Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters. This significantly decreases the computational and storage costs. Recent state-of-the-art PEFT techniques achieve performance comparable to fully fine-tuned models.
PEFT is integrated with Transformers for easy model training and inference, Diffusers for conveniently managing different adapters, and Accelerate for distributed training and inference for really big models.
> [!TIP]
> Visit the [PEFT](https://huggingface.co/PEFT) organization to read about the PEFT methods implemented in the library and to see notebooks demonstrating how to apply these methods to a variety of downstream tasks. Click the "Watch repos" button on the organization page to be notified of newly implemented methods and notebooks!
Check the PEFT Adapters API Reference section for a list of supported PEFT methods, and read the [Adapters](https://huggingface.co/docs/peft/en/conceptual_guides/adapter), [Soft prompts](https://huggingface.co/docs/peft/en/conceptual_guides/prompting), and [IA3](https://huggingface.co/docs/peft/en/conceptual_guides/ia3) conceptual guides to learn more about how these methods work.
## Quickstart
Install PEFT from pip:
```bash
pip install peft
```
Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration with `get_peft_model`. For the bigscience/mt0-large model, you're only training 0.19% of the parameters!
```python
from transformers import AutoModelForCausalLM
from peft import LoraConfig, TaskType, get_peft_model
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model_id = "Qwen/Qwen2.5-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
task_type=TaskType.CAUSAL_LM,
# target_modules=["q_proj", "v_proj", ...] # optionally indicate target modules
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# prints: trainable params: 3,686,400 || all params: 3,089,625,088 || trainable%: 0.1193
# now perform training on your dataset, e.g. using transformers Trainer, then save the model
model.save_pretrained("qwen2.5-3b-lora")
```
To load a PEFT model for inference:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model_id = "Qwen/Qwen2.5-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
model = PeftModel.from_pretrained(model, "qwen2.5-3b-lora")
inputs = tokenizer("Preheat the oven to 350 degrees and place the cookie dough", return_tensors="pt")
outputs = model.generate(**inputs.to(device), max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# prints something like: Preheat the oven to 350 degrees and place the cookie dough in a baking dish [...]
```
## Why you should use PEFT
There are many benefits of using PEFT but the main one is the huge savings in compute and storage, making PEFT applicable to many different use cases.
### High performance on consumer hardware
Consider the memory requirements for training the following models on the [ought/raft/twitter_complaints](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) dataset with an A100 80GB GPU with more than 64GB of CPU RAM.
| Model | Full Finetuning | PEFT-LoRA PyTorch | PEFT-LoRA DeepSpeed with CPU Offloading |
| --------- | ---- | ---- | ---- |
| bigscience/T0_3B (3B params) | 47.14GB GPU / 2.96GB CPU | 14.4GB GPU / 2.96GB CPU | 9.8GB GPU / 17.8GB CPU |
| bigscience/mt0-xxl (12B params) | OOM GPU | 56GB GPU / 3GB CPU | 22GB GPU / 52GB CPU |
| bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU |
With LoRA you can fully finetune a 12B parameter model that would've otherwise run out of memory on the 80GB GPU, and comfortably fit and train a 3B parameter model. When you look at the 3B parameter model's performance, it is comparable to a fully finetuned model at a fraction of the GPU memory.
| Submission Name | Accuracy |
| --------- | ---- |
| Human baseline (crowdsourced) | 0.897 |
| Flan-T5 | 0.892 |
| lora-t0-3b | 0.863 |
> [!TIP]
> The bigscience/T0_3B model performance isn't optimized in the table above. You can squeeze even more performance out of it by playing around with the input instruction templates, LoRA hyperparameters, and other training related hyperparameters. The final checkpoint size of this model is just 19MB compared to 11GB of the full bigscience/T0_3B model. Learn more about the advantages of finetuning with PEFT in this [blog post](https://www.philschmid.de/fine-tune-flan-t5-peft).
### Quantization
Quantization is another method for reducing the memory requirements of a model by representing the data in a lower precision. It can be combined with PEFT methods to make it even easier to train and load LLMs for inference.
* Learn how to finetune [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) with QLoRA and the [TRL](https://huggingface.co/docs/trl/index) library on a 16GB GPU in the [Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem](https://pytorch.org/blog/finetune-llms/) blog post.
* Learn how to finetune a [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) model for multilingual automatic speech recognition with LoRA and 8-bit quantization in this [notebook](https://colab.research.google.com/drive/1DOkD_5OUjFa0r5Ik3SgywJLJtEo2qLxO?usp=sharing) (see this [notebook](https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) instead for an example of streaming a dataset).
### Save compute and storage
PEFT can help you save storage by avoiding full finetuning of models on each of downstream task or dataset. In many cases, you're only finetuning a very small fraction of a model's parameters and each checkpoint is only a few MBs in size (instead of GBs). These smaller PEFT adapters demonstrate performance comparable to a fully finetuned model. If you have many datasets, you can save a lot of storage with a PEFT model and not have to worry about catastrophic forgetting or overfitting the backbone or base model.
## PEFT integrations
PEFT is widely supported across the Hugging Face ecosystem because of the massive efficiency it brings to training and inference.
### Diffusers
The iterative diffusion process consumes a lot of memory which can make it difficult to train. PEFT can help reduce the memory requirements and reduce the storage size of the final model checkpoint. For example, consider the memory required for training a Stable Diffusion model with LoRA on an A100 80GB GPU with more than 64GB of CPU RAM. The final model checkpoint size is only 8.8MB!
| Model | Full Finetuning | PEFT-LoRA | PEFT-LoRA with Gradient Checkpointing |
| --------- | ---- | ---- | ---- |
| CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU |
> [!TIP]
> Take a look at the [examples/lora_dreambooth/train_dreambooth.py](examples/lora_dreambooth/train_dreambooth.py) training script to try training your own Stable Diffusion model with LoRA, and play around with the [smangrul/peft-lora-sd-dreambooth](https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth) Space which is running on a T4 instance. Learn more about the PEFT integration in Diffusers in this [tutorial](https://huggingface.co/docs/peft/main/en/tutorial/peft_integrations#diffusers).
### Transformers
PEFT is directly integrated with [Transformers](https://huggingface.co/docs/transformers/main/en/peft). After loading a model, call `add_adapter` to add a new PEFT adapter to the model:
```python
from peft import LoraConfig
model = ... # transformers model
peft_config = LoraConfig(...)
model.add_adapter(lora_config, adapter_name="lora_1")
```
To load a trained PEFT adapter, call `load_adapter`:
```python
model = ... # transformers model
model.load_adapter(, adapter_name="lora_1")
```
And to switch between different adapters, call `set_adapter`:
```python
model.set_adapter("lora_2")
```
The Transformers integration doesn't include all the functionalities offered in PEFT, such as methods for merging the adapter into the base model.
### Accelerate
[Accelerate](https://huggingface.co/docs/accelerate/index) is a library for distributed training and inference on various training setups and hardware (GPUs, TPUs, Apple Silicon, etc.). PEFT models work with Accelerate out of the box, making it really convenient to train really large models or use them for inference on consumer hardware with limited resources.
### TRL
PEFT can also be applied to training LLMs with RLHF components such as the ranker and policy. Get started by reading:
* [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) with PEFT and the [TRL](https://huggingface.co/docs/trl/index) library to learn more about the Direct Preference Optimization (DPO) method and how to apply it to a LLM.
* [Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU](https://huggingface.co/blog/trl-peft) with PEFT and the [TRL](https://huggingface.co/docs/trl/index) library, and then try out the [gpt2-sentiment_peft.ipynb](https://github.com/huggingface/trl/blob/main/examples/notebooks/gpt2-sentiment.ipynb) notebook to optimize GPT2 to generate positive movie reviews.
* [StackLLaMA: A hands-on guide to train LLaMA with RLHF](https://huggingface.co/blog/stackllama) with PEFT, and then try out the [stack_llama/scripts](https://github.com/huggingface/trl/tree/main/examples/research_projects/stack_llama/scripts) for supervised finetuning, reward modeling, and RL finetuning.
## Model support
Use this [Space](https://stevhliu-peft-methods.hf.space) or check out the [docs](https://huggingface.co/docs/peft/main/en/index) to find which models officially support a PEFT method out of the box. Even if you don't see a model listed below, you can manually configure the model config to enable PEFT for a model. Read the [New transformers architecture](https://huggingface.co/docs/peft/main/en/developer_guides/custom_models#new-transformers-architectures) guide to learn how.
## Contribute
If you would like to contribute to PEFT, please check out our [contribution guide](https://huggingface.co/docs/peft/developer_guides/contributing).
## Citing 🤗 PEFT
To use 🤗 PEFT in your publication, please cite it by using the following BibTeX entry.
```bibtex
@Misc{peft,
title = {{PEFT}: State-of-the-art Parameter-Efficient Fine-Tuning methods},
author = {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes Belkada and Sayak Paul and Benjamin Bossan and Marian Tietz},
howpublished = {\url{https://github.com/huggingface/peft}},
year = {2022}
}
```
================================================
FILE: docker/README.md
================================================
# PEFT Docker images
Here we store all PEFT Docker images used in our testing infrastructure. We use python 3.11 for now on all our images.
- `peft-cpu`: PEFT compiled on CPU with all other HF libraries installed on main branch
- `peft-gpu`: PEFT complied for NVIDIA GPUs with all other HF libraries installed on main branch
================================================
FILE: docker/peft-cpu/Dockerfile
================================================
# Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.11
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
RUN apt-get update && \
apt-get install -y curl git wget git-lfs ffmpeg libsndfile1-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists*
RUN git lfs install
# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
RUN python3 -m pip install --no-cache-dir --upgrade pip
# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
# We don't install pytorch here yet since CUDA isn't available
# instead we use the direct torch wheel
ENV PATH=/opt/conda/envs/peft/bin:$PATH
# Activate our bash shell
RUN chsh -s /bin/bash
SHELL ["/bin/bash", "-c"]
# Activate the conda env and install transformers + accelerate from source
RUN source activate peft && \
python3 -m pip install --no-cache-dir \
librosa \
"soundfile>=0.12.1" \
scipy \
git+https://github.com/huggingface/transformers \
git+https://github.com/huggingface/accelerate \
peft[test]@git+https://github.com/huggingface/peft
# Install apt libs
RUN apt-get update && \
apt-get install -y curl git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists*
RUN echo "source activate peft" >> ~/.profile
# Activate the virtualenv
CMD ["/bin/bash"]
================================================
FILE: docker/peft-gpu/Dockerfile
================================================
# Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.11
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
# Install audio-related libraries
RUN apt-get update && \
apt-get install -y curl git wget git-lfs ffmpeg libsndfile1-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists*
RUN git lfs install
# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
# We don't install pytorch here yet since CUDA isn't available
# instead we use the direct torch wheel
ENV PATH=/opt/conda/envs/peft/bin:$PATH
# Activate our bash shell
RUN chsh -s /bin/bash
SHELL ["/bin/bash", "-c"]
# Stage 2
FROM nvidia/cuda:12.8.1-cudnn-devel-ubuntu22.04 AS build-image
COPY --from=compile-image /opt/conda /opt/conda
ENV PATH=/opt/conda/bin:$PATH
# Install apt libs
RUN apt-get update && \
apt-get install -y curl git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists*
RUN chsh -s /bin/bash
SHELL ["/bin/bash", "-c"]
RUN conda run -n peft pip install --no-cache-dir bitsandbytes optimum
# GPTQmodel doesn't find torch without build isolation
#
# Note: we are hard-coding CUDA_ARCH_LIST here since `gptqmodel` requires either nvidia-smi
# or CUDA_ARCH_LIST for compute capability information. Since the docker build is unlikely
# to have compute hardware available we use the information from the CI runner (which hosts
# a NVIDIA L4). So we fix the compute capability to 8.9. In the future we might extend this
# to a list of compute capabilities (separated by ;).
RUN CUDA_ARCH_LIST=8.9 conda run -n peft pip install --no-build-isolation gptqmodel
RUN \
# Add eetq for quantization testing; needs to run without build isolation since the setup
# script directly imports torch from the environment which would fail with isolation.
conda run -n peft pip install --no-build-isolation git+https://github.com/NetEase-FuXi/EETQ.git
RUN \
conda run -n peft pip install --no-build-isolation "transformer_engine[pytorch]"
# Activate the conda env and install transformers + accelerate from source
RUN conda run -n peft pip install -U --no-cache-dir \
librosa \
"soundfile>=0.12.1" \
scipy \
torchao \
fbgemm-gpu-genai>=1.2.0 \
git+https://github.com/huggingface/transformers \
git+https://github.com/huggingface/accelerate \
peft[test]@git+https://github.com/huggingface/peft \
# Add aqlm for quantization testing
aqlm[gpu]>=1.0.2 \
# Add HQQ for quantization testing
hqq \
deepspeed
RUN conda run -n peft pip freeze | grep transformers
RUN echo "source activate peft" >> ~/.profile
# Activate the virtualenv
CMD ["/bin/bash"]
================================================
FILE: docs/Makefile
================================================
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SOURCEDIR = source
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
================================================
FILE: docs/README.md
================================================
# Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
you can install them with the following command, at the root of the code repository:
```bash
pip install -e ".[docs]"
```
Then you need to install our special tool that builds the documentation:
```bash
pip install git+https://github.com/huggingface/doc-builder
```
---
**NOTE**
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to commit to the built documentation.
---
## Building the documentation
Once you have setup the `doc-builder` and additional packages, you can generate the documentation by
typing the following command:
```bash
doc-builder build peft docs/source/ --build_dir ~/tmp/test-build
```
You can adapt the `--build_dir` to set any temporary folder you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.
## Previewing the documentation
To preview the docs, first install the `watchdog` module with:
```bash
pip install watchdog
```
Then run the following command:
```bash
doc-builder preview {package_name} {path_to_docs}
```
For example:
```bash
doc-builder preview peft docs/source
```
The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
---
**NOTE**
The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
---
## Adding a new element to the navigation bar
Accepted files are Markdown (.md or .mdx).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/peft/blob/main/docs/source/_toctree.yml) file.
## Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
```
Sections that were moved:
[ Section A ]
```
and of course, if you moved it to another file, then:
```
Sections that were moved:
[ Section A ]
```
Use the relative style to link to the new file so that the versioned docs continue to work.
## Writing Documentation - Specification
The `huggingface/peft` documentation follows the
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
although we can write them directly in Markdown.
### Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
- Link that file in `./source/_toctree.yml` on the correct toc-tree.
Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
depending on the intended targets (beginners, more advanced users, or researchers) it should go into sections two, three, or
four.
### Writing source documentation
Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
and objects like True, None, or any strings should usually be put in `code`.
When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
function to be in the main package.
If you want to create a link to some internal class or function, you need to
provide its path. For instance: \[\`utils.gather\`\]. This will be converted into a link with
`utils.gather` in the description. To get rid of the path and only keep the name of the object you are
linking to in the description, add a ~: \[\`~utils.gather\`\] will generate a link with `gather` in the description.
The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
#### Defining arguments in a method
Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
description:
```
Args:
n_layers (`int`): The number of layers of the model.
```
If the description is too long to fit in one line (more than 119 characters in total), another indentation is necessary
before writing the description after the argument.
Finally, to maintain uniformity if any *one* description is too long to fit on one line, the
rest of the parameters should follow suit and have an indention before their description.
Here's an example showcasing everything so far:
```
Args:
gradient_accumulation_steps (`int`, *optional*, default to 1):
The number of steps that should pass before gradients are accumulated. A number > 1 should be combined with `Accelerator.accumulate`.
cpu (`bool`, *optional*):
Whether or not to force the script to execute on CPU. Will ignore GPU available if set to `True` and force the execution on one process only.
```
For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
following signature:
```
def my_function(x: str = None, a: float = 1):
```
then its documentation should look like this:
```
Args:
x (`str`, *optional*):
This argument controls ... and has a description longer than 119 chars.
a (`float`, *optional*, defaults to 1):
This argument is used to ... and has a description longer than 119 chars.
```
Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
if the first line describing your argument type and its default gets long, you can't break it into several lines. You can
however write as many lines as you want in the indented description (see the example above with `input_ids`).
#### Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
````
```python
# first line of code
# second line
# etc
```
````
#### Writing a return block
The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
building the return.
Here's an example of a single value return:
```
Returns:
`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
```
Here's an example of a tuple return, comprising several objects:
```
Returns:
`tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
- ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
- **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
```
## Styling the docstring
We have an automatic script running with the `make style` comment that will make sure that:
- the docstrings fully take advantage of the line width
- all code examples are formatted using black, like the code of the Transformers library
This script may have some weird failures if you make a syntax mistake or if you uncover a bug. Therefore, it's
recommended to commit your changes before running `make style`, so you can revert the changes done by that script
easily.
## Writing documentation examples
The syntax, for example, docstrings can look as follows:
```
Example:
```python
>>> import time
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> if accelerator.is_main_process:
... time.sleep(2)
>>> else:
... print("I'm waiting for the main process to finish its sleep...")
>>> accelerator.wait_for_everyone()
>>> # Should print on every process at the same time
>>> print("Everyone is here")
```
```
The docstring should give a minimal, clear example of how the respective function
is to be used in inference and also include the expected (ideally sensible)
output.
Often, readers will try out the example before even going through the function
or class definitions. Therefore, it is of utmost importance that the example
works as expected.
================================================
FILE: docs/source/_config.py
================================================
# docstyle-ignore
INSTALL_CONTENT = """
# PEFT installation
! pip install peft accelerate transformers
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/peft.git
"""
================================================
FILE: docs/source/_toctree.yml
================================================
- title: Get started
sections:
- local: index
title: 🤗 PEFT
- local: quicktour
title: Quicktour
- local: install
title: Installation
- title: Tutorial
sections:
- local: tutorial/peft_model_config
title: Configurations and models
- local: tutorial/peft_integrations
title: Integrations
- title: PEFT method guides
sections:
- local: task_guides/prompt_based_methods
title: Prompt-based methods
- local: task_guides/lora_based_methods
title: LoRA methods
- local: task_guides/ia3
title: IA3
- title: Developer guides
sections:
- local: developer_guides/model_merging
title: Model merging
- local: developer_guides/quantization
title: Quantization
- local: developer_guides/lora
title: LoRA
- local: developer_guides/custom_models
title: Custom models
- local: developer_guides/low_level_api
title: Adapter injection
- local: developer_guides/mixed_models
title: Mixed adapter types
- local: developer_guides/torch_compile
title: torch.compile
- local: developer_guides/contributing
title: Contribute to PEFT
- local: developer_guides/troubleshooting
title: Troubleshooting
- local: developer_guides/checkpoint
title: PEFT checkpoint format
- title: 🤗 Accelerate integrations
sections:
- local: accelerate/deepspeed
title: DeepSpeed
- local: accelerate/fsdp
title: Fully Sharded Data Parallel
- title: Conceptual guides
sections:
- local: conceptual_guides/adapter
title: Adapters
- local: conceptual_guides/prompting
title: Soft prompts
- local: conceptual_guides/ia3
title: IA3
- local: conceptual_guides/oft
title: OFT/BOFT
- sections:
- sections:
- local: package_reference/auto_class
title: AutoPeftModel
- local: package_reference/peft_model
title: PEFT model
- local: package_reference/peft_types
title: PEFT types
- local: package_reference/config
title: Configuration
- local: package_reference/tuners
title: Tuner
title: Main classes
- sections:
- local: package_reference/adalora
title: AdaLoRA
- local: package_reference/ia3
title: IA3
- local: package_reference/llama_adapter
title: Llama-Adapter
- local: package_reference/loha
title: LoHa
- local: package_reference/lokr
title: LoKr
- local: package_reference/lora
title: LoRA
- local: package_reference/osf
title: OSF
- local: package_reference/xlora
title: X-LoRA
- local: package_reference/adapter_utils
title: LyCORIS
- local: package_reference/multitask_prompt_tuning
title: Multitask Prompt Tuning
- local: package_reference/oft
title: OFT
- local: package_reference/boft
title: BOFT
- local: package_reference/psoft
title: PSOFT
- local: package_reference/poly
title: Polytropon
- local: package_reference/p_tuning
title: P-tuning
- local: package_reference/prefix_tuning
title: Prefix tuning
- local: package_reference/cartridges
title: Cartridges
- local: package_reference/prompt_tuning
title: Prompt tuning
- local: package_reference/layernorm_tuning
title: Layernorm tuning
- local: package_reference/vera
title: VeRA
- local: package_reference/pvera
title: PVeRA
- local: package_reference/fourierft
title: FourierFT
- local: package_reference/gralora
title: GraLoRA
- local: package_reference/vblora
title: VB-LoRA
- local: package_reference/hra
title: HRA
- local: package_reference/cpt
title: CPT
- local: package_reference/trainable_tokens
title: Trainable Tokens
- local: package_reference/randlora
title: RandLora
- local: package_reference/shira
title: SHiRA
- local: package_reference/c3a
title: C3A
- local: package_reference/miss
title: MiSS
- local: package_reference/road
title: RoAd
- local: package_reference/waveft
title: WaveFT
- local: package_reference/delora
title: DeLoRA
- local: package_reference/lily
title: Lily
- local: package_reference/peanut
title: PEANuT
title: Adapters
- sections:
- local: package_reference/merge_utils
title: Model merge
- local: package_reference/helpers
title: Helpers
- local: package_reference/hotswap
title: Hotswapping adapters
- local: package_reference/functional
title: Functions for PEFT integration
- local: package_reference/lora_conversion
title: Converting non-LoRA adapters to LoRA
title: Utilities
title: API reference
================================================
FILE: docs/source/accelerate/deepspeed.md
================================================
# DeepSpeed
[DeepSpeed](https://www.deepspeed.ai/) is a library designed for speed and scale for distributed training of large models with billions of parameters. At its core is the Zero Redundancy Optimizer (ZeRO) that shards optimizer states (ZeRO-1), gradients (ZeRO-2), and parameters (ZeRO-3) across data parallel processes. This drastically reduces memory usage, allowing you to scale your training to billion parameter models. To unlock even more memory efficiency, ZeRO-Offload reduces GPU compute and memory by leveraging CPU resources during optimization.
Both of these features are supported in 🤗 Accelerate, and you can use them with 🤗 PEFT.
## Compatibility with `bitsandbytes` quantization + LoRA
Below is a table that summarizes the compatibility between PEFT's LoRA, [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) library and DeepSpeed Zero stages with respect to fine-tuning. DeepSpeed Zero-1 and 2 will have no effect at inference as stage 1 shards the optimizer states and stage 2 shards the optimizer states and gradients:
| DeepSpeed stage | Is compatible? |
|---|---|
| Zero-1 | 🟢 |
| Zero-2 | 🟢 |
| Zero-3 | 🟢 |
For DeepSpeed Stage 3 + QLoRA, please refer to the section [Use PEFT QLoRA and DeepSpeed with ZeRO3 for finetuning large models on multiple GPUs](#use-peft-qlora-and-deepspeed-with-zero3-for-finetuning-large-models-on-multiple-gpus) below.
For confirming these observations, we ran the SFT (Supervised Fine-tuning) [offical example scripts](https://github.com/huggingface/trl/tree/main/examples) of the [Transformers Reinforcement Learning (TRL) library](https://github.com/huggingface/trl) using QLoRA + PEFT and the accelerate configs available [here](https://github.com/huggingface/trl/tree/main/examples/accelerate_configs). We ran these experiments on a 2x NVIDIA T4 GPU.
# Use PEFT and DeepSpeed with ZeRO3 for finetuning large models on multiple devices and multiple nodes
This section of guide will help you learn how to use our DeepSpeed [training script](https://github.com/huggingface/peft/blob/main/examples/sft/train.py) for performing SFT. You'll configure the script to do SFT (supervised fine-tuning) of Llama-70B model with LoRA and ZeRO-3 on 8xH100 80GB GPUs on a single machine. You can configure it to scale to multiple machines by changing the accelerate config.
## Configuration
Start by running the following command to [create a DeepSpeed configuration file](https://huggingface.co/docs/accelerate/quicktour#launching-your-distributed-script) with 🤗 Accelerate. The `--config_file` flag allows you to save the configuration file to a specific location, otherwise it is saved as a `default_config.yaml` file in the 🤗 Accelerate cache.
The configuration file is used to set the default options when you launch the training script.
```bash
accelerate config --config_file deepspeed_config.yaml
```
You'll be asked a few questions about your setup, and configure the following arguments. In this example, you'll use ZeRO-3 so make sure you pick those options.
```bash
`zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning
`gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them. Pass the same value as you would pass via cmd argument else you will encounter mismatch error.
`gradient_clipping`: Enable gradient clipping with value. Don't set this as you will be passing it via cmd arguments.
`offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2. Set this as `none` as don't want to enable offloading.
`offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3. Set this as `none` as don't want to enable offloading.
`zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3. Set this to `True`.
`zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3. Set this to `True`.
`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training. Set this to `True`.
```
Once this is done, the corresponding config should look like below and you can find it in config folder at [deepspeed_config.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/deepspeed_config.yaml):
```yml
compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
deepspeed_multinode_launcher: standard
gradient_accumulation_steps: 4
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
## Launch command
The launch command is available at [run_peft_deepspeed.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_deepspeed.sh) and it is also shown below:
```bash
accelerate launch --config_file "configs/deepspeed_config.yaml" train.py \
--seed 100 \
--model_name_or_path "meta-llama/Llama-2-70b-hf" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len 2048 \
--num_train_epochs 1 \
--logging_steps 5 \
--log_level "info" \
--logging_strategy "steps" \
--eval_strategy "epoch" \
--save_strategy "epoch" \
--push_to_hub \
--hub_private_repo True \
--hub_strategy "every_save" \
--bf16 True \
--packing True \
--learning_rate 1e-4 \
--lr_scheduler_type "cosine" \
--weight_decay 1e-4 \
--warmup_steps 0 \
--max_grad_norm 1.0 \
--output_dir "llama-sft-lora-deepspeed" \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--gradient_accumulation_steps 4 \
--gradient_checkpointing True \
--use_reentrant False \
--dataset_text_field "content" \
--use_flash_attn True \
--use_peft_lora True \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.1 \
--lora_target_modules "all-linear" \
--use_4bit_quantization False
```
Notice that we are using LoRA with rank=8, alpha=16 and targeting all linear layers. We are passing the deepspeed config file and finetuning 70B Llama model on a subset of the ultrachat dataset.
## The important parts
Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
The first thing to know is that the script uses DeepSpeed for distributed training as the DeepSpeed config has been passed. The [`~trl.SFTTrainer`] class handles all the heavy lifting of creating the PEFT model using the peft config that is passed. After that, when you call `trainer.train()`, [`~trl.SFTTrainer`] internally uses 🤗 Accelerate to prepare the model, optimizer and trainer using the DeepSpeed config to create DeepSpeed engine which is then trained. The main code snippet is below:
```python
# trainer
trainer = SFTTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
)
trainer.accelerator.print(f"{trainer.model}")
# train
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
trainer.train(resume_from_checkpoint=checkpoint)
# saving final model
trainer.save_model()
```
## Memory usage
In the above example, the memory consumed per GPU is 64 GB (80%) as seen in the screenshot below:
GPU memory usage for the training run
## More resources
You can also refer this blog post [Falcon 180B Finetuning using 🤗 PEFT and DeepSpeed](https://medium.com/@sourabmangrulkar/falcon-180b-finetuning-using-peft-and-deepspeed-b92643091d99) on how to finetune 180B Falcon model on 16 A100 GPUs on 2 machines.
# Use PEFT QLoRA and DeepSpeed with ZeRO3 for finetuning large models on multiple GPUs
In this section, we will look at how to use QLoRA and DeepSpeed Stage-3 for finetuning 70B llama model on 2X40GB GPUs.
For this, we first need `bitsandbytes>=0.43.3`, `accelerate>=1.0.1`, `transformers>4.44.2`, `trl>0.11.4` and `peft>0.13.0`. We need to set `zero3_init_flag` to true when using Accelerate config. Below is the config which can be found at [deepspeed_config_z3_qlora.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/deepspeed_config_z3_qlora.yaml):
```yml
compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
deepspeed_multinode_launcher: standard
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
Launch command is given below which is available at [run_peft_qlora_deepspeed_stage3.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_qlora_deepspeed_stage3.sh):
```
accelerate launch --config_file "configs/deepspeed_config_z3_qlora.yaml" train.py \
--seed 100 \
--model_name_or_path "meta-llama/Llama-2-70b-hf" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len 2048 \
--num_train_epochs 1 \
--logging_steps 5 \
--log_level "info" \
--logging_strategy "steps" \
--eval_strategy "epoch" \
--save_strategy "epoch" \
--push_to_hub \
--hub_private_repo True \
--hub_strategy "every_save" \
--bf16 True \
--packing True \
--learning_rate 1e-4 \
--lr_scheduler_type "cosine" \
--weight_decay 1e-4 \
--warmup_steps 0 \
--max_grad_norm 1.0 \
--output_dir "llama-sft-qlora-dsz3" \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 2 \
--gradient_checkpointing True \
--use_reentrant True \
--dataset_text_field "content" \
--use_flash_attn True \
--use_peft_lora True \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.1 \
--lora_target_modules "all-linear" \
--use_4bit_quantization True \
--use_nested_quant True \
--bnb_4bit_compute_dtype "bfloat16" \
--bnb_4bit_quant_storage_dtype "bfloat16"
```
Notice the new argument being passed `bnb_4bit_quant_storage_dtype` which denotes the data type for packing the 4-bit parameters. For example, when it is set to `bfloat16`, **32/4 = 8** 4-bit params are packed together post quantization.
In terms of training code, the important code changes are:
```diff
...
bnb_config = BitsAndBytesConfig(
load_in_4bit=args.use_4bit_quantization,
bnb_4bit_quant_type=args.bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=args.use_nested_quant,
+ bnb_4bit_quant_storage=quant_storage_dtype,
)
...
model = AutoModelForCausalLM.from_pretrained(
args.model_name_or_path,
quantization_config=bnb_config,
trust_remote_code=True,
attn_implementation="flash_attention_2" if args.use_flash_attn else "eager",
+ dtype=quant_storage_dtype or torch.float32,
)
```
Notice that `dtype` for `AutoModelForCausalLM` is same as the `bnb_4bit_quant_storage` data type. That's it. Everything else is handled by Trainer and TRL.
## Memory usage
In the above example, the memory consumed per GPU is **36.6 GB**. Therefore, what took 8X80GB GPUs with DeepSpeed Stage 3+LoRA and a couple of 80GB GPUs with DDP+QLoRA now requires 2X40GB GPUs. This makes finetuning of large models more accessible.
# Use PEFT and DeepSpeed with ZeRO3 and CPU Offloading for finetuning large models on a single GPU
This section of guide will help you learn how to use our DeepSpeed [training script](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py). You'll configure the script to train a large model for conditional generation with ZeRO-3 and CPU Offload.
> [!TIP]
> 💡 To help you get started, check out our example training scripts for [causal language modeling](https://github.com/huggingface/peft/blob/main/examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py) and [conditional generation](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py). You can adapt these scripts for your own applications or even use them out of the box if your task is similar to the one in the scripts.
## Configuration
Start by running the following command to [create a DeepSpeed configuration file](https://huggingface.co/docs/accelerate/quicktour#launching-your-distributed-script) with 🤗 Accelerate. The `--config_file` flag allows you to save the configuration file to a specific location, otherwise it is saved as a `default_config.yaml` file in the 🤗 Accelerate cache.
The configuration file is used to set the default options when you launch the training script.
```bash
accelerate config --config_file ds_zero3_cpu.yaml
```
You'll be asked a few questions about your setup, and configure the following arguments. In this example, you'll use ZeRO-3 along with CPU-Offload so make sure you pick those options.
```bash
`zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning
`gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them.
`gradient_clipping`: Enable gradient clipping with value.
`offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2.
`offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3.
`zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3.
`zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3.
`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training.
```
An example [configuration file](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml) might look like the following. The most important thing to notice is that `zero_stage` is set to `3`, and `offload_optimizer_device` and `offload_param_device` are set to the `cpu`.
```yml
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 1.0
offload_optimizer_device: cpu
offload_param_device: cpu
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config: {}
machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: 'no'
num_machines: 1
num_processes: 1
rdzv_backend: static
same_network: true
use_cpu: false
```
## The important parts
Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
Within the [`main`](https://github.com/huggingface/peft/blob/2822398fbe896f25d4dac5e468624dc5fd65a51b/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py#L103) function, the script creates an [`~accelerate.Accelerator`] class to initialize all the necessary requirements for distributed training.
> [!TIP]
> 💡 Feel free to change the model and dataset inside the `main` function. If your dataset format is different from the one in the script, you may also need to write your own preprocessing function.
The script also creates a configuration for the 🤗 PEFT method you're using, which in this case, is LoRA. The [`LoraConfig`] specifies the task type and important parameters such as the dimension of the low-rank matrices, the matrices scaling factor, and the dropout probability of the LoRA layers. If you want to use a different 🤗 PEFT method, make sure you replace `LoraConfig` with the appropriate [class](../package_reference/tuners).
```diff
def main():
+ accelerator = Accelerator()
model_name_or_path = "facebook/bart-large"
dataset_name = "twitter_complaints"
+ peft_config = LoraConfig(
task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
)
```
Throughout the script, you'll see the [`~accelerate.Accelerator.main_process_first`] and [`~accelerate.Accelerator.wait_for_everyone`] functions which help control and synchronize when processes are executed.
The [`get_peft_model`] function takes a base model and the [`peft_config`] you prepared earlier to create a [`PeftModel`]:
```diff
model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
+ model = get_peft_model(model, peft_config)
```
Pass all the relevant training objects to 🤗 Accelerate's [`~accelerate.Accelerator.prepare`] which makes sure everything is ready for training:
```py
model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler = accelerator.prepare(
model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler
)
```
The next bit of code checks whether the DeepSpeed plugin is used in the `Accelerator`, and if the plugin exists, then we check if we are using ZeRO-3. This conditional flag is used when calling `generate` function call during inference for syncing GPUs when the model parameters are sharded:
```py
is_ds_zero_3 = False
if getattr(accelerator.state, "deepspeed_plugin", None):
is_ds_zero_3 = accelerator.state.deepspeed_plugin.zero_stage == 3
```
Inside the training loop, the usual `loss.backward()` is replaced by 🤗 Accelerate's [`~accelerate.Accelerator.backward`] which uses the correct `backward()` method based on your configuration:
```diff
for epoch in range(num_epochs):
with TorchTracemalloc() as tracemalloc:
model.train()
total_loss = 0
for step, batch in enumerate(tqdm(train_dataloader)):
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
+ accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
```
That is all! The rest of the script handles the training loop, evaluation, and even pushes it to the Hub for you.
## Train
Run the following command to launch the training script. Earlier, you saved the configuration file to `ds_zero3_cpu.yaml`, so you'll need to pass the path to the launcher with the `--config_file` argument like this:
```bash
accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py
```
You'll see some output logs that track memory usage during training, and once it's completed, the script returns the accuracy and compares the predictions to the labels:
```bash
GPU Memory before entering the train : 1916
GPU Memory consumed at the end of the train (end-begin): 66
GPU Peak Memory consumed during the train (max-begin): 7488
GPU Total Peak Memory consumed during the train (max): 9404
CPU Memory before entering the train : 19411
CPU Memory consumed at the end of the train (end-begin): 0
CPU Peak Memory consumed during the train (max-begin): 0
CPU Total Peak Memory consumed during the train (max): 19411
epoch=4: train_ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0')
100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:27<00:00, 3.92s/it]
GPU Memory before entering the eval : 1982
GPU Memory consumed at the end of the eval (end-begin): -66
GPU Peak Memory consumed during the eval (max-begin): 672
GPU Total Peak Memory consumed during the eval (max): 2654
CPU Memory before entering the eval : 19411
CPU Memory consumed at the end of the eval (end-begin): 0
CPU Peak Memory consumed during the eval (max-begin): 0
CPU Total Peak Memory consumed during the eval (max): 19411
accuracy=100.0
eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
```
# Caveats
1. Merging when using PEFT and DeepSpeed is currently unsupported and will raise error.
2. When using CPU offloading, the major gains from using PEFT to shrink the optimizer states and gradients to that of the adapter weights would be realized on CPU RAM and there won't be savings with respect to GPU memory.
3. DeepSpeed Stage 3 and qlora when used with CPU offloading leads to more GPU memory usage when compared to disabling CPU offloading.
> [!TIP]
> 💡 When you have code that requires merging (and unmerging) of weights, try to manually collect the parameters with DeepSpeed Zero-3 beforehand:
>
> ```python
> import deepspeed
>
> is_ds_zero_3 = ... # check if Zero-3
>
> with deepspeed.zero.GatheredParameters(list(model.parameters()), enabled= is_ds_zero_3):
> model.merge_adapter()
> # do whatever is needed, then unmerge in the same context if unmerging is required
> ...
> model.unmerge_adapter()
> ```
================================================
FILE: docs/source/accelerate/fsdp.md
================================================
# Fully Sharded Data Parallel
[Fully sharded data parallel](https://pytorch.org/docs/stable/fsdp.html) (FSDP) is developed for distributed training of large pretrained models up to 1T parameters. FSDP achieves this by sharding the model parameters, gradients, and optimizer states across data parallel processes and it can also offload sharded model parameters to a CPU. The memory efficiency afforded by FSDP allows you to scale training to larger batch or model sizes.
Both of these features are supported in 🤗 Accelerate, and you can use them with 🤗 PEFT.
# Use PEFT and FSDP
This section of guide will help you learn how to use our DeepSpeed [training script](https://github.com/huggingface/peft/blob/main/examples/sft/train.py) for performing SFT. You'll configure the script to do SFT (supervised fine-tuning) of Llama-70B model with LoRA and FSDP on 8xH100 80GB GPUs on a single machine. You can configure it to scale to multiple machines by changing the accelerate config.
## Configuration
Start by running the following command to [create a FSDP configuration file](https://huggingface.co/docs/accelerate/quicktour#launching-your-distributed-script) with 🤗 Accelerate. The `--config_file` flag allows you to save the configuration file to a specific location, otherwise it is saved as a `default_config.yaml` file in the 🤗 Accelerate cache.
The configuration file is used to set the default options when you launch the training script.
```bash
accelerate config --config_file fsdp_config.yaml
```
You'll be asked a few questions about your setup, and configure the following arguments. In this example, you'll answer the questionnaire as shown in the image below.
Creating Accelerate's config to use FSDP
Once this is done, the corresponding config should look like below and you can find it in config folder at [fsdp_config.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/fsdp_config.yaml):
```yml
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: FSDP
downcast_bf16: 'no'
fsdp_config:
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_backward_prefetch: BACKWARD_PRE
fsdp_cpu_ram_efficient_loading: true
fsdp_forward_prefetch: false
fsdp_offload_params: false
fsdp_sharding_strategy: FULL_SHARD
fsdp_state_dict_type: SHARDED_STATE_DICT
fsdp_sync_module_states: true
fsdp_use_orig_params: false
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
## Launch command
The launch command is available at [run_peft_fsdp.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_fsdp.sh) and it is also shown below:
```bash
accelerate launch --config_file "configs/fsdp_config.yaml" train.py \
--seed 100 \
--model_name_or_path "meta-llama/Llama-2-70b-hf" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len 2048 \
--num_train_epochs 1 \
--logging_steps 5 \
--log_level "info" \
--logging_strategy "steps" \
--eval_strategy "epoch" \
--save_strategy "epoch" \
--push_to_hub \
--hub_private_repo True \
--hub_strategy "every_save" \
--bf16 True \
--packing True \
--learning_rate 1e-4 \
--lr_scheduler_type "cosine" \
--weight_decay 1e-4 \
--warmup_steps 0 \
--max_grad_norm 1.0 \
--output_dir "llama-sft-lora-fsdp" \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--gradient_accumulation_steps 4 \
--gradient_checkpointing True \
--use_reentrant False \
--dataset_text_field "content" \
--use_flash_attn True \
--use_peft_lora True \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.1 \
--lora_target_modules "all-linear" \
--use_4bit_quantization False
```
Notice that we are using LoRA with rank=8, alpha=16 and targeting all linear layers. We are passing the FSDP config file and finetuning the 70B Llama model on a subset of the [ultrachat dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k).
## The important parts
Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
The first thing to know is that the script uses FSDP for distributed training as the FSDP config has been passed. The [`~trl.SFTTrainer`] class handles all the heavy lifting of creating PEFT model using the peft config that is passed. After that when you call `trainer.train()`, Trainer internally uses 🤗 Accelerate to prepare model, optimizer and trainer using the FSDP config to create FSDP wrapped model which is then trained. The main code snippet is below:
```python
# trainer
trainer = SFTTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
)
trainer.accelerator.print(f"{trainer.model}")
if model_args.use_peft_lora:
# handle PEFT+FSDP case
trainer.model.print_trainable_parameters()
if getattr(trainer.accelerator.state, "fsdp_plugin", None):
from peft.utils.other import fsdp_auto_wrap_policy
fsdp_plugin = trainer.accelerator.state.fsdp_plugin
fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(trainer.model)
# train
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
trainer.train(resume_from_checkpoint=checkpoint)
# saving final model
if trainer.is_fsdp_enabled:
trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT")
trainer.save_model()
```
Here, one main thing to note currently when using FSDP with PEFT is that `use_orig_params` needs to be `False` to realize GPU memory savings. Due to `use_orig_params=False`, the auto wrap policy for FSDP needs to change so that trainable and non-trainable parameters are wrapped separately. This is done by the code snippt below which uses the util function `fsdp_auto_wrap_policy` from PEFT:
```
if getattr(trainer.accelerator.state, "fsdp_plugin", None):
from peft.utils.other import fsdp_auto_wrap_policy
fsdp_plugin = trainer.accelerator.state.fsdp_plugin
fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(trainer.model)
```
## Memory usage
In the above example, the memory consumed per GPU is 72-80 GB (90-98%) as seen in the screenshot below. The slight increase in GPU memory at the end is when saving the model using `FULL_STATE_DICT` state dict type instead of the `SHARDED_STATE_DICT` so that the model has adapter weights that can be loaded normally with `from_pretrained` method during inference:
GPU memory usage for the training run
# Use PEFT QLoRA and FSDP for finetuning large models on multiple GPUs
In this section, we will look at how to use QLoRA and FSDP for finetuning 70B llama model on 2X24GB GPUs. [Answer.AI](https://www.answer.ai/) in collaboration with bitsandbytes and Hugging Face 🤗 open sourced code enabling the usage of FSDP+QLoRA and explained the whole process in their insightful blogpost [You can now train a 70b language model at home](https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html). This is now integrated in Hugging Face ecosystem.
For this, we first need `bitsandbytes>=0.43.3`, `accelerate>=1.0.1`, `transformers>4.44.2`, `trl>0.11.4` and `peft>0.13.0`. We need to set `fsdp_cpu_ram_efficient_loading=true`, `fsdp_use_orig_params=false` and `fsdp_offload_params=true`(cpu offloading) when using Accelerate config. When not using accelerate launcher, you can alternately set the environment variable `export FSDP_CPU_RAM_EFFICIENT_LOADING=true`. Here, we will be using accelerate config and below is the config which can be found at [fsdp_config_qlora.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/fsdp_config_qlora.yaml):
```yml
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: FSDP
downcast_bf16: 'no'
fsdp_config:
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_backward_prefetch: BACKWARD_PRE
fsdp_cpu_ram_efficient_loading: true
fsdp_forward_prefetch: false
fsdp_offload_params: true
fsdp_sharding_strategy: FULL_SHARD
fsdp_state_dict_type: SHARDED_STATE_DICT
fsdp_sync_module_states: true
fsdp_use_orig_params: false
machine_rank: 0
main_training_function: main
mixed_precision: 'no'
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
Launch command is given below which is available at [run_peft_qlora_fsdp.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_qlora_fsdp.sh):
```
accelerate launch --config_file "configs/fsdp_config_qlora.yaml" train.py \
--seed 100 \
--model_name_or_path "meta-llama/Llama-2-70b-hf" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len 2048 \
--num_train_epochs 1 \
--logging_steps 5 \
--log_level "info" \
--logging_strategy "steps" \
--eval_strategy "epoch" \
--save_strategy "epoch" \
--push_to_hub \
--hub_private_repo True \
--hub_strategy "every_save" \
--bf16 True \
--packing True \
--learning_rate 1e-4 \
--lr_scheduler_type "cosine" \
--weight_decay 1e-4 \
--warmup_steps 0 \
--max_grad_norm 1.0 \
--output_dir "llama-sft-qlora-fsdp" \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 2 \
--gradient_checkpointing True \
--use_reentrant True \
--dataset_text_field "content" \
--use_flash_attn True \
--use_peft_lora True \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.1 \
--lora_target_modules "all-linear" \
--use_4bit_quantization True \
--use_nested_quant True \
--bnb_4bit_compute_dtype "bfloat16" \
--bnb_4bit_quant_storage_dtype "bfloat16"
```
Notice the new argument being passed, `bnb_4bit_quant_storage_dtype`, which denotes the data type for packing the 4-bit parameters. For example, when it is set to `bfloat16`, **16/4 = 4** 4-bit params are packed together post quantization. When using mixed precision training with `bfloat16`, `bnb_4bit_quant_storage_dtype` can be either `bfloat16` for pure `bfloat16` finetuning, or `float32` for automatic mixed precision (this consumes more GPU memory). When using mixed precision training with `float16`, `bnb_4bit_quant_storage_dtype` should be set to `float32` for stable automatic mixed precision training.
In terms of training code, the important code changes are:
```diff
...
bnb_config = BitsAndBytesConfig(
load_in_4bit=args.use_4bit_quantization,
bnb_4bit_quant_type=args.bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=args.use_nested_quant,
+ bnb_4bit_quant_storage=quant_storage_dtype,
)
...
model = AutoModelForCausalLM.from_pretrained(
args.model_name_or_path,
quantization_config=bnb_config,
trust_remote_code=True,
attn_implementation="flash_attention_2" if args.use_flash_attn else "eager",
+ dtype=quant_storage_dtype or torch.float32,
)
```
Notice that `dtype` for `AutoModelForCausalLM` is same as the `bnb_4bit_quant_storage` data type. That's it. Everything else is handled by Trainer and TRL.
## Memory usage
In the above example, the memory consumed per GPU is **19.6 GB** while CPU RAM usage is around **107 GB**. When disabling CPU offloading, the GPU memory usage is **35.6 GB/ GPU**. Therefore, what took 16X80GB GPUs for full finetuning, 8X80GB GPUs with FSDP+LoRA, and a couple of 80GB GPUs with DDP+QLoRA, now requires 2X24GB GPUs. This makes finetuning of large models more accessible.
## More resources
You can also refer the [llama-recipes](https://github.com/facebookresearch/llama-recipes/?tab=readme-ov-file#fine-tuning) repo and [Getting started with Llama](https://llama.meta.com/get-started/#fine-tuning) guide on how to finetune using FSDP and PEFT.
## Caveats
1. Merging when using PEFT and FSDP is currently unsupported and will raise error.
2. Passing `modules_to_save` config parameter to is untested at present.
3. GPU Memory saving when using CPU Offloading is untested at present.
4. When using FSDP+QLoRA, `paged_adamw_8bit` currently results in an error when saving a checkpoint.
5. DoRA training with FSDP should work (albeit at lower speed than LoRA). If combined with bitsandbytes (QDoRA), 4-bit quantization should also work, but 8-bit quantization has known issues and is not recommended.
================================================
FILE: docs/source/conceptual_guides/adapter.md
================================================
# Adapters
Adapter-based methods add extra trainable parameters after the attention and fully-connected layers of a frozen pretrained model to reduce memory-usage and speed up training. The method varies depending on the adapter, it could simply be an extra added layer or it could be expressing the weight updates ∆W as a low-rank decomposition of the weight matrix. Either way, the adapters are typically small but demonstrate comparable performance to a fully finetuned model and enable training larger models with fewer resources.
This guide will give you a brief overview of the adapter methods supported by PEFT (if you're interested in learning more details about a specific method, take a look at the linked paper).
## Low-Rank Adaptation (LoRA)
> [!TIP]
> LoRA is one of the most popular PEFT methods and a good starting point if you're just getting started with PEFT. It was originally developed for large language models but it is a tremendously popular training method for diffusion models because of its efficiency and effectiveness.
As mentioned briefly earlier, [LoRA](https://hf.co/papers/2106.09685) is a technique that accelerates finetuning large models while consuming less memory.
LoRA represents the weight updates ∆W with two smaller matrices (called *update matrices*) through low-rank decomposition. These new matrices can be trained to adapt to the new data while keeping the overall number of parameters low. The original weight matrix remains frozen and doesn't receive any further updates. To produce the final results, the original and extra adapted weights are combined. You could also merge the adapter weights with the base model to eliminate inference latency.
This approach has a number of advantages:
* LoRA makes finetuning more efficient by drastically reducing the number of trainable parameters.
* The original pretrained weights are kept frozen, which means you can have multiple lightweight and portable LoRA models for various downstream tasks built on top of them.
* LoRA is orthogonal to other parameter-efficient methods and can be combined with many of them.
* Performance of models finetuned using LoRA is comparable to the performance of fully finetuned models.
In principle, LoRA can be applied to any subset of weight matrices in a neural network to reduce the number of trainable parameters. However, for simplicity and further parameter efficiency, LoRA is typically only applied to the attention blocks in Transformer models. The resulting number of trainable parameters in a LoRA model depends on the size of the update matrices, which is determined mainly by the rank `r` and the shape of the original weight matrix.
Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
## Mixture of LoRA Experts (X-LoRA)
[X-LoRA](https://huggingface.co/papers/2402.07148) is a mixture of experts method for LoRA which works by using dense or sparse gating to dynamically activate LoRA experts. The LoRA experts as well as the base model are frozen during training, resulting in a low parameter count as only the gating layers must be trained. In particular, the gating layers output scalings which (depending on config) are granular on the layer and token level. Additionally, during inference, X-LoRA dynamically activates LoRA adapters to recall knowledge and effectively mix them:
The below graphic demonstrates how the scalings change for different prompts for each token. This highlights the activation of different adapters as the generation progresses and the sequence creates new context.
For each step, X-LoRA requires the base model to be run twice: first, to get hidden states without any LoRA adapters, and secondly, the hidden states are used to calculate scalings which are applied to the LoRA adapters and the model is run a second time. The output of the second run is the result of the model step.
Ultimately, X-LoRA allows the model to reflect upon its knowledge because of the dual forward pass scheme, and dynamically reconfigure the architecture.
## Low-Rank Hadamard Product (LoHa)
Low-rank decomposition can impact performance because the weight updates are limited to the low-rank space, which can constrain a model's expressiveness. However, you don't necessarily want to use a larger rank because it increases the number of trainable parameters. To address this, [LoHa](https://huggingface.co/papers/2108.06098) (a method originally developed for computer vision) was applied to diffusion models where the ability to generate diverse images is an important consideration. LoHa should also work with general model types, but the embedding layers aren't currently implemented in PEFT.
LoHa uses the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_(matrices)) (element-wise product) instead of the matrix product. ∆W is represented by four smaller matrices instead of two - like in LoRA - and each pair of these low-rank matrices are combined with the Hadamard product. As a result, ∆W can have the same number of trainable parameters but a higher rank and expressivity.
## Low-Rank Kronecker Product (LoKr)
[LoKr](https://hf.co/papers/2309.14859) is very similar to LoRA and LoHa, and it is also mainly applied to diffusion models, though you could also use it with other model types. LoKr replaces the matrix product with the [Kronecker product](https://en.wikipedia.org/wiki/Kronecker_product) instead. The Kronecker product decomposition creates a block matrix which preserves the rank of the original weight matrix. Another benefit of the Kronecker product is that it can be vectorized by stacking the matrix columns. This can speed up the process because you're avoiding fully reconstructing ∆W.
## Orthogonal Finetuning (OFT)
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
[OFT](https://hf.co/papers/2306.07280) is a method that primarily focuses on preserving a pretrained model's generative performance in the finetuned model. It tries to maintain the same cosine similarity (hyperspherical energy) between all pairwise neurons in a layer because this better captures the semantic information among neurons. This means OFT is more capable at preserving the subject and it is better for controllable generation (similar to [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet)).
OFT preserves the hyperspherical energy by learning an orthogonal transformation for neurons to keep the cosine similarity between them unchanged. In practice, this means taking the matrix product of an orthogonal matrix with the pretrained weight matrix. However, to be parameter-efficient, the orthogonal matrix is represented as a block-diagonal matrix with rank `r` blocks. Whereas LoRA reduces the number of trainable parameters with low-rank structures, OFT reduces the number of trainable parameters with a sparse block-diagonal matrix structure.
## Orthogonal Butterfly (BOFT)
[BOFT](https://hf.co/papers/2311.06243) is an improved orthogonal finetuning method that focuses on preserving a pretrained model's generative capabilities while being significantly more parameter-efficient than standard OFT. Like OFT, BOFT maintains the same cosine similarity (hyperspherical energy) between all pairwise neurons in a layer by applying an orthogonal transformation to the pretrained weight matrix, ensuring the semantic relationships among neurons are preserved.
Instead of using a block-diagonal orthogonal matrix, BOFT factorizes the orthogonal transformation into a product of **sparse butterfly matrices** (originally introduced in the [Cooley–Tukey FFT](https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm)). Unlike OFT's block-diagonal rotations, which only mix inputs within each block, the butterfly structure guarantees that every input can influence every output, producing a **dense connectivity** with just `O(d log d)` parameters. This factorization preserves expressivity while drastically reducing the parameter count compared to OFT (at the expense of computation time).
In practice, BOFT multiplies each pretrained weight matrix by a sequence of butterfly-structured orthogonal factors, enabling efficient and expressive neuron rotations. This makes BOFT well-suited for controllable generation and tasks where maintaining the pretrained model's subject representation is critical, while also scaling to larger models with lower memory and compute overhead.
## Adaptive Low-Rank Adaptation (AdaLoRA)
[AdaLoRA](https://hf.co/papers/2303.10512) manages the parameter budget introduced from LoRA by allocating more parameters - in other words, a higher rank `r` - for important weight matrices that are better adapted for a task and pruning less important ones. The rank is controlled by a method similar to singular value decomposition (SVD). The ∆W is parameterized with two orthogonal matrices and a diagonal matrix which contains singular values. This parametrization method avoids iteratively applying SVD which is computationally expensive. Based on this method, the rank of ∆W is adjusted according to an importance score. ∆W is divided into triplets and each triplet is scored according to its contribution to model performance. Triplets with low importance scores are pruned and triplets with high importance scores are kept for finetuning.
Training with AdaLoRA has three phases: the init phase, the budgeting phase and the final phase. In the initial phase, no budgeting is applied, therefore the ranks are not touched. During the budgeting phase the process described above is applied and the rank is redistributed according to a budget, aiming to give more important adapters more rank and less important layers less. When reaching the final phase, budgeting has ended, the ranks are redistributed but we may continue training for a while with the redistributed ranks to further improve performance.
## Llama-Adapter
[Llama-Adapter](https://hf.co/papers/2303.16199) is a method for adapting Llama into an instruction-following model. To help adapt the model for instruction-following, the adapter is trained with a 52K instruction-output dataset.
A set of learnable adaption prompts are prefixed to the input instruction tokens. These are inserted into the upper layers of the model because it is better to learn with the higher-level semantics of the pretrained model. The instruction-output tokens prefixed to the input guide the adaption prompt to generate a contextual response.
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
To avoid adding noise to the tokens, the adapter uses zero-initialized attention. On top of this, the adapter adds a learnable gating factor (initialized with zeros) to progressively add information to the model during training. This prevents overwhelming the model's pretrained knowledge with the newly learned instructions.
## Householder Reflection Adaptation (HRA)
[HRA](https://huggingface.co/papers/2405.17484) provides a new perspective connecting LoRA to OFT, which means it can harness the advantages of both strategies, reduce parameters and computation costs while penalizing the loss of pre-training knowledge.
Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation
HRA constructs a chain of `r` trainable Householder reflections (HRs). Because the Householder reflection matrix is an orthogonal matrix and the product of orthogonal matrices is also an orthogonal matrix, HRA satisfies the theoretical guarantee of Orthogonal Finetuning (OFT). Meanwhile, HRA can also be viewed as a low-rank fine-tuning adapter by rewriting formula.
The higher `r`, the more trainable parameters, resulting in a larger model capacity and better performance. Besides, due to the chain structure, the orthogonality of HR planes impacts the capacity and regularity of HRA. To achieve a trade-off between the model capacity and regularity, an orthogonality regularizer of the HR planes is added to the loss function. The weight \\(\lambda\\) can control the strength of the regularizer.
## Bone
Bone was deprecated and removed in PEFT v0.19.0 in favor of [MiSS](https://huggingface.co/papers/2409.15371) (new version of paper: "MiSS: Balancing LoRA Performance and Efficiency with Simple Shard Sharing"). If you already have a Bone checkpoint, you can use `/scripts/convert-bone-to-miss.py` to convert it into a MiSS checkpoint and proceed with training using MiSS.
## MiSS
[MiSS](https://huggingface.co/papers/2409.15371) MiSS (Matrix Shard Sharing) is a novel Parameter-Efficient Fine-Tuning (PEFT) method designed to address the trade-off between adaptability and efficiency in Large Language Models. The core approach of MiSS involves a simple shard-sharing mechanism. It achieves low-rank adaptation by decomposing a weight matrix into multiple fragments and then utilizing a shared, trainable "common fragment." The final low-rank update matrix is constructed by replicating these shared, partitioned shards. (MiSS is a novel PEFT method that adopts a low-rank structure, requires only a single trainable matrix, and introduces a new update mechanism distinct from LoRA, achieving an excellent balance between performance and efficiency.)
MiSS: Balancing LoRA Performance and Efficiency with Simple Shard Sharing
Intuitively, the shape of a single trainable matrix in MiSS is consistent with `lora_B`, so the `r` parameter in MiSS is less than the `r` in LoRA by (`in_feature * r`).
Note: Bat's r (b) is special and requires that weight W satisfies the conditions `in_features % r == 0` and `out_features % r == 0`. Additionally, when `in_features == out_features` and MiSS-r equals LoRA-r, MiSS's number of trainable parameters is only half that of LoRA.
Although the nonlinear updates of Bat bring some performance improvements, they also increase computational overhead. Its main purpose is to provide researchers with a direction for improvement. Therefore, we recommend fine-tuning the comprehensive MiSS model instead.
================================================
FILE: docs/source/conceptual_guides/ia3.md
================================================
# IA3
This conceptual guide gives a brief overview of [IA3](https://huggingface.co/papers/2205.05638), a parameter-efficient fine tuning technique that is
intended to improve over [LoRA](./lora).
To make fine-tuning more efficient, IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations)
rescales inner activations with learned vectors. These learned vectors are injected in the attention and feedforward modules
in a typical transformer-based architecture. These learned vectors are the only trainable parameters during fine-tuning, and thus the original
weights remain frozen. Dealing with learned vectors (as opposed to learned low-rank updates to a weight matrix like LoRA)
keeps the number of trainable parameters much smaller.
Being similar to LoRA, IA3 carries many of the same advantages:
* IA3 makes fine-tuning more efficient by drastically reducing the number of trainable parameters. (For T0, an IA3 model only has about 0.01% trainable parameters, while even LoRA has > 0.1%)
* The original pre-trained weights are kept frozen, which means you can have multiple lightweight and portable IA3 models for various downstream tasks built on top of them.
* Performance of models fine-tuned using IA3 is comparable to the performance of fully fine-tuned models.
* IA3 does not add any inference latency because adapter weights can be merged with the base model.
In principle, IA3 can be applied to any subset of weight matrices in a neural network to reduce the number of trainable
parameters. Following the authors' implementation, IA3 weights are added to the key, value and feedforward layers
of a Transformer model. To be specific, for transformer models, IA3 weights are added to the outputs of key and value layers, and to the input of the second feedforward layer
in each transformer block.
Given the target layers for injecting IA3 parameters, the number of trainable parameters
can be determined based on the size of the weight matrices.
## Common IA3 parameters in PEFT
As with other methods supported by PEFT, to fine-tune a model using IA3, you need to:
1. Instantiate a base model.
2. Create a configuration (`IA3Config`) where you define IA3-specific parameters.
3. Wrap the base model with `get_peft_model()` to get a trainable `PeftModel`.
4. Train the `PeftModel` as you normally would train the base model.
`IA3Config` allows you to control how IA3 is applied to the base model through the following parameters:
- `target_modules`: The modules (for example, attention blocks) to apply the IA3 vectors.
- `feedforward_modules`: The list of modules to be treated as feedforward layers in `target_modules`. While learned vectors are multiplied with
the output activation for attention blocks, the vectors are multiplied with the input for classic feedforward layers. Note that `feedforward_modules` must be a subset of `target_modules`.
- `modules_to_save`: List of modules apart from IA3 layers to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task.
## Example Usage
For the task of sequence classification, one can initialize the IA3 config for a Llama model as follows:
```py
peft_config = IA3Config(
task_type=TaskType.SEQ_CLS, target_modules=["k_proj", "v_proj", "down_proj"], feedforward_modules=["down_proj"]
)
```
================================================
FILE: docs/source/conceptual_guides/oft.md
================================================
# Orthogonal Finetuning (OFT and BOFT)
This conceptual guide gives a brief overview of [OFT](https://huggingface.co/papers/2306.07280), [OFTv2](https://huggingface.co/papers/2506.19847) and [BOFT](https://huggingface.co/papers/2311.06243), a parameter-efficient fine-tuning technique that utilizes orthogonal matrix to multiplicatively transform the pretrained weight matrices.
To achieve efficient fine-tuning, OFT represents the weight updates with an orthogonal transformation. The orthogonal transformation is parameterized by an orthogonal matrix multiplied to the pretrained weight matrix. These new matrices can be trained to adapt to the new data while keeping the overall number of changes low. The original weight matrix remains frozen and doesn't receive any further adjustments. To produce the final results, both the original and the adapted weights are multiplied togethor.
Orthogonal Butterfly (BOFT) generalizes OFT with Butterfly factorization and further improves its parameter efficiency and finetuning flexibility. In short, OFT can be viewed as a special case of BOFT. Different from LoRA that uses additive low-rank weight updates, BOFT uses multiplicative orthogonal weight updates. The comparison is shown below.
BOFT has some advantages compared to LoRA:
* BOFT proposes a simple yet generic way to finetune pretrained models to downstream tasks, yielding a better preservation of pretraining knowledge and a better parameter efficiency.
* Through the orthogonality, BOFT introduces a structural constraint, i.e., keeping the [hyperspherical energy](https://huggingface.co/papers/1805.09298) unchanged during finetuning. This can effectively reduce the forgetting of pretraining knowledge.
* BOFT uses the butterfly factorization to efficiently parameterize the orthogonal matrix, which yields a compact yet expressive learning space (i.e., hypothesis class).
* The sparse matrix decomposition in BOFT brings in additional inductive biases that are beneficial to generalization.
In principle, BOFT can be applied to any subset of weight matrices in a neural network to reduce the number of trainable parameters. Given the target layers for injecting BOFT parameters, the number of trainable parameters can be determined based on the size of the weight matrices.
## Merge OFT/BOFT weights into the base model
Similar to LoRA, the weights learned by OFT/BOFT can be integrated into the pretrained weight matrices using the merge_and_unload() function. This function merges the adapter weights with the base model which allows you to effectively use the newly merged model as a standalone model.
This works because during training, the orthogonal weight matrix (R in the diagram above) and the pretrained weight matrices are separate. But once training is complete, these weights can actually be merged (multiplied) into a new weight matrix that is equivalent.
## Utils for OFT / BOFT
### Common OFT / BOFT parameters in PEFT
As with other methods supported by PEFT, to fine-tune a model using OFT or BOFT, you need to:
1. Instantiate a base model.
2. Create a configuration (`OFTConfig` or `BOFTConfig`) where you define OFT/BOFT-specific parameters.
3. Wrap the base model with `get_peft_model()` to get a trainable `PeftModel`.
4. Train the `PeftModel` as you normally would train the base model.
### OFT-specific parameters
`OFTConfig` allows you to control how OFT is applied to the base model through the following parameters:
- `r`: OFT rank, number of OFT blocks per injected layer. **Bigger** `r` results in more sparse update matrices with **fewer** trainable paramters. **Note**: You can only specify either `r` or `oft_block_size`, but not both simultaneously, because `r` × `oft_block_size` = layer dimension. For simplicity, we let the user speficy either `r` or `oft_block_size` and infer the other one. Default set to `r = 0`, the user is advised to set the `oft_block_size` instead for better clarity.
- `oft_block_size`: OFT block size across different layers. **Bigger** `oft_block_size` results in more dense update matrices with **more** trainable parameters. **Note**: Please choose `oft_block_size` to be divisible by layer's input dimension (`in_features`), e.g., 4, 8, 16. You can only specify either `r` or `oft_block_size`, but not both simultaneously, because `r` × `oft_block_size` = layer dimension. For simplicity, we let the user speficy either `r` or `oft_block_size` and infer the other one. Default set to `oft_block_size = 32`.
- `use_cayley_neumann`: Specifies whether to use the Cayley-Neumann parameterization (efficient but approximate) or the vanilla Cayley parameterization (exact but computationally expensive because of matrix inverse). We recommend to set it to `True` for better efficiency, but performance may be slightly worse because of the approximation error. Please test both settings (`True` and `False`) depending on your needs. Default is `False`.
- `module_dropout`: The multiplicative dropout probability, by setting OFT blocks to identity during training, similar to the dropout layer in LoRA.
- `bias`: specify if the `bias` parameters should be trained. Can be `"none"`, `"all"` or `"oft_only"`.
- `target_modules`: The modules (for example, attention blocks) to inject the OFT matrices.
- `modules_to_save`: List of modules apart from OFT matrices to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task.
### BOFT-specific parameters
`BOFTConfig` allows you to control how BOFT is applied to the base model through the following parameters:
- `boft_block_size`: the BOFT matrix block size across different layers, expressed in `int`. **Bigger** `boft_block_size` results in more dense update matrices with **more** trainable parameters. **Note**, please choose `boft_block_size` to be divisible by most layer's input dimension (`in_features`), e.g., 4, 8, 16. Also, please only
specify either `boft_block_size` or `boft_block_num`, but not both simultaneously or leaving both to 0, because `boft_block_size` x `boft_block_num` must equal the layer's input dimension.
- `boft_block_num`: the number of BOFT matrix blocks across different layers, expressed in `int`. **Bigger** `boft_block_num` result in sparser update matrices with **fewer** trainable parameters. **Note**, please choose `boft_block_num` to be divisible by most layer's input dimension (`in_features`), e.g., 4, 8, 16. Also, please only
specify either `boft_block_size` or `boft_block_num`, but not both simultaneously or leaving both to 0, because `boft_block_size` x `boft_block_num` must equal the layer's input dimension.
- `boft_n_butterfly_factor`: the number of butterfly factors. **Note**, for `boft_n_butterfly_factor=1`, BOFT is the same as vanilla OFT, for `boft_n_butterfly_factor=2`, the effective block size of OFT becomes twice as big and the number of blocks become half.
- `bias`: specify if the `bias` parameters should be trained. Can be `"none"`, `"all"` or `"boft_only"`.
- `boft_dropout`: specify the probability of multiplicative dropout.
- `target_modules`: The modules (for example, attention blocks) to inject the OFT/BOFT matrices.
- `modules_to_save`: List of modules apart from OFT/BOFT matrices to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task.
## OFT Example Usage
For using OFT for quantized finetuning with [TRL](https://github.com/huggingface/trl) for `SFT`, `PPO`, or `DPO` fine-tuning, follow the following outline:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from trl import SFTTrainer
from peft import OFTConfig
if use_quantization:
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_storage=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
"model_name",
quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained("model_name")
# Configure OFT
peft_config = OFTConfig(
oft_block_size=32,
use_cayley_neumann=True,
target_modules="all-linear",
bias="none",
task_type="CAUSAL_LM"
)
trainer = SFTTrainer(
model=model,
train_dataset=ds['train'],
peft_config=peft_config,
processing_class=tokenizer,
args=training_arguments,
data_collator=collator,
)
trainer.train()
```
## BOFT Example Usage
For an example of the BOFT method application to various downstream tasks, please refer to the following guides:
Take a look at the following step-by-step guides on how to finetune a model with BOFT:
- [Dreambooth finetuning with BOFT](https://github.com/huggingface/peft/blob/main/examples/boft_dreambooth/boft_dreambooth.md)
- [Controllable generation finetuning with BOFT (ControlNet)](https://github.com/huggingface/peft/blob/main/examples/boft_controlnet/boft_controlnet.md)
For the task of image classification, one can initialize the BOFT config for a DinoV2 model as follows:
```py
import transformers
from transformers import AutoModelForSeq2SeqLM, BOFTConfig
from peft import BOFTConfig, get_peft_model
config = BOFTConfig(
boft_block_size=4,
boft_n_butterfly_factor=2,
target_modules=["query", "value", "key", "output.dense", "mlp.fc1", "mlp.fc2"],
boft_dropout=0.1,
bias="boft_only",
modules_to_save=["classifier"],
)
model = transformers.Dinov2ForImageClassification.from_pretrained(
"facebook/dinov2-large",
num_labels=100,
)
boft_model = get_peft_model(model, config)
```
================================================
FILE: docs/source/conceptual_guides/prompting.md
================================================
# Soft prompts
Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is increasing interest in more efficient training methods such as *prompting*. Prompting primes a frozen pretrained model for a specific downstream task by including a text prompt that describes the task or even demonstrates an example of the task. With prompting, you can avoid fully training a separate model for each downstream task, and use the same frozen pretrained model instead. This is a lot easier because you can use the same model for several different tasks, and it is significantly more efficient to train and store a smaller set of prompt parameters than to train all the model's parameters.
There are two categories of prompting methods:
- hard prompts are manually handcrafted text prompts with discrete input tokens; the downside is that it requires a lot of effort to create a good prompt
- soft prompts are learnable tensors concatenated with the input embeddings that can be optimized to a dataset; the downside is that they aren't human readable because you aren't matching these "virtual tokens" to the embeddings of a real word
This conceptual guide provides a brief overview of the soft prompt methods included in 🤗 PEFT: prompt tuning, prefix tuning, P-tuning, and multitask prompt tuning.
## Prompt tuning
Only train and store a significantly smaller set of task-specific prompt parameters (image source).
[Prompt tuning](https://hf.co/papers/2104.08691) was developed for text classification tasks on T5 models, and all downstream tasks are cast as a text generation task. For example, sequence classification usually assigns a single class label to a sequence of text. By casting it as a text generation task, the tokens that make up the class label are *generated*. Prompts are added to the input as a series of tokens. Typically, the model parameters are fixed which means the prompt tokens are also fixed by the model parameters.
The key idea behind prompt tuning is that prompt tokens have their own parameters that are updated independently. This means you can keep the pretrained model's parameters frozen, and only update the gradients of the prompt token embeddings. The results are comparable to the traditional method of training the entire model, and prompt tuning performance scales as model size increases.
Take a look at [Prompt tuning for causal language modeling](../task_guides/clm-prompt-tuning) for a step-by-step guide on how to train a model with prompt tuning.
## Prefix tuning
Optimize the prefix parameters for each task (image source).
[Prefix tuning](https://hf.co/papers/2101.00190) was designed for natural language generation (NLG) tasks on GPT models. It is very similar to prompt tuning; prefix tuning also prepends a sequence of task-specific vectors to the input that can be trained and updated while keeping the rest of the pretrained model's parameters frozen.
The main difference is that the prefix parameters are inserted in **all** of the model layers, whereas prompt tuning only adds the prompt parameters to the model input embeddings. The prefix parameters are also optimized by a separate feed-forward network (FFN) instead of training directly on the soft prompts because it causes instability and hurts performance. The FFN is discarded after updating the soft prompts.
As a result, the authors found that prefix tuning demonstrates comparable performance to fully finetuning a model, despite having 1000x fewer parameters, and it performs even better in low-data settings.
Take a look at [Prefix tuning for conditional generation](../task_guides/seq2seq-prefix-tuning) for a step-by-step guide on how to train a model with prefix tuning.
## P-tuning
Prompt tokens can be inserted anywhere in the input sequence, and they are optimized by a prompt encoder (image source).
[P-tuning](https://hf.co/papers/2103.10385) is designed for natural language understanding (NLU) tasks and all language models.
It is another variation of a soft prompt method; P-tuning also adds a trainable embedding tensor that can be optimized to find better prompts, and it uses a prompt encoder (a bidirectional long-short term memory network or LSTM) to optimize the prompt parameters. Unlike prefix tuning though:
- the prompt tokens can be inserted anywhere in the input sequence, and it isn't restricted to only the beginning
- the prompt tokens are only added to the input instead of adding them to every layer of the model
- introducing *anchor* tokens can improve performance because they indicate characteristics of a component in the input sequence
The results suggest that P-tuning is more efficient than manually crafting prompts, and it enables GPT-like models to compete with BERT-like models on NLU tasks.
Take a look at [P-tuning for sequence classification](../task_guides/ptuning-seq-classification) for a step-by-step guide on how to train a model with P-tuning.
## Multitask prompt tuning
Multitask prompt tuning enables parameter-efficient transfer learning.
[Multitask prompt tuning (MPT)](https://hf.co/papers/2303.02861) learns a single prompt from data for multiple task types that can be shared for different target tasks. Other existing approaches learn a separate soft prompt for each task that need to be retrieved or aggregated for adaptation to target tasks. MPT consists of two stages:
1. source training - for each task, its soft prompt is decomposed into task-specific vectors. The task-specific vectors are multiplied together to form another matrix W, and the Hadamard product is used between W and a shared prompt matrix P to generate a task-specific prompt matrix. The task-specific prompts are distilled into a single prompt matrix that is shared across all tasks. This prompt is trained with multitask training.
2. target adaptation - to adapt the single prompt for a target task, a target prompt is initialized and expressed as the Hadamard product of the shared prompt matrix and the task-specific low-rank prompt matrix.
CPT optimizing only specific token embeddings while keeping the rest of the model frozen (image source).
[Context-Aware Prompt Tuning (CPT)](https://huggingface.co/papers/2410.17222) is designed to enhance few-shot classification by refining only context embeddings.
This approach combines ideas from In-Context Learning (ICL), Prompt Tuning (PT), and adversarial optimization, focusing on making model adaptation both parameter-efficient and effective.
In CPT, only specific context token embeddings are optimized, while the rest of the model remains frozen.
To prevent overfitting and maintain stability, CPT uses controlled perturbations to limit the allowed changes to context embeddings within a defined range.
Additionally, to address the phenomenon of recency bias—where examples near the end of the context tend to be prioritized over earlier ones—CPT applies a decay loss factor.
Take a look at [Example](https://github.com/huggingface/peft/blob/main/examples/cpt_finetuning/README.md) for a step-by-step guide on how to train a model with CPT.
================================================
FILE: docs/source/developer_guides/checkpoint.md
================================================
# PEFT checkpoint format
This document describes how PEFT's checkpoint files are structured and how to convert between the PEFT format and other formats.
## PEFT files
PEFT (parameter-efficient fine-tuning) methods only update a small subset of a model's parameters rather than all of them. This is nice because checkpoint files can generally be much smaller than the original model files and are easier to store and share. However, this also means that to load a PEFT model, you need to have the original model available as well.
When you call [`~PeftModel.save_pretrained`] on a PEFT model, the PEFT model saves three files, described below:
1. `adapter_model.safetensors` or `adapter_model.bin`
By default, the model is saved in the `safetensors` format, a secure alternative to the `bin` format, which is known to be susceptible to [security vulnerabilities](https://huggingface.co/docs/hub/security-pickle) because it uses the pickle utility under the hood. Both formats store the same `state_dict` though, and are interchangeable.
The `state_dict` only contains the parameters of the adapter module, not the base model. To illustrate the difference in size, a normal BERT model requires ~420MB of disk space, whereas an IA³ adapter on top of this BERT model only requires ~260KB.
2. `adapter_config.json`
The `adapter_config.json` file contains the configuration of the adapter module, which is necessary to load the model. Below is an example of an `adapter_config.json` for an IA³ adapter with standard settings applied to a BERT model:
```json
{
"auto_mapping": {
"base_model_class": "BertModel",
"parent_library": "transformers.models.bert.modeling_bert"
},
"base_model_name_or_path": "bert-base-uncased",
"fan_in_fan_out": false,
"feedforward_modules": [
"output.dense"
],
"inference_mode": true,
"init_ia3_weights": true,
"modules_to_save": null,
"peft_type": "IA3",
"revision": null,
"target_modules": [
"key",
"value",
"output.dense"
],
"task_type": null
}
```
The configuration file contains:
- the adapter module type stored, `"peft_type": "IA3"`
- information about the base model like `"base_model_name_or_path": "bert-base-uncased"`
- the revision of the model (if any), `"revision": null`
If the base model is not a pretrained Transformers model, the latter two entries will be `null`. Other than that, the settings are all related to the specific IA³ adapter that was used to fine-tune the model.
3. `README.md`
The generated `README.md` is the model card of a PEFT model and contains a few pre-filled entries. The intent of this is to make it easier to share the model with others and to provide some basic information about the model. This file is not needed to load the model.
## Convert to PEFT format
When converting from another format to the PEFT format, we require both the `adapter_model.safetensors` (or `adapter_model.bin`) file and the `adapter_config.json` file.
### adapter_model
For the model weights, it is important to use the correct mapping from parameter name to value for PEFT to load the file. Getting this mapping right is an exercise in checking the implementation details, as there is no generally agreed upon format for PEFT adapters.
Fortunately, figuring out this mapping is not overly complicated for common base cases. Let's look at a concrete example, the [`LoraLayer`](https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py):
```python
# showing only part of the code
class LoraLayer(BaseTunerLayer):
# All names of layers that may contain (trainable) adapter weights
adapter_layer_names = ("lora_A", "lora_B", "lora_embedding_A", "lora_embedding_B")
# All names of other parameters that may contain adapter-related parameters
other_param_names = ("r", "lora_alpha", "scaling", "lora_dropout")
def __init__(self, base_layer: nn.Module, **kwargs) -> None:
self.base_layer = base_layer
self.r = {}
self.lora_alpha = {}
self.scaling = {}
self.lora_dropout = nn.ModuleDict({})
self.lora_A = nn.ModuleDict({})
self.lora_B = nn.ModuleDict({})
# For Embedding layer
self.lora_embedding_A = nn.ParameterDict({})
self.lora_embedding_B = nn.ParameterDict({})
# Mark the weight as unmerged
self._disable_adapters = False
self.merged_adapters = []
self.use_dora: dict[str, bool] = {}
self.lora_magnitude_vector: Optional[torch.nn.ParameterDict] = None # for DoRA
self._caches: dict[str, Any] = {}
self.kwargs = kwargs
```
In the `__init__` code used by all `LoraLayer` classes in PEFT, there are a bunch of parameters used to initialize the model, but only a few are relevant for the checkpoint file: `lora_A`, `lora_B`, `lora_embedding_A`, and `lora_embedding_B`. These parameters are listed in the class attribute `adapter_layer_names` and contain the learnable parameters, so they must be included in the checkpoint file. All the other parameters, like the rank `r`, are derived from the `adapter_config.json` and must be included there (unless the default value is used).
Let's check the `state_dict` of a PEFT LoRA model applied to BERT. When printing the first five keys using the default LoRA settings (the remaining keys are the same, just with different layer numbers), we get:
- `base_model.model.encoder.layer.0.attention.self.query.lora_A.weight`
- `base_model.model.encoder.layer.0.attention.self.query.lora_B.weight`
- `base_model.model.encoder.layer.0.attention.self.value.lora_A.weight`
- `base_model.model.encoder.layer.0.attention.self.value.lora_B.weight`
- `base_model.model.encoder.layer.1.attention.self.query.lora_A.weight`
- etc.
Let's break this down:
- By default, for BERT models, LoRA is applied to the `query` and `value` layers of the attention module. This is why you see `attention.self.query` and `attention.self.value` in the key names for each layer.
- LoRA decomposes the weights into two low-rank matrices, `lora_A` and `lora_B`. This is where `lora_A` and `lora_B` come from in the key names.
- These LoRA matrices are implemented as `nn.Linear` layers, so the parameters are stored in the `.weight` attribute (`lora_A.weight`, `lora_B.weight`).
- By default, LoRA isn't applied to BERT's embedding layer, so there are _no entries_ for `lora_A_embedding` and `lora_B_embedding`.
- The keys of the `state_dict` always start with `"base_model.model."`. The reason is that, in PEFT, we wrap the base model inside a tuner-specific model (`LoraModel` in this case), which itself is wrapped in a general PEFT model (`PeftModel`). For this reason, these two prefixes are added to the keys. When converting to the PEFT format, it is required to add these prefixes.
> [!TIP]
> This last point is not true for prefix tuning techniques like prompt tuning. There, the extra embeddings are directly stored in the `state_dict` without any prefixes added to the keys.
When inspecting the parameter names in the loaded model, you might be surprised to find that they look a bit different, e.g. `base_model.model.encoder.layer.0.attention.self.query.lora_A.default.weight`. The difference is the *`.default`* part in the second to last segment. This part exists because PEFT generally allows the addition of multiple adapters at once (using an `nn.ModuleDict` or `nn.ParameterDict` to store them). For example, if you add another adapter called "other", the key for that adapter would be `base_model.model.encoder.layer.0.attention.self.query.lora_A.other.weight`.
When you call [`~PeftModel.save_pretrained`], the adapter name is stripped from the keys. The reason is that the adapter name is not an important part of the model architecture; it is just an arbitrary name. When loading the adapter, you could choose a totally different name, and the model would still work the same way. This is why the adapter name is not stored in the checkpoint file.
> [!TIP]
> If you call `save_pretrained("some/path")` and the adapter name is not `"default"`, the adapter is stored in a sub-directory with the same name as the adapter. So if the name is "other", it would be stored inside of `some/path/other`.
In some circumstances, deciding which values to add to the checkpoint file can become a bit more complicated. For example, in PEFT, DoRA is implemented as a special case of LoRA. If you want to convert a DoRA model to PEFT, you should create a LoRA checkpoint with extra entries for DoRA. You can see this in the `__init__` of the previous `LoraLayer` code:
```python
self.lora_magnitude_vector: Optional[torch.nn.ParameterDict] = None # for DoRA
```
This indicates that there is an optional extra parameter per layer for DoRA.
### adapter_config
All the other information needed to load a PEFT model is contained in the `adapter_config.json` file. Let's check this file for a LoRA model applied to BERT:
```json
{
"alpha_pattern": {},
"auto_mapping": {
"base_model_class": "BertModel",
"parent_library": "transformers.models.bert.modeling_bert"
},
"base_model_name_or_path": "bert-base-uncased",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 8,
"lora_dropout": 0.0,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 8,
"rank_pattern": {},
"revision": null,
"target_modules": [
"query",
"value"
],
"task_type": null,
"use_dora": false,
"use_rslora": false
}
```
This contains a lot of entries, and at first glance, it could feel overwhelming to figure out all the right values to put in there. However, most of the entries are not necessary to load the model. This is either because they use the default values and don't need to be added or because they only affect the initialization of the LoRA weights, which is irrelevant when it comes to loading the model. If you find that you don't know what a specific parameter does, e.g., `"use_rslora",` don't add it, and you should be fine. Also note that as more options are added, this file will get more entries in the future, but it should be backward compatible.
At the minimum, you should include the following entries:
```json
{
"target_modules": ["query", "value"],
"peft_type": "LORA"
}
```
However, adding as many entries as possible, like the rank `r` or the `base_model_name_or_path` (if it's a Transformers model) is recommended. This information can help others understand the model better and share it more easily. To check which keys and values are expected, check out the [config.py](https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/config.py) file (as an example, this is the config file for LoRA) in the PEFT source code.
## Model storage
In some circumstances, you might want to store the whole PEFT model, including the base weights. This can be necessary if, for instance, the base model is not available to the users trying to load the PEFT model. You can merge the weights first or convert it into a Transformer model.
### Merge the weights
The most straightforward way to store the whole PEFT model is to merge the adapter weights into the base weights:
```python
merged_model = model.merge_and_unload()
merged_model.save_pretrained(...)
```
There are some disadvantages to this approach, though:
- Once [`~LoraModel.merge_and_unload`] is called, you get a basic model without any PEFT-specific functionality. This means you can't use any of the PEFT-specific methods anymore.
- You cannot unmerge the weights, load multiple adapters at once, disable the adapter, etc.
- Not all PEFT methods support merging weights.
- Some PEFT methods may generally allow merging, but not with specific settings (e.g. when using certain quantization techniques).
- The whole model will be much larger than the PEFT model, as it will contain all the base weights as well.
But inference with a merged model should be a bit faster.
### Convert to a Transformers model
Another way to save the whole model, assuming the base model is a Transformers model, is to use this hacky approach to directly insert the PEFT weights into the base model and save it, which only works if you "trick" Transformers into believing the PEFT model is not a PEFT model. This only works with LoRA because other adapters are not implemented in Transformers.
```python
model = ... # the PEFT model
...
# after you finish training the model, save it in a temporary location
model.save_pretrained()
# now load this model directly into a transformers model, without the PEFT wrapper
# the PEFT weights are directly injected into the base model
model_loaded = AutoModel.from_pretrained()
# now make the loaded model believe that it is _not_ a PEFT model
model_loaded._hf_peft_config_loaded = False
# now when we save it, it will save the whole model
model_loaded.save_pretrained()
# or upload to Hugging Face Hub
model_loaded.push_to_hub()
```
================================================
FILE: docs/source/developer_guides/contributing.md
================================================
# Contribute to PEFT
We are happy to accept contributions to PEFT. If you plan to contribute, please read this to make the process as smooth as possible.
## Installation
For code contributions to PEFT, you should choose the ["source"](../install#source) installation method.
If you are new to creating a pull request, follow the [Creating a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) guide by GitHub.
## Tests and code quality checks
Regardless of the contribution type (unless it’s only about the docs), you should run tests and code quality checks before creating a PR to ensure your contribution doesn’t break anything and follows the project standards.
We provide a Makefile to execute the necessary tests. Run the code below for the unit test:
```sh
make test
```
Run one of the following to either only check or check and fix code quality and style:
```sh
make quality # just check
make style # check and fix
```
You can also set up [`pre-commit`](https://pre-commit.com/) to run these fixes
automatically as Git commit hooks.
```bash
$ pip install pre-commit
$ pre-commit install
```
Running all the tests can take a while, so during development it can be more efficient to only [run tests specific to your change](https://docs.pytest.org/en/6.2.x/usage.html#specifying-tests-selecting-tests), e.g. via:
```sh
pytest tests/ -k
```
This should finish much quicker and allow for faster iteration.
If your change is specific to a hardware setting (e.g., it requires CUDA), take a look at [tests/test_gpu_examples.py](https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/tests/test_gpu_examples.py) and [tests/test_common_gpu.py](https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/tests/test_common_gpu.py) to see if it makes sense to add tests there. If your change could have an effect on saving and loading models, please run the tests with the `--regression` flag to trigger regression tests.
It can happen that while you’re working on your PR, the underlying code base changes due to other changes being merged. If that happens – especially when there is a merge conflict – please update your branch with the latest changes. This can be a merge or a rebase, and we'll squash and merge the PR once it’s ready. If possible, avoid force pushes to make reviews easier.
## PR description
When opening a PR, please provide a nice description of the change you're proposing. If it relates to other issues or PRs, please reference them. Providing a good description not only helps the reviewers review your code better and faster, it can also be used later (as a basis) for the commit message which helps with long term maintenance of the project.
If your code makes some non-trivial changes, it may also be a good idea to add comments to the code to explain those changes. For example, if you had to iterate on your implementation multiple times because the most obvious way didn’t work, it’s a good indication that a code comment is needed.
## Bugfixes
Please give a description of the circumstances that led to the bug. If there is an existing issue, please link to it (e.g., “Resolves #12345”).
Ideally when a bugfix is provided, it should be accompanied by a test for the bug. The test should fail with the current code and pass with the bugfix. Add a comment to the test that references the issue or PR. Without a test, it is more difficult to prevent regressions in the future.
## Documentation improvements
We are happy to have fixes for broken links and missing or unclear documentation. Taking care of examples, making
sure that they are up-to-date and running fine in this fast moving environment is also highly appreciated.
Please refrain from sending pull requests that *only* correct typing errors as these generally create more work
than they safe. Such changes are better combined with more substantial fixes (such as fixing broken links or
extending/updating documentation).
## Add a new fine-tuning method
New parameter-efficient fine-tuning methods are developed all the time. If you would like to add a new and promising method to PEFT, please follow these steps.
1. Before you start to implement the new method, please open a [GitHub issue](https://github.com/huggingface/peft/issues) with your proposal. This way, the maintainers can give you some early feedback.
2. Please add a link to the source (usually a paper) of the method. The paper should be in a final state to avoid changing requirements during development (e.g. due to reviewer feedback).
3. When implementing the method, it makes sense to look for existing implementations that already exist as a guide. Moreover, when you structure your code, please take inspiration from the other PEFT methods. For example, if your method is similar to LoRA, it makes sense to structure your code similarly or even reuse some functions or classes where it makes sense (some code duplication is okay, but don’t overdo it).
4. Ideally, in addition to the implementation of the new method, there should also be
- [examples](https://github.com/huggingface/peft/tree/main/examples) (notebooks, scripts)
- [documentation](https://github.com/huggingface/peft/tree/main/docs/source)
- [extensive test suite](https://github.com/huggingface/peft/tree/main/tests) that proves the method correctly integrates with PEFT
- [experimental setup](https://github.com/huggingface/peft/tree/main/method_comparison#creating-new-experiments) to run benchmarks
5. Once you have something that seems to be working, don’t hesitate to create a draft PR even if it’s not in a mergeable state yet. The maintainers are happy to give you feedback and guidance along the way.
## Add other features
It is best if you first open an issue on GitHub with a proposal to add the new feature. This way, you can discuss with the maintainers if it makes sense to add the feature before spending too much time on implementing it.
New features should generally be accompanied by tests and documentation or examples. Without the latter, users will have a hard time discovering your cool new feature.
Changes to the code should be implemented in a backward-compatible way. For example, existing code should continue to work the same way after the feature is merged.
================================================
FILE: docs/source/developer_guides/custom_models.md
================================================
# Custom models
Some fine-tuning techniques, such as prompt tuning, are specific to language models. That means in 🤗 PEFT, it is
assumed a 🤗 Transformers model is being used. However, other fine-tuning techniques - like
[LoRA](../conceptual_guides/lora) - are not restricted to specific model types.
In this guide, we will see how LoRA can be applied to a multilayer perceptron, a computer vision model from the [timm](https://huggingface.co/docs/timm/index) library, or a new 🤗 Transformers architecture.
## Multilayer perceptron
Let's assume that we want to fine-tune a multilayer perceptron with LoRA. Here is the definition:
```python
from torch import nn
class MLP(nn.Module):
def __init__(self, num_units_hidden=2000):
super().__init__()
self.seq = nn.Sequential(
nn.Linear(20, num_units_hidden),
nn.ReLU(),
nn.Linear(num_units_hidden, num_units_hidden),
nn.ReLU(),
nn.Linear(num_units_hidden, 2),
nn.LogSoftmax(dim=-1),
)
def forward(self, X):
return self.seq(X)
```
This is a straightforward multilayer perceptron with an input layer, a hidden layer, and an output layer.
> [!TIP]
> For this toy example, we choose an exceedingly large number of hidden units to highlight the efficiency gains
> from PEFT, but those gains are in line with more realistic examples.
There are a few linear layers in this model that could be tuned with LoRA. When working with common 🤗 Transformers
models, PEFT will know which layers to apply LoRA to, but in this case, it is up to us as a user to choose the layers.
To determine the names of the layers to tune:
```python
print([(n, type(m)) for n, m in MLP().named_modules()])
```
This should print:
```
[('', __main__.MLP),
('seq', torch.nn.modules.container.Sequential),
('seq.0', torch.nn.modules.linear.Linear),
('seq.1', torch.nn.modules.activation.ReLU),
('seq.2', torch.nn.modules.linear.Linear),
('seq.3', torch.nn.modules.activation.ReLU),
('seq.4', torch.nn.modules.linear.Linear),
('seq.5', torch.nn.modules.activation.LogSoftmax)]
```
Let's say we want to apply LoRA to the input layer and to the hidden layer, those are `'seq.0'` and `'seq.2'`. Moreover,
let's assume we want to update the output layer without LoRA, that would be `'seq.4'`. The corresponding config would
be:
```python
from peft import LoraConfig
config = LoraConfig(
target_modules=["seq.0", "seq.2"],
modules_to_save=["seq.4"],
)
```
With that, we can create our PEFT model and check the fraction of parameters trained:
```python
from peft import get_peft_model
model = MLP()
peft_model = get_peft_model(model, config)
peft_model.print_trainable_parameters()
# prints trainable params: 56,164 || all params: 4,100,164 || trainable%: 1.369798866581922
```
Finally, we can use any training framework we like, or write our own fit loop, to train the `peft_model`.
For a complete example, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb).
## timm models
The [timm](https://huggingface.co/docs/timm/index) library contains a large number of pretrained computer vision models.
Those can also be fine-tuned with PEFT. Let's check out how this works in practice.
To start, ensure that timm is installed in the Python environment:
```bash
python -m pip install -U timm
```
Next we load a timm model for an image classification task:
```python
import timm
num_classes = ...
model_id = "timm/poolformer_m36.sail_in1k"
model = timm.create_model(model_id, pretrained=True, num_classes=num_classes)
```
Again, we need to make a decision about what layers to apply LoRA to. Since LoRA supports 2D conv layers, and since
those are a major building block of this model, we should apply LoRA to the 2D conv layers. To identify the names of
those layers, let's look at all the layer names:
```python
print([(n, type(m)) for n, m in model.named_modules()])
```
This will print a very long list, we'll only show the first few:
```
[('', timm.models.metaformer.MetaFormer),
('stem', timm.models.metaformer.Stem),
('stem.conv', torch.nn.modules.conv.Conv2d),
('stem.norm', torch.nn.modules.linear.Identity),
('stages', torch.nn.modules.container.Sequential),
('stages.0', timm.models.metaformer.MetaFormerStage),
('stages.0.downsample', torch.nn.modules.linear.Identity),
('stages.0.blocks', torch.nn.modules.container.Sequential),
('stages.0.blocks.0', timm.models.metaformer.MetaFormerBlock),
('stages.0.blocks.0.norm1', timm.layers.norm.GroupNorm1),
('stages.0.blocks.0.token_mixer', timm.models.metaformer.Pooling),
('stages.0.blocks.0.token_mixer.pool', torch.nn.modules.pooling.AvgPool2d),
('stages.0.blocks.0.drop_path1', torch.nn.modules.linear.Identity),
('stages.0.blocks.0.layer_scale1', timm.models.metaformer.Scale),
('stages.0.blocks.0.res_scale1', torch.nn.modules.linear.Identity),
('stages.0.blocks.0.norm2', timm.layers.norm.GroupNorm1),
('stages.0.blocks.0.mlp', timm.layers.mlp.Mlp),
('stages.0.blocks.0.mlp.fc1', torch.nn.modules.conv.Conv2d),
('stages.0.blocks.0.mlp.act', torch.nn.modules.activation.GELU),
('stages.0.blocks.0.mlp.drop1', torch.nn.modules.dropout.Dropout),
('stages.0.blocks.0.mlp.norm', torch.nn.modules.linear.Identity),
('stages.0.blocks.0.mlp.fc2', torch.nn.modules.conv.Conv2d),
('stages.0.blocks.0.mlp.drop2', torch.nn.modules.dropout.Dropout),
('stages.0.blocks.0.drop_path2', torch.nn.modules.linear.Identity),
('stages.0.blocks.0.layer_scale2', timm.models.metaformer.Scale),
('stages.0.blocks.0.res_scale2', torch.nn.modules.linear.Identity),
('stages.0.blocks.1', timm.models.metaformer.MetaFormerBlock),
('stages.0.blocks.1.norm1', timm.layers.norm.GroupNorm1),
('stages.0.blocks.1.token_mixer', timm.models.metaformer.Pooling),
('stages.0.blocks.1.token_mixer.pool', torch.nn.modules.pooling.AvgPool2d),
...
('head.global_pool.flatten', torch.nn.modules.linear.Identity),
('head.norm', timm.layers.norm.LayerNorm2d),
('head.flatten', torch.nn.modules.flatten.Flatten),
('head.drop', torch.nn.modules.linear.Identity),
('head.fc', torch.nn.modules.linear.Linear)]
]
```
Upon closer inspection, we see that the 2D conv layers have names such as `"stages.0.blocks.0.mlp.fc1"` and
`"stages.0.blocks.0.mlp.fc2"`. How can we match those layer names specifically? You can write a [regular
expressions](https://docs.python.org/3/library/re.html) to match the layer names. For our case, the regex
`r".*\.mlp\.fc\d"` should do the job.
Furthermore, as in the first example, we should ensure that the output layer, in this case the classification head, is
also updated. Looking at the end of the list printed above, we can see that it's named `'head.fc'`. With that in mind,
here is our LoRA config:
```python
config = LoraConfig(target_modules=r".*\.mlp\.fc\d", modules_to_save=["head.fc"])
```
Then we only need to create the PEFT model by passing our base model and the config to `get_peft_model`:
```python
peft_model = get_peft_model(model, config)
peft_model.print_trainable_parameters()
# prints trainable params: 1,064,454 || all params: 56,467,974 || trainable%: 1.88505789139876
```
This shows us that we only need to train less than 2% of all parameters, which is a huge efficiency gain.
For a complete example, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/image_classification/image_classification_timm_peft_lora.ipynb).
## New transformers architectures
When new popular transformers architectures are released, we do our best to quickly add them to PEFT. If you come across a transformers model that is not supported out of the box, don't worry, it will most likely still work if the config is set correctly. Specifically, you have to identify the layers that should be adapted and set them correctly when initializing the corresponding config class, e.g. `LoraConfig`. Here are some tips to help with this.
As a first step, it is a good idea to check the existing models for inspiration. You can find them inside of [constants.py](https://github.com/huggingface/peft/blob/main/src/peft/utils/constants.py) in the PEFT repository. Often, you'll find a similar architecture that uses the same names. For example, if the new model architecture is a variation of the "mistral" model and you want to apply LoRA, you can see that the entry for "mistral" in `TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING` contains `["q_proj", "v_proj"]`. This tells you that for "mistral" models, the `target_modules` for LoRA should be `["q_proj", "v_proj"]`:
```python
from peft import LoraConfig, get_peft_model
my_mistral_model = ...
config = LoraConfig(
target_modules=["q_proj", "v_proj"],
..., # other LoRA arguments
)
peft_model = get_peft_model(my_mistral_model, config)
```
If that doesn't help, check the existing modules in your model architecture with the `named_modules` method and try to identify the attention layers, especially the key, query, and value layers. Those will often have names such as `c_attn`, `query`, `q_proj`, etc. The key layer is not always adapted, and ideally, you should check whether including it results in better performance.
Additionally, linear layers are common targets to be adapted (e.g. in [QLoRA paper](https://huggingface.co/papers/2305.14314), authors suggest to adapt them as well). Their names will often contain the strings `fc` or `dense`.
If you want to add a new model to PEFT, please create an entry in [constants.py](https://github.com/huggingface/peft/blob/main/src/peft/utils/constants.py) and open a pull request on the [repository](https://github.com/huggingface/peft/pulls). Don't forget to update the [README](https://github.com/huggingface/peft#models-support-matrix) as well.
## Verify parameters and layers
You can verify whether you've correctly applied a PEFT method to your model in a few ways.
* Check the fraction of parameters that are trainable with the [`~PeftModel.print_trainable_parameters`] method. If this number is lower or higher than expected, check the model `repr` by printing the model. This shows the names of all the layer types in the model. Ensure that only the intended target layers are replaced by the adapter layers. For example, if LoRA is applied to `nn.Linear` layers, then you should only see `lora.Linear` layers being used.
```py
peft_model.print_trainable_parameters()
```
* Another way you can view the adapted layers is to use the `targeted_module_names` attribute to list the name of each module that was adapted.
```python
print(peft_model.targeted_module_names)
```
## Unsupported module types
Methods like LoRA only work if the target modules are supported by PEFT. For example, it's possible to apply LoRA to `nn.Linear` and `nn.Conv2d` layers, but not, for instance, to `nn.LSTM`. If you find a layer class you want to apply PEFT to is not supported, you can:
- define a custom mapping to dynamically dispatch custom modules in LoRA
- open an [issue](https://github.com/huggingface/peft/issues) and request the feature where maintainers will implement it or guide you on how to implement it yourself if demand for this module type is sufficiently high
### Experimental support for dynamic dispatch of custom modules in LoRA
> [!WARNING]
> This feature is experimental and subject to change, depending on its reception by the community. We will introduce a public and stable API if there is significant demand for it.
PEFT supports an experimental API for custom module types for LoRA. Let's assume you have a LoRA implementation for LSTMs. Normally, you would not be able to tell PEFT to use it, even if it would theoretically work with PEFT. However, this is possible with dynamic dispatch of custom layers.
The experimental API currently looks like this:
```python
class MyLoraLSTMLayer:
...
base_model = ... # load the base model that uses LSTMs
# add the LSTM layer names to target_modules
config = LoraConfig(..., target_modules=["lstm"])
# define a mapping from base layer type to LoRA layer type
custom_module_mapping = {nn.LSTM: MyLoraLSTMLayer}
# register the new mapping
config._register_custom_module(custom_module_mapping)
# after registration, create the PEFT model
peft_model = get_peft_model(base_model, config)
# do training
```
> [!TIP]
> When you call [`get_peft_model`], you will see a warning because PEFT does not recognize the targeted module type. In this case, you can ignore this warning.
By supplying a custom mapping, PEFT first checks the base model's layers against the custom mapping and dispatches to the custom LoRA layer type if there is a match. If there is no match, PEFT checks the built-in LoRA layer types for a match.
Therefore, this feature can also be used to override existing dispatch logic, e.g. if you want to use your own LoRA layer for `nn.Linear` instead of using the one provided by PEFT.
When creating your custom LoRA module, please follow the same rules as the [existing LoRA modules](https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py). Some important constraints to consider:
- The custom module should inherit from `nn.Module` and `peft.tuners.lora.layer.LoraLayer`.
- The `__init__` method of the custom module should have the positional arguments `base_layer` and `adapter_name`. After this, there are additional `**kwargs` that you are free to use or ignore.
- The learnable parameters should be stored in an `nn.ModuleDict` or `nn.ParameterDict`, where the key corresponds to the name of the specific adapter (remember that a model can have more than one adapter at a time).
- The name of these learnable parameter attributes should start with `"lora_"`, e.g. `self.lora_new_param = ...`.
- Some methods are optional, e.g. you only need to implement `merge` and `unmerge` if you want to support weight merging.
Currently, the information about the custom module does not persist when you save the model. When loading the model, you have to register the custom modules again.
```python
# saving works as always and includes the parameters of the custom modules
peft_model.save_pretrained()
# loading the model later:
base_model = ...
# load the LoRA config that you saved earlier
config = LoraConfig.from_pretrained()
# register the custom module again, the same way as the first time
custom_module_mapping = {nn.LSTM: MyLoraLSTMLayer}
config._register_custom_module(custom_module_mapping)
# pass the config instance to from_pretrained:
peft_model = PeftModel.from_pretrained(model, tmp_path / "lora-custom-module", config=config)
```
If you use this feature and find it useful, or if you encounter problems, let us know by creating an issue or a discussion on GitHub. This allows us to estimate the demand for this feature and add a public API if it is sufficiently high.
================================================
FILE: docs/source/developer_guides/lora.md
================================================
# LoRA
LoRA is low-rank decomposition method to reduce the number of trainable parameters which speeds up finetuning large models and uses less memory. In PEFT, using LoRA is as easy as setting up a [`LoraConfig`] and wrapping it with [`get_peft_model`] to create a trainable [`PeftModel`].
This guide explores in more detail other options and features for using LoRA.
## Initialization
The initialization of LoRA weights is controlled by the parameter `init_lora_weights` in [`LoraConfig`]. By default, PEFT initializes LoRA weights with Kaiming-uniform for weight A and zeros for weight B resulting in an identity transform (same as the reference [implementation](https://github.com/microsoft/LoRA)).
It is also possible to pass `init_lora_weights="gaussian"`. As the name suggests, this initializes weight A with a Gaussian distribution and zeros for weight B (this is how [Diffusers](https://huggingface.co/docs/diffusers/index) initializes LoRA weights).
```py
from peft import LoraConfig
config = LoraConfig(init_lora_weights="gaussian", ...)
```
There is also an option to set `init_lora_weights=False` which is useful for debugging and testing. This should be the only time you use this option. When choosing this option, the LoRA weights are initialized such that they do *not* result in an identity transform.
```py
from peft import LoraConfig
config = LoraConfig(init_lora_weights=False, ...)
```
### PiSSA
[PiSSA](https://huggingface.co/papers/2404.02948) initializes the LoRA adapter using the principal singular values and singular vectors. This straightforward modification allows PiSSA to converge more rapidly than LoRA and ultimately attain superior performance. Moreover, PiSSA reduces the quantization error compared to QLoRA, leading to further enhancements.
Configure the initialization method to "pissa", which may take several minutes to execute SVD on the pre-trained model:
```python
from peft import LoraConfig
config = LoraConfig(init_lora_weights="pissa", ...)
```
Alternatively, execute fast SVD, which takes only a few seconds. The number of iterations determines the trade-off between the error and computation time:
```python
lora_config = LoraConfig(init_lora_weights="pissa_niter_[number of iters]", ...)
```
For detailed instruction on using PiSSA, please follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/pissa_finetuning).
### CorDA
[CorDA](https://huggingface.co/papers/2406.05223) builds task-aware LoRA adapters from weight decomposition oriented by the context of downstream task to learn (instruction-previewed mode, IPM) or world knowledge to maintain (knowledge-preserved mode, KPM).
The KPM not only achieves better performance than LoRA on fine-tuning tasks, but also mitigates the catastrophic forgetting of pre-trained world knowledge.
When preserving pre-trained knowledge is not a concern,
the IPM is favored because it can further accelerate convergence and enhance the fine-tuning performance.
You need to configure the initialization method to "corda", and specify the mode of IPM or KPM and the dataset to collect covariance matrices.
```py
@torch.no_grad()
def run_model():
# Assume `model` and `dataset` is in context...
model.eval()
for batch in dataset:
model(**batch)
corda_config = CordaConfig(
corda_method="kpm",
)
lora_config = LoraConfig(
init_lora_weights="corda",
corda_config=corda_config,
)
preprocess_corda(model, lora_config, run_model=run_model)
peft_model = get_peft_model(model, lora_config)
```
For detailed instruction on using CorDA, please follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/corda_finetuning).
### OLoRA
[OLoRA](https://huggingface.co/papers/2406.01775) utilizes QR decomposition to initialize the LoRA adapters. OLoRA translates the base weights of the model by a factor of their QR decompositions, i.e., it mutates the weights before performing any training on them. This approach significantly improves stability, accelerates convergence speed, and ultimately achieves superior performance.
You just need to pass a single additional option to use OLoRA:
```python
from peft import LoraConfig
config = LoraConfig(init_lora_weights="olora", ...)
```
For more advanced usage, please refer to our [documentation](https://github.com/huggingface/peft/tree/main/examples/olora_finetuning).
### EVA
[EVA](https://huggingface.co/papers/2410.07170) performs SVD on the input activations of each layer and uses the right-singular vectors to initialize LoRA weights. It is therefore a data-driven initialization scheme. Furthermore EVA adaptively allocates ranks across layers based on their "explained variance ratio" - a metric derived from the SVD analysis.
You can use EVA by setting `init_lora_weights="eva"` and defining [`EvaConfig`] in [`LoraConfig`]:
```python
from peft import LoraConfig, EvaConfig
peft_config = LoraConfig(
init_lora_weights = "eva",
eva_config = EvaConfig(rho = 2.0),
...
)
```
The parameter `rho` (≥ 1.0) determines how much redistribution is allowed. When `rho=1.0` and `r=16`, LoRA adapters are limited to exactly 16 ranks, preventing any redistribution from occurring. A recommended value for EVA with redistribution is 2.0, meaning the maximum rank allowed for a layer is 2r.
It is recommended to perform EVA initialization on an accelerator(e.g. CUDA GPU, Intel XPU) as it is much faster. To optimize the amount of available memory for EVA, you can use the `low_cpu_mem_usage` flag in [`get_peft_model`]:
```python
peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
```
Then, call [`initialize_lora_eva_weights`] to initialize the EVA weights (in most cases the dataloader used for eva initialization can be the same as the one used for finetuning):
```python
initialize_lora_eva_weights(peft_model, dataloader)
```
EVA works out of the box with bitsandbytes. Simply initialize the model with `quantization_config` and call [`initialize_lora_eva_weights`] as usual.
> [!TIP]
> For further instructions on using EVA, please refer to our [documentation](https://github.com/huggingface/peft/tree/main/examples/eva_finetuning).
### LoftQ
#### Standard approach
When quantizing the base model for QLoRA training, consider using the [LoftQ initialization](https://huggingface.co/papers/2310.08659), which has been shown to improve performance when training quantized models. The idea is that the LoRA weights are initialized such that the quantization error is minimized. To use LoftQ, follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning).
> [!TIP]
> Learn more about how PEFT works with quantization and how to use LoftQ in the [Quantization](quantization) guide.
### Rank-stabilized LoRA
Another way to initialize [`LoraConfig`] is with the [rank-stabilized LoRA (rsLoRA)](https://huggingface.co/papers/2312.03732) method. The LoRA architecture scales each adapter during every forward pass by a fixed scalar which is set at initialization and depends on the rank `r`. The scalar is given by `lora_alpha/r` in the original implementation, but rsLoRA uses `lora_alpha/math.sqrt(r)` which stabilizes the adapters and increases the performance potential from using a higher `r`.
```py
from peft import LoraConfig
config = LoraConfig(use_rslora=True, ...)
```
### LoRA-GA
[LoRA-GA](../package_reference/lora#lora-ga) (Low-Rank Adaptation with Gradient Approximation) initializes the adapter
weights by performing SVD on estimated gradients, so that the weights are aligning closer to full-finetuning for faster
convergence.
This method requires an initialization function to estimate the gradients
before beginning the actual training:
```python
from peft.tuners.lora import preprocess_loraga
def train_step():
"""Run forward and backward passes for gradient estimation."""
dataloader_iter = iter(grad_dataloader)
for _ in range(N):
batch = next(dataloader_iter)
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
preprocess_loraga(model, lora_config, train_step)
```
## Variants
PEFT implements LoRA variants that improve upon the original LoRA.
### Weight-Decomposed Low-Rank Adaptation (DoRA)
This technique decomposes the updates of the weights into two parts, magnitude and direction. Direction is handled by normal LoRA, whereas the magnitude is handled by a separate learnable parameter. This can improve the performance of LoRA, especially at low ranks. For more information on DoRA, see https://huggingface.co/papers/2402.09353.
```py
from peft import LoraConfig
config = LoraConfig(use_dora=True, ...)
```
If parts of the model or the DoRA adapter are offloaded to CPU you can get a significant speedup at the cost of some temporary (ephemeral) VRAM overhead by using `ephemeral_gpu_offload=True` in `config.runtime_config`.
```py
from peft import LoraConfig, LoraRuntimeConfig
config = LoraConfig(use_dora=True, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=True), ...)
```
A `PeftModel` with a DoRA adapter can also be loaded with `ephemeral_gpu_offload=True` flag using the `from_pretrained` method as well as the `load_adapter` method.
```py
from peft import PeftModel
model = PeftModel.from_pretrained(base_model, peft_model_id, ephemeral_gpu_offload=True)
```
#### Optimization
DoRA is optimized (computes faster and takes less memory) for models in the evaluation mode, or when dropout is set to 0. We reuse the
base result at those times to get the speedup.
Running [dora finetuning](https://github.com/huggingface/peft/blob/main/examples/dora_finetuning/dora_finetuning.py)
with `CUDA_VISIBLE_DEVICES=0 ZE_AFFINITY_MASK=0 time python examples/dora_finetuning/dora_finetuning.py --quantize --lora_dropout 0 --batch_size 16 --eval_step 2 --use_dora` on a 4090 with gradient accumulation set to 2 and max step to 20 resulted with the following observations:
| | Without Optimization | With Optimization |
| :--: | :--: | :--: |
| train runtime (sec) | 359.7298 | **279.2676** |
| train samples per second | 1.779 | **2.292** |
| train steps per second | 0.056 | **0.072** |
Moreover, it is possible to further increase runtime performance of DoRA by using the [`DoraCaching`] helper context. This requires the model to be in `eval` mode:
```py
from peft.helpers import DoraCaching
model.eval()
with DoraCaching():
output = model(inputs)
```
For [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B), the [DoRA caching benchmark script](https://github.com/huggingface/peft/blob/main/examples/dora_finetuning/dora-caching.py) shows that, compared to LoRA:
- DoRA without caching requires 139% more time
- DoRA without caching requires 4% more memory
- DoRA with caching requires 17% more time
- DoRA with caching requires 41% more memory
Caching can thus make inference with DoRA significantly faster but it also requires signficantly more memory. Ideally, if the use case allows it, just merge the DoRA adapter to avoid both memory and runtime overhead.
#### Caveats
- DoRA only supports embedding, linear, and Conv2d layers at the moment.
- DoRA introduces a bigger overhead than pure LoRA, so it is recommended to merge weights for inference, see [`LoraModel.merge_and_unload`].
- DoRA should work with weights quantized with bitsandbytes ("QDoRA"). However, issues have been reported when using QDoRA with DeepSpeed Zero2.
## Training
This section shows how to handle more complex training scenarios instead of only applying a low-rank adapter
to the model and feed data.
### QLoRA-style training
The default LoRA settings in PEFT add trainable weights to the query and value layers of each attention block. But [QLoRA](https://hf.co/papers/2305.14314), which adds trainable weights to all the linear layers of a transformer model, can provide performance equal to a fully finetuned model. To apply LoRA to all the linear layers, like in QLoRA, set `target_modules="all-linear"` (easier than specifying individual modules by name which can vary depending on the architecture).
```py
config = LoraConfig(target_modules="all-linear", ...)
```
For more information about how to apply quantization to PEFT adapters, refer to the [quantization guide](quantization).
### Memory efficient Layer Replication with LoRA
An approach used to improve the performance of models is to expand a model by duplicating layers in the model to build a larger model from a pretrained model of a given size. For example increasing a 7B model to a 10B model as described in the [SOLAR](https://huggingface.co/papers/2312.15166) paper. PEFT LoRA supports this kind of expansion in a memory efficient manner that supports further fine-tuning using LoRA adapters attached to the layers post replication of the layers. The replicated layers do not take additional memory as they share the underlying weights so the only additional memory required is the memory for the adapter weights. To use this feature you would create a config with the `layer_replication` argument.
```py
config = LoraConfig(layer_replication=[[0,4], [2,5]], ...)
```
Assuming the original model had 5 layers `[0, 1, 2 ,3, 4]`, this would create a model with 7 layers arranged as `[0, 1, 2, 3, 2, 3, 4]`. This follows the [mergekit](https://github.com/arcee-ai/mergekit) pass through merge convention where sequences of layers specified as start inclusive and end exclusive tuples are stacked to build the final model. Each layer in the final model gets its own distinct set of LoRA adapters.
[Fewshot-Metamath-OrcaVicuna-Mistral-10B](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B) is an example of a model trained using this method on Mistral-7B expanded to 10B. The
[adapter_config.json](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B/blob/main/adapter_config.json) shows a sample LoRA adapter config applying this method for fine-tuning.
### Fine grained control over ranks and alpha (scaling)
By default, all layers targeted with LoRA will have the same rank `r` and the same `lora_alpha` (which determines the LoRA scaling), depending on what was specified in the [`LoraConfig`]. In some cases, however, you may want to indicate different values for different layers. This is possible by passing the `rank_pattern` and `alpha_pattern` arguments to [`LoraConfig`]. These arguments should be dictionaries with the key being the layer name and the value being the rank/alpha value. The keys can be [regular expressions](https://docs.python.org/3/library/re.html) (regex). All LoRA layers that are not explicitly mentioned in `rank_pattern` and `alpha_pattern` will take the default `r` and `lora_alpha` values.
To give an example, let's assume that we have a model with the following structure:
```python
>>> print(model)
Outer(
(foo): Linear(...)
(module): Middle(
(foo): Linear(...)
(foobar): Linear(...)
(module): Inner(
(foo): Linear(...)
(barfoo): Linear(...)
)
)
)
```
- `rank_pattern={"foo": 42}` will match all 3 `foo` layers. Neither `foobar` nor `barfoo` are matched.
- `rank_pattern={"^foo": 42}` will only match the `foo` layer of the model, but neither `module.foo` nor `module.module.foo`. This is because the `^` means "start of string" when using regular expressions, and only `foo` starts with `"foo"`, the other layer names have prefixes.
- `rank_pattern={"^module.foo": 42}` matches only `module.foo`, but not `module.module.foo`, for the same reason.
- `rank_pattern={"module.foo": 42}` matches both `module.foo` and `module.module.foo`, but not `foo`.
- `rank_pattern={"^foo": 42, "^module.module.foo": 55}` matches `foo` and `module.module.foo`, respectively, but not `module.foo`.
- There is no need to indicate `$` to mark the end of the match, as this is added automatically by PEFT.
The same logic applies to `alpha_pattern`. If you're in doubt, don't try to get fancy with regular expressions -- just pass the full name for each module with a different rank/alpha, preceded by the `^` prefix, and you should be good.
### Targeting `nn.Parameter` directly
> [!WARNING]
> This feature is experimental and subject to change.
Generally, you should use `target_modules` to target the module (e.g. `nn.Linear`). However, in some circumstances, this is not possible. E.g., in many mixture of expert (MoE) layers in HF Transformers, instead of using `nn.Linear`, an `nn.Parameter` is used. PEFT normally overwrites the `forward` method for LoRA, but for `nn.Parameter`, there is none. Therefore, to apply LoRA to that parameter, it needs to be targeted with `target_parameters`. As an example, for [Llama4](https://huggingface.co/collections/meta-llama/llama-4-67f0c30d9fe03840bc9d0164), you can pass: `target_parameters=['feed_forward.experts.gate_up_proj', 'feed_forward.experts.down_proj]`.
#### Caveats
- At the moment, this argument allows to target 2-dim or 3-dim `nn.Parameter`s. It is assumed that in the case of a 3-dim parameter, the 0th dimension is the expert dimension.
- It is currently not possible to add multiple LoRA adapters (via `model.add_adapter` or `model.load_adapter`) that use `target_parameters` at the same time.
#### MoE expert parameters and vLLM
Some MoE models in Transformers store expert weights as `nn.Parameter` tensors (often 3D), not `nn.Linear` modules.
To apply LoRA to those experts, use `target_parameters` and set a per-layer rank with `rank_pattern`:
```python
num_experts = getattr(model.config, "num_local_experts", None) or model.config.num_experts
effective_r = max(1, r // num_experts)
config = LoraConfig(
r=r,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
target_parameters=[
# Mixtral / Qwen3-MoE / GPT-OSS
"mlp.experts.gate_up_proj",
"mlp.experts.down_proj",
# Llama4
# "feed_forward.experts.gate_up_proj",
# "feed_forward.experts.down_proj",
],
rank_pattern={
"experts.gate_up_proj": effective_r,
"experts.down_proj": effective_r,
},
)
```
This keeps the total LoRA parameter budget similar to dense layers (see
[LoRA Without Regret](https://thinkingmachines.ai/blog/lora/) by Schulman et. al.).
Non-expert modules use the default rank `r`.
Accelerated inference with the fine-tuned model is possible with, for example, [vLLM](https://vllm.ai/) which supports fused MoE expert layers since v0.11.2.
### Efficiently train tokens alongside LoRA
PEFT LoRA adapters support adding new tokens with the `trainable_token_indices` parameter. This allows tuning of other tokens alongside fine-tuning specific layers. Only the specified tokens are trained and all other tokens are untouched. It saves memory and doesn't throw away learned context from existing token embeddings unlike training the whole embedding matrix. Under the hood this method uses the layer of [`TrainableTokensModel`].
```py
# for layer 'embed_tokens'
config = LoraConfig(trainable_token_indices=[idx_1, idx_2, ...], ...)
# specific embedding layer
config = LoraConfig(trainable_token_indices={'emb_tokens': [idx_1, idx_2, ...]}, ...)
```
In the snippet below we show how to add new tokens to the model and how to train it alongside the other layers in the model.
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import get_peft_model, LoraConfig
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
# we define our new tokens and add them to the tokenizer as special tokens
special_tokens = ['<|start_think|>', '<|stop_think|>']
tokenizer.add_special_tokens({'additional_special_tokens': special_tokens})
# make room for new tokens in the embedding matrix if it isn't big enough already
base_model.resize_token_embeddings(max(len(tokenizer), base_model.model.embed_tokens.num_embeddings))
# typical LoRA config with `trainable_token_indices` targeting embedding layer `embed_tokens`
# and specifically our new tokens we just added
lora_config = LoraConfig(
target_modules='all-linear',
trainable_token_indices={'embed_tokens': tokenizer.convert_tokens_to_ids(special_tokens)},
)
peft_model = get_peft_model(base_model, lora_config)
# proceed to train the model like normal
[...]
```
The token weights are saved as a part of the adapter state dict alongside the LoRA weights. Full fine-tuning and saving the embedding matrix would have stored a much bigger file.
To give a bit of an indication how much VRAM can be saved, a rudimentary comparison of the above example was made between training the embedding matrix fully (`modules_to_save=["embed_tokens"]`), using a LoRA for the embedding matrix (`target_modules=[..., "embed_tokens"]`, rank 32) and trainable tokens (`trainable_token_indices=[...]`, 6 tokens):
| | Trainable Tokens | LoRA | Full Fine-tuning |
| --------: | :--------------: | :--------: | :--------------: |
| VRAM | 15,562 MB | 15,581MB | ~16,500MB |
| Influence | 6 tokens | all tokens | all tokens |
## Optimizers
LoRA training can optionally include special purpose optimizers. Currently PEFT supports LoRA-FA and LoRA+.
### LoRA-FA Optimizer
LoRA training can be more effective and efficient using LoRA-FA, as described in [LoRA-FA](https://huggingface.co/papers/2308.03303). LoRA-FA reduces activation memory consumption by fixing the matrix A and only tuning the matrix B. During training, the gradient of B is optimized to approximate the full parameter fine-tuning gradient. Moreover, the memory consumption of LoRA-FA is not sensitive to the rank (since it erases the activation of $A$), therefore it can improve performance by enlarging lora rank without increasing memory consumption.
```py
from peft import LoraConfig, get_peft_model
from peft.optimizers import create_lorafa_optimizer
from transformers import Trainer, get_cosine_schedule_with_warmup
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
config = LoraConfig(...)
model = get_peft_model(base_model, config)
optimizer = create_lorafa_optimizer(
model=model,
r=128,
lora_alpha=32,
lr=7e-5,
)
scheduler = get_cosine_schedule_with_warmup(
optimizer,
num_warmup_steps=100,
num_training_steps=1000,
)
trainer = Trainer(
...,
optimizers=(optimizer, scheduler),
)
```
### LoRA+ optimized LoRA
LoRA training can be optimized using [LoRA+](https://huggingface.co/papers/2402.12354), which uses different learning rates for the adapter matrices A and B, shown to increase finetuning speed by up to 2x and performance by 1-2%.
```py
from peft import LoraConfig, get_peft_model
from peft.optimizers import create_loraplus_optimizer
from transformers import Trainer
import bitsandbytes as bnb
base_model = ...
config = LoraConfig(...)
model = get_peft_model(base_model, config)
optimizer = create_loraplus_optimizer(
model=model,
optimizer_cls=bnb.optim.Adam8bit,
lr=5e-5,
loraplus_lr_ratio=16,
)
scheduler = None
...
trainer = Trainer(
...,
optimizers=(optimizer, scheduler),
)
```
## Post-Training
This section shows potential post-processing methods for trained adapters.
### Merge LoRA weights into the base model
While LoRA is significantly smaller and faster to train, you may encounter latency issues during inference due to separately loading the base model and the LoRA adapter. To eliminate latency, use the [`~LoraModel.merge_and_unload`] function to merge the adapter weights with the base model. This allows you to use the newly merged model as a standalone model. The [`~LoraModel.merge_and_unload`] function doesn't keep the adapter weights in memory.
Below is a diagram that explains the intuition of LoRA adapter merging:
We show in the snippets below how to run that using PEFT.
```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model = model.merge_and_unload()
```
It is important to assign the returned model to a variable and use it, [`~LoraModel.merge_and_unload`] is not an in-place operation. If you need to keep a copy of the weights so you can unmerge the adapter later or delete and load different ones, you should use the [`~LoraModel.merge_adapter`] function instead. Now you have the option to use [`~LoraModel.unmerge_adapter`] to return the base model.
```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_adapter()
# unmerge the LoRA layers from the base model
model.unmerge_adapter()
```
The [`~LoraModel.add_weighted_adapter`] function is useful for merging multiple LoRAs into a new adapter based on a user provided weighting scheme in the `weights` parameter. Below is an end-to-end example.
First load the base model:
```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
import torch
base_model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-v0.1", dtype=torch.float16, device_map="auto"
)
```
Then we load the first adapter:
```python
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id, adapter_name="sft")
```
Then load a different adapter and merge it with the first one:
```python
weighted_adapter_name = "sft-dpo"
model.load_adapter("alignment-handbook/zephyr-7b-dpo-lora", adapter_name="dpo")
model.add_weighted_adapter(
adapters=["sft", "dpo"],
weights=[0.7, 0.3],
adapter_name=weighted_adapter_name,
combination_type="linear"
)
model.set_adapter(weighted_adapter_name)
```
> [!TIP]
> There are several supported methods for `combination_type`. Refer to the [documentation](../package_reference/lora#peft.LoraModel.add_weighted_adapter) for more details. Note that "svd" as the `combination_type` is not supported when using `torch.float16` or `torch.bfloat16` as the datatype.
Now, perform inference:
```python
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
generate_ids = model.generate(**inputs, max_length=30)
outputs = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(outputs)
```
### Recovering base model performance via intruder dimension reduction
The paper [LoRA vs Full Fine-tuning: An Illusion of Equivalence](https://huggingface.co/papers/2410.21228) argues
that LoRA training introduces extra dimensions into the weights that have very little in common with the already
learnt weights and lead to forgetting of already learned information. PEFT implements the suggested mitigation
in [`peft.tuners.lora.intruders.reduce_intruder_dimension`].
The mitigation will take a PEFT model with a loaded LoRA and create a new, modified adapter that is loaded alongside
the existing adapter and now the active adapter.
Example usage:
```python
from peft.tuners.lora.intruders import reduce_intruder_dimension
peft_model = AutoPeftModelForCausalLM.from_pretrained('hubnemo/llama-3.2b-metamathqa-lora64')
reduce_intruder_dimension(
peft_model,
mitigation_lambda=0.75,
)
peft_model.generate(...)
```
There are a few hyper-parameters that can be used for tuning the effectiveness of the mitigation but, as evidenced
in Figure 8 of the paper, it will always be a trade-off between task accuracy learned by the adapter and forgetting
of the base model's knowledge. The mitigation will remove information from the adapter to reduce the impact on
forgetting previous knowledge but this also means that some information about the task learned by the adapter is
lost as well.
While the defaults are set to deliver a good trade-off between the two factors it is not guaranteed that the defaults
will hold for your adapter, your model and your data, therefore it is wise to have a benchmark ready to measure
the effect.
## Load adapters
Adapters can be loaded onto a pretrained model with [`~PeftModel.load_adapter`], which is useful for trying out different adapters whose weights aren't merged. Set the active adapter weights with the [`~LoraModel.set_adapter`] function.
```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
# load different adapter
model.load_adapter("alignment-handbook/zephyr-7b-dpo-lora", adapter_name="dpo")
# set adapter as active
model.set_adapter("dpo")
```
To return the base model, you could use [`~LoraModel.unload`] to unload all of the LoRA modules or [`~LoraModel.delete_adapter`] to delete the adapter entirely. [`~LoraModel.unload`] is not an in-place operation, remember to assign the returned model to a variable and use it.
```py
# unload adapter
model = model.unload()
# delete adapter
model.delete_adapter("dpo")
```
## Tensor Parallelism
LoRA supports [Tensor Parallelism (TP)](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_many#tensor-parallelism) as provided by Transformers. When a base model is loaded with a `tp_plan`, PEFT automatically detects the TP configuration of each target module and adds the appropriate hooks to the LoRA adapter weights so that they participate correctly in the tensor-parallel computation.
> [!WARNING]
> Tensor Parallelism support for LoRA requires `transformers >= 5.4.0`.
Usage is identical to the standard LoRA workflow — simply load the base model with a `tp_plan` before wrapping it with PEFT:
```py
from transformers import AutoModelForCausalLM
from peft import get_peft_model, LoraConfig
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B", tp_plan="auto")
lora_config = LoraConfig(r=16, target_modules=["q_proj", "v_proj"])
model = get_peft_model(model, lora_config)
```
Saving and loading work as usual via `save_pretrained` / `from_pretrained`. PEFT gathers the sharded adapter weights back to full tensors before saving, so checkpoints are portable and independent of the number of devices used during training.
## Inference
This section showcases what you can do during inference time with LoRA, such as uncoupling the adapter.
### Activated LoRA (aLoRA)
Activated LoRA (aLoRA) is a low rank adapter architecture for causal LMs that reuses the existing base model KV cache for more efficient inference. This approach is best suited for inference pipelines which rely on the base model for most tasks/generations, but use aLoRA adapter(s) to perform specialized task(s) within the chain. For example, checking or correcting generated outputs of the base model. In these settings, inference times can be sped up by an order of magnitude or more. For more information on aLoRA and many example use cases, see the aLoRA [paper](https://huggingface.co/papers/2504.12397).
This technique scans for the last occurrence of an invocation sequence (`alora_invocation_tokens`) in each input (this can be as short as 1 token). It activates the adapter weights on tokens starting with the beginning of the invocation sequence. Any inputs after the invocation sequence are also adapted, and all generated tokens will use the adapted weights. Weights on prior tokens are left un-adapted, making the cache for those tokens interchangeable with base model cache due to the causal attention mask in causal LMs. Usage is very similar to standard LoRA. The key difference is that the invocation sequence must be specified when the adapter is created:
```py
from peft import LoraConfig
config = LoraConfig(alora_invocation_tokens=alora_invocation_tokens, task_type="CAUSAL_LM", ...)
```
alora_invocation_tokens` is a list of integer token ids. Given a desired invocation string, this can be obtained as:
```py
invocation_string = "placeholder"
alora_invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False).
```
The tokenizer is the base model's tokenizer. Use `add_special_tokens=False` to avoid adding `SOS`/`EOS` tokens in our search string (which will most likely cause the search to fail).
**Notes**
* aLoRA is only supported for `task_type=CAUSAL_LM` tasks due to its focus on cache reuse.
* Since the weights are adapted on fewer tokens, often (not always) aLoRA requires higher rank (`r`) than LoRA. `r=32` can be a good starting point.
* aLoRA weights cannot be merged into the base model by definition, since the adapter weights are selectively applied to a subset of tokens. Attempts to merge will throw errors.
* Beam search is not yet supported.
* It is generally not recommended to add new tokens to the tokenizer that are not present in the base model. This can complicate the target use case of both the base model and adapter model operating on overlapping context. You can workaround this by adding [trainable tokens](../package_reference/trainable_tokens) to the base model prior to training the adapter.
#### Choice of invocation sequence and SFT design
You must add the `alora_invocation_tokens` sequence because it is not added automatically. We recommend activating the adapter weights early (at the start of any adapter-specific prompting), but after any long inputs, to maximize model performance without compromising cache reuse. As with any model,
formatting should be consistent between train and test.
Consider the following example, where the base model has a chat template,
and the goal is to train the adapter to generate a desired output.
* Option 1: If there is no task-specific prompt, i.e. the input is a chat history with the `assistant` prompt, then the chat template's `assistant` prompt (e.g. `<|start_of_role|>assistant<|end_of_role|>`) is a natural choice for the invocation string. See the model's chat template to find the prompt for the model.
* Option 2: If there is a task-specific prompt for the adapter that describes the task the adapter is learning, and that prompt is put as a `user` turn immediately prior to the generation, then the chat template's `user` prompt (e.g. `<|start_of_role|>user<|end_of_role|>`) is a natural choice for the invocation string.
After deciding on an invocation string, get the model tokenizer and obtain `alora_invocation_tokens` as
```py
alora_invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False).
```
An example inference setup is at [alora finetuning](https://github.com/huggingface/peft/blob/main/examples/alora_finetuning/alora_finetuning.py).
> [!NOTE]
> If using custom strings for the invocation string, make sure that the start and end of the string are special tokens to avoid issues with tokenization at the boundaries.
To see why, imagine that 'a', 'b', 'c', and 'ab' are tokens in your tokenizer (numbers 1, 2, 3, 4 respectively). Suppose that your alora_invocation_tokens = [2, 3]. Now imagine your input string is "abc". Because "ab" is a token, this will get tokenized as [4,3]. So the alora_invocation_tokens will fail to be found, despite the string "bc" being in it. If the start and end of the invocation string are special tokens, however, this failure case will never happen since special tokens are never tokenized into the same token with other characters.
#### Using (and reusing) cache for generation
The main purpose of aLoRA is to make KV cache interchangeable between the base model and aLoRA adapter models **prior to the invocation sequence** since base and adapted KV values are not compatible. Specifically, keys and values stored during one model generation can be used in subsequent generations to avoid expensive prefill operations for context tokens. When sharing cache between the base model and aLoRA adapters, there are 2 main patterns:
1. The base model has generated something, and an aLoRA adapter is then called to do a follow-up generation. For example, the base model answers a question, and an aLoRA trained to detect hallucinations checks the base model response.
2. An aLoRA adapter has generated something, and the base model or a different aLoRA adapter is called to do a follow-up generation where there is partial context overlap with the original aLoRA. For example, the user provides a query, and an aLoRA rewrites the query to be more self-contained and improve retrieval in a RAG system. Then, documents are retrieved and loaded into context, aLoRA checks if these documents are relevant to the question, and then the base model generates an answer.
To demonstrate the above behaviors when using caching, we're using [DynamicCache](https://huggingface.co/docs/transformers/en/kv_cache) from `transformers`. Take care to ensure that adapted cache values are not mixed with base cache values. In particular, an extra step is required for sharing the cache when there is partial context overlap (pattern 2).
**Pattern 1: Base model followed by aLoRA** Here, the entire input and generation from the base model is input into the aLoRA adapter, along with the invocation sequence:
```
from transformers import DynamicCache
...
cache = DynamicCache()
inputs_base = tokenizer(prompt_base, return_tensors="pt")
# Generate from base model and save cache
with model_alora.disable_adapter():
output = model_alora.generate(inputs_base["input_ids"].to(device),attention_mask=inputs_base["attention_mask"].to(device),past_key_values = cache,return_dict_in_generate=True)
output_text_base = tokenizer.decode(output.sequences[0])
cache = output.past_key_values
# Generate with aLoRA adapter from cache
prompt_alora = output_text + INVOCATION_STRING
inputs_alora = tokenizer(prompt_alora, return_tensors="pt").to(device)
output = model_alora.generate(**inputs_alora, past_key_values=cache)
output_text_alora = tokenizer.decode(output[0])
# Note: cache is now tainted with adapter values and cannot be used in base model from here on!
```
**Pattern 2: aLoRA generation followed by base model (or another aLoRA) with partial context overlap** Here, we prefill the shared context using the base model, and then generate.
```
from transformers import DynamicCache
import copy
...
cache = DynamicCache()
inputs_shared = tokenizer(prompt_shared, return_tensors="pt").to(device)
# Prefill from base model and save cache
with model_alora.disable_adapter():
with torch.no_grad():
model_alora(**inputs_shared, past_key_values=cache)
cache_copy = copy.deepcopy(cache)
# Generate from aLoRA using prefilled cache
prompt_alora = prompt_shared + INVOCATION_STRING
inputs_alora = tokenizer(prompt_alora, return_tensors="pt").to(device)
output = model_alora.generate(**inputs_alora, past_key_values=cache)
output_text_alora = tokenizer.decode(output[0])
# Generate from base model using saved cache not tainted by aLoRA KV values
prompt_base = prompt_shared
inputs_base = tokenizer(prompt_base, return_tensors="pt").to(device)
with model_alora.disable_adapter():
output = model_alora.generate(**inputs_base, past_key_values=cache_copy)
output_text_base = tokenizer.decode(output[0])
```
### Inference with different LoRA adapters in the same batch
Normally, each inference batch has to use the same adapter(s) in PEFT. This can sometimes be annoying, because we may have batches that contain samples intended to be used with different LoRA adapters. For example, we could have a base model that works well in English and two more LoRA adapters, one for French and one for German. Usually, we would have to split our batches such that each batch only contains samples of one of the languages, we cannot combine different languages in the same batch.
Thankfully, it is possible to mix different LoRA adapters in the same batch using the `adapter_name` argument. Below, we show an example of how this works in practice. First, let's load the base model, English, and the two adapters, French and German, like this:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
model_id = ...
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# load the LoRA adapter for French
peft_model = PeftModel.from_pretrained(model, , adapter_name="adapter_fr")
# next, load the LoRA adapter for German
peft_model.load_adapter(, adapter_name="adapter_de")
```
Now, we want to generate text on a sample that contains all three languages: The first three samples are in English, the next three are in French, and the last three are in German. We can use the `adapter_names` argument to specify which adapter to use for each sample. Since our base model is used for English, we use the special string `"__base__"` for these samples. For the next three samples, we indicate the adapter name of the French LoRA fine-tune, in this case `"adapter_fr"`. For the last three samples, we indicate the adapter name of the German LoRA fine-tune, in this case `"adapter_de"`. This way, we can use the base model and the two adapters in a single batch.
```python
inputs = tokenizer(
[
"Hello, my dog is cute",
"Hello, my cat is awesome",
"Hello, my fish is great",
"Salut, mon chien est mignon",
"Salut, mon chat est génial",
"Salut, mon poisson est super",
"Hallo, mein Hund ist süß",
"Hallo, meine Katze ist toll",
"Hallo, mein Fisch ist großartig",
],
return_tensors="pt",
padding=True,
)
adapter_names = [
"__base__", "__base__", "__base__",
"adapter_fr", "adapter_fr", "adapter_fr",
"adapter_de", "adapter_de", "adapter_de",
]
output = peft_model.generate(**inputs, adapter_names=adapter_names, max_new_tokens=20)
```
Note that the order does not matter here, i.e. the samples in the batch don't need to be grouped by adapter as in the example above. We just need to ensure that the `adapter_names` argument is aligned correctly with the samples.
Additionally, the same approach also works with the `modules_to_save` feature, which allows for saving and reusing specific neural network layers, such as custom heads for classification tasks, across different LoRA adapters.
#### Caveats
Using this feature has some drawbacks, namely:
- It only works for inference, not for training.
- Disabling adapters using the `with model.disable_adapter()` context takes precedence over `adapter_names`.
- You cannot pass `adapter_names` when some adapter weights were merged with base weight using the `merge_adapter` method. Please unmerge all adapters first by calling `model.unmerge_adapter()`.
- For obvious reasons, this cannot be used after calling `merge_and_unload()`, since all the LoRA adapters will be merged into the base weights in this case.
- This feature does not currently work with DoRA, so set `use_dora=False` in your `LoraConfig` if you want to use it.
- The `modules_to_save` feature is currently only supported for the layers of types `Linear`, `Embedding`, `Conv2d` and `Conv1d`.
- There is an expected overhead for inference with `adapter_names`, especially if the amount of different adapters in the batch is high. This is because the batch size is effectively reduced to the number of samples per adapter. If runtime performance is your top priority, try the following:
- Increase the batch size.
- Try to avoid having a large number of different adapters in the same batch, prefer homogeneous batches. This can be achieved by buffering samples with the same adapter and only perform inference with a small handful of different adapters.
- Take a look at alternative implementations such as [LoRAX](https://github.com/predibase/lorax), [punica](https://github.com/punica-ai/punica), or [S-LoRA](https://github.com/S-LoRA/S-LoRA), which are specialized to work with a large number of different adapters.
### Composing and Reusing LoRA Adapters
#### Arrow
[Arrow](https://huggingface.co/papers/2405.11157) is a modular routing algorithm designed to combine multiple pre-trained task-specific LoRA adapters to solve a given task. Rather than merging all adapters naively, Arrow introduces a **gradient-free, token-wise mixture-of-experts (MoE) routing mechanism**. At inference time, it first computes a _prototype_ for each LoRA by extracting the top right singular vector from its SVD decomposition. Each token representation is then compared to these prototypes via cosine similarity to obtain routing coefficients. Tokens are assigned to the top-k most relevant LoRA adapters, with the coefficients normalized through softmax, and their outputs linearly combined. This allows effective reuse of existing LoRA modules for new tasks and leads to stronger zero-shot generalization.
In PEFT, Arrow is enabled through [`ArrowConfig]` and `create_arrow_model`. You can also configure parameters such as `top_k` (the number of LoRA adapters combined per token), `router_temperature` (the softmax temperature applied to the routing coefficients), and `rng_seed` (for reproducibility).
```py
from peft import create_arrow_model, ArrowConfig
from transformers import AutoModelForCausalLM
# Loading the model
base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
# Creating the Arrow config
arrow_config = ArrowConfig(
top_k=3,
router_temperature=1.0,
rng_seed=42,
)
# The LoRA adapters below were trained on a clustered FLAN dataset.
# Task clustering was performed using the Model-Based Clustering (MBC) method,
# as described in the Arrow paper.
# While one could train a separate LoRA for each task and let Arrow route tokens among them,
# training LoRAs on clusters of tasks instead provides an indirect optimization for
# transfer across the multi-task dataset.
task_specific_adapter_paths = [
f"TahaBa/phi3-mini-clustered-flan/ts_expert_{i}" for i in range(10)
]
# Creating the Arrow model
model = create_arrow_model(
base_model=base_model,
task_specific_adapter_paths=task_specific_adapter_paths,
arrow_config=arrow_config,
)
# Now the forward path could be called on this model, like a normal PeftModel.
```
Furthermore, you can add or remove adapters after calling ```create_arrow_model```—for example, to fine-tune a new adapter or discard an unnecessary one. Once the adapters are in place, you can activate the ```"arrow_router"``` for inference to use Arrow. Note that if you add a new LoRA adapter after ```create_arrow_model``` and want to fine-tune it, you must explicitly set the new adapter as active, since ```"arrow_router"``` is activated by default in ```create_arrow_model```.
```py
from trl import SFTTrainer, SFTConfig
# Adding a new adapter and activating it
model.add_adapter(adapter_name='new_adapter')
model.set_adapter('new_adapter')
# Now the model could be trained along the `new_adapter`.
trainer = SFTTrainer(
model=model,
args=SFTConfig(...),
...
)
# Once the training is done, you can activate `arrow_router` and use it in inference
model.set_adapter('arrow_router') # Model is ready to be used at inference time now
```
#### GenKnowSub
[GenKnowSub](https://aclanthology.org/2025.acl-short.54/) augments Arrow by purifying task-specific LoRA adapters before routing. The key idea is to subtract general knowledge encoded in LoRA space—based on the [forgetting-via-negation principle](https://huggingface.co/papers/2212.04089)—so that task adapters become more isolated and focused on task-relevant signals. Concretely, GenKnowSub estimates a low-dimensional “general” subspace from a set of general (non task-specific) LoRA adapters and removes this component from each task adapter’s LoRA update prior to Arrow’s token-wise routing. This typically improves compositionality and reduces interference when combining many task adapters.
In PEFT, enable GenKnowSub by setting ```use_gks=True``` in ArrowConfig, and providing ```general_adapter_paths``` in ```create_arrow_model```:
```py
from peft import create_arrow_model, ArrowConfig
from transformers import AutoModelForCausalLM
# Loading the model
base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
# Creating the Arrow config
arrow_config = ArrowConfig(
top_k=3,
router_temperature=1.0,
use_gks=True,
rng_seed=42,
)
# Path to task-specific, trained on flan clustered dataset (as we explained before.)
task_specific_adapter_paths = [
f"TahaBa/phi3-mini-clustered-flan/ts_expert_{i}" for i in range(10)
]
# These general adapters are trained on English, German, and French Wikipedia dataset,
# with causal language modelling objective, each pair like: (507 token tsentence, 5 token completion), and the loss computed on the completion
general_adapter_paths = [
"TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langen/checkpoint-17",
"TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langfr/checkpoint-35",
"TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langger/checkpoint-17"
]
# Creating the Arrow model
model = create_arrow_model(
base_model=base_model,
task_specific_adapter_paths=task_specific_adapter_paths,
general_adapter_paths=general_adapter_paths,
arrow_config=arrow_config,
)
# Now the forward path could be called on this model, like a normal PeftModel.
```
To encode general knowledge, GenKnowSub subtracts the average of the provided general adapters from each task-specific adapter once, before routing begins. Furthermore, the ability to add or remove adapters after calling ```create_arrow_model``` (as described in the Arrow section) is still supported in this case.
> [!TIP]
> **Things to keep in mind when using Arrow + GenKnowSub:**
>
> - All LoRA adapters (task-specific and general) must share the same ```rank``` and ```target_modules```.
>
> - Any inconsistency in these settings will raise an error in ```create_arrow_model```.
>
> - Having different scaling factors (```lora_alpha```) across task adapters is supported — Arrow handles them automatically.
>
> - Merging the ```"arrow_router"``` is not supported, due to its dynamic routing behavior.
>
> - In create_arrow_model, task adapters are loaded as ```task_i``` and general adapters as ```gks_j``` (where ```i``` and ```j``` are indices). The function ensures consistency of ```target_modules```, ```rank```, and whether adapters are applied to ```Linear``` or ```Linear4bit``` layers. It then adds the ```"arrow_router"``` module and activates it. Any customization of this process requires overriding ```create_arrow_model```.
>
> - This implementation is compatible with 4-bit quantization (via bitsandbytes):
>
> ```py
> from transformers import AutoModelForCausalLM, BitsAndBytesConfig
> import torch
>
> # Quantisation config
> bnb_config = BitsAndBytesConfig(
> load_in_4bit=True,
> bnb_4bit_quant_type="nf4",
> bnb_4bit_compute_dtype=torch.bfloat16,
> bnb_4bit_use_double_quant=False,
> )
>
> # Loading the model
> base_model = AutoModelForCausalLM.from_pretrained(
> "microsoft/Phi-3-mini-4k-instruct",
> dtype=torch.bfloat16,
> device_map="auto",
> quantization_config=bnb_config,
> )
>
> # Now call create_arrow_model() as we explained before.
> ```
================================================
FILE: docs/source/developer_guides/low_level_api.md
================================================
# Adapter injection
With PEFT, you can inject trainable adapters into any `torch` module which allows you to use adapter methods without relying on the modeling classes in PEFT. This works for all adapters except for those based on prompt learning (e.g. prefix tuning or p-tuning).
Check the table below to see when you should inject adapters.
| Pros | Cons |
|---|---|
| the model is modified inplace, keeping all the original attributes and methods | manually write the `from_pretrained` and `save_pretrained` utility functions from Hugging Face to save and load adapters |
| works for any `torch` module and modality | doesn't work with any of the utility methods provided by `PeftModel` such as disabling and merging adapters |
## Creating a new PEFT model
To perform the adapter injection, use the [`inject_adapter_in_model`] method. This method takes 3 arguments, the PEFT config, the model, and an optional adapter name. You can also attach multiple adapters to the model if you call [`inject_adapter_in_model`] multiple times with different adapter names.
For example, to inject LoRA adapters into the `linear` submodule of the `DummyModel` module:
```python
import torch
from peft import inject_adapter_in_model, LoraConfig
class DummyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.embedding = torch.nn.Embedding(10, 10)
self.linear = torch.nn.Linear(10, 10)
self.lm_head = torch.nn.Linear(10, 10)
def forward(self, input_ids):
x = self.embedding(input_ids)
x = self.linear(x)
x = self.lm_head(x)
return x
lora_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
target_modules=["linear"],
)
model = DummyModel()
model = inject_adapter_in_model(lora_config, model)
dummy_inputs = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]])
dummy_outputs = model(dummy_inputs)
```
Print the model to see that the adapters have been correctly injected.
```bash
DummyModel(
(embedding): Embedding(10, 10)
(linear): Linear(
in_features=10, out_features=10, bias=True
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=10, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=10, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(lm_head): Linear(in_features=10, out_features=10, bias=True)
)
```
### Injection based on a `state_dict`
Sometimes, it is possible that there is a PEFT adapter checkpoint but the corresponding PEFT config is not known for whatever reason. To inject the PEFT layers for this checkpoint, you would usually have to reverse-engineer the corresponding PEFT config, most notably the `target_modules` argument, based on the `state_dict` from the checkpoint. This can be cumbersome and error prone. To avoid this, it is also possible to call [`inject_adapter_in_model`] and pass the loaded `state_dict` as an argument:
```python
from safetensors.torch import load_file
model = ...
state_dict = load_file()
lora_config = LoraConfig(...)
model = inject_adapter_in_model(lora_config, model, state_dict=state_dict)
```
In this case, PEFT will use the `state_dict` as reference for which layers to target instead of using the PEFT config. As a user, you don't have to set the exact `target_modules` of the PEFT config for this to work. However, you should still pass a PEFT config of the right type, in this example `LoraConfig`, you can leave the `target_modules` as `None`.
Be aware that this still only creates the uninitialized PEFT layers, the values from the `state_dict` are not used to populate the model weights. To populate the weights, proceed with calling [`set_peft_model_state_dict`] as described below.
⚠️ Note that if there is a mismatch between what is configured in the PEFT config and what is found in the `state_dict`, PEFT will warn you about this. You can ignore the warning if you know that the PEFT config is not correctly specified.
> [!WARNING]
> If the original PEFT adapters was using `target_parameters` instead of `target_modules`, injecting from a `state_dict` will not work correctly. In this case, it is mandatory to use the correct PEFT config for injection.
## Saving the model
To only save the adapter, use the [`get_peft_model_state_dict`] function:
```python
from peft import get_peft_model_state_dict
peft_state_dict = get_peft_model_state_dict(model)
print(peft_state_dict)
```
Otherwise, `model.state_dict()` returns the full state dict of the model.
## Loading the model
After loading the saved `state_dict`, it can be applied using the [`set_peft_model_state_dict`] function:
```python
from peft import set_peft_model_state_dict
model = DummyModel()
model = inject_adapter_in_model(lora_config, model)
outcome = set_peft_model_state_dict(model, peft_state_dict)
# check that there were no wrong keys
print(outcome.unexpected_keys)
```
If injecting the adapter is slow or you need to load a large number of adapters, you may use an optimization that allows to create an "empty" adapter on meta device and only fills the weights with real weights when the [`set_peft_model_state_dict`] is called. To do this, pass `low_cpu_mem_usage=True` to both [`inject_adapter_in_model`] and [`set_peft_model_state_dict`].
```python
model = DummyModel()
model = inject_adapter_in_model(lora_config, model, low_cpu_mem_usage=True)
print(model.linear.lora_A["default"].weight.device.type == "meta") # should be True
set_peft_model_state_dict(model, peft_state_dict, low_cpu_mem_usage=True)
print(model.linear.lora_A["default"].weight.device.type == "cpu") # should be True
```
================================================
FILE: docs/source/developer_guides/mixed_models.md
================================================
# Mixed adapter types
Normally, it isn't possible to mix different adapter types in 🤗 PEFT. You can create a PEFT model with two different LoRA adapters (which can have different config options), but it is not possible to combine a LoRA and LoHa adapter. With [`PeftMixedModel`] however, this works as long as the adapter types are compatible. The main purpose of allowing mixed adapter types is to combine trained adapters for inference. While it is possible to train a mixed adapter model, this has not been tested and is not recommended.
To load different adapter types into a PEFT model, use [`PeftMixedModel`] instead of [`PeftModel`]:
```py
from peft import PeftMixedModel
base_model = ... # load the base model, e.g. from transformers
# load first adapter, which will be called "default"
peft_model = PeftMixedModel.from_pretrained(base_model, )
peft_model.load_adapter(, adapter_name="other")
peft_model.set_adapter(["default", "other"])
```
The [`~PeftMixedModel.set_adapter`] method is necessary to activate both adapters, otherwise only the first adapter would be active. You can keep adding more adapters by calling [`~PeftModel.add_adapter`] repeatedly.
[`PeftMixedModel`] does not support saving and loading mixed adapters. The adapters should already be trained, and loading the model requires a script to be run each time.
## Tips
- Not all adapter types can be combined. See [`peft.tuners.mixed.COMPATIBLE_TUNER_TYPES`](https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/src/peft/tuners/mixed/model.py#L35) for a list of compatible types. An error will be raised if you try to combine incompatible adapter types.
- It is possible to mix multiple adapters of the same type which can be useful for combining adapters with very different configs.
- If you want to combine a lot of different adapters, the most performant way to do it is to consecutively add the same adapter types. For example, add LoRA1, LoRA2, LoHa1, LoHa2 in this order, instead of LoRA1, LoHa1, LoRA2, and LoHa2. While the order can affect the output, there is no inherently *best* order, so it is best to choose the fastest one.
================================================
FILE: docs/source/developer_guides/model_merging.md
================================================
# Model merging
Training a model for each task can be costly, take up storage space, and the models aren't able to learn new information to improve their performance. Multitask learning can overcome some of these limitations by training a model to learn several tasks, but it is expensive to train and designing a dataset for it is challenging. *Model merging* offers a solution to these challenges by combining multiple pretrained models into one model, giving it the combined abilities of each individual model without any additional training.
PEFT provides several methods for merging models like a linear or SVD combination. This guide focuses on two methods that are more efficient for merging LoRA adapters by eliminating redundant parameters:
* [TIES](https://hf.co/papers/2306.01708) - TrIm, Elect, and Merge (TIES) is a three-step method for merging models. First, redundant parameters are trimmed, then conflicting signs are resolved into an aggregated vector, and finally the parameters whose signs are the same as the aggregate sign are averaged. This method takes into account that some values (redundant and sign disagreement) can degrade performance in the merged model.
* [DARE](https://hf.co/papers/2311.03099) - Drop And REscale is a method that can be used to prepare for other model merging methods like TIES. It works by randomly dropping parameters according to a drop rate and rescaling the remaining parameters. This helps to reduce the number of redundant and potentially interfering parameters among multiple models.
Models are merged with the [`~LoraModel.add_weighted_adapter`] method, and the specific model merging method is specified in the `combination_type` parameter.
## Merge method
With TIES and DARE, merging is enabled by setting `combination_type` and `density` to a value of the weights to keep from the individual models. For example, let's merge three finetuned [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) models: [tinyllama_lora_nobots](https://huggingface.co/smangrul/tinyllama_lora_norobots), [tinyllama_lora_sql](https://huggingface.co/smangrul/tinyllama_lora_sql), and [tinyllama_lora_adcopy](https://huggingface.co/smangrul/tinyllama_lora_adcopy).
When you're attempting to merge fully trained models with TIES, you should be aware of any special tokens each model may have added to the embedding layer which are not a part of the original checkpoint's vocabulary. This may cause an issue because each model may have added a special token to the same embedding position. If this is the case, you should use the [`~transformers.PreTrainedModel.resize_token_embeddings`] method to avoid merging the special tokens at the same embedding index.
This shouldn't be an issue if you're only merging LoRA adapters trained from the same base model.
Load a base model and can use the [`~PeftModel.load_adapter`] method to load and assign each adapter a name:
```py
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
config = PeftConfig.from_pretrained("smangrul/tinyllama_lora_norobots")
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_4bit=True, device_map="auto").eval()
tokenizer = AutoTokenizer.from_pretrained("smangrul/tinyllama_lora_norobots")
model.config.vocab_size = 32005
model.resize_token_embeddings(32005)
model = PeftModel.from_pretrained(model, "smangrul/tinyllama_lora_norobots", adapter_name="norobots")
_ = model.load_adapter("smangrul/tinyllama_lora_sql", adapter_name="sql")
_ = model.load_adapter("smangrul/tinyllama_lora_adcopy", adapter_name="adcopy")
```
Set the adapters, weights, `adapter_name`, `combination_type`, and `density` with the [`~LoraModel.add_weighted_adapter`] method.
Weight values greater than `1.0` typically produce better results because they preserve the correct scale. A good default starting value for the weights is to set all values to `1.0`.
```py
adapters = ["norobots", "adcopy", "sql"]
weights = [2.0, 1.0, 1.0]
adapter_name = "merge"
density = 0.2
model.add_weighted_adapter(adapters, weights, adapter_name, combination_type="ties", density=density)
```
```py
adapters = ["norobots", "adcopy", "sql"]
weights = [2.0, 0.3, 0.7]
adapter_name = "merge"
density = 0.2
model.add_weighted_adapter(adapters, weights, adapter_name, combination_type="dare_ties", density=density)
```
Set the newly merged model as the active model with the [`~LoraModel.set_adapter`] method.
```py
model.set_adapter("merge")
```
Now you can use the merged model as an instruction-tuned model to write ad copy or SQL queries!
```py
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
messages = [
{"role": "user", "content": "Write an essay about Generative AI."},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))
```
```py
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
messages = [
{"role": "system", "content": "Create a text ad given the following product and description."},
{"role": "user", "content": "Product: Sony PS5 PlayStation Console\nDescription: The PS5 console unleashes new gaming possibilities that you never anticipated."},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))
```
```py
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
text = """Table: 2-11365528-2
Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location']
Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic?
SQL Query:"""
inputs = tokenizer(text, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1, eos_token_id=tokenizer("").input_ids[-1])
print(tokenizer.decode(outputs[0]))
```
## Merging (IA)³ Models
The (IA)³ models facilitate linear merging of adapters. To merge adapters in an (IA)³ model, utilize the `add_weighted_adapter` method from the `IA3Model` class. This method is analogous to the `add_weighted_adapter` method used in `LoraModel`, with the key difference being the absence of the `combination_type` parameter. For example, to merge three (IA)³ adapters into a PEFT model, you would proceed as follows:
```py
adapters = ["adapter1", "adapter2", "adapter3"]
weights = [0.4, 0.3, 0.3]
adapter_name = "merge"
model.add_weighted_adapter(adapters, weights, adapter_name)
```
It is recommended that the weights sum to 1.0 to preserve the scale of the model. The merged model can then be set as the active model using the `set_adapter` method:
```py
model.set_adapter("merge")
```
================================================
FILE: docs/source/developer_guides/quantization.md
================================================
# Quantization
Quantization represents data with fewer bits, making it a useful technique for reducing memory-usage and accelerating inference especially when it comes to large language models (LLMs). There are several ways to quantize a model including:
* optimizing which model weights are quantized with the [AWQ](https://hf.co/papers/2306.00978) algorithm
* independently quantizing each row of a weight matrix with the [GPTQ](https://hf.co/papers/2210.17323) algorithm
* quantizing to 8-bit and 4-bit precision with the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library
* quantizing to as low as 2-bit precision with the [AQLM](https://huggingface.co/papers/2401.06118) algorithm
However, after a model is quantized it isn't typically further trained for downstream tasks because training can be unstable due to the lower precision of the weights and activations. But since PEFT methods only add *extra* trainable parameters, this allows you to train a quantized model with a PEFT adapter on top! Combining quantization with PEFT can be a good strategy for training even the largest models on a single GPU. For example, [QLoRA](https://hf.co/papers/2305.14314) is a method that quantizes a model to 4-bits and then trains it with LoRA. This method allows you to finetune a 65B parameter model on a single 48GB GPU!
In this guide, you'll see how to quantize a model to 4-bits and train it with LoRA.
## Quantize a model
[bitsandbytes](https://github.com/TimDettmers/bitsandbytes) is a quantization library with a Transformers integration. With this integration, you can quantize a model to 8 or 4-bits and enable many other options by configuring the [`~transformers.BitsAndBytesConfig`] class. For example, you can:
* set `load_in_4bit=True` to quantize the model to 4-bits when you load it
* set `bnb_4bit_quant_type="nf4"` to use a special 4-bit data type for weights initialized from a normal distribution
* set `bnb_4bit_use_double_quant=True` to use a nested quantization scheme to quantize the already quantized weights
* set `bnb_4bit_compute_dtype=torch.bfloat16` to use bfloat16 for faster computation
```py
import torch
from transformers import BitsAndBytesConfig
config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
```
Pass the `config` to the [`~transformers.AutoModelForCausalLM.from_pretrained`] method.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", quantization_config=config)
```
Next, you should call the [`~peft.utils.prepare_model_for_kbit_training`] function to preprocess the quantized model for training.
```py
from peft import prepare_model_for_kbit_training
model = prepare_model_for_kbit_training(model)
```
Now that the quantized model is ready, let's set up a configuration.
## LoraConfig
Create a [`LoraConfig`] with the following parameters (or choose your own):
```py
from peft import LoraConfig
config = LoraConfig(
r=16,
lora_alpha=8,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
```
Then use the [`get_peft_model`] function to create a [`PeftModel`] from the quantized model and configuration.
```py
from peft import get_peft_model
model = get_peft_model(model, config)
```
You're all set for training with whichever training method you prefer!
### LoftQ initialization
[LoftQ](https://hf.co/papers/2310.08659) initializes LoRA weights such that the quantization error is minimized, and it can improve performance when training quantized models. To get started, follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning).
In general, for LoftQ to work best, it is recommended to target as many layers with LoRA as possible, since those not targeted cannot have LoftQ applied. This means that passing `LoraConfig(..., target_modules="all-linear")` will most likely give the best results. Also, you should use `nf4` as quant type in your quantization config when using 4bit quantization, i.e. `BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")`.
In general, for LoftQ to work best, it is recommended to target as many layers with LoRA as possible, since those not targeted cannot have LoftQ applied. This means that passing `LoraConfig(..., target_modules="all-linear")` will most likely give the best results. Also, you should use `nf4` as quant type in your quantization config when using 4bit quantization, i.e. `BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")`.
There are currently two intended ways of applying LoftQ:
1. load the non-quantized base model, apply LoftQ with your intended quantization level (e.g., `bits=4` for nf4) and
save the resulting adapter. Then quantize the base model using, for example,
`BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")` and load the LoftQ-initialized adapter on top
2. load the quantized base model, initialize a PEFT model with LoRA (no loftq) and use `replace_lora_weights_loftq` to
apply LoftQ initialization by streaming the reference weights of the non-quantized base model from the weights file
(explained further in the next section)
#### Using `replace_lora_weights_loftq` for on-the-fly LoftQ application
An easier but more limited way to apply LoftQ initialization is to use the convenience function `replace_lora_weights_loftq`. This takes the quantized PEFT model as input and replaces the LoRA weights in-place with their LoftQ-initialized counterparts.
```python
from peft import replace_lora_weights_loftq
from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_4bit=True, ...)
base_model = AutoModelForCausalLM.from_pretrained(..., quantization_config=bnb_config)
# note: don't pass init_lora_weights="loftq" or loftq_config!
lora_config = LoraConfig(task_type="CAUSAL_LM")
peft_model = get_peft_model(base_model, lora_config)
replace_lora_weights_loftq(peft_model)
```
`replace_lora_weights_loftq` also allows you to pass a `callback` argument to give you more control over which layers should be modified or not, which empirically can improve the results quite a lot. To see a more elaborate example of this, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb).
`replace_lora_weights_loftq` implements only one iteration step of LoftQ. This means that only the LoRA weights are updated, instead of iteratively updating LoRA weights and quantized base model weights. This may lead to lower performance but has the advantage that we can use the original quantized weights derived from the base model, instead of having to keep an extra copy of modified quantized weights. Whether this tradeoff is worthwhile depends on the use case.
At the moment, `replace_lora_weights_loftq` has these additional limitations:
- Model files must be stored as a `safetensors` file.
- Only bitsandbytes 4bit quantization is supported.
### QLoRA-style training
QLoRA adds trainable weights to all the linear layers in the transformer architecture. Since the attribute names for these linear layers can vary across architectures, set `target_modules` to `"all-linear"` to add LoRA to all the linear layers:
```py
config = LoraConfig(target_modules="all-linear", ...)
```
## GPTQ quantization
You can learn more about gptq based `[2, 3, 4, 8]` bits quantization at [GPTQModel](https://github.com/ModelCloud/GPTQModel) and the Transformers [GPTQ](https://huggingface.co/docs/transformers/quantization/gptq) doc. Post-quant training, PEFT can use both [GPTQModel](https://github.com/ModelCloud/GPTQModel) or [AutoGPTQ](https://github.com/autogptq/autogptq) libraries, but we recommend GPTQModel because AutoGPTQ will be deprecated in a future release.
```bash
# gptqmodel install
pip install gptqmodel --no-build-isolation
```
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_id = "facebook/opt-125m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
gptq_config = GPTQConfig(bits=4, group_size=128, dataset="wikitext2", tokenizer=tokenizer)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config)
# save quantized model
quantized_model.save_pretrained("./opt-125m-gptq")
tokenizer.save_pretrained("./opt-125m-gptq")
```
Once quantized, you can post-train GPTQ models with PEFT APIs.
## AQLM quantization
Additive Quantization of Language Models ([AQLM](https://huggingface.co/papers/2401.06118)) is a Large Language Models compression method. It quantizes multiple weights together and takes advantage of interdependencies between them. AQLM represents groups of 8-16 weights as a sum of multiple vector codes. This allows it to compress models down to as low as 2-bit with considerably low accuracy losses.
Since the AQLM quantization process is computationally expensive, the use of prequantized models is recommended. A partial list of available models can be found in the official aqlm [repository](https://github.com/Vahe1994/AQLM).
The models support LoRA adapter tuning. To tune the quantized model you'll need to install the `aqlm` inference library: `pip install aqlm>=1.0.2`. Finetuned LoRA adapters shall be saved separately, as merging them with AQLM quantized weights is not possible.
```py
quantized_model = AutoModelForCausalLM.from_pretrained(
"BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch",
dtype="auto", device_map="auto", low_cpu_mem_usage=True,
)
peft_config = LoraConfig(...)
quantized_model = get_peft_model(quantized_model, peft_config)
```
You can refer to the [Google Colab](https://colab.research.google.com/drive/12GTp1FCj5_0SnnNQH18h_2XFh9vS_guX?usp=sharing) example for an overview of AQLM+LoRA finetuning.
## EETQ quantization
You can also perform LoRA fine-tuning on EETQ quantized models. [EETQ](https://github.com/NetEase-FuXi/EETQ) package offers simple and efficient way to perform 8-bit quantization, which is claimed to be faster than the `LLM.int8()` algorithm. First, make sure that you have a transformers version that is compatible with EETQ (e.g. by installing it from latest pypi or from source).
```py
import torch
from transformers import EetqConfig
config = EetqConfig("int8")
```
Pass the `config` to the [`~transformers.AutoModelForCausalLM.from_pretrained`] method.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", quantization_config=config)
```
and create a `LoraConfig` and pass it to `get_peft_model`:
```py
from peft import LoraConfig, get_peft_model
config = LoraConfig(
r=16,
lora_alpha=8,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
model = get_peft_model(model, config)
```
## HQQ quantization
The models that are quantized using Half-Quadratic Quantization of Large Machine Learning Models ([HQQ](https://mobiusml.github.io/hqq_blog/)) support LoRA adapter tuning. To tune the quantized model, you'll need to install the `hqq` library with: `pip install hqq`.
```python
from hqq.engine.hf import HQQModelForCausalLM
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
quantized_model = HQQModelForCausalLM.from_quantized(save_dir_or_hfhub, device=device)
peft_config = LoraConfig(...)
quantized_model = get_peft_model(quantized_model, peft_config)
```
Or using transformers version that is compatible with HQQ (e.g. by installing it from latest pypi or from source).
```python
from transformers import HqqConfig, AutoModelForCausalLM
quant_config = HqqConfig(nbits=4, group_size=64)
quantized_model = AutoModelForCausalLM.from_pretrained(save_dir_or_hfhub, device_map=device_map, quantization_config=quant_config)
peft_config = LoraConfig(...)
quantized_model = get_peft_model(quantized_model, peft_config)
```
## torchao (PyTorch Architecture Optimization)
PEFT supports models quantized with [torchao](https://github.com/pytorch/ao) ("ao") for int8 quantization.
```python
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, TorchAoConfig
model_id = ...
quantization_config = TorchAoConfig(quant_type="int8_weight_only")
base_model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
peft_config = LoraConfig(...)
model = get_peft_model(base_model, peft_config)
```
### Caveats:
- Use the most recent versions of torchao (>= v0.4.0) and transformers (> 4.42).
- Only linear layers are currently supported.
- `quant_type = "int4_weight_only"` is currently not supported.
- `NF4` is not implemented in transformers as of yet and is thus also not supported.
- DoRA only works with `quant_type = "int8_weight_only"` at the moment.
- There is explicit support for torchao when used with LoRA. However, when torchao quantizes a layer, its class does not change, only the type of the underlying tensor. For this reason, PEFT methods other than LoRA will generally also work with torchao, even if not explicitly supported. Be aware, however, that **merging only works correctly with LoRA and with `quant_type = "int8_weight_only"`**. If you use a different PEFT method or dtype, merging will likely result in an error, and even it doesn't, the results will still be incorrect.
## INC quantization
Intel Neural Compressor ([INC](https://github.com/intel/neural-compressor)) enables model quantization for various devices,
including Intel Gaudi accelerators (also known as HPU devices). You can perform LoRA fine-tuning on models that have been
quantized using INC. To use INC with PyTorch models, install the library with: `pip install neural-compressor[pt]`.
Quantizing a model to FP8 precision for HPU devices can be done with the following single-step quantization workflow:
```python
import torch
from neural_compressor.torch.quantization import FP8Config, convert, finalize_calibration, prepare
quant_configs = {
...
}
config = FP8Config(**quant_configs)
```
Pass the config to the `prepare` method, run inference to gather calibration stats, and call `finalize_calibration`
and `convert` methods to quantize model to FP8 precision:
```python
model = prepare(model, config)
# Run inference to collect calibration statistics
...
# Finalize calibration and convert the model to FP8 precision
finalize_calibration(model)
model = convert(model)
# Load PEFT LoRA adapter as usual
...
```
An example demonstrating how to load a PEFT LoRA adapter into an INC-quantized FLUX text-to-image model for HPU
devices is provided [here](https://github.com/huggingface/peft/blob/main/examples/stable_diffusion/inc_flux_lora_hpu.py).
### Caveats:
- `merge()` and `unmerge()` methods are currently not supported for INC-quantized models.
- Currently, only **Linear** INC-quantized layers are supported when loading PEFT adapters.
## Other Supported PEFT Methods
Besides LoRA, the following PEFT methods also support quantization:
- **VeRA** (supports bitsandbytes quantization)
- **AdaLoRA** (supports both bitsandbytes and GPTQ quantization)
- **(IA)³** (supports bitsandbytes quantization)
## Transformer Engine (TE) LoRA
PEFT supports LoRA adapters on top of [NVIDIA Transformer Engine](https://docs.nvidia.com/deeplearning/transformer-engine/) layers (`te.pytorch.Linear`, `te.pytorch.LayerNormLinear`, and `te.pytorch.LayerNormMLP`). TE layers use FP8 and fused kernels to accelerate transformer training, and attaching LoRA adapters lets you parameter-efficiently fine-tune TE-accelerated models.
```python
from peft import LoraConfig, get_peft_model
config = LoraConfig(
r=8,
lora_alpha=16,
target_modules=["q_proj", "v_proj"],
task_type="TOKEN_CLS",
)
model = get_peft_model(te_model, config)
```
When Transformer Engine is installed, PEFT automatically dispatches matching layers to the `TeLinear` adapter — no extra configuration is needed.
### Caveats
- `merge()` and `unmerge()` are not yet supported for TE layers.
- DoRA is not supported with TE layers.
A complete ESM2 token-classification example is available under `examples/lora_finetuning_transformer_engine/`.
## Next steps
If you're interested in learning more about quantization, the following may be helpful:
* Learn more details about QLoRA and check out some benchmarks on its impact in the [Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes) blog post.
* Read more about different quantization schemes in the Transformers [Quantization](https://hf.co/docs/transformers/main/quantization) guide.
================================================
FILE: docs/source/developer_guides/torch_compile.md
================================================
# torch.compile
In PEFT, [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) works for some but not all features. The reason why it won't always work is because PEFT is highly dynamic in certain places (loading and switching between multiple adapters, for instance), which can cause trouble for `torch.compile`. In other places, `torch.compile` may work, but won't be as fast as expected because of graph breaks.
If you don't see an error, it doesn't necessarily mean that `torch.compile` worked correctly. It might give you an output, but the output is incorrect. This guide describes what works with `torch.compile` and what doesn't. For your own testing, we recommend using the latest PyTorch version, as `torch.compile` is constantly being improved.
> [!TIP]
> Unless indicated otherwise, the default `torch.compile` settings were used.
## Training and inference with `torch.compile`
These features **work** with `torch.compile`. Everything listed below was tested with a causal LM:
- Training with `Trainer` from 🤗 transformers
- Training with a custom PyTorch loop
- Inference
- Generation
The following adapters were tested successfully:
- AdaLoRA
- BOFT
- IA³
- Layer Norm Tuning
- LoHa
- LoKr
- LoRA
- LoRA + DoRA
- LoRA applied to embedding layers
- OFT
- VeRA
- HRA
## Advanced PEFT features with `torch.compile`
Below are some of the more advanced PEFT features that **work**. They were all tested with LoRA.
- `modules_to_save` (i.e. `config = LoraConfig(..., modules_to_save=...)`)
- Merging adapters (one or multiple)
- Merging multiple adapters into one adapter (i.e. calling `model.add_weighted_adapter(...)`)
- Using PEFT adapters with quantization (bitsandbytes)
- Disabling adapters (i.e. using `with model.disable_adapter()`)
- Unloading (i.e. calling `model.merge_and_unload()`)
- Mixed adapter batches (i.e. calling `model(batch, adapter_names=["__base__", "default", "other", ...])`)
- Inference with multiple adapters (i.e. using `model.add_adapter` or `model.load_adapter` to load more than 1 adapter); for this, only call `torch.compile` _after_ loading all adapters
Generally, we can expect that if a feature works correctly with LoRA and is also supported by other adapter types, it should also work for that adapter type.
## Test cases
All the use cases listed above are tested inside of [`peft/tests/test_torch_compile.py`](https://github.com/huggingface/peft/blob/main/tests/test_torch_compile.py). If you want to check in more detail how we tested a certain feature, please go to that file and check the test that corresponds to your use case.
> [!TIP]
> If you have another use case where you know that `torch.compile` does or does not work with PEFT, please contribute by letting us know or by opening a PR to add this use case to the covered test cases.
================================================
FILE: docs/source/developer_guides/troubleshooting.md
================================================
# Troubleshooting
If you encounter any issue when using PEFT, please check the following list of common issues and their solutions.
## Examples don't work
Examples often rely on the most recent package versions, so please ensure they're up-to-date. In particular, check the following package versions:
- `peft`
- `transformers`
- `accelerate`
- `torch`
In general, you can update the package version by running this command inside your Python environment:
```bash
python -m pip install -U
```
Installing PEFT from source is useful for keeping up with the latest developments:
```bash
python -m pip install git+https://github.com/huggingface/peft
```
## Dtype-related issues
### ValueError: Attempting to unscale FP16 gradients
This error probably occurred because the model was loaded with `dtype=torch.float16` and then used in an automatic mixed precision (AMP) context, e.g. by setting `fp16=True` in the [`~transformers.Trainer`] class from 🤗 Transformers. The reason is that when using AMP, trainable weights should never use fp16. To make this work without loading the whole model in fp32, add the following to your code:
```python
peft_model = get_peft_model(...)
# add this:
for param in model.parameters():
if param.requires_grad:
param.data = param.data.float()
# proceed as usual
trainer = Trainer(model=peft_model, fp16=True, ...)
trainer.train()
```
Alternatively, you can use the [`~utils.cast_mixed_precision_params`] function to correctly cast the weights:
```python
from peft import cast_mixed_precision_params
peft_model = get_peft_model(...)
cast_mixed_precision_params(peft_model, dtype=torch.float16)
# proceed as usual
trainer = Trainer(model=peft_model, fp16=True, ...)
trainer.train()
```
> [!TIP]
> Starting from PEFT version v0.12.0, PEFT automatically promotes the dtype of adapter weights from `torch.float16` and `torch.bfloat16` to `torch.float32` where appropriate. To _prevent_ this behavior, you can pass `autocast_adapter_dtype=False` to [`~get_peft_model`], to [`~PeftModel.from_pretrained`], and to [`~PeftModel.load_adapter`].
### Selecting the dtype of the adapter
Most PEFT methods, like LoRA, work by adding trainable adapter weights. By default, those weights are stored in float32 dtype (fp32), i.e. at a relatively high precision. Therefore, even if the base model is loaded in float16 (fp16) or bfloat16 (bf16), the adapter weights are float32. When the adapter results are calculated during the forward pass, the input will typically be in the dtype of the base model, thus it will be upcast to float32 if necessary, then cast back to the original dtype.
If you prefer to have the adapter weights in the lower precision of the base model, i.e. in float16 or bfloat16, you can pass `autocast_adapter_dtype=False` when creating the model ([`~get_peft_model`]) or loading the model ([`~PeftModel.from_pretrained`]). There are some advantages and disadvantages to this:
Advantages of half precision adapter:
- computation slightly faster
- slightly less memory
- smaller file size of checkpoint (half the size)
Disadvantages of half precision adapter:
- slightly worse loss
- higher risk of overflow or underflow
Note that for most use cases, overall runtime and memory cost will be determined by the size of the base model and by the dataset, while the dtype of the PEFT adapter will only have a small impact.
## Bad results from a loaded PEFT model
There can be several reasons for getting a poor result from a loaded PEFT model which are listed below. If you're still unable to troubleshoot the problem, see if anyone else had a similar [issue](https://github.com/huggingface/peft/issues) on GitHub, and if you can't find any, open a new issue.
When opening an issue, it helps a lot if you provide a minimal code example that reproduces the issue. Also, please report if the loaded model performs at the same level as the model did before fine-tuning, if it performs at a random level, or if it is only slightly worse than expected. This information helps us identify the problem more quickly.
### Random deviations
If your model outputs are not exactly the same as previous runs, there could be an issue with random elements. For example:
1. please ensure it is in `.eval()` mode, which is important, for instance, if the model uses dropout
2. if you use [`~transformers.GenerationMixin.generate`] on a language model, there could be random sampling, so obtaining the same result requires setting a random seed
3. if you used quantization and merged the weights, small deviations are expected due to rounding errors
### Incorrectly loaded model
Please ensure that you load the model correctly. A common error is trying to load a _trained_ model with [`get_peft_model`] which is incorrect. Instead, the loading code should look like this:
```python
from peft import PeftModel, PeftConfig
base_model = ... # to load the base model, use the same code as when you trained it
config = PeftConfig.from_pretrained(peft_model_id)
peft_model = PeftModel.from_pretrained(base_model, peft_model_id)
```
### Randomly initialized layers
For some tasks, it is important to correctly configure `modules_to_save` in the config to account for randomly initialized layers.
As an example, this is necessary if you use LoRA to fine-tune a language model for sequence classification because 🤗 Transformers adds a randomly initialized classification head on top of the model. If you do not add this layer to `modules_to_save`, the classification head won't be saved. The next time you load the model, you'll get a _different_ randomly initialized classification head, resulting in completely different results.
PEFT tries to correctly guess the `modules_to_save` if you provide the `task_type` argument in the config. This should work for transformers models that follow the standard naming scheme. It is always a good idea to double check though because we can't guarantee all models follow the naming scheme.
When you load a transformers model that has randomly initialized layers, you should see a warning along the lines of:
```
Some weights of were not initialized from the model checkpoint at and are newly initialized: [].
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
The mentioned layers should be added to `modules_to_save` in the config to avoid the described problem.
> [!TIP]
> As an example, when loading a model that is using the DeBERTa architecture for sequence classification, you'll see a warning that the following weights are newly initialized: `['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']`. From this, it follows that the `classifier` and `pooler` layers should be added to: `modules_to_save=["classifier", "pooler"]`.
### Extending the vocabulary
For many language fine-tuning tasks, extending the model's vocabulary is necessary since new tokens are being introduced. This requires extending the embedding layer to account for the new tokens and, depending on the fine-tuning method, also storing the embedding layer in addition to the adapter weights when saving the adapter. There are a few ways of achieving this ordered by parameter effectiveness:
- [trainable tokens](../package_reference/trainable_tokens), train only the specified tokens, optionally store only the updated values
- training an adapter on the embedding matrix, optionally store only the updated values
- full-finetuning of the embedding layer
#### Using trainable tokens
Let's start with trainable tokens, in this case its [LoRA integration](../developer_guides/lora#efficiently-train-tokens-alongside-lora). If you're interested in only training the new embeddings and nothing else, refer to the [standalone documentation](../package_reference/trainable_tokens).
To enable selective token training of the embedding layer, you'll need to supply the token ids of your newly added tokens via the `trainable_token_indices` parameter. Optionally you can specify which layer to target if there is more than one embedding layer. For a Mistral model this could look like this:
```python
new_tokens = ['', '']
tokenizer.add_tokens(new_tokens)
base_model.resize_token_embeddings(len(tokenizer))
lora_config = LoraConfig(
...,
trainable_token_indices={'embed_tokens': tokenizer.convert_tokens_to_ids(new_tokens)},
)
```
If your model uses tied weights (such as the `lm_head`), trainable tokens will try to resolve those and keep them updated as well, so in that case there should be no need for adding `modules_to_save=["lm_head"]`. This only works if the model uses the Transformers convention for tying weights.
Saving the model with `model.save_pretrained` may save the full embedding matrix instead of
only the difference as a precaution because the embedding matrix was resized. To save space you can disable this behavior by setting `save_embedding_layers=False` when calling `save_pretrained`. This is safe to do as long as you don't modify the embedding matrix through other means as well, as such changes will be not tracked by trainable tokens.
#### Using an adapter, e.g. LoRA
Prepare the embedding layer by adding it to the `target_modules` of your adapter config. For example, the Mistral config could look like this:
```python
config = LoraConfig(..., target_modules=["embed_tokens", "lm_head", "q_proj", "v_proj"])
```
Once added to `target_modules`, PEFT automatically stores the embedding layer when saving the adapter if the model has the [`~transformers.PreTrainedModel.get_input_embeddings`] and [`~transformers.PreTrainedModel.get_output_embeddings`]. This is generally the case for Transformers models.
If the model's embedding layer doesn't follow the Transformer's naming scheme but nevertheless implements `get_input_embeddings`, you can still save it by manually passing `save_embedding_layers=True` when saving the adapter:
```python
model = get_peft_model(...)
# train the model
model.save_pretrained("my_adapter", save_embedding_layers=True)
```
For inference, load the base model first and resize it the same way you did before you trained the model. After you've resized the base model, you can load the PEFT checkpoint.
For a complete example, please check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/causal_language_modeling/peft_lora_clm_with_additional_tokens.ipynb).
#### Full fine-tuning
Full fine-tuning is more costly in terms of VRAM or storage space but if all else fails, you can fall back to this and see if it works for you. Achieve it by adding the name of the embedding layer to `modules_to_save`. Note that you need to add tied layers as well, e.g. `lm_head`. Example for a Mistral model with LoRA:
```python
config = LoraConfig(..., modules_to_save=["embed_tokens", "lm_head"], target_modules=["q_proj", "v_proj"])
```
### Getting a warning about "weights not being initialized from the model checkpoint"
When you load your PEFT model which has been trained on a task (for example, classification), you may get a warning like:
> Some weights of LlamaForSequenceClassification were not initialized from the model checkpoint at meta-llama/Llama-3.2-1B and are newly initialized: ['score.weight']. You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Although this looks scary, it is most likely nothing to worry about. This warning comes from Transformers, and it isn't a PEFT specific warning. It lets you know that a randomly initialized classification head (`score`) is attached to the base model, and the head must be trained to produce sensible predictions.
When you get this warning _before_ training the model, PEFT automatically takes care of making the classification head trainable if you correctly passed the `task_type` argument to the PEFT config.
```python
from peft import LoraConfig, TaskType
lora_config = LoraConfig(..., task_type=TaskType.SEQ_CLS)
```
If your classification head does not follow the usual naming conventions from Transformers (which is rare), you have to explicitly tell PEFT the name of the head in `modules_to_save`.
```python
lora_config = LoraConfig(..., modules_to_save=["name-of-classification-head"])
```
To check the name of the classification head, print the model and it should be the last module.
If you get this warning from your inference code, i.e. _after_ training the model, when you load the PEFT model, you always have to load the Transformers model first. Since Transformers does not know that you will load PEFT weights afterwards, it still gives the warning.
As always, it is best practice to ensure the model works correctly for inference by running some validation on it.
### Check layer and model status
Sometimes a PEFT model can end up in a bad state, especially when handling multiple adapters. There can be some confusion around what adapters exist, which one is active, which one is merged, etc. To help investigate this issue, call the [`~peft.PeftModel.get_layer_status`] and the [`~peft.PeftModel.get_model_status`] methods.
The [`~peft.PeftModel.get_layer_status`] method gives you a detailed overview of each targeted layer's active, merged, and available adapters.
```python
>>> from transformers import AutoModel
>>> from peft import get_peft_model, LoraConfig
>>> model_id = "google/flan-t5-small"
>>> model = AutoModel.from_pretrained(model_id)
>>> model = get_peft_model(model, LoraConfig())
>>> model.get_layer_status()
[TunerLayerStatus(name='model.encoder.block.0.layer.0.SelfAttention.q',
module_type='lora.Linear',
enabled=True,
active_adapters=['default'],
merged_adapters=[],
requires_grad={'default': True},
available_adapters=['default']),
TunerLayerStatus(name='model.encoder.block.0.layer.0.SelfAttention.v',
module_type='lora.Linear',
enabled=True,
active_adapters=['default'],
merged_adapters=[],
requires_grad={'default': True},
available_adapters=['default']),
...]
>>> model.get_model_status()
TunerModelStatus(
base_model_type='T5Model',
adapter_model_type='LoraModel',
peft_types={'default': 'LORA'},
trainable_params=344064,
total_params=60855680,
num_adapter_layers=48,
enabled=True,
active_adapters=['default'],
merged_adapters=[],
requires_grad={'default': True},
available_adapters=['default'],
)
```
In the model state output, you should look out for entries that say `"irregular"`. This means PEFT detected an inconsistent state in the model. For instance, if `merged_adapters="irregular"`, it means that for at least one adapter, it was merged on some target modules but not on others. The inference results will most likely be incorrect as a result.
The best way to resolve this issue is to reload the whole model and adapter checkpoint(s). Ensure that you don't perform any incorrect operations on the model, e.g. manually merging adapters on some modules but not others.
Convert the layer status into a pandas `DataFrame` for an easier visual inspection.
```python
from dataclasses import asdict
import pandas as pd
df = pd.DataFrame(asdict(layer) for layer in model.get_layer_status())
```
It is possible to get this information for non-PEFT models if they are using PEFT layers under the hood, but some information like the `base_model_type` or the `peft_types` cannot be determined in that case. As an example, you can call this on a [diffusers](https://huggingface.co/docs/diffusers/index) model like so:
```python
>>> import torch
>>> from diffusers import StableDiffusionPipeline
>>> from peft import get_model_status, get_layer_status
>>> path = "runwayml/stable-diffusion-v1-5"
>>> lora_id = "takuma104/lora-test-text-encoder-lora-target"
>>> pipe = StableDiffusionPipeline.from_pretrained(path, dtype=torch.float16)
>>> pipe.load_lora_weights(lora_id, adapter_name="adapter-1")
>>> pipe.load_lora_weights(lora_id, adapter_name="adapter-2")
>>> pipe.set_lora_device(["adapter-2"], "cuda")
>>> get_layer_status(pipe.text_encoder)
[TunerLayerStatus(name='text_model.encoder.layers.0.self_attn.k_proj',
module_type='lora.Linear',
enabled=True,
active_adapters=['adapter-2'],
merged_adapters=[],
requires_grad={'adapter-1': False, 'adapter-2': True},
available_adapters=['adapter-1', 'adapter-2'],
devices={'adapter-1': ['cpu'], 'adapter-2': ['cuda']}),
TunerLayerStatus(name='text_model.encoder.layers.0.self_attn.v_proj',
module_type='lora.Linear',
enabled=True,
active_adapters=['adapter-2'],
merged_adapters=[],
requires_grad={'adapter-1': False, 'adapter-2': True},
devices={'adapter-1': ['cpu'], 'adapter-2': ['cuda']}),
...]
>>> get_model_status(pipe.unet)
TunerModelStatus(
base_model_type='other',
adapter_model_type='None',
peft_types={},
trainable_params=797184,
total_params=861115332,
num_adapter_layers=128,
enabled=True,
active_adapters=['adapter-2'],
merged_adapters=[],
requires_grad={'adapter-1': False, 'adapter-2': True},
available_adapters=['adapter-1', 'adapter-2'],
devices={'adapter-1': ['cpu'], 'adapter-2': ['cuda']},
)
```
## Speed
### Loading adapter weights is slow
Loading adapters like LoRA weights should generally be fast compared to loading the base model. However, there can be use cases where the adapter weights are quite large or where users need to load a large number of adapters -- the loading time can add up in this case. The reason for this is that the adapter weights are first initialized and then overridden by the loaded weights, which is wasteful. To speed up the loading time, you can pass the `low_cpu_mem_usage=True` argument to [`~PeftModel.from_pretrained`] and [`~PeftModel.load_adapter`].
> [!TIP]
> If this option works well across different use cases, it may become the default for adapter loading in the future.
## Reproducibility
### Models using batch norm
When loading a trained PEFT model where the base model uses batch norm (e.g. `torch.nn.BatchNorm1d` or `torch.nn.BatchNorm2d`), you may find that you cannot reproduce the exact same outputs. This is because the batch norm layers keep track of running stats during training, but these stats are not part of the PEFT checkpoint. Therefore, when you load the PEFT model, the running stats of the base model will be used (i.e. from before training with PEFT).
Depending on your use case, this may not be a big deal. If, however, you need your outputs to be 100% reproducible, you can achieve this by adding the batch norm layers to `modules_to_save`. Below is an example of this using resnet and LoRA. Notice that we set `modules_to_save=["classifier", "normalization"]`. We need the `"classifier"` argument because our task is image classification, and we add the `"normalization"` argument to ensure that the batch norm layers are saved in the PEFT checkpoint.
```python
from transformers import AutoModelForImageClassification
from peft import LoraConfig, get_peft_model
model_id = "microsoft/resnet-18"
base_model = AutoModelForImageClassification.from_pretrained(self.model_id)
config = LoraConfig(
target_modules=["convolution"],
modules_to_save=["classifier", "normalization"],
),
```
Depending on the type of model you use, the batch norm layers could have different names than `"normalization"`, so please ensure that the name matches your model architecture.
## Version mismatch
### Error while loading the config because of an unexpected keyword argument
When you encounter an error like the one shown below, it means the adapter you're trying to load was trained with a more recent version of PEFT than the version you have installed on your system.
```
TypeError: LoraConfig.__init__() got an unexpected keyword argument
```
The best way to resolve this issue is to install the latest PEFT version:
```sh
python -m pip install -U PEFT
```
If the adapter was trained from a source install of PEFT (an unreleased version of PEFT), then you also need to install PEFT from source.
```sh
python -m pip install -U git+https://github.com/huggingface/peft.git
```
If it is not possible for you to upgrade PEFT, there is a workaround you can try.
Assume the error message says that the unknown keyword argument is named `foobar`. Search inside the `adapter_config.json` of this PEFT adapter for the `foobar` entry and delete it from the file. Then save the file and try loading the model again.
This solution works most of the time. As long as it is the default value for `foobar`, it can be ignored. However, when it is set to some other value, you will get incorrect results. Upgrading PEFT is the recommended solution.
## Adapter handling
### Using multiple adapters at the same time
PEFT allows you to create more than one adapter on the same model. This can be useful in many situations. For example, for inference, you may want to serve two fine-tuned models from the same base model instead of loading the base model once for each fine-tuned model, which would cost more memory. However, multiple adapters can be activated at the same time. This way, the model may leverage the learnings from all those adapters at the same time. As an example, if you have a diffusion model, you may want to use one LoRA adapter to change the style and a different one to change the subject.
Activating multiple adapters at the same time is generally possible on all PEFT methods (LoRA, LoHa, IA³, etc.) except for prompt learning methods (p-tuning, prefix tuning, etc.). The following example illustrates how to achieve this:
```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
model_id = ...
base_model = AutoModelForCausalLM.from_pretrained(model_id)
model = PeftModel.from_pretrained(base_model, lora_path_0) # default adapter_name is 'default'
model.load_adapter(lora_path_1, adapter_name="other")
# the 'other' adapter was loaded but it's not active yet, so to activate both adapters:
model.base_model.set_adapter(["default", "other"])
```
> [!TIP]
> In the example above, you can see that we need to call `model.base_model.set_adapter(["default", "other"])`. Why can we not call `model.set_adapter(["default", "other"])`? This is unfortunately not possible because, as explained earlier, some PEFT methods don't support activating more than one adapter at a time.
It is also possible to train two adapters at the same time, but you should be careful to ensure that the weights of both adapters are known to the optimizer. Otherwise, only one adapter will receive updates.
```python
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model
model_id = ...
base_model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config_0 = LoraConfig(...)
lora_config_1 = LoraConfig(...)
model = get_peft_model(base_model, lora_config_0)
model.add_adapter(adapter_name="other", peft_config=lora_config_1)
```
If we would now call:
```python
from transformers import Trainer
trainer = Trainer(model=model, ...)
trainer.train()
```
or
```python
optimizer = torch.optim.AdamW([param for param in model.parameters() if param.requires_grad], ...)
```
then the second LoRA adapter (`"other"`) would not be trained. This is because it is inactive at this moment, which means the `requires_grad` attribute on its parameters is set to `False` and the optimizer will ignore it. Therefore, make sure to activate all adapters that should be trained _before_ initializing the optimizer:
```python
# activate all adapters
model.base_model.set_adapter(["default", "other"])
trainer = Trainer(model=model, ...)
trainer.train()
```
> [!TIP]
> This section deals with using multiple adapters _of the same type_ on the same model, for example, using multiple LoRA adapters at the same time. It does not apply to using _different types_ of adapters on the same model, for example one LoRA adapter and one LoHa adapter. For this, please check [`PeftMixedModel`](https://huggingface.co/docs/peft/developer_guides/mixed_models).
================================================
FILE: docs/source/index.md
================================================
# PEFT
🤗 PEFT (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting large pretrained models to various downstream applications without fine-tuning all of a model's parameters because it is prohibitively costly. PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational and storage costs - while yielding performance comparable to a fully fine-tuned model. This makes it more accessible to train and store large language models (LLMs) on consumer hardware.
PEFT is integrated with the Transformers, Diffusers, and Accelerate libraries to provide a faster and easier way to load, train, and use large models for inference.
================================================
FILE: docs/source/install.md
================================================
# Installation
Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 PEFT. 🤗 PEFT is tested on **Python 3.9+**.
🤗 PEFT is available on PyPI, as well as GitHub:
## PyPI
To install 🤗 PEFT from PyPI:
```bash
pip install peft
```
## Source
New features that haven't been released yet are added every day, which also means there may be some bugs. To try them out, install from the GitHub repository:
```bash
pip install git+https://github.com/huggingface/peft
```
If you're working on contributing to the library or wish to play with the source code and see live
results as you run the code, an editable version can be installed from a locally-cloned version of the
repository:
```bash
git clone https://github.com/huggingface/peft
cd peft
pip install -e .[test]
```
================================================
FILE: docs/source/package_reference/adalora.md
================================================
# AdaLoRA
[AdaLoRA](https://hf.co/papers/2303.10512) is a method for optimizing the number of trainable parameters to assign to weight matrices and layers, unlike LoRA, which distributes parameters evenly across all modules. More parameters are budgeted for important weight matrices and layers while less important ones receive fewer parameters.
The abstract from the paper is:
*Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream tasks are present. Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e.g., low-rank increments. These methods often evenly distribute the budget of incremental updates across all pre-trained weight matrices, and overlook the varying importance of different weight parameters. As a consequence, the fine-tuning performance is suboptimal. To bridge this gap, we propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. In particular, AdaLoRA parameterizes the incremental updates in the form of singular value decomposition. Such a novel approach allows us to effectively prune the singular values of unimportant updates, which is essentially to reduce their parameter budget but circumvent intensive exact SVD computations. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA. Results demonstrate that AdaLoRA manifests notable improvement over baselines, especially in the low budget settings. Our code is publicly available at https://github.com/QingruZhang/AdaLoRA*.
## AdaLoraConfig
[[autodoc]] tuners.adalora.config.AdaLoraConfig
## AdaLoraModel
[[autodoc]] tuners.adalora.model.AdaLoraModel
================================================
FILE: docs/source/package_reference/adapter_utils.md
================================================
# LyCORIS
[LyCORIS](https://hf.co/papers/2309.14859) (Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion) are LoRA-like matrix decomposition adapters that modify the cross-attention layer of the UNet. The [LoHa](loha) and [LoKr](lokr) methods inherit from the `Lycoris` classes here.
## LycorisConfig
[[autodoc]] tuners.lycoris_utils.LycorisConfig
## LycorisLayer
[[autodoc]] tuners.lycoris_utils.LycorisLayer
## LycorisTuner
[[autodoc]] tuners.lycoris_utils.LycorisTuner
================================================
FILE: docs/source/package_reference/auto_class.md
================================================
# AutoPeftModels
The `AutoPeftModel` classes loads the appropriate PEFT model for the task type by automatically inferring it from the configuration file. They are designed to quickly and easily load a PEFT model in a single line of code without having to worry about which exact model class you need or manually loading a [`PeftConfig`].
## AutoPeftModel
[[autodoc]] auto.AutoPeftModel
- from_pretrained
## AutoPeftModelForCausalLM
[[autodoc]] auto.AutoPeftModelForCausalLM
## AutoPeftModelForSeq2SeqLM
[[autodoc]] auto.AutoPeftModelForSeq2SeqLM
## AutoPeftModelForSequenceClassification
[[autodoc]] auto.AutoPeftModelForSequenceClassification
## AutoPeftModelForTokenClassification
[[autodoc]] auto.AutoPeftModelForTokenClassification
## AutoPeftModelForQuestionAnswering
[[autodoc]] auto.AutoPeftModelForQuestionAnswering
## AutoPeftModelForFeatureExtraction
[[autodoc]] auto.AutoPeftModelForFeatureExtraction
================================================
FILE: docs/source/package_reference/boft.md
================================================
# BOFT
[Orthogonal Butterfly (BOFT)](https://hf.co/papers/2311.06243) is a generic method designed for finetuning foundation models. It improves the parameter efficiency of the finetuning paradigm -- Orthogonal Finetuning (OFT), by taking inspiration from Cooley-Tukey fast Fourier transform, showing favorable results across finetuning different foundation models, including large vision transformers, large language models and text-to-image diffusion models.
The abstract from the paper is:
*Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in vision and language*.
## BOFTConfig
[[autodoc]] tuners.boft.config.BOFTConfig
## BOFTModel
[[autodoc]] tuners.boft.model.BOFTModel
================================================
FILE: docs/source/package_reference/c3a.md
================================================
# C3A: Parameter-Efficient Fine-Tuning via Circular Convolution
[C3A](https://huggingface.co/papers/2407.19342) is a parameter-efficient fine-tuning technique that leverages Circular Convolution to achieve high rank adaptation within reasonable resource limits.
Note that you should use a much larger learning rate (LR) for C3A than for other methods. For example, a LR of 1e-1 for C3A is a good starting point. Besides, a much smaller weight decay should be used. You can refer to the `method_comparison` folder for more details.
For the `block_size`, it affects tunable parameters and performance. To start with, you can choose a $\mathrm{gcd}(d_1,d_2)$ near $\frac{\sqrt{d_1 \times d_2}}{r}$, where $r$ is the rank for LoRA you would use for this task.
C3A currently has the following constraints:
- Only `nn.Linear` layers are supported.
- Quantized layers are not supported.
- The block size should be a common divisor of both the input and output sizes of target layers.
If these constraints don't work for your use case, consider other methods instead.
The abstract from the paper is:
> Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices $\mathbf{A}$ and $\mathbf{B}$ to represent weight changes (i.e., $\Delta \mathbf{W} = \mathbf{B} \mathbf{A}$). This method reduces trainable parameters and mitigates heavy memory consumption associated with full delta matrices by sequentially multiplying $\mathbf{A}$ and $\mathbf{B}$ with the activation. Despite its success, the intrinsic low-rank characteristic may limit its performance. Although several variants have been proposed to address this issue, they often overlook the crucial computational and memory efficiency brought by LoRA. In this paper, we propose Circular Convolution Adaptation (C3A), which not only achieves high-rank adaptation with enhanced performance but also excels in both computational power and memory utilization. Extensive experiments demonstrate that C3A consistently outperforms LoRA and its variants across various fine-tuning tasks.
## C3AConfig
[[autodoc]] tuners.c3a.config.C3AConfig
## C3AModel
[[autodoc]] tuners.c3a.model.C3AModel
================================================
FILE: docs/source/package_reference/cartridges.md
================================================
# Cartridges
Cartridges are a prompt-learning method that stores a compressed long-context representation as a parameterized KV-cache
prefix. The core idea comes from the paper
[Cartridges: Lightweight and general-purpose long context representations via self-study](https://huggingface.co/papers/2506.06266).
For a high-level overview and motivation, see the blog post
[Cartridges: Storing long contexts in tiny caches with self-study](https://hazyresearch.stanford.edu/blog/2025-06-08-cartridges).
## How Cartridges differ from Prefix Tuning
Both Prefix Tuning and Cartridges are served by injecting `past_key_values` (a prefix KV cache) into the base model.
- Prefix Tuning learns virtual token embeddings (and optionally an MLP projection) and produces a KV prefix.
- Cartridges learn the KV prefix itself directly (the per-layer key/value vectors for `p` virtual tokens), and are
designed to be initialized from real prefill KV (for example, the first `p` tokens of a corpus/system prompt).
The paper also recommends freezing the first token as an attention sink for stability (`num_frozen_tokens=1` is the
default).
## Usage (inference)
Load a trained CARTRIDGE adapter and run generation:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_path = "path/to/cartridge_adapter"
base = AutoModelForCausalLM.from_pretrained(model_id)
model = PeftModel.from_pretrained(base, adapter_path)
tok = AutoTokenizer.from_pretrained(model_id)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
out = model.generate(**tok("Question about the corpus:", return_tensors="pt"), max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))
```
If you need to create and initialize a cartridge before training, see the initialization options below.
## Initialization options
The paper discusses a few practical initialization strategies:
- Random KV (default): create a `CartridgeConfig` and start training. This initializes the KV prefix randomly.
- KV from the first tokens of a prompt/corpus: use `initialize_kv_prefix_from_text(model, tokenizer, text=...)`. This
runs a prefill on `text` and copies the resulting KV cache for the first `num_virtual_tokens` into the adapter.
- KV from an existing cache: use `initialize_kv_prefix_from_past_key_values(model, past_key_values=...)` if you already
have a `past_key_values` object from a base-model prefill.
## Training
The Cartridges paper proposes a SELF-STUDY distillation objective (a frozen base model provides teacher logits; the
CARTRIDGE adapter is trained so the student matches the teacher’s next-token distribution over the target segment).
PEFT keeps training logic out of the core library; see
`https://github.com/huggingface/peft/tree/main/examples/cartridge_self_study` for a reference workflow.
The example scripts use the frozen base model as the teacher and the adapted model as the student, so both share the
same underlying checkpoint.
## Composition
To concatenate independently trained cartridges into a single adapter, use `compose_cartridge_adapters(...)`.
## CartridgeConfig
[[autodoc]] tuners.cartridge.config.CartridgeConfig
## CartridgeEncoder
[[autodoc]] tuners.cartridge.model.CartridgeEncoder
================================================
FILE: docs/source/package_reference/config.md
================================================
# Configuration
[`PeftConfigMixin`] is the base configuration class for storing the adapter configuration of a [`PeftModel`], and [`PromptLearningConfig`] is the base configuration class for soft prompt methods (p-tuning, prefix tuning, and prompt tuning). These base classes contain methods for saving and loading model configurations from the Hub, specifying the PEFT method to use, type of task to perform, and model configurations like number of layers and number of attention heads.
## PeftConfigMixin
[[autodoc]] config.PeftConfigMixin
- all
## PeftConfig
[[autodoc]] PeftConfig
- all
## PromptLearningConfig
[[autodoc]] PromptLearningConfig
- all
================================================
FILE: docs/source/package_reference/cpt.md
================================================
# Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods
[CPT](https://huggingface.co/papers/2410.17222) combines In-Context Learning (ICL), Prompt Tuning (PT), and adversarial optimization to improve few-shot learning by refining context embeddings. CPT updates the context tokens by optimizing both the context and the training examples, encapsulating them into a novel loss design that minimizes overfitting, enables more effective optimization, and drives significant improvements in classification tasks.
[//]: # ([CPT](https://huggingface.co/papers/2410.17222) for the paper)
The abstract from the paper is:
> Large Language Models (LLMs) can perform few-shot learning using either optimization-based approaches or In-Context Learning (ICL). Optimization-based methods often suffer from overfitting, as they require updating a large number of parameters with limited data. In contrast, ICL avoids overfitting but typically underperforms compared to optimization-based methods and is highly sensitive to the selection, order, and format of demonstration examples. To overcome these challenges, we introduce Context-aware Prompt Tuning (CPT), a method inspired by ICL, Prompt Tuning (PT), and adversarial attacks. CPT builds on the ICL strategy of concatenating examples before the input, extending it by incorporating PT-like learning to refine the context embedding through iterative optimization, extracting deeper insights from the training examples. Our approach carefully modifies specific context tokens, considering the unique structure of the examples within the context. In addition to updating the context with PT-like optimization, CPT draws inspiration from adversarial attacks, adjusting the input based on the labels present in the context while preserving the inherent value of the user-provided data. To ensure robustness and stability during optimization, we employ a projected gradient descent algorithm, constraining token embeddings to remain close to their original values and safeguarding the quality of the context. Our method has demonstrated superior accuracy across multiple classification tasks using various LLM models, outperforming existing baselines and effectively addressing the overfitting challenge in few-shot learning.
Take a look at [Example](https://github.com/huggingface/peft/blob/main/examples/cpt_finetuning/README.md) for a step-by-step guide on how to train a model with CPT.
## CPTConfig
[[autodoc]] tuners.cpt.config.CPTConfig
## CPTEmbedding
[[autodoc]] tuners.cpt.model.CPTEmbedding
================================================
FILE: docs/source/package_reference/delora.md
================================================
# DeLoRA: Decoupled Low-rank Adaptation
[DeLoRA](https://huggingface.co/papers/2503.18225) is a parameter-efficient fine-tuning technique that implicitly maintains a Frobenius boundary with respect to the pretrained weights by normalizing and scaling learnable low-rank matrices. This effectively decouples the learning of directions (BA term) and magnitude (boundary term) of the weight updates, avoiding catastrophic shifts in the adapted weights and enhancing robustness to hyperparameter choices.
Note:
- use a learning rate 10-100x larger than for standard LoRA variants (typical values from 1e-3/1e-2/..)
- ensure the initial boundary parameter lambda is not too small (typical values around 10/15/..). Setting different lambdas to different layers is possible
DeLoRA currently has the following constraints:
- Only nn.Linear layers are supported.
- Quantized layers are not supported.
If these constraints don't work for your use case, consider other methods instead.
The abstract from the paper is:
> Parameter-Efficient FineTuning (PEFT) methods have recently gained significant popularity thanks to the widespread availability of large-scale pretrained models. These methods allow for quick adaptation to downstream tasks with minimal computational cost. However, popular finetuning methods such as LoRA exhibit limited robustness when it comes to hyperparameter choices or extended training regimes, preventing optimal out-of-the-box performance. In contrast, bounded approaches, such as ETHER, provide greater robustness but are limited to extremely low-rank adaptations and fixed-strength transformations, reducing their adaptation expressive power. In this work, we propose Decoupled Low-rank Adaptation (DeLoRA), a novel finetuning method that normalizes and scales learnable low-rank matrices. By bounding the distance of the transformation, DeLoRA effectively decouples the angular learning from the adaptation strength, enhancing robustness without compromising performance. Through evaluations on subject-driven image generation, natural language understanding, and instruction tuning, we show that DeLoRA matches or surpasses performance of competing PEFT methods, while exhibiting stronger robustness.
## DeloraConfig
[[autodoc]] tuners.delora.config.DeloraConfig
## DeloraModel
[[autodoc]] tuners.delora.model.DeloraModel
================================================
FILE: docs/source/package_reference/fourierft.md
================================================
# FourierFT: Discrete Fourier Transformation Fine-Tuning
[FourierFT](https://huggingface.co/papers/2405.03003) is a parameter-efficient fine-tuning technique that leverages Discrete Fourier Transform to compress the model's tunable weights. This method outperforms LoRA in the GLUE benchmark and common ViT classification tasks using much less parameters.
FourierFT currently has the following constraints:
- Only `nn.Linear` layers are supported.
- Quantized layers are not supported.
If these constraints don't work for your use case, consider other methods instead.
The abstract from the paper is:
> Low-rank adaptation (LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank matrices A and B to represent the weight change, i.e., Delta W=BA. Despite LoRA's progress, it faces storage challenges when handling extensive customization adaptations or larger base models. In this work, we aim to further compress trainable parameters by enjoying the powerful expressiveness of the Fourier transform. Specifically, we introduce FourierFT, which treats Delta W as a matrix in the spatial domain and learns only a small fraction of its spectral coefficients. With the trained spectral coefficients, we implement the inverse discrete Fourier transform to recover Delta W. Empirically, our FourierFT method shows comparable or better performance with fewer parameters than LoRA on various tasks, including natural language understanding, natural language generation, instruction tuning, and image classification. For example, when performing instruction tuning on the LLaMA2-7B model, FourierFT surpasses LoRA with only 0.064M trainable parameters, compared to LoRA's 33.5M.
## FourierFTConfig
[[autodoc]] tuners.fourierft.config.FourierFTConfig
## FourierFTModel
[[autodoc]] tuners.fourierft.model.FourierFTModel
================================================
FILE: docs/source/package_reference/functional.md
================================================
# Functions for PEFT integration
A collection of functions that could be useful for non-PeftModel models, e.g. transformers or diffusers integration
The functions provided here can be considered "public API" of PEFT and hence are safe to be used by packages that provide PEFT integrations.
## Cast the adapter weight dtypes
[[autodoc]] functional.cast_adapter_dtype
- all
## Delete the PEFT adapter from model
[[autodoc]] functional.delete_adapter
- all
## Get the state dict of the PEFT adapter
[[autodoc]] functional.get_peft_model_state_dict
- all
## Inject a PEFT adapter into the model based on a PEFT config
[[autodoc]] functional.inject_adapter_in_model
- all
## Set the active PEFT adapter(s) of the model
[[autodoc]] functional.set_adapter
- all
## Set the `requires_grad` attribute of the specified adapters
[[autodoc]] functional.set_requires_grad
- all
## Load the weights of the PEFT state dict into the model
[[autodoc]] functional.set_peft_model_state_dict
- all
================================================
FILE: docs/source/package_reference/gralora.md
================================================
# GraLoRA
[**Granular Low-Rank Adaptation (GraLoRA)**](https://huggingface.co/papers/2505.20355) is a PEFT method designed to enhance the **expressivity** of low-rank adaptation while improving **robustness to outlier** activations, based on insights from well-known issues in quantization.
Unlike standard LoRA, which applies a single low-rank adapter across the entire feature space, GraLoRA introduces a structured and fine-grained adaptation scheme. It divides the adaptation space into a grid of $𝑘^2$ smaller, independent adapter pairs, each responsible for a localized subset of the input and output dimensions. As a result, each adapter operates on a subspace that is $k$ times smaller in both dimensions than the original LoRA adapter.
This granular decomposition enables spatially localized and context-aware updates, effectively increasing representational capacity without additional parameters or computational cost. By isolating the influence of extreme activations within smaller subspaces, GraLoRA mitigates gradient distortion and preserves inter-channel balance during adaptation.
---
The abstract from the paper is:
*Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-
tuning (PEFT) of generative models, valued for its simplicity and effectiveness.
Despite recent enhancements, LoRA still suffers from a fundamental limitation:
overfitting when the bottleneck is widened. It performs best at ranks 32–64, yet its
accuracy stagnates or declines at higher ranks, still falling short of full fine-tuning
(FFT) performance. We identify the root cause as LoRA’s structural bottleneck,
which introduces gradient entanglement to the unrelated input channels and distorts
gradient propagation. To address this, we introduce a novel structure, Granular
Low-Rank Adaptation (GraLoRA) that partitions weight matrices into sub-blocks,
each with its own low-rank adapter. With negligible computational or storage cost,
GraLoRA overcomes LoRA’s limitations, effectively increases the representational
capacity, and more closely approximates FFT behavior. Experiments on code
generation, commonsense reasoning, mathematical reasoning, general language
understanding, and image generation benchmarks show that GraLoRA consistently
outperforms LoRA and other baselines, achieving up to +8.5% absolute gain in
Pass@1 on HumanEval+. These improvements hold across model sizes and rank
settings, making GraLoRA a scalable and robust solution for PEFT.*
================================================
FILE: docs/source/package_reference/helpers.md
================================================
# Helper methods
A collection of helper functions for PEFT.
## Checking if a model is a PEFT model
[[autodoc]] helpers.check_if_peft_model
- all
## Temporarily Rescaling Adapter Scale in LoraLayer Modules
[[autodoc]] helpers.rescale_adapter_scale
- all
## Context manager to disable input dtype casting in the `forward` method of LoRA layers
[[autodoc]] helpers.disable_input_dtype_casting
- all
## Context manager to enable DoRA caching (faster at inference time but requires more memory)
[[autodoc]] helpers.DoraCaching
- all
================================================
FILE: docs/source/package_reference/hotswap.md
================================================
# Hotswapping adapters
The idea of hotswapping an adapter is the following: We can already load multiple adapters, e.g. two LoRAs, at the same time. But sometimes, we want to load one LoRA and then replace its weights in-place with the LoRA weights of another adapter. This is now possible the `hotswap_adapter` function.
In general, this should be faster than deleting one adapter and loading the adapter in its place, which would be the how to achieve the same final outcome without hotswapping. Another advantage of hotswapping is that it prevents re-compilation in case the PEFT model is already compiled using `torch.compile`. This can save quite a lot of time.
## Example without `torch.compile`
```python
import torch
from transformers import AutoModelForCausalLM
from peft import PeftModel
from peft.utils.hotswap import hotswap_adapter
model_id = ...
inputs = ...
device = ...
model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
# load lora 0
model = PeftModel.from_pretrained(model, )
with torch.inference_mode():
output_adapter_0 = model(inputs)
# replace the "default" lora adapter with the new one
hotswap_adapter(model, , adapter_name="default", torch_device=device)
with torch.inference_mode():
output_adapter_1 = model(inputs).logits
```
## Example with `torch.compile`
```python
import torch
from transformers import AutoModelForCausalLM
from peft import PeftModel
from peft.utils.hotswap import hotswap_adapter, prepare_model_for_compiled_hotswap
model_id = ...
inputs = ...
device = ...
max_rank = ... # maximum rank among all LoRA adapters that will be used
model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
# load lora 0
model = PeftModel.from_pretrained(model, )
# Prepare the model to allow hotswapping even if ranks/scalings of 2nd adapter differ.
# You can skip this step if all ranks and scalings are identical.
prepare_model_for_compiled_hotswap(model, target_rank=max_rank)
model = torch.compile(model)
with torch.inference_mode():
output_adapter_0 = model(inputs)
# replace the "default" lora adapter with the new one
hotswap_adapter(model, , adapter_name="default", torch_device=device)
with torch.inference_mode():
output_adapter_1 = model(inputs).logits
```
## Caveats
Hotswapping works with transformers models and diffusers models. However, there are some caveats:
- Right now, only LoRA is properly supported.
- It only works for the same PEFT method, so no swapping LoRA and LoHa, for example.
- The adapter that is being swapped in must target the same layers as the previous adapter or a subset of those layers. It cannot target new layers. Therefore, if possible, start with the adapter that targets most layers.
## API
[[autodoc]] utils.hotswap.hotswap_adapter
- all
[[autodoc]] utils.hotswap.hotswap_adapter_from_state_dict
- all
================================================
FILE: docs/source/package_reference/hra.md
================================================
# Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation (HRA)
[HRA](https://huggingface.co/papers/2405.17484) is a simple but effective adapter-based fine-tuning method by leveraging Householder reflections. This method harnesses the advantages of both strategies, reducing parameters and computation costs while penalizing the loss of pre-training knowledge. It consistently achieves better performance with fewer trainable parameters and outperforms state-of-the-art adapters across different models, including large language models (LLMs) and conditional image generators.
The abstract from the paper is:
> While following different technical routes, both low-rank and orthogonal adaptation techniques can efficiently adapt large-scale pre-training models in specific tasks or domains based on a small piece of trainable parameters. In this study, we bridge the gap between these two techniques, proposing a simple but effective adaptation method based on Householder reflections. Given a pre-trained model, our method fine-tunes its layers by multiplying each frozen weight matrix with an orthogonal matrix constructed by a chain of learnable Householder reflections (HRs). This HR-based orthogonal fine-tuning is equivalent to an adaptive low-rank adaptation. Moreover, we show that the orthogonality of the reflection planes corresponding to the HRs impacts the model capacity and regularity. The analysis motivates us to regularize the orthogonality of the HRs, leading to different implementations of the proposed Householder reflection adaptation (HRA) method. Compared with state-of-the-art methods, HRA achieves superior performance with fewer learnable parameters when adapting large language models and conditional image generators. The code is available at [peft](https://github.com/huggingface/peft/tree/main/src/peft/tuners/hra) and [HRA](https://github.com/DaShenZi721/HRA).
## HRAConfig
[[autodoc]] tuners.hra.config.HRAConfig
## HRAModel
[[autodoc]] tuners.hra.model.HRAModel
================================================
FILE: docs/source/package_reference/ia3.md
================================================
# IA3
Infused Adapter by Inhibiting and Amplifying Inner Activations, or [IA3](https://hf.co/papers/2205.05638), is a method that adds three learned vectors to rescale the keys and values of the self-attention and encoder-decoder attention layers, and the intermediate activation of the position-wise feed-forward network.
The abstract from the paper is:
*Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made. Parameter-efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning, sparse update methods, etc.) offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task. In this paper, we rigorously compare few-shot ICL and PEFT and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs. Along the way, we introduce a new PEFT method called (IA)^3 that scales activations by learned vectors, attaining stronger performance while only introducing a relatively tiny amount of new parameters. We also propose a simple recipe based on the T0 model called T-Few that can be applied to new tasks without task-specific tuning or modifications. We validate the effectiveness of T-Few on completely unseen tasks by applying it to the RAFT benchmark, attaining super-human performance for the first time and outperforming the state-of-the-art by 6% absolute. All of the code used in our experiments is publicly available*.
## IA3Config
[[autodoc]] tuners.ia3.config.IA3Config
## IA3Model
[[autodoc]] tuners.ia3.model.IA3Model
================================================
FILE: docs/source/package_reference/layernorm_tuning.md
================================================
# LayerNorm Tuning
LayerNorm Tuning ([LN Tuning](https://huggingface.co/papers/2312.11420)) is a PEFT method that only fine-tunes the parameters of the LayerNorm layers in a model.
The paper has tested the performance of this method on large language models and has shown that it can achieve strong performance with a significant reduction in the number of trainable parameters and GPU memory usage.
However, the method is not limited to language models and can be applied to any model that uses LayerNorm layers.
In this implementation, the default is that all layernorm layers inside a model is finetuned, but it could be used to target other layer types such as `MLP` or `Attention` layers, this can be done by specifying the `target_modules` in the `LNTuningConfig`.
The abstract from the paper is:
*This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a domain adaptation process, i.e., transitioning from text understanding to embracing multiple modalities, we intriguingly note that, within each attention block, tuning LayerNorm suffices to yield strong performance. Moreover, when benchmarked against other tuning approaches like full parameter finetuning or LoRA, its benefits on efficiency are substantial. For example, when compared to LoRA on a 13B model scale, performance can be enhanced by an average of over 20% across five multi-modal tasks, and meanwhile, results in a significant reduction of trainable parameters by 41.9% and a decrease in GPU memory usage by 17.6%. On top of this LayerNorm strategy, we showcase that selectively tuning only with conversational data can improve efficiency further. Beyond these empirical outcomes, we provide a comprehensive analysis to explore the role of LayerNorm in adapting LLMs to the multi-modal domain and improving the expressive power of the model.*
## LNTuningConfig
[[autodoc]] tuners.ln_tuning.config.LNTuningConfig
## LNTuningModel
[[autodoc]] tuners.ln_tuning.model.LNTuningModel
================================================
FILE: docs/source/package_reference/lily.md
================================================
# Lily: Low-Rank Interconnected Adaptation across Layers
[Lily](https://huggingface.co/papers/2407.09946) is a parameter-efficient fine-tuning technique that introduces cross-layer weight sharing for adapter matrices. Instead of learning an independent AB pair per layer as in LoRA, Lily uses **locally shared A adapters** (each A is shared across a block of `stride_A` consecutive layers) and **globally shared B experts** (a small pool of `num_B` B adapters is shared across all layers). At each forward pass, a lightweight data-dependent router computes a softmax-weighted combination of the B experts to produce the effective B for that layer and input.
This sharing can reduce the total number of adapter matrices from `2N` (standard LoRA) to `N / stride_A + num_B`, freeing up the parameter budget to use a **much larger rank `r`** — typically `2×`–`4×` what you would use in LoRA. Higher rank and better interconnectivity increase the effective rank of the weight update `ΔW = A × combined_B`, leading to better adaptation performance.
Because the B combination is **data-dependent** (the router weights depend on the input activations at runtime), `merge` and `unmerge` are **not supported**. If weight merging is required for your deployment, consider other methods such as LoRA instead.
Lily currently has the following additional constraints:
- Only `nn.Linear` layers are supported.
- Quantized layers are not supported.
If these constraints don't work for your use case, consider other methods instead.
The abstract from the paper is:
> Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method that learns weight updates ΔW = AB for pretrained weights W through low-rank adapters A and B. While LoRA ensures hardware efficiency, its low-rank weight updates limit adaptation performance. In this paper, we propose low-rank interconnected adaptation across layers (Lily), a novel PEFT method that introduces an interconnected framework with locally shared A and globally shared B experts. This structure eliminates redundant per-layer AB pairs, enabling higher-rank ΔW with equal or fewer parameters. To enhance expressiveness, we use data-dependent routers to determine A-B interconnections, preventing B experts from converging to the same behavior and improving representational power across domains. Experiments across modalities, architectures, and model sizes demonstrate Lily's superior performance and efficiency.
## LilyConfig
[[autodoc]] tuners.lily.config.LilyConfig
## LilyModel
[[autodoc]] tuners.lily.model.LilyModel
================================================
FILE: docs/source/package_reference/llama_adapter.md
================================================
# Llama-Adapter
[Llama-Adapter](https://hf.co/papers/2303.16199) is a PEFT method specifically designed for turning Llama into an instruction-following model. The Llama model is frozen and only a set of adaptation prompts prefixed to the input instruction tokens are learned. Since randomly initialized modules inserted into the model can cause the model to lose some of its existing knowledge, Llama-Adapter uses zero-initialized attention with zero gating to progressively add the instructional prompts to the model.
The abstract from the paper is:
*We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the input text tokens at higher transformer layers. Then, a zero-init attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With efficient training, LLaMA-Adapter generates high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Furthermore, our approach can be simply extended to multi-modal input, e.g., images, for image-conditioned LLaMA, which achieves superior reasoning capacity on ScienceQA. We release our code at https://github.com/ZrrSkywalker/LLaMA-Adapter*.
## AdaptionPromptConfig
[[autodoc]] tuners.adaption_prompt.config.AdaptionPromptConfig
## AdaptionPromptModel
[[autodoc]] tuners.adaption_prompt.model.AdaptionPromptModel
================================================
FILE: docs/source/package_reference/loha.md
================================================
# LoHa
Low-Rank Hadamard Product ([LoHa](https://huggingface.co/papers/2108.06098)), is similar to LoRA except it approximates the large weight matrix with more low-rank matrices and combines them with the Hadamard product. This method is even more parameter-efficient than LoRA and achieves comparable performance.
The abstract from the paper is:
*In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our method re-parameterizes weight parameters of layers using low-rank weights followed by the Hadamard product. Compared to the conventional low-rank parameterization, our FedPara method is not restricted to low-rank constraints, and thereby it has a far larger capacity. This property enables to achieve comparable performance while requiring 3 to 10 times lower communication costs than the model with the original layers, which is not achievable by the traditional low-rank methods. The efficiency of our method can be further improved by combining with other efficient FL optimizers. In addition, we extend our method to a personalized FL application, pFedPara, which separates parameters into global and local ones. We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters*.
## LoHaConfig
[[autodoc]] tuners.loha.config.LoHaConfig
## LoHaModel
[[autodoc]] tuners.loha.model.LoHaModel
================================================
FILE: docs/source/package_reference/lokr.md
================================================
# LoKr
Low-Rank Kronecker Product ([LoKr](https://hf.co/papers/2309.14859)), is a LoRA-variant method that approximates the large weight matrix with two low-rank matrices and combines them with the Kronecker product. LoKr also provides an optional third low-rank matrix to provide better control during fine-tuning.
## LoKrConfig
[[autodoc]] tuners.lokr.config.LoKrConfig
## LoKrModel
[[autodoc]] tuners.lokr.model.LoKrModel
================================================
FILE: docs/source/package_reference/lora.md
================================================
# LoRA
Low-Rank Adaptation ([LoRA](https://huggingface.co/papers/2309.15223)) is a PEFT method that decomposes a large matrix into two smaller low-rank matrices in the attention layers. This drastically reduces the number of parameters that need to be fine-tuned.
The abstract from the paper is:
*We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have shown superior performance in second-pass rescoring, the high computational cost of scaling up the pretraining stage and adapting the pretrained models to specific domains limit their practical use in rescoring. Here we present a method based on low-rank decomposition to train a rescoring BERT model and adapt it to new domains using only a fraction (0.08%) of the pretrained parameters. These inserted matrices are optimized through a discriminative training objective along with a correlation-based regularization loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is evaluated on LibriSpeech and internal datasets with decreased training times by factors between 5.4 and 3.6.*.
## LoraConfig
[[autodoc]] tuners.lora.config.LoraConfig
## LoraModel
[[autodoc]] tuners.lora.model.LoraModel
## Utility
### ArrowConfig
[[autodoc]] tuners.lora.config.ArrowConfig
### LoftQ
[[autodoc]] utils.loftq_utils.replace_lora_weights_loftq
### Eva
#### EvaConfig
[[autodoc]] tuners.lora.config.EvaConfig
#### initialize_lora_eva_weights
[[autodoc]] tuners.lora.eva.initialize_lora_eva_weights
#### get_eva_state_dict
[[autodoc]] tuners.lora.eva.get_eva_state_dict
## Variants
### LoRA-GA
[LoRA-GA](https://hf.co/papers/2407.05000) (Low-Rank Adaptation with Gradient Approximation) improves upon standard LoRA by using gradient information during initialization to achieve faster convergence. Instead of random initialization, LoRA-GA performs SVD on estimated gradients to initialize adapter weights in a direction that aligns with full fine-tuning, resulting in 2-4x faster convergence with the same final performance.
The abstract from the paper is:
*Low-rank adaptation (LoRA) is a popular technique for parameter-efficient fine-tuning of large language models. However, LoRA's random initialization of adapter weights leads to slow convergence during the initial training phase. In this paper, we propose LoRA-GA (Low-Rank Adaptation with Gradient Approximation), a novel initialization method that leverages gradient information to initialize LoRA adapters. Specifically, we estimate gradients on a small set of training samples and perform singular value decomposition (SVD) to extract principal components. These components are used to initialize the adapter matrices, aligning the initial update direction with that of full fine-tuning. Our experiments across various tasks and model scales demonstrate that LoRA-GA achieves 2-4x faster convergence compared to standard LoRA while maintaining the same final performance. The method is orthogonal to existing LoRA variants and can be easily integrated with techniques like DoRA and LoRA+.*
#### Usage Tips
- **Gradient Estimation**: LoRA-GA requires a gradient estimation phase before model initialization. Use `preprocess_loraga()` with a `train_step` callback to compute gradients over a small number of training batches (typically 64-128 batches).
- **Initialization Strategies**: LoRA-GA supports four direction strategies (`direction`): `"ArBr"`, `"A2rBr"`, `"ArB2r"` (default), and `"random"`, and four scaling strategies (`scale`): `"stable"` (default), `"weight_svd"`, `"gd_scale"`, and `"unit"`. The default combination provides the best balance of convergence speed and stability.
- **Base Weight Modification**: Unlike standard LoRA, LoRA-GA modifies the base model weights during initialization by subtracting a scaled version of the low-rank approximation. This enables better alignment with full fine-tuning gradients. Since base weights are modified, use `save_pretrained()` with the `save_embedding_layers` argument or `save_mutated_as_lora` pattern to properly save the adapter.
- **Computational Overhead**: The gradient estimation adds a small overhead during initialization (typically 1-2 minutes for 64 batches), but this is quickly amortized by faster convergence during training.
- **Compatibility**: LoRA-GA requires full-precision weights and does not support quantized models. Can be combined with other LoRA variants like DoRA.
#### LoraGAConfig
[[autodoc]] tuners.lora.config.LoraGAConfig
#### Utilities
[[autodoc]] tuners.lora.loraga.estimate_gradients
[[autodoc]] tuners.lora.loraga.preprocess_loraga
## Intruder Dimension Reduction
[[autodoc]] tuners.lora.intruders.reduce_intruder_dimension
================================================
FILE: docs/source/package_reference/lora_conversion.md
================================================
# LoRA conversion
Functions that allow to convert non-LoRA PEFT models to LoRA models.
## Description
PEFT supports dozens of different parameter effficient fine-tuning techniques. The most popular one by far is LoRA. This means that many other packages support LoRA too. For example, [Diffusers](https://huggingface.co/docs/diffusers/main/en/api/loaders/lora) allows to load LoRA adapters to change the capabilities of diffusion models. [vLLM](https://docs.vllm.ai/en/stable/features/lora/) allows serving models with LoRA adapters. This is nice but unfortunately, all the other, non-LoRA PEFT methods are rarely supported. Therefore, even if another PEFT method would work better for your specific use case, you may be prevented from using it because downstream packages offer no support.
Here we present a potential solution. PEFT offers two functions, [`save_as_lora`] and [`convert_to_lora`], which allow to convert a PEFT adapter into a LoRA adapter. Not all PEFT methods support this for now, but if they do, it means you can start with the PEFT method that works best for you and then later use it as if it were a LoRA adapter.
## Example
The LoRA rank for the converted adapter can either be set to a fixed rank by passing an int > 0 to the `rank` argument, or a dynamic rank, which adapts to each layer, by passing a float between 0 and 1 to the `rank` argument. Dynamic ranks can potentially be more efficient (same performance with fewer parameters).
### Fixed LoRA rank
The usage of [`save_as_lora`] is relatively straightforward:
```python
from peft import get_peft_model, save_as_lora
# first load and train your non-LoRA PEFT model as normal
base_model = ...
non_lora_config = ...
model = get_peft_model(base_model, non_lora_config)
# check that this PEFT method can indeed be converted to LoRA
assert model.supports_lora_conversion()
... # train the model
# the rank of the LoRA adapter that you want to convert to
target_rank = 64
# save as a LoRA checkpoint
save_as_lora(output_path, model, rank=target_rank)
```
This will create a LoRA checkpoint at `output_path` that you can load like any other LoRA adapter, or use in downstream packages such as Diffusers or vLLM.
The [`convert_to_lora`] function is useful if you don't want to save the converted LoRA adapter but instead want to use the converted weights right away, for example to perform evaluations:
```python
from peft import convert_to_lora, get_peft_model, set_peft_model_state_dict
base_model = ...
non_lora_config = ...
model = get_peft_model(base_model, non_lora_config)
... # train the model
# get the lora config and state dict of the converted lora model
lora_config, lora_state_dict = convert_to_lora(model, rank=target_rank)
# reload the base model, or use model.unload()
base_model = ...
# apply the lora config to the base model
lora_model = get_peft_model(base_model, lora_config)
# load the LoRA weights onto the base model
set_peft_model_state_dict(lora_model, state_dict)
```
### Dynamic LoRA rank
In the examples above, we used a fixed LoRA rank for conversion. However, it is conceivable that some layers don't require a high rank to be accurately converted, while other layers require a higher rank. To accomodate this, PEFT offers the option to pass a float between 0 and 1 as the `rank` argument. Let's say you pass `rank=0.5`. This means that for each layer, the rank for the LoRA adapter is chosen such that the LoRA adapter explains 50% of the variance in weight introduced by original adapter. In more technical terms, under the hood we perform a [Singular Value Decomposition](https://en.wikipedia.org/wiki/Singular_value_decomposition) on the weight contribution of the adapter and then take the top singular values that, when normalized, sum up to the passed value.
```python
# set a dynamic rank by passing a float
threshold = 0.7
# save as a LoRA checkpoint
save_as_lora(output_path, model, rank=threshold)
# get the lora config and state dict directly:
lora_config, lora_state_dict = convert_to_lora(model, rank=threshold)
# inspect the different ranks per layer:
print(lora_config.rank_pattern)
```
Using this type of dynamic LoRA rank can be useful if the contribution of the different layers varies a lot. The disadvantage is that it could mean that some layers will have a very high LoRA rank, which can lead to memory spikes. Please test what works best for your use case.
### LoRA to LoRA conversion
It is also possible to convert a LoRA adapter into another LoRA adapter. Why would you want to do that? There is one reason, namely if you want to reduce the rank of the LoRA adapter. If, after training, you want to shrink the LoRA adapter, use [`save_as_lora`] or [`convert_to_lora`] and pass a smaller rank. This will give you a new LoRA adapter that has a smaller memory and storage footprint.
## Metrics
### Non-LoRA to LoRA conversion
Of course, converting one PEFT adapter into another adapter is a lossy process. The new adapter will most likely not perform as well as the initial adapter. Therefore, it is highly advised to **evaluate the converted LoRA adapter**. This way, you can make sure that the converted adapter performs well enough for your use case. The general rule applies that the higher the rank of the LoRA adaper, the better it will approximate your initial adapter. This means that the converted LoRA adapter may require more parameters than the original adapter to achieve a similar performanace.
To give an example, here are some numbers that were derived on the [PEFT MetaMathQA benchmark](https://github.com/huggingface/peft/tree/main/method_comparison/MetaMathQA). For this, a [LoHa](https://huggingface.co/docs/peft/package_reference/loha) was used to fine-tune `meta-llama/Llama-3.2-3B` on MetaMathQA and evaluated on GSM8K. The initial LoKr adapter had rank 32, resulting in 18,350,080 trainable parameters, and a test accuracy of 41.85%. Evaluation required 12.25 GB of memory. The checkpoint was converted into LoRA with different values for the `rank`. The resulting outcome is:
| rank | trainable parameters | test accuracy (%) | accuracy change | memory reserved (max, GB) | memory increase |
|------|---------------------:|------------------:|----------------:|--------------------------:|----------------:|
| 8 | 2293760 | 37.60 | -4.25 | 12.41 | 0.16 |
| 16 | 4587520 | 38.89 | -2.96 | 12.15 | -0.10 |
| 32 | 9175040 | 40.11 | -1.74 | 12.41 | 0.16 |
| 64 | 18350080 | 39.20 | -2.65 | 12.18 | -0.07 |
| | | | | | |
| 0.4 | 2428928 | 37.60 | -4.25 | 12.41 | 0.16 |
| 0.5 | 4761600 | 40.18 | -1.67 | 12.41 | 0.16 |
| 0.6 | 8857600 | 39.42 | -2.43 | 12.41 | 0.16 |
| 0.7 | 16230400 | 39.04 | -2.81 | 12.15 | -0.10 |
As you can see, we can attain a test accuracy that comes close to the original LoHa adapter if the rank is sufficiently high. Choosing the right rank is a tradeoff between model performance and model efficiency. To reproduce this experiment, follow the script at https://github.com/huggingface/peft/tree/main/scripts/evaluate-lora-conversion.py.
Note that the number of trainable parameters cannot be translated one to one into memory usage. Some PEFT methods require more, some less memory, even with the same number of trainable parameters. Therefore, even if after conversion, the LoRA adapter has more parameters than the original one, it could still be more memory efficient when serving.
### LoRA to LoRA conversion
Similar to the experiment above, we can also evaluate LoRA to LoRA conversion (i.e. LoRA compression). Here, we start with a LoRA adapter of rank 64 trained on the same setup as above with RS-LoRA. The initial adapter has 18,350,080 trainable parameters, a test accuracy of 52.92%, and requires 12.58 GB of memory for evaluation. The following table shows the results of converting this adapter to LoRA adapters of smaller rank:
| rank | trainable parameters | test accuracy (%) | accuracy change | memory reserved (max, GB) | memory increase |
|------|---------------------:|------------------:|----------------:|--------------------------:|----------------:|
| 8 | 2293760 | 43.37 | -9.55 | 12.38 | -0.20 |
| 16 | 4587520 | 48.90 | -4.02 | 12.38 | -0.20 |
| 32 | 9175040 | 51.48 | -1.44 | 12.49 | -0.09 |
| 48 | 13762560 | 52.01 | -0.91 | 12.38 | -0.20 |
| | | | | | |
| 0.5 | 2150400 | 44.12 | -8.80 | 12.37 | -0.21 |
| 0.6 | 3082240 | 47.54 | -5.38 | 12.37 | -0.21 |
| 0.7 | 4448256 | 50.49 | -2.43 | 12.37 | -0.21 |
| 0.8 | 6510592 | 50.11 | -2.81 | 12.37 | -0.21 |
| 0.9 | 10022912 | 51.55 | -1.37 | 12.38 | -0.20 |
| 0.95 | 12976128 | 52.62 | -0.30 | 12.39 | -0.19 |
So for instance for rank 0.95, we can close the accuracy gap to just 0.3 percentage points while reducing the number of parameters by 30%. Also note that these compressed LoRAs can be better than directly training them on the lower rank -- e.g. for rank 32, training directly results in a test accuracy of 48.22%, while conversion from rank 64 results in 51.48%.
## Caveats
There are some limitations to the LoRA conversion. As mentioned above, a reduction in performance is expected and the converted LoRA will most likely be less parameter efficient than the original adapter. Morever, LoRA conversion has these limitations:
- Right now, only adapters applied to linear layers can be converted.
- Not all PEFT methods currently support LoRA conversion.
If there is a lot of demand to extend LoRA conversion, please let us know and we will make it work with more layer types and PEFT methods.
## API
### Convert a non-LoRA model to a LoRA model, return the `LoraConfig` and `state_dict`
[[autodoc]] tuners.lora.conversion.convert_to_lora
- all
### Convert a non-LoRA model to a LoRA model, save the adapter checkpoint and config at the given path
[[autodoc]] tuners.lora.conversion.save_as_lora
- all
================================================
FILE: docs/source/package_reference/merge_utils.md
================================================
# Model merge
PEFT provides several internal utilities for [merging LoRA adapters](../developer_guides/model_merging) with the TIES and DARE methods.
[[autodoc]] utils.merge_utils.prune
[[autodoc]] utils.merge_utils.calculate_majority_sign_mask
[[autodoc]] utils.merge_utils.disjoint_merge
[[autodoc]] utils.merge_utils.task_arithmetic
[[autodoc]] utils.merge_utils.ties
[[autodoc]] utils.merge_utils.dare_linear
[[autodoc]] utils.merge_utils.dare_ties
================================================
FILE: docs/source/package_reference/miss.md
================================================
# MiSS
MiSS: Balancing LoRA Performance and Efficiency with Simple Shard Sharing([MiSS](https://huggingface.co/papers/2409.15371)) is a novel PEFT method that adopts a low-rank structure, requires only a single trainable matrix, and introduces a new update mechanism distinct from LoRA, achieving an excellent balance between performance and efficiency.
The abstract from the paper is:
*Parameter-Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), effectively reduce the number of trainable parameters in Large Language Models (LLMs). However, as model scales continue to grow, the demand for computational resources remains a significant challenge. Existing LoRA variants often struggle to strike an optimal balance between adaptability (model performance and convergence speed) and efficiency (computational overhead, memory usage, and initialization time). This paper introduces MiSS(Matrix Shard Sharing ), a novel PEFT approach that addresses this trade-off through a simple shard-sharing mechanism. MiSS leverages the insight that a low-rank adaptation can be achieved by decomposing the weight matrix into multiple fragment matrices and utilizing a shared, trainable common fragment. This method constructs the low-rank update matrix through the replication of these shared, partitioned shards. We also propose a hardware-efficient and broadly applicable implementation for MiSS. Extensive experiments conducted on a range of tasks, alongside a systematic analysis of computational performance, demonstrate MiSS's superiority. The results show that MiSS significantly outperforms standard LoRA and its prominent variants in both model performance metrics and computational efficiency, including initialization speed and training throughput. By effectively balancing expressive power and resource utilization, MiSS offers a compelling solution for efficiently adapting large-scale models*.
## MissConfig
[[autodoc]] tuners.miss.config.MissConfig
## MissModel
[[autodoc]] tuners.miss.model.MissModel
================================================
FILE: docs/source/package_reference/multitask_prompt_tuning.md
================================================
# Multitask prompt tuning
[Multitask prompt tuning](https://huggingface.co/papers/2303.02861) decomposes the soft prompts of each task into a single learned transferable prompt instead of a separate prompt for each task. The single learned prompt can be adapted for each task by multiplicative low rank updates.
The abstract from the paper is:
*Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks. However, existing methods typically learn soft prompt vectors from scratch, and it has not been clear how to exploit the rich cross-task knowledge with prompt vectors in a multitask learning setting. We propose multitask prompt tuning (MPT), which first learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task. Extensive experiments on 23 NLP datasets demonstrate that our proposed approach outperforms the state-of-the-art methods, including the full finetuning baseline in some cases, despite only tuning 0.035% as many task-specific parameters*.
## MultitaskPromptTuningConfig
[[autodoc]] tuners.multitask_prompt_tuning.config.MultitaskPromptTuningConfig
## MultitaskPromptEmbedding
[[autodoc]] tuners.multitask_prompt_tuning.model.MultitaskPromptEmbedding
================================================
FILE: docs/source/package_reference/oft.md
================================================
# OFT
[Orthogonal Finetuning (OFT)](https://hf.co/papers/2306.07280) is a method developed for adapting text-to-image diffusion models. It works by reparameterizing the pretrained weight matrices with its orthogonal matrix to preserve information in the pretrained model. To reduce the number of parameters, OFT introduces a block-diagonal structure in the orthogonal matrix.
The abstract from the paper is:
*Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models to perform different downstream tasks becomes an important open problem. To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks. Unlike existing methods, OFT can provably preserve hyperspherical energy which characterizes the pairwise neuron relationship on the unit hypersphere. We find that this property is crucial for preserving the semantic generation ability of text-to-image diffusion models. To improve finetuning stability, we further propose Constrained Orthogonal Finetuning (COFT) which imposes an additional radius constraint to the hypersphere. Specifically, we consider two important finetuning text-to-image tasks: subject-driven generation where the goal is to generate subject-specific images given a few images of a subject and a text prompt, and controllable generation where the goal is to enable the model to take in additional control signals. We empirically show that our OFT framework outperforms existing methods in generation quality and convergence speed*.
## OFTConfig
[[autodoc]] tuners.oft.config.OFTConfig
## OFTModel
[[autodoc]] tuners.oft.model.OFTModel
================================================
FILE: docs/source/package_reference/osf.md
================================================
# OSF (Orthogonal Subspace Fine-tuning)
Orthogonal Subspace Fine-tuning ([OSF](https://huggingface.co/papers/2504.07097)) is a PEFT method designed for continual learning that constrains parameter updates to be orthogonal to previously important directions. This approach enables full fine-tuning while preventing catastrophic forgetting without requiring additional parameters or storing previous gradients.
The abstract from the paper is:
*Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. Existing methods typically rely on low-rank, parameter-efficient updates that limit the model's expressivity and introduce additional parameters per task, leading to scalability issues. To address these limitations, we propose a novel continual full fine-tuning approach leveraging adaptive singular value decomposition (SVD). Our method dynamically identifies task-specific low-rank parameter subspaces and constrains updates to be orthogonal to critical directions associated with prior tasks, thus effectively minimizing interference without additional parameter overhead or storing previous task gradients. We evaluate our approach extensively on standard continual learning benchmarks using both encoder-decoder (T5-Large) and decoder-only (LLaMA-2 7B) models, spanning diverse tasks including classification, generation, and reasoning. Empirically, our method achieves state-of-the-art results, up to 7% higher average accuracy than recent baselines like O-LoRA, and notably maintains the model's general linguistic capabilities, instruction-following accuracy, and safety throughout the continual learning process by reducing forgetting to near-negligible levels. Our adaptive SVD framework effectively balances model plasticity and knowledge retention, providing a practical, theoretically grounded, and computationally scalable solution for continual learning scenarios in large language models.*
## How OSF Works
OSF decomposes each weight matrix into high-rank (frozen) and low-rank (trainable) components using SVD:
```
W = U_high * S_high * V_high^T + U_low * S_low * V_low^T
```
Where:
- `U_high, S_high, V_high`: Preserve important directions from previous tasks (frozen)
- `U_low, S_low, V_low`: Allow adaptation to new tasks (trainable)
During training, gradients are projected to be orthogonal to the high-rank subspace, ensuring updates don't interfere with previously learned knowledge.
## Basic Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import OSFConfig, get_peft_model
# Load base model
model = AutoModelForCausalLM.from_pretrained("gpt2")
# Configure OSF
config = OSFConfig(
target_modules=["c_attn", "c_proj"], # Target attention layers
effective_rank=8, # Default rank for decomposition
rank_pattern={"c_attn": 16} # Override rank for specific modules
)
# Apply OSF
model = get_peft_model(model, config)
# Train as usual
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer("Hello world", return_tensors="pt", padding=True)
loss = model(**inputs, labels=inputs.input_ids).loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
```
## Configuration Options
### Target Modules
You can specify target modules in several ways:
```python
# Specific module names
config = OSFConfig(target_modules=["q_proj", "k_proj", "v_proj", "o_proj"])
# All linear layers
config = OSFConfig(target_modules="all-linear")
# Model-specific defaults (automatically detected)
config = OSFConfig() # Uses model-appropriate defaults
```
### Effective Rank Configuration
Control the preserved/trainable subspaces:
```python
# Global preserved rank (applies to all target modules)
config = OSFConfig(effective_rank=16) # preserves top-16 singular directions; trains the rest
# Automatic preserved rank (50% of the smaller matrix dimension per target)
config = OSFConfig(effective_rank=None)
# Per-module preserved-rank overrides
config = OSFConfig(
effective_rank=8,
rank_pattern={
"q_proj": 16, # Higher rank for query projection
"gate_proj": 4 # Lower rank for gate projection
}
)
# Fractional preserved rank is supported (interpreted per-target as fraction * min_dim)
config = OSFConfig(effective_rank=0.8) # preserve 80% of min_dim; train remaining 20%
config = OSFConfig(rank_pattern={"q_proj": 0.5}) # preserve 50% on q_proj, others use global/default
```
Note: OSF's `effective_rank` is the preserved (frozen) rank, not the trainable rank. The trainable rank equals `min(weight.shape) - effective_rank`. This differs from LoRA's `r`, which directly specifies the trainable rank.
## Training Advice for Continual Learning
### Sequential Task Learning
OSF is specifically designed for learning tasks sequentially. Between tasks, recompute the SVD so the preserved subspace reflects the latest weights. One simple way is to re-wrap the updated base model with OSF again:
```python
# Task 1: train on domain A with initial preserved subspace
r = 8 # initial effective rank to preserve
model = get_peft_model(base_model, OSFConfig(effective_rank=r))
train_task(model, task_1_data)
# Task 2: recompute SVD on updated weights and increase preserved subspace
base_model = model.unload() # unwrap base model without assuming internals
r += 4 # grow preserved subspace to include Task 1 knowledge
model = get_peft_model(base_model, OSFConfig(effective_rank=r))
train_task(model, task_2_data)
# Task 3: recompute again and expand preserved subspace further
base_model = model.unload()
r += 4
model = get_peft_model(base_model, OSFConfig(effective_rank=r))
train_task(model, task_3_data)
```
### Budget Allocation for Task Sequences
When training on a known sequence of n tasks, one effective strategy is to progressively allocate model capacity to balance learning new tasks while preserving previous knowledge:
- **Task 1**: Use full capacity (train everything)
- **Task 2**: Freeze 1/n of model capacity, train remaining (n-1)/n capacity
- **Task 3**: Freeze 2/n of model capacity, train remaining (n-2)/n capacity
- **Task n**: Freeze (n-1)/n of model capacity, use 1/n capacity for final task
This approach ensures each task gets adequate learning capacity while progressively preserving more knowledge from previous tasks.
```python
# Example: 4-task sequence with progressive budget allocation
n_tasks = 4
max_preserved_rank = 512 # Upper bound for preserved rank per target (heuristic)
for task_id in range(n_tasks):
# Freeze increases over time; trainable capacity shrinks
preserved_fraction = (task_id + 1) / n_tasks
preserved_rank = int(max_preserved_rank * preserved_fraction)
config = OSFConfig(
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
effective_rank=preserved_rank,
)
print(
f"Task {task_id + 1}: Preserving rank {preserved_rank} "
f"({preserved_fraction:.1%} of max_preserved_rank - {max_preserved_rank} frozen); trainable rank = min_dim - preserved_rank"
)
model = get_peft_model(base_model, config)
train_task(model, task_data[task_id])
```
### Best Practices
1. **Effective Rank Selection**: Start with `effective_rank=None` (auto sets rank to 50% of the smaller weight dimension per target module) and adjust based on task complexity
2. **Learning Rate**: Use smaller learning rates (1e-5 to 1e-4) compared to standard fine-tuning
3. **Task Importance**: Use `rank_pattern` to allocate more capacity to critical modules
4. **Model Architecture**: OSF works best with transformer architectures having clear attention and MLP separations
5. **Capacity Planning**: For known task sequences, use progressive budget allocation (1/n, 2/n, ..., (n-1)/n freezing) to balance plasticity and stability
### Memory Considerations
OSF modifies weights in-place and doesn't add parameters, making it memory-efficient:
```python
# Memory usage remains close to base model
print(f"Base model parameters: {base_model.num_parameters():,}")
print(f"OSF model parameters: {osf_model.num_parameters():,}") # Similar count
```
## Advanced Usage
### Custom Target Modules
For models with non-standard architectures:
```python
config = OSFConfig(
target_modules=["dense", "intermediate.dense"], # Custom layer names
effective_rank=12,
rank_pattern={"dense": 8, "intermediate.dense": 16}
)
```
### Integration with Other Methods
OSF can be combined with other techniques:
```python
# Use with gradient checkpointing for memory efficiency
model.gradient_checkpointing_enable()
# Apply weight decay selectively (regularizes low-rank factors to limit drift/overfitting in continual updates; keep small)
optimizer = torch.optim.AdamW([
{"params": [p for n, p in model.named_parameters() if "U_low" in n], "weight_decay": 0.01},
{"params": [p for n, p in model.named_parameters() if "S_low" in n], "weight_decay": 0.001},
{"params": [p for n, p in model.named_parameters() if "V_low" in n], "weight_decay": 0.01},
], lr=1e-4)
```
## OSFConfig
[[autodoc]] tuners.osf.config.OSFConfig
## OSFModel
[[autodoc]] tuners.osf.model.OSFModel
## Utility Functions
### Weight Decomposition
[[autodoc]] tuners.osf.utils.decompose_weight_matrix
[[autodoc]] tuners.osf.utils.reconstruct_weight_matrix
### Gradient Projection
[[autodoc]] tuners.osf.utils.project_gradient_to_orthogonal_space
================================================
FILE: docs/source/package_reference/p_tuning.md
================================================
# P-tuning
[P-tuning](https://hf.co/papers/2103.10385) adds trainable prompt embeddings to the input that is optimized by a prompt encoder to find a better prompt, eliminating the need to manually design prompts. The prompt tokens can be added anywhere in the input sequence, and p-tuning also introduces anchor tokens for improving performance.
The abstract from the paper is:
*While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), we show that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning -- which employs trainable continuous prompt embeddings. On the knowledge probing (LAMA) benchmark, the best GPT recovers 64\% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning. Importantly, we find that P-tuning also improves BERTs' performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark.*.
## PromptEncoderConfig
[[autodoc]] tuners.p_tuning.config.PromptEncoderConfig
## PromptEncoder
[[autodoc]] tuners.p_tuning.model.PromptEncoder
================================================
FILE: docs/source/package_reference/peanut.md
================================================
# PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers
[PEANuT](https://arxiv.org/abs/2410.01870) is a parameter-efficient fine-tuning technique that introduces
weight-aware neural tweakers to generate adapter updates from the frozen pretrained weights themselves. Instead of
learning a purely linear low-rank update as in LoRA, PEANuT conditions the adapter transformation on the base weight,
which makes the update rule more expressive while keeping the number of trainable parameters small.
PEANuT uses an input projection `A`, an output projection `B`, and optional intermediate residual encoder/decoder
pairs with non-linear activations. This makes it possible to model more complex update patterns than weight-agnostic
linear adapters while still remaining within the PEFT setting.
PEANuT currently has the following tradeoffs:
Pros:
- Higher theoretical expressiveness than linear low-rank updates.
- Better performance than LoRA on a range of tasks under similar budgets.
- Works well in very low-parameter regimes, for example around `0.2M` trainable parameters.
Cons:
- Higher memory usage than LoRA, because `ΔW` is explicitly constructed before being applied.
- Slower training and inference than LoRA, and deeper intermediate layers increase the overhead further.
- The non-linearity can require more careful hyperparameter tuning, especially learning rate and related optimization settings.
If these tradeoffs do not fit your use case, consider other PEFT methods such as LoRA.
The abstract from the paper is:
> Fine-tuning large pre-trained foundation models often yields excellent downstream performance but is prohibitively expensive when updating all parameters. Parameter-efficient fine-tuning (PEFT) methods such as LoRA alleviate this by introducing lightweight update modules, yet they commonly rely on weight-agnostic linear approximations, limiting their expressiveness. In this work, we propose PEANuT, a novel PEFT framework that introduces weight-aware neural tweakers, compact neural modules that generate task-adaptive updates conditioned on frozen pre-trained weights. PEANuT provides a flexible yet efficient way to capture complex update patterns without full model tuning. We theoretically show that PEANuT achieves equivalent or greater expressivity than existing linear PEFT methods with comparable or fewer parameters. Extensive experiments across four benchmarks with over twenty datasets demonstrate that PEANuT consistently outperforms strong baselines in both NLP and vision tasks, while maintaining low computational overhead.
## PeanutConfig
[[autodoc]] tuners.peanut.config.PeanutConfig
## PeanutModel
[[autodoc]] tuners.peanut.model.PeanutModel
================================================
FILE: docs/source/package_reference/peft_model.md
================================================
# Models
[`PeftModel`] is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base `PeftModel` contains methods for loading and saving models from the Hub.
## PeftModel
[[autodoc]] PeftModel
- all
## PeftModelForSequenceClassification
A `PeftModel` for sequence classification tasks.
[[autodoc]] PeftModelForSequenceClassification
- all
## PeftModelForTokenClassification
A `PeftModel` for token classification tasks.
[[autodoc]] PeftModelForTokenClassification
- all
## PeftModelForCausalLM
A `PeftModel` for causal language modeling.
[[autodoc]] PeftModelForCausalLM
- all
## PeftModelForSeq2SeqLM
A `PeftModel` for sequence-to-sequence language modeling.
[[autodoc]] PeftModelForSeq2SeqLM
- all
## PeftModelForQuestionAnswering
A `PeftModel` for question answering.
[[autodoc]] PeftModelForQuestionAnswering
- all
## PeftModelForFeatureExtraction
A `PeftModel` for getting extracting features/embeddings from transformer models.
[[autodoc]] PeftModelForFeatureExtraction
- all
## PeftMixedModel
A `PeftModel` for mixing different adapter types (e.g. LoRA and LoHa).
[[autodoc]] PeftMixedModel
- all
## Utilities
[[autodoc]] utils.cast_mixed_precision_params
[[autodoc]] get_peft_model
[[autodoc]] inject_adapter_in_model
[[autodoc]] utils.get_peft_model_state_dict
[[autodoc]] utils.prepare_model_for_kbit_training
[[autodoc]] get_layer_status
[[autodoc]] get_model_status
================================================
FILE: docs/source/package_reference/peft_types.md
================================================
# PEFT types
[`PeftType`] includes the supported adapters in PEFT, and [`TaskType`] includes PEFT-supported tasks.
## PeftType
[[autodoc]] utils.peft_types.PeftType
## TaskType
[[autodoc]] utils.peft_types.TaskType
================================================
FILE: docs/source/package_reference/poly.md
================================================
# Polytropon
[Polytropon](https://hf.co/papers/2202.13914) is a multitask model with a number of different LoRA adapters in its "inventory". The model learns the correct combination of adapters from the inventory with a routing function to choose the best subset of modules for a specific task. PEFT also supports [Multi-Head Adapter Routing (MHR)](https://hf.co/papers/2211.03831) for Polytropon which builds on and improves the routing function by combining the adapter heads more granularly. The adapter heads are separated into disjoint blocks and a different routing function is learned for each one, allowing for more expressivity.
The abstract from the paper is:
*A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is associated with a subset of latent discrete skills from a (potentially small) inventory. In turn, skills correspond to parameter-efficient (sparse / low-rank) model parameterisations. By jointly learning these and a task-skill allocation matrix, the network for each task is instantiated as the average of the parameters of active skills. To favour non-trivial soft partitions of skills across tasks, we experiment with a series of inductive biases, such as an Indian Buffet Process prior and a two-speed learning rate. We evaluate our latent-skill model on two main settings: 1) multitask reinforcement learning for grounded instruction following on 8 levels of the BabyAI platform; and 2) few-shot adaptation of pre-trained text-to-text generative models on CrossFit, a benchmark comprising 160 NLP tasks. We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning, compared to baselines with fully shared, task-specific, or conditionally generated parameters where knowledge is entangled across tasks. In addition, we show how discrete skills help interpretability, as they yield an explicit hierarchy of tasks.*
The abstract from the paper is:
*Parameter-efficient fine-tuning (PEFT) for cross-task generalization consists in pre-training adapters on a multi-task training set before few-shot adaptation to test tasks. Polytropon [Ponti et al., 2023] (Poly) jointly learns an inventory of adapters and a routing function that selects a (variable-size) subset of adapters for each task during both pre-training and few-shot adaptation. In this paper, we investigate the role that adapter routing plays in its success and design new variants based on our findings. First, we build on the intuition that finer-grained routing provides more expressivity. Hence, we propose MHR (Multi-Head Routing), which combines subsets of adapter parameters and outperforms Poly under a comparable parameter budget; by only fine-tuning the routing function and not the adapters (MHR-z), we achieve competitive performance with extreme parameter efficiency. Second, we find that Poly/MHR performance is a result of better multi-task optimization, rather than modular inductive biases that facilitate adapter recombination and local adaptation, as previously hypothesized. In fact, we find that MHR exhibits higher gradient alignment between tasks than any other method. Since this implies that routing is only crucial during multi-task pre-training, we propose MHR-mu, which discards routing and fine-tunes the average of the pre-trained adapters during few-shot adaptation. This establishes MHR-mu as an effective method for single-adapter fine-tuning.*.
## PolyConfig
[[autodoc]] tuners.poly.config.PolyConfig
## PolyModel
[[autodoc]] tuners.poly.model.PolyModel
================================================
FILE: docs/source/package_reference/prefix_tuning.md
================================================
# Prefix tuning
[Prefix tuning](https://hf.co/papers/2101.00190) prefixes a series of task-specific vectors to the input sequence that can be learned while keeping the pretrained model frozen. The prefix parameters are inserted in all of the model layers.
**Note** For encoder-decoder models (seq2seq), the prefix is only applied to the decoder, which does not correspond to the paper specification (see e.g. Figure 2). Prefix tuning can still be fine-tuned on these model architectures but the performance could be sub-par; consider using other PEFT methods for encoder-decoder models.
## Initialize from a KV cache prefix
By default, prefix tuning is randomly initialized.
PEFT also provides utilities to initialize a prefix-tuning adapter from an existing KV cache prefix (for example, from
the first `p` tokens of a prompt/corpus). This is only supported when `prefix_projection=False` (the default), because
in that case the learned parameters are the KV prefix itself.
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PrefixTuningConfig, get_peft_model, initialize_kv_prefix_from_text
base = AutoModelForCausalLM.from_pretrained("gpt2")
tok = AutoTokenizer.from_pretrained("gpt2")
peft_cfg = PrefixTuningConfig(task_type="CAUSAL_LM", num_virtual_tokens=20, prefix_projection=False)
model = get_peft_model(base, peft_cfg)
initialize_kv_prefix_from_text(
model,
tok,
text="...a long context with at least num_virtual_tokens tokens...",
use_chat_template=False,
)
```
Make sure the text is long enough to produce at least `num_virtual_tokens` tokens, otherwise initialization will fail.
The abstract from the paper is:
*Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks. However, it modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen, but optimizes a small continuous task-specific vector (called the prefix). Prefix-tuning draws inspiration from prompting, allowing subsequent tokens to attend to this prefix as if it were "virtual tokens". We apply prefix-tuning to GPT-2 for table-to-text generation and to BART for summarization. We find that by learning only 0.1\% of the parameters, prefix-tuning obtains comparable performance in the full data setting, outperforms fine-tuning in low-data settings, and extrapolates better to examples with topics unseen during training*.
## PrefixTuningConfig
[[autodoc]] tuners.prefix_tuning.config.PrefixTuningConfig
## PrefixEncoder
[[autodoc]] tuners.prefix_tuning.model.PrefixEncoder
================================================
FILE: docs/source/package_reference/prompt_tuning.md
================================================
# Prompt tuning
[Prompt tuning](https://hf.co/papers/2104.08691) adds task-specific prompts to the input, and these prompt parameters are updated independently of the pretrained model parameters which are frozen.
The abstract from the paper is:
*In this work, we explore "prompt tuning", a simple yet effective mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signal from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3's "few-shot" learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant in that large models are costly to share and serve, and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed "prefix tuning" of Li and Liang (2021), and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer, as compared to full model tuning*.
## PromptTuningConfig
[[autodoc]] tuners.prompt_tuning.config.PromptTuningConfig
## PromptEmbedding
[[autodoc]] tuners.prompt_tuning.model.PromptEmbedding
================================================
FILE: docs/source/package_reference/psoft.md
================================================
# PSOFT
[PSOFT](https://hf.co/papers/2505.11235) is an Orthogonal Fine-Tuning (OFT)-based parameter-efficient fine-tuning method that preserves the geometric relationships of pre-trained weight column vectors while achieving a balanced trade-off between performance and multi-dimensional efficiency, including parameter count, memory usage, and computational cost. By restricting orthogonal transformations to a low-rank principal subspace derived from pre-trained weights, PSOFT bridges the gap between LoRA and OFT, providing both theoretical guarantees and practical adaptability. Its effectiveness is validated through extensive evaluations on diverse benchmarks, including GLUE, VTAB-1K, GSM8K, MATH, and commonsense reasoning benchmarks.
- Only `nn.Linear` layers are supported.
- Quantized layers are not supported.
The abstract from the paper is:
*Driven by the rapid growth of model parameters, parameter-efficient fine-tuning (PEFT) has become essential for adapting large models to diverse downstream tasks under constrained computational resources. Within this paradigm, orthogonal fine-tuning and its variants preserve semantic representations of pre-trained models, but struggle to achieve both expressiveness and efficiency in terms of parameter counts, memory, and computation. To overcome this limitation, we propose efficient Orthogonal Fine-Tuning with Principal Subspace adaptation (PSOFT), which confines orthogonal transformations to the principal subspace of pre-trained weights. Specifically, PSOFT constructs this subspace via matrix decomposition to enable compatible transformations, establishes a theoretical condition that strictly maintains the geometry of this subspace for essential semantic preservation, and introduces efficient tunable vectors that gradually relax orthogonality during training to enhance adaptability. Extensive experiments on 35 NLP and CV tasks across four representative models demonstrate that PSOFT offers a practical and scalable solution to simultaneously achieve semantic preservation, expressiveness, and multi-dimensional efficiency in PEFT.*
## How PSOFT Works
PSOFT decomposes each weight matrix $W_{pre}$ into $W_{pri}$ and $W_{res}$ using SVD:
$W_{\text{pre}} = U S V^\top$
The principal subspace $W_{\text{pri}} = U_r S_r V_r^\top = AB$ is constructed from the top-$r$ singular components:
$W_{\text{pre}} = W_{\text{pri}} + W_{\text{res}} = AB + W_{\text{res}},$
$W_{\text{ps-tuned}} = ARB + W_{\text{res}}.$ (PSOFT-SO: PSOFT with strict orthogonality)
$W_{\text{ps-tuned}} = A \, \mathrm{diag}(\alpha) \, R \, \mathrm{diag}(\beta) \, B + W_{\text{res}}.$ (PSOFT-RO: PSOFT with relaxed orthogonality)
During training, $A$, $B$, and $W_{\text{res}}$ are frozen, and only $R$ (or $R$ with $\alpha$ and $\beta$) is trainable.
For compatibility with the PEFT framework (which expects additive weight updates), PSOFT is implemented in the following additive form:
$W_{\text{ps-tuned}} = W_{\text{pre}} + A (R - I_r) B$
## Trainable Parameters
After applying PSOFT:
- The original model weights ($A$, $B$, and $W_{\text{res}}$) are frozen.
- Only the orthogonal matrix $R$ (and optionally $\alpha$, $\beta$) are trainable.
- No additional bias parameters are introduced.
## Basic Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PsoftConfig, get_peft_model
# Load base model
model_id = "facebook/opt-125m"
model = AutoModelForCausalLM.from_pretrained(model_id)
# Configure PSOFT
config = PsoftConfig(
r=32, # the dimension of trainable matrix R,
psoft_alpha=32, # scaling factor (typically set to r in PSOFT),
target_modules=["q_proj", "v_proj"], # target attention projection layers
ab_svd_init="psoft_init", # principal subspace initialization
psoft_svd="full", # SVD method
psoft_orth=True, # enable orthogonal R (Cayley parameterization)
psoft_mag_a=True, # enable tunable vector alpha
psoft_mag_b=True, # enable tunable vector beta
use_cayley_neumann=False, # disable Cayley–Neumann approximation
num_cayley_neumann_terms=5, # number of Neumann series terms
cayley_neumann_eps=None, # improve numerical stability
)
# Apply PSOFT
model = get_peft_model(model, config)
model.train()
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
# Train
inputs = tokenizer("Hello world", return_tensors="pt", padding=True)
loss = model(**inputs, labels=inputs["input_ids"]).loss
loss.backward()
trainable = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.AdamW(trainable, lr=5e-4)
optimizer.step()
optimizer.zero_grad(set_to_none=True)
```
## Configuration Options
### Different Mode
(PSOFT-SO: PSOFT with strict orthogonality)
```python
config = PsoftConfig(psoft_orth=True,psoft_mag_a=False,psoft_mag_b=False)
```
(PSOFT-RO: PSOFT with relaxed orthogonality)
```python
config = PsoftConfig(psoft_orth=True,psoft_mag_a=True,psoft_mag_b=True)
```
### Best Practices
1. **Rank Choice**: Smaller ranks (e.g., `32–128`) are suitable for simpler tasks, while larger ranks (e.g., `64–256`) provide greater expressiveness for more complex tasks at the cost of increased parameters and computation.
2. **Scaling Factor**: The scaling factor is typically set to $r$ in PSOFT.
3. **Learning Rate**: Use standard learning rates (e.g., `1e-4` to `5e-3`) for stable training.
4. **SVD Initialization**: The `lowrank` option is more memory- and compute-efficient than `full`, making it more suitable for large models.
5. **Cayley–Neumann Approximation**: When the rank is large, enabling the Cayley–Neumann approximation can significantly improve computational efficiency, while the benefit is less pronounced for small ranks. In practice, a small number of Neumann series terms (typically `5`) usually provides a good balance between accuracy and efficiency.
## PsoftConfig
[[autodoc]] tuners.psoft.config.PsoftConfig
## PsoftModel
[[autodoc]] tuners.psoft.model.PsoftModel
================================================
FILE: docs/source/package_reference/pvera.md
================================================
# PVeRA: Probabilistic Vector-Based Random Matrix Adaptation
[PVeRA](https://huggingface.co/papers/2512.07703) is a parameter-efficient fine-tuning technique that is base on VeRA, in the family of the LoRA-based adapters. It keeps the very low parameter budget of VeRA, but increases the performance by learning a distribution of latent adaptations. This also enables models adapted with PVeRA to generate Monte Carlo confidence interval estimates, by sampling from the learned distribution at inference.
When saving the adapter parameters, it's possible to eschew storing the low rank matrices by setting `save_projection=False` on the `PveraConfig`. In that case, these matrices will be restored based on the fixed random seed from the `projection_prng_key` argument. This cuts down on the size of the checkpoint, but we cannot guarantee reproducibility on all devices and for all future versions of PyTorch. If you want to ensure reproducibility, set `save_projection=True` (which is the default).
To handle different shapes of adapted layers, PVeRA initializes shared A and B matrices with the largest required size for each dimension. During the forward pass, submatrices A and B for a given layer are sliced out from these shared matrices and used as described in the paper. For example, adapting two linear layers of shapes (100, 20) and (80, 50) will create A and B matrices of shapes (rank, 50) and (100, rank) respectively. Then, to adapt a layer of shape (100, 20), submatrices A and B of shapes (rank, 20) and (100, rank) will be extracted.
PVeRA currently has the following constraint:
- Only `nn.Linear` layers are supported.
- The latent representation is not easily accessible, for training using the KL divergence.
The abstract from the paper is:
> Large foundation models have emerged in the last years and are pushing performance boundaries for a variety of tasks. Training or even finetuning such models demands vast datasets and computational resources, which are often scarce and costly. Adaptation methods provide a computationally efficient solution to address these limitations by allowing such models to be finetuned on small amounts of data and computing power. This is achieved by appending new trainable modules to frozen backbones with only a fraction of the trainable parameters and fitting only these modules on novel tasks. Recently, the VeRA adapter was shown to excel in parameter-efficient adaptations by utilizing a pair of frozen random low-rank matrices shared across all layers. In this paper, we propose PVeRA, a probabilistic version of the VeRA adapter, which modifies the low-rank matrices of VeRA in a probabilistic manner. This modification naturally allows handling inherent ambiguities in the input and allows for different sampling configurations during training and testing. A comprehensive evaluation was performed on the VTAB-1k benchmark and seven adapters, with PVeRA outperforming VeRA and other adapters.
## PveraConfig
[[autodoc]] tuners.pvera.config.PveraConfig
## PveraModel
[[autodoc]] tuners.pvera.model.PveraModel
================================================
FILE: docs/source/package_reference/randlora.md
================================================
# RandLora: Full-rank parameter-efficient fine-tuning of large models
[RandLora](https://huggingface.co/papers/2502.00987) is a parameter-efficient fine-tuning technique that is similar to [LoRA](https://huggingface.co/papers/2106.09685) and [VeRA](https://huggingface.co/papers/2310.11454) but performs full rank updates to improve performance. RandLora can be particulary usefull when adapting large model to hard tasks that require complex updates while preserving the parameter efficiency of LoRA. The full rank update of RandLora is achieved by linearly scaling random bases. The random bases are a collection of multiple low rank matrices such that the summation of their ranks if greater or equal to the full rank of the parameter matrices. The trainable parameters of RandLora are two diagonal matrices (vectors) that get multiplied with the right hand low rank random bases, in a similar way to VeRA's update. To maintain low memory usage, RandLora uses a custom function that prevents storing unnecessary bases in memory for backpropagation.
RandLora presents the noteworthy difference that contrary to other LoRA-like PEFT algorithm, increasing RandLora's random base ranks increases the amount of trainable parameters. Because number of bases x bases rank is constant in RandLora, reducing the rank will increase the number of random bases, hence the number of base-specific trainable diagonal bases.
Because reducing the rank of RandLora's random bases will increase their number, RandLora can become slower to train than LoRA for very small ranks where typically, ranks below 4 with result in a large training time increase. This does not affect inference though as the RandLora adapters can be merged into the pretrained weight matrices.
RandLora additionally supports training with sparse, ternary random bases (only containing -1, 0 and 1). These bases are as described in [Bingham et al.](https://cs-people.bu.edu/evimaria/cs565/kdd-rp.pdf) and [Ping et al.](https://hastie.su.domains/Papers/Ping/KDD06_rp.pdf) and could theoretically be used to reduce compute needs by performing aggregations instead of matrix multiplications to create the weight update. This is not currently supported. Although it does not currently reduce compute, using sparse random bases in RandLora can reduce overfitting in some cases. For users intersted in using sparse ternary bases, the `sparse` option is recommended over the `very_sparse` one that can reduce perfromance.
Similarly to VeRA, when saving the RandLora's parameters, it's possible to eschew storing the low rank matrices by setting `save_projection=False` on the `VeraConfig`. In that case, these matrices will be restored based on the fixed random seed from the `projection_prng_key` argument. This cuts down on the size of the checkpoint, but we cannot guarantee reproducibility on all devices and for all future versions of PyTorch. If you want to ensure reproducibility, set `save_projection=True` (which is the default).
As in Vera and to handle different shapes of adapted layers, RandLora initializes shared A and B matrices with the largest required size for each dimension. During the forward pass, submatrices A and B for a given layer are sliced out from these shared matrices and used as described in the paper. For example, adapting two linear layers of shapes (100, 20) and (80, 50) will create A and B matrices of shapes (rank, 50) and (100, rank) respectively. Then, to adapt a layer of shape (100, 20), submatrices A and B of shapes (rank, 20) and (100, rank) will be extracted.
RandLora currently has the following constraint:
- Only `nn.Linear` layers are supported.
The abstract from the paper is:
> Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while maintaining fine-tuning performance. The low-rank nature of the weight update inherently limits the representation power of fine-tuned models, however, thus potentially compromising performance on complex tasks. This raises a critical question: when a performance gap between LoRA and standard fine-tuning is observed, is it due to the reduced number of trainable parameters or the rank deficiency?
This paper aims to answer this question by introducing RandLora, a parameter-efficient method that performs full-rank updates using a learned linear combinations of low-rank, non-trainable random matrices. Our method limits the number of trainable parameters by restricting optimization to diagonal scaling matrices applied to the fixed random matrices. This allows us to effectively overcome the low-rank limitations while maintaining parameter and memory efficiency during training. Through extensive experimentation across vision, language, and vision-language benchmarks, we systematically evaluate the limitations of LoRA and existing random basis methods. Our findings reveal that full-rank updates are beneficial across vision and language tasks individually, and even more so for vision-language tasks, where RandLora significantly reduces---and sometimes eliminates---the performance gap between standard fine-tuning and LoRA, demonstrating its efficacy.
## RandLoraConfig
[[autodoc]] tuners.randlora.config.RandLoraConfig
## RandLoraModel
[[autodoc]] tuners.randlora.model.RandLoraModel
================================================
FILE: docs/source/package_reference/road.md
================================================
# RoAd
[RoAd](https://huggingface.co/papers/2409.00119) is a parameter‑efficient fine‑tuning technique that adapts large language models by learning a small set of 2×2 rotation matrices (and optional scaling factors) applied to pairs of hidden dimensions. RoAd achieves competitive or superior performance compared to other PEFT methods with under 0.1% trainable parameters. Unlike LoRA’s batched low‑rank updates, RoAd’s sparse rotations reformulate to simple element‑wise operations, yielding significantly higher serving throughput when handling heterogeneous requests in the same batch, i.e. serving multiple adapters simulatenously. Moreover, RoAd integrates seamlessly into a distributed interchange intervention framework, interpreting its sparse 2D rotations as task-specific interventions within learned subspaces of hidden representations. These orthogonal subspaces can be composed to merge multiple task-specific behaviors—like multilingual capabilities or instruction following—without additional fine-tuning, enabling modular, interpretable adaptations in LLMs.
Finetuning with RoAd typically requires higher learning rate compared to LoRA or similar methods, around 1e-3. Currently RoAd only supports linear layers and it can be used on models quantized with bitsandbytes (4-bit or 8-bit).
For running inference with different RoAd adapters in the same batch see [Inference with different LoRA adapters in the same batch](../developer_guides/lora#inference-with-different-lora-adapters-in-the-same-batch).
## RoadConfig
[[autodoc]] tuners.road.config.RoadConfig
## RoadModel
[[autodoc]] tuners.road.model.RoadModel
================================================
FILE: docs/source/package_reference/shira.md
================================================
# Sparse High Rank Adapters
Sparse High Rank Adapters or [SHiRA](https://huggingface.co/papers/2406.13175) is an alternate type of adapter and has been found to have significant advantages over the low rank adapters. Specifically, SHiRA achieves better accuracy than LoRA for a variety of vision and language tasks. It also offers simpler and higher quality multi-adapter fusion by significantly reducing concept loss, a common problem faced by low rank adapters. SHiRA directly finetunes a small number of the base model's parameters to finetune the model on any adaptation task.
SHiRA currently has the following constraint:
- Only `nn.Linear` layers are supported.
The abstract from the paper is:
> Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models, adding no overhead during inference. However, from a mobile deployment standpoint, we can either avoid inference overhead in the fused mode but lose the ability to switch adapters rapidly, or suffer significant (up to 30% higher) inference latency while enabling rapid switching in the unfused mode. LoRA also exhibits concept-loss when multiple adapters are used concurrently. In this paper, we propose Sparse High Rank Adapters (SHiRA), a new paradigm which incurs no inference overhead, enables rapid switching, and significantly reduces concept-loss. Specifically, SHiRA can be trained by directly tuning only 1-2% of the base model weights while leaving others unchanged. This results in a highly sparse adapter which can be switched directly in the fused mode. We further provide theoretical and empirical insights on how high sparsity in SHiRA can aid multi-adapter fusion by reducing concept loss. Our extensive experiments on LVMs and LLMs demonstrate that finetuning only a small fraction of the parameters in the base model significantly outperforms LoRA while enabling both rapid switching and multi-adapter fusion. Finally, we provide a latency- and memory-efficient SHiRA implementation based on Parameter-Efficient Finetuning (PEFT) Library which trains at nearly the same speed as LoRA while consuming up to 16% lower peak GPU memory, thus making SHiRA easy to adopt for practical use cases. To demonstrate rapid switching benefits during inference, we show that loading SHiRA on a base model can be 5x-16x faster than LoRA fusion on a CPU.
## ShiraConfig
[[autodoc]] tuners.shira.config.ShiraConfig
## ShiraModel
[[autodoc]] tuners.shira.model.ShiraModel
================================================
FILE: docs/source/package_reference/trainable_tokens.md
================================================
# Trainable Tokens
The Trainable Tokens method provides a way to target specific token embeddings for fine-tuning without resorting to
training the full embedding matrix or using an adapter on the embedding matrix. It is based on the initial implementation from
[here](https://github.com/huggingface/peft/pull/1541).
The method only targets specific tokens and selectively trains the token indices you specify. Consequently the
required RAM will be lower and disk memory is also significantly lower than storing the full fine-tuned embedding matrix.
Some preliminary benchmarks acquired with [this script](https://github.com/huggingface/peft/blob/main/scripts/train_memory.py)
suggest that for `gemma-2-2b` (which has a rather large embedding matrix) you can save ~4 GiB VRAM with Trainable Tokens
over fully fine-tuning the embedding matrix. While LoRA will use comparable amounts of VRAM it might also target
tokens you don't want to be changed. Note that these are just indications and varying embedding matrix sizes might skew
these numbers a bit.
Note that this method does not add tokens for you, you have to add tokens to the tokenizer yourself and resize the
embedding matrix of the model accordingly. This method will only re-train the embeddings for the tokens you specify.
This method can also be used in conjunction with LoRA layers! See [the LoRA developer guide](../developer_guides/lora#efficiently-train-tokens-alongside-lora).
> [!TIP]
> Saving the model with [`~PeftModel.save_pretrained`] or retrieving the state dict using
> [`get_peft_model_state_dict`] when adding new tokens may save the full embedding matrix instead of only the difference
> as a precaution because the embedding matrix was resized. To save space you can disable this behavior by setting
> `save_embedding_layers=False` when calling `save_pretrained`. This is safe to do as long as you don't modify the
> embedding matrix through other means as well, as such changes will be not tracked by trainable tokens.
## TrainableTokensConfig
[[autodoc]] tuners.trainable_tokens.config.TrainableTokensConfig
## TrainableTokensModel
[[autodoc]] tuners.trainable_tokens.model.TrainableTokensModel
================================================
FILE: docs/source/package_reference/tuners.md
================================================
# Tuners
A tuner (or adapter) is a module that can be plugged into a `torch.nn.Module`. [`BaseTuner`] base class for other tuners and provides shared methods and attributes for preparing an adapter configuration and replacing a target module with the adapter module. [`BaseTunerLayer`] is a base class for adapter layers. It offers methods and attributes for managing adapters such as activating and disabling adapters.
## BaseTuner
[[autodoc]] tuners.tuners_utils.BaseTuner
## BaseTunerLayer
[[autodoc]] tuners.tuners_utils.BaseTunerLayer
================================================
FILE: docs/source/package_reference/vblora.md
================================================
# VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
## Overview
[VB-LoRA](https://huggingface.co/papers/2405.15179) is a parameter-efficient fine-tuning technique that extends LoRA by learning a fine-grained parameter-sharing scheme at the sub-vector level, achieving significantly higher parameter efficiency. This makes VB-LoRA especially useful in scenarios where storage and transmission costs are critical. It works by decomposing low-rank matrices—from different layers and modules such as K, Q, V, and FFN—into sub-vectors, which are then globally shared through a vector bank.
The abstract from the paper is:
*As the adoption of large language models increases and the need for per-user or per-task model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and its variants, incur substantial storage and transmission costs. To further reduce stored parameters, we introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules and layers by sharing parameters globally via a vector bank. As an instantiation of the paradigm to LoRA, our proposed VB-LoRA composites all the low-rank matrices of LoRA from a shared vector bank with a differentiable top-k admixture module. VB-LoRA achieves extreme parameter efficiency while maintaining comparable or better performance compared to state-of-the-art PEFT methods. Extensive experiments demonstrate the effectiveness of VB-LoRA on natural language understanding, natural language generation, and instruction tuning tasks. When fine-tuning the Llama2-13B model, VB-LoRA only uses 0.4% of LoRA's stored parameters, yet achieves superior results.*
## Usage Tips
- VB-LoRA utilizes a sparse top-k module to learn the sharing machanism. When saving adapter parameters, you can either save only the top-k weights and their indices by setting `save_only_topk_weights = True` in `VBLoRAConfig`, or save all the trainable logits by setting it to `False`. Enabling `save_only_topk_weights = True` significantly reduces storage space; for instance, in Llama2-7B, the storage file size decreases from 308MB to 2.5MB. Note that models saved with `save_only_topk_weights = True` are intended for merging or inference only and cannot be used to resume training.
- VB-LoRA has two sets of training parameters: vector bank parameters and logit parameters. In practice, we found that logit parameters require a higher learning rate, while vector bank parameters require a lower learning rate. When using the AdamW optimizer, typical learning rates are 0.01 for logits and 0.001 for vector bank parameters.
## VBLoRAConfig
[[autodoc]] tuners.vblora.config.VBLoRAConfig
## VBLoRAModel
[[autodoc]] tuners.vblora.model.VBLoRAModel
================================================
FILE: docs/source/package_reference/vera.md
================================================
# VeRA: Vector-based Random Matrix Adaptation
[VeRA](https://huggingface.co/papers/2310.11454) is a parameter-efficient fine-tuning technique that is similar to LoRA but requires even fewer extra parameters while promising similar or even better performance. As such, it is particularly useful when the parameter budget is very limited, e.g. when scaling to very large models. The reduction of the count of trainable parameters is achieved by sharing the same low-rank matrices across all layers, and only training two additional vectors per layer.
When saving the adapter parameters, it's possible to eschew storing the low rank matrices by setting `save_projection=False` on the `VeraConfig`. In that case, these matrices will be restored based on the fixed random seed from the `projection_prng_key` argument. This cuts down on the size of the checkpoint, but we cannot guarantee reproducibility on all devices and for all future versions of PyTorch. If you want to ensure reproducibility, set `save_projection=True` (which is the default).
To handle different shapes of adapted layers, VeRA initializes shared A and B matrices with the largest required size for each dimension. During the forward pass, submatrices A and B for a given layer are sliced out from these shared matrices and used as described in the paper. For example, adapting two linear layers of shapes (100, 20) and (80, 50) will create A and B matrices of shapes (rank, 50) and (100, rank) respectively. Then, to adapt a layer of shape (100, 20), submatrices A and B of shapes (rank, 20) and (100, rank) will be extracted.
VeRA currently has the following constraint:
- Only `nn.Linear` layers are supported.
The abstract from the paper is:
> Low-rank adapation (LoRA) is a popular method that reduces the number of trainable parameters when finetuning large language models, but still faces acute storage challenges when scaling to even larger models or deploying numerous per-user or per-task adapted models. In this work, we present Vector-based Random Matrix Adaptation (VeRA), which significantly reduces the number of trainable parameters compared to LoRA, yet maintains the same performance. It achieves this by using a single pair of low-rank matrices shared across all layers and learning small scaling vectors instead. We demonstrate its effectiveness on the GLUE and E2E benchmarks, image classification tasks, and show its application in instruction-tuning of 7B and 13B language models.
## VeRAConfig
[[autodoc]] tuners.vera.config.VeraConfig
## VeRAModel
[[autodoc]] tuners.vera.model.VeraModel
================================================
FILE: docs/source/package_reference/waveft.md
================================================
# WaveFT: Wavelet Fine-Tuning
[WaveFT](https://huggingface.co/papers/2505.12532) is a novel parameter-efficient fine-tuning (PEFT) method that introduces sparse updates in the **wavelet domain** of residual matrices. Unlike LoRA, which is constrained by discrete low-rank choices, WaveFT enables fine-grained control over the number of trainable parameters by directly learning a sparse set of coefficients in the transformed space. These coefficients are then mapped back to the weight domain via the Inverse Discrete Wavelet Transform (IDWT), producing high-rank updates without incurring inference overhead.
WaveFT currently has the following constraint:
- Only `nn.Linear` layers are supported.
The abstract from the paper is:
>Efficiently adapting large foundation models is critical, especially with tight compute and memory budgets. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA offer limited granularity and effectiveness in few-parameter regimes. We propose Wavelet Fine-Tuning (WaveFT), a novel PEFT method that learns highly sparse updates in the wavelet domain of residual matrices. WaveFT allows precise control of trainable parameters, offering fine-grained capacity adjustment and excelling with remarkably low parameter count, potentially far fewer than LoRA’s minimum—ideal for extreme parameter-efficient scenarios. Evaluated on personalized text-to-image generation using Stable Diffusion XL as baseline, WaveFT significantly outperforms LoRA and other PEFT methods, especially at low parameter counts; achieving superior subject fidelity, prompt alignment, and image diversity.
## WaveFTConfig
[[autodoc]] tuners.waveft.config.WaveFTConfig
## WaveFTModel
[[autodoc]] tuners.waveft.model.WaveFTModel
================================================
FILE: docs/source/package_reference/xlora.md
================================================
# X-LoRA
Mixture of LoRA Experts ([X-LoRA](https://huggingface.co/papers/2402.07148)) is a PEFT method enabling sparse or dense mixture of LoRA experts based on a high granularity (token, layer, sequence) scalings matrix. This leverages frozen LoRA adapters and a frozen base model to drastically reduces the number of parameters that need to be fine-tuned.
A unique aspect of X-LoRA is its versatility: it can be applied to any `transformers` base model with LoRA adapters. This means that, despite the mixture of experts strategy, no changes to the model code must be made.
The below graphic demonstrates how the scalings change for different prompts for each token. This highlights the activation of different adapters as the generation progresses and the sequence creates new context.
The abstract from the paper is:
*We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and create never-before-used deep layer-wise combinations to solve tasks. The design is inspired by the biological principles of universality and diversity, where neural network building blocks are reused in different hierarchical manifestations. Hence, the X-LoRA model can be easily implemented for any existing large language model (LLM) without a need for modifications of the underlying structure. We develop a tailored X-LoRA model that offers scientific capabilities including forward/inverse analysis tasks and enhanced reasoning capability, focused on biomaterial analysis, protein mechanics and design. The impact of this work include access to readily expandable and adaptable models with strong domain knowledge and the capability to integrate across areas of knowledge. Featuring experts in biology, mathematics, reasoning, bio-inspired materials, mechanics and materials, chemistry, protein biophysics, mechanics and quantum-mechanics based molecular properties, we conduct a series of physics-focused case studies. We examine knowledge recall, protein mechanics forward/inverse tasks, protein design, adversarial agentic modeling including ontological knowledge graph construction, as well as molecular design. The model is capable not only of making quantitative predictions of nanomechanical properties of proteins or quantum mechanical molecular properties, but also reasons over the results and correctly predicts likely mechanisms that explain distinct molecular behaviors.*.
Please cite X-LoRA as:
```bibtex
@article{10.1063/5.0203126,
author = {Buehler, Eric L. and Buehler, Markus J.},
title = "{X-LoRA: Mixture of low-rank adapter experts, a flexible framework for large language models with applications in protein mechanics and molecular design}",
journal = {APL Machine Learning},
volume = {2},
number = {2},
pages = {026119},
year = {2024},
month = {05},
abstract = "{We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and create never-before-used deep layer-wise combinations to solve tasks. The design is inspired by the biological principles of universality and diversity, where neural network building blocks are reused in different hierarchical manifestations. Hence, the X-LoRA model can be easily implemented for any existing large language model without a need for modifications of the underlying structure. We develop a tailored X-LoRA model that offers scientific capabilities, including forward/inverse analysis tasks and enhanced reasoning capability, focused on biomaterial analysis, protein mechanics, and design. The impact of this work includes access to readily expandable and adaptable models with strong domain knowledge and the capability to integrate across areas of knowledge. Featuring experts in biology, mathematics, reasoning, bio-inspired materials, mechanics and materials, chemistry, protein biophysics, mechanics, and quantum-mechanics based molecular properties, we conduct a series of physics-focused case studies. We examine knowledge recall, protein mechanics forward/inverse tasks, protein design, adversarial agentic modeling including ontological knowledge graph construction, and molecular design. The model is capable not only of making quantitative predictions of nanomechanical properties of proteins or quantum mechanical molecular properties but also reasoning over the results and correctly predicting likely mechanisms that explain distinct molecular behaviors.}",
issn = {2770-9019},
doi = {10.1063/5.0203126},
url = {https://doi.org/10.1063/5.0203126},
eprint = {https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0203126/19964043/026119\_1\_5.0203126.pdf},
}
```
## XLoraConfig
[[autodoc]] tuners.xlora.config.XLoraConfig
## XLoraModel
[[autodoc]] tuners.xlora.model.XLoraModel
================================================
FILE: docs/source/quicktour.md
================================================
# Quicktour
PEFT offers parameter-efficient methods for finetuning large pretrained models. The traditional paradigm is to finetune all of a model's parameters for each downstream task, but this is becoming exceedingly costly and impractical because of the enormous number of parameters in models today. Instead, it is more efficient to train a smaller number of prompt parameters or use a reparametrization method like low-rank adaptation (LoRA) to reduce the number of trainable parameters.
This quicktour will show you PEFT's main features and how you can train or run inference on large models that would typically be inaccessible on consumer devices.
## Train
Each PEFT method is defined by a [`PeftConfig`] class that stores all the important parameters for building a [`PeftModel`]. For example, to train with LoRA, load and create a [`LoraConfig`] class and specify the following parameters:
- `task_type`: the task to train for (sequence-to-sequence language modeling in this case)
- `inference_mode`: whether you're using the model for inference or not
- `r`: the dimension of the low-rank matrices
- `lora_alpha`: the scaling factor for the low-rank matrices
- `lora_dropout`: the dropout probability of the LoRA layers
```python
from peft import LoraConfig, TaskType
peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)
```
> [!TIP]
> See the [`LoraConfig`] reference for more details about other parameters you can adjust, such as the modules to target or the bias type.
Once the [`LoraConfig`] is setup, create a [`PeftModel`] with the [`get_peft_model`] function. It takes a base model - which you can load from the Transformers library - and the [`LoraConfig`] containing the parameters for how to configure a model for training with LoRA.
Load the base model you want to finetune.
```python
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/mt0-large")
```
Wrap the base model and `peft_config` with the [`get_peft_model`] function to create a [`PeftModel`]. To get a sense of the number of trainable parameters in your model, use the [`print_trainable_parameters`] method.
```python
from peft import get_peft_model
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
"output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282"
```
Out of [bigscience/mt0-large's](https://huggingface.co/bigscience/mt0-large) 1.2B parameters, you're only training 0.19% of them!
That is it 🎉! Now you can train the model with the Transformers [`~transformers.Trainer`], Accelerate, or any custom PyTorch training loop.
For example, to train with the [`~transformers.Trainer`] class, setup a [`~transformers.TrainingArguments`] class with some training hyperparameters.
```py
training_args = TrainingArguments(
output_dir="your-name/bigscience/mt0-large-lora",
learning_rate=1e-3,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
num_train_epochs=2,
weight_decay=0.01,
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
)
```
Pass the model, training arguments, dataset, tokenizer, and any other necessary component to the [`~transformers.Trainer`], and call [`~transformers.Trainer.train`] to start training.
```py
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
processing_class=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics,
)
trainer.train()
```
### Save model
After your model is finished training, you can save your model to a directory using the [`~transformers.PreTrainedModel.save_pretrained`] function.
```py
model.save_pretrained("output_dir")
```
You can also save your model to the Hub (make sure you're logged in to your Hugging Face account first) with the [`~transformers.PreTrainedModel.push_to_hub`] function.
```python
from huggingface_hub import notebook_login
notebook_login()
model.push_to_hub("your-name/bigscience/mt0-large-lora")
```
Both methods only save the extra PEFT weights that were trained, meaning it is super efficient to store, transfer, and load. For example, this [facebook/opt-350m](https://huggingface.co/ybelkada/opt-350m-lora) model trained with LoRA only contains two files: `adapter_config.json` and `adapter_model.safetensors`. The `adapter_model.safetensors` file is just 6.3MB!
The adapter weights for a opt-350m model stored on the Hub are only ~6MB compared to the full size of the model weights, which can be ~700MB.
## Inference
> [!TIP]
> Take a look at the [AutoPeftModel](package_reference/auto_class) API reference for a complete list of available `AutoPeftModel` classes.
Easily load any PEFT-trained model for inference with the [`AutoPeftModel`] class and the [`~transformers.PreTrainedModel.from_pretrained`] method:
```py
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import torch
model = AutoPeftModelForCausalLM.from_pretrained("ybelkada/opt-350m-lora")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = model.to("cuda")
model.eval()
inputs = tokenizer("Preheat the oven to 350 degrees and place the cookie dough", return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])
"Preheat the oven to 350 degrees and place the cookie dough in the center of the oven. In a large bowl, combine the flour, baking powder, baking soda, salt, and cinnamon. In a separate bowl, combine the egg yolks, sugar, and vanilla."
```
For other tasks that aren't explicitly supported with an `AutoPeftModelFor` class - such as automatic speech recognition - you can still use the base [`AutoPeftModel`] class to load a model for the task.
```py
from peft import AutoPeftModel
model = AutoPeftModel.from_pretrained("smangrul/openai-whisper-large-v2-LORA-colab")
```
## Next steps
Now that you've seen how to train a model with one of the PEFT methods, we encourage you to try out some of the other methods like prompt tuning. The steps are very similar to the ones shown in the quicktour:
1. prepare a [`PeftConfig`] for a PEFT method
2. use the [`get_peft_model`] method to create a [`PeftModel`] from the configuration and base model
Then you can train it however you like! To load a PEFT model for inference, you can use the [`AutoPeftModel`] class.
Feel free to also take a look at the task guides if you're interested in training a model with another PEFT method for a specific task such as semantic segmentation, multilingual automatic speech recognition, DreamBooth, token classification, and more.
================================================
FILE: docs/source/task_guides/ia3.md
================================================
# IA3
[IA3](../conceptual_guides/ia3) multiplies the model's activations (the keys and values in the self-attention and encoder-decoder attention blocks, and the intermediate activation of the position-wise feedforward network) by three learned vectors. This PEFT method introduces an even smaller number of trainable parameters than LoRA which introduces weight matrices instead of vectors. The original model's parameters are kept frozen and only these vectors are updated. As a result, it is faster, cheaper and more efficient to finetune for a new downstream task.
This guide will show you how to train a sequence-to-sequence model with IA3 to *generate a sentiment* given some financial news.
> [!TIP]
> Some familiarity with the general process of training a sequence-to-sequence would be really helpful and allow you to focus on how to apply IA3. If you’re new, we recommend taking a look at the [Translation](https://huggingface.co/docs/transformers/tasks/translation) and [Summarization](https://huggingface.co/docs/transformers/tasks/summarization) guides first from the Transformers documentation. When you’re ready, come back and see how easy it is to drop PEFT in to your training!
## Dataset
You'll use the [zeroshot/twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset. This dataset contains financial tweets labeled with sentiment (bearish, bullish, or neutral). Take a look at the [dataset viewer](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment/viewer) for a better idea of the data and sentences you'll be working with.
Load the dataset with the [`~datasets.load_dataset`] function. This dataset only contains a train split, so use the [`~datasets.train_test_split`] function to create a train and validation split. Create a new `text_label` column so it is easier to understand what the `label` values `0`, `1`, and `2` mean.
```py
from datasets import load_dataset
ds = load_dataset("zeroshot/twitter-financial-news-sentiment")
ds = ds["train"].train_test_split(test_size=0.1)
ds["validation"] = ds["test"]
del ds["test"]
classes = ds["train"].features["label"].names
ds = ds.map(
lambda x: {"text_label": [classes[label] for label in x["label"]]},
batched=True,
num_proc=1,
)
ds["train"][0]
{'text': 'Morrisons reports first sales rise in four years',
'label': 1,
'text_label': 'bullish'}
```
Load a tokenizer and create a preprocessing function that:
1. tokenizes the inputs, pads and truncates the sequence to the `max_length`
2. apply the same tokenizer to the labels but with a shorter `max_length` that corresponds to the label
3. mask the padding tokens
```py
from transformers import AutoTokenizer
text_column = "text"
label_column = "text_label"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-large")
def preprocess_function(examples):
inputs = examples[text_column]
targets = examples[label_column]
model_inputs = tokenizer(inputs, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt")
labels = tokenizer(targets, max_length=3, padding="max_length", truncation=True, return_tensors="pt")
labels = labels["input_ids"]
labels[labels == tokenizer.pad_token_id] = -100
model_inputs["labels"] = labels
return model_inputs
```
Use the [`~datasets.Dataset.map`] function to apply the preprocessing function to the entire dataset.
```py
processed_ds = ds.map(
preprocess_function,
batched=True,
num_proc=1,
remove_columns=ds["train"].column_names,
load_from_cache_file=False,
desc="Running tokenizer on dataset",
)
```
Create a training and evaluation [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), and set `pin_memory=True` to speed up data transfer to the accelerator during training if your dataset samples are on a CPU.
```py
from torch.utils.data import DataLoader
from transformers import default_data_collator
train_ds = processed_ds["train"]
eval_ds = processed_ds["validation"]
batch_size = 8
train_dataloader = DataLoader(
train_ds, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
)
eval_dataloader = DataLoader(eval_ds, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)
```
## Model
Now you can load a pretrained model to use as the base model for IA3. This guide uses the [bigscience/mt0-large](https://huggingface.co/bigscience/mt0-large) model, but you can use any sequence-to-sequence model you like.
```py
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/mt0-large")
```
### PEFT configuration and model
All PEFT methods need a configuration that contains and specifies all the parameters for how the PEFT method should be applied. Create an [`IA3Config`] with the task type and set the inference mode to `False`. You can find additional parameters for this configuration in the [API reference](../package_reference/ia3#ia3config).
> [!TIP]
> Call the [`~PeftModel.print_trainable_parameters`] method to compare the number of trainable parameters of [`PeftModel`] versus the number of parameters in the base model!
Once the configuration is setup, pass it to the [`get_peft_model`] function along with the base model to create a trainable [`PeftModel`].
```py
from peft import IA3Config, get_peft_model
peft_config = IA3Config(task_type="SEQ_2_SEQ_LM")
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
"trainable params: 282,624 || all params: 1,229,863,936 || trainable%: 0.022980103060766553"
```
### Training
Set up an optimizer and learning rate scheduler.
```py
import torch
from transformers import get_linear_schedule_with_warmup
lr = 8e-3
num_epochs = 3
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=(len(train_dataloader) * num_epochs),
)
```
Move the model to the accelerator and create a training loop that reports the loss and perplexity for each epoch.
```py
from tqdm import tqdm
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model = model.to(device)
for epoch in range(num_epochs):
model.train()
total_loss = 0
for step, batch in enumerate(tqdm(train_dataloader)):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
eval_loss = 0
eval_preds = []
for step, batch in enumerate(tqdm(eval_dataloader)):
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
loss = outputs.loss
eval_loss += loss.detach().float()
eval_preds.extend(
tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)
)
eval_epoch_loss = eval_loss / len(eval_dataloader)
eval_ppl = torch.exp(eval_epoch_loss)
train_epoch_loss = total_loss / len(train_dataloader)
train_ppl = torch.exp(train_epoch_loss)
print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}")
```
## Share your model
After training is complete, you can upload your model to the Hub with the [`~transformers.PreTrainedModel.push_to_hub`] method. You'll need to login to your Hugging Face account first and enter your token when prompted.
```py
from huggingface_hub import notebook_login
account =
peft_model_id = f"{account}/mt0-large-ia3"
model.push_to_hub(peft_model_id)
```
## Inference
To load the model for inference, use the [`~AutoPeftModelForSeq2SeqLM.from_pretrained`] method. Let's also load a sentence of financial news from the dataset to generate a sentiment for.
```py
from peft import AutoPeftModelForSeq2SeqLM
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model = AutoPeftModelForSeq2SeqLM.from_pretrained("/mt0-large-ia3").to(device)
tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-large")
i = 15
inputs = tokenizer(ds["validation"][text_column][i], return_tensors="pt")
print(ds["validation"][text_column][i])
"The robust growth was the result of the inclusion of clothing chain Lindex in the Group in December 2007 ."
```
Call the [`~transformers.GenerationMixin.generate`] method to generate the predicted sentiment label.
```py
with torch.no_grad():
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
['positive']
```
================================================
FILE: docs/source/task_guides/lora_based_methods.md
================================================
# LoRA methods
A popular way to efficiently train large models is to insert (typically in the attention blocks) smaller trainable matrices that are a low-rank decomposition of the delta weight matrix to be learnt during finetuning. The pretrained model's original weight matrix is frozen and only the smaller matrices are updated during training. This reduces the number of trainable parameters, reducing memory usage and training time which can be very expensive for large models.
There are several different ways to express the weight matrix as a low-rank decomposition, but [Low-Rank Adaptation (LoRA)](../conceptual_guides/adapter#low-rank-adaptation-lora) is the most common method. The PEFT library supports several other LoRA variants, such as [Low-Rank Hadamard Product (LoHa)](../conceptual_guides/adapter#low-rank-hadamard-product-loha), [Low-Rank Kronecker Product (LoKr)](../conceptual_guides/adapter#low-rank-kronecker-product-lokr), and [Adaptive Low-Rank Adaptation (AdaLoRA)](../conceptual_guides/adapter#adaptive-low-rank-adaptation-adalora). You can learn more about how these methods work conceptually in the [Adapters](../conceptual_guides/adapter) guide. If you're interested in applying these methods to other tasks and use cases like semantic segmentation, token classification, take a look at our [notebook collection](https://huggingface.co/collections/PEFT/notebooks-6573b28b33e5a4bf5b157fc1)!
Additionally, PEFT supports the [X-LoRA](../conceptual_guides/adapter#mixture-of-lora-experts-x-lora) Mixture of LoRA Experts method.
This guide will show you how to quickly train an image classification model - with a low-rank decomposition method - to identify the class of food shown in an image.
> [!TIP]
> Some familiarity with the general process of training an image classification model would be really helpful and allow you to focus on the low-rank decomposition methods. If you're new, we recommend taking a look at the [Image classification](https://huggingface.co/docs/transformers/tasks/image_classification) guide first from the Transformers documentation. When you're ready, come back and see how easy it is to drop PEFT in to your training!
Before you begin, make sure you have all the necessary libraries installed.
```bash
pip install -q peft transformers datasets
```
## Dataset
In this guide, you'll use the [Food-101](https://huggingface.co/datasets/food101) dataset which contains images of 101 food classes (take a look at the [dataset viewer](https://huggingface.co/datasets/food101/viewer/default/train) to get a better idea of what the dataset looks like).
Load the dataset with the [`~datasets.load_dataset`] function.
```py
from datasets import load_dataset
ds = load_dataset("food101")
```
Each food class is labeled with an integer, so to make it easier to understand what these integers represent, you'll create a `label2id` and `id2label` dictionary to map the integer to its class label.
```py
labels = ds["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = i
id2label[i] = label
id2label[2]
"baklava"
```
Load an image processor to properly resize and normalize the pixel values of the training and evaluation images.
```py
from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
```
You can also use the image processor to prepare some transformation functions for data augmentation and pixel scaling.
```py
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
train_transforms = Compose(
[
RandomResizedCrop(image_processor.size["height"]),
RandomHorizontalFlip(),
ToTensor(),
normalize,
]
)
val_transforms = Compose(
[
Resize(image_processor.size["height"]),
CenterCrop(image_processor.size["height"]),
ToTensor(),
normalize,
]
)
def preprocess_train(example_batch):
example_batch["pixel_values"] = [train_transforms(image.convert("RGB")) for image in example_batch["image"]]
return example_batch
def preprocess_val(example_batch):
example_batch["pixel_values"] = [val_transforms(image.convert("RGB")) for image in example_batch["image"]]
return example_batch
```
Define the training and validation datasets, and use the [`~datasets.Dataset.set_transform`] function to apply the transformations on-the-fly.
```py
train_ds = ds["train"]
val_ds = ds["validation"]
train_ds.set_transform(preprocess_train)
val_ds.set_transform(preprocess_val)
```
Finally, you'll need a data collator to create a batch of training and evaluation data and convert the labels to `torch.tensor` objects.
```py
import torch
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
labels = torch.tensor([example["label"] for example in examples])
return {"pixel_values": pixel_values, "labels": labels}
```
## Model
Now let's load a pretrained model to use as the base model. This guide uses the [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) model, but you can use any image classification model you want. Pass the `label2id` and `id2label` dictionaries to the model so it knows how to map the integer labels to their class labels, and you can optionally pass the `ignore_mismatched_sizes=True` parameter if you're finetuning a checkpoint that has already been finetuned.
```py
from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
model = AutoModelForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
label2id=label2id,
id2label=id2label,
ignore_mismatched_sizes=True,
)
```
### PEFT configuration and model
Every PEFT method requires a configuration that holds all the parameters specifying how the PEFT method should be applied. Once the configuration is setup, pass it to the [`~peft.get_peft_model`] function along with the base model to create a trainable [`PeftModel`].
> [!TIP]
> Call the [`~PeftModel.print_trainable_parameters`] method to compare the number of parameters of [`PeftModel`] versus the number of parameters in the base model!
[LoRA](../conceptual_guides/adapter#low-rank-adaptation-lora) decomposes the weight update matrix into *two* smaller matrices. The size of these low-rank matrices is determined by its *rank* or `r`. A higher rank means the model has more parameters to train, but it also means the model has more learning capacity. You'll also want to specify the `target_modules` which determine where the smaller matrices are inserted. For this guide, you'll target the *query* and *value* matrices of the attention blocks. Other important parameters to set are `lora_alpha` (scaling factor), `bias` (whether `none`, `all` or only the LoRA bias parameters should be trained), and `modules_to_save` (the modules apart from the LoRA layers to be trained and saved). All of these parameters - and more - are found in the [`LoraConfig`].
```py
from peft import LoraConfig, get_peft_model
config = LoraConfig(
r=16,
lora_alpha=16,
target_modules=["query", "value"],
lora_dropout=0.1,
bias="none",
modules_to_save=["classifier"],
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
"trainable params: 667,493 || all params: 86,543,818 || trainable%: 0.7712775047664294"
```
[LoHa](../conceptual_guides/adapter#low-rank-hadamard-product-loha) decomposes the weight update matrix into *four* smaller matrices and each pair of smaller matrices is combined with the Hadamard product. This allows the weight update matrix to keep the same number of trainable parameters when compared to LoRA, but with a higher rank (`r^2` for LoHA when compared to `2*r` for LoRA). The size of the smaller matrices is determined by its *rank* or `r`. You'll also want to specify the `target_modules` which determines where the smaller matrices are inserted. For this guide, you'll target the *query* and *value* matrices of the attention blocks. Other important parameters to set are `alpha` (scaling factor), and `modules_to_save` (the modules apart from the LoHa layers to be trained and saved). All of these parameters - and more - are found in the [`LoHaConfig`].
```py
from peft import LoHaConfig, get_peft_model
config = LoHaConfig(
r=16,
alpha=16,
target_modules=["query", "value"],
module_dropout=0.1,
modules_to_save=["classifier"],
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
"trainable params: 1,257,317 || all params: 87,133,642 || trainable%: 1.4429753779831676"
```
[LoKr](../conceptual_guides/adapter#low-rank-kronecker-product-lokr) expresses the weight update matrix as a decomposition of a Kronecker product, creating a block matrix that is able to preserve the rank of the original weight matrix. The size of the smaller matrices are determined by its *rank* or `r`. You'll also want to specify the `target_modules` which determines where the smaller matrices are inserted. For this guide, you'll target the *query* and *value* matrices of the attention blocks. Other important parameters to set are `alpha` (scaling factor), and `modules_to_save` (the modules apart from the LoKr layers to be trained and saved). All of these parameters - and more - are found in the [`LoKrConfig`].
```py
from peft import LoKrConfig, get_peft_model
config = LoKrConfig(
r=16,
alpha=16,
target_modules=["query", "value"],
module_dropout=0.1,
modules_to_save=["classifier"],
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
"trainable params: 116,069 || all params: 87,172,042 || trainable%: 0.13314934162033282"
```
[AdaLoRA](../conceptual_guides/adapter#adaptive-low-rank-adaptation-adalora) efficiently manages the LoRA parameter budget by assigning important weight matrices more parameters and pruning less important ones. In contrast, LoRA evenly distributes parameters across all modules. You can control the average desired *rank* or `r` of the matrices, and which modules to apply AdaLoRA to with `target_modules`. Other important parameters to set are `lora_alpha` (scaling factor), and `modules_to_save` (the modules apart from the AdaLoRA layers to be trained and saved). All of these parameters - and more - are found in the [`AdaLoraConfig`].
```py
from peft import AdaLoraConfig, get_peft_model
config = AdaLoraConfig(
r=8,
init_r=12,
tinit=200,
tfinal=1000,
deltaT=10,
target_modules=["query", "value"],
modules_to_save=["classifier"],
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
"trainable params: 520,325 || all params: 87,614,722 || trainable%: 0.5938785036606062"
```
### Training
For training, let's use the [`~transformers.Trainer`] class from Transformers. The [`Trainer`] contains a PyTorch training loop, and when you're ready, call [`~transformers.Trainer.train`] to start training. To customize the training run, configure the training hyperparameters in the [`~transformers.TrainingArguments`] class. With LoRA-like methods, you can afford to use a higher batch size and learning rate.
> [!WARNING]
> AdaLoRA has an [`~AdaLoraModel.update_and_allocate`] method that should be called at each training step to update the parameter budget and mask, otherwise the adaptation step is not performed. This requires writing a custom training loop or subclassing the [`~transformers.Trainer`] to incorporate this method. As an example, take a look at this [custom training loop](https://github.com/huggingface/peft/blob/912ad41e96e03652cabf47522cd876076f7a0c4f/examples/conditional_generation/peft_adalora_seq2seq.py#L120).
```py
from transformers import TrainingArguments, Trainer
account = "stevhliu"
peft_model_id = f"{account}/google/vit-base-patch16-224-in21k-lora"
batch_size = 128
args = TrainingArguments(
peft_model_id,
remove_unused_columns=False,
eval_strategy="epoch",
save_strategy="epoch",
learning_rate=5e-3,
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=4,
per_device_eval_batch_size=batch_size,
fp16=True,
num_train_epochs=5,
logging_steps=10,
load_best_model_at_end=True,
label_names=["labels"],
)
```
Begin training with [`~transformers.Trainer.train`].
```py
trainer = Trainer(
model,
args,
train_dataset=train_ds,
eval_dataset=val_ds,
processing_class=image_processor,
data_collator=collate_fn,
)
trainer.train()
```
## Share your model
Once training is complete, you can upload your model to the Hub with the [`~transformers.PreTrainedModel.push_to_hub`] method. You’ll need to login to your Hugging Face account first and enter your token when prompted.
```py
from huggingface_hub import notebook_login
notebook_login()
```
Call [`~transformers.PreTrainedModel.push_to_hub`] to save your model to your repositoy.
```py
model.push_to_hub(peft_model_id)
```
## Inference
Let's load the model from the Hub and test it out on a food image.
```py
from peft import PeftConfig, PeftModel
from transformers import AutoImageProcessor
from PIL import Image
import requests
config = PeftConfig.from_pretrained("stevhliu/vit-base-patch16-224-in21k-lora")
model = AutoModelForImageClassification.from_pretrained(
config.base_model_name_or_path,
label2id=label2id,
id2label=id2label,
ignore_mismatched_sizes=True,
)
model = PeftModel.from_pretrained(model, "stevhliu/vit-base-patch16-224-in21k-lora")
url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/beignets.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
image
```
Convert the image to RGB and return the underlying PyTorch tensors.
```py
encoding = image_processor(image.convert("RGB"), return_tensors="pt")
```
Now run the model and return the predicted class!
```py
with torch.no_grad():
outputs = model(**encoding)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
"Predicted class: beignets"
```
================================================
FILE: docs/source/task_guides/prompt_based_methods.md
================================================
# Prompt-based methods
A prompt can describe a task or provide an example of a task you want the model to learn. Instead of manually creating these prompts, soft prompting methods add learnable parameters to the input embeddings that can be optimized for a specific task while keeping the pretrained model's parameters frozen. This makes it both faster and easier to finetune large language models (LLMs) for new downstream tasks.
The PEFT library supports several types of prompting methods (p-tuning, prefix tuning, prompt tuning) and you can learn more about how these methods work conceptually in the [Soft prompts](../conceptual_guides/prompting) guide. If you're interested in applying these methods to other tasks and use cases, take a look at our [notebook collection](https://huggingface.co/spaces/PEFT/soft-prompting)!
This guide will show you how to train a causal language model - with a soft prompting method - to *generate a classification* for whether a tweet is a complaint or not.
> [!TIP]
> Some familiarity with the general process of training a causal language model would be really helpful and allow you to focus on the soft prompting methods. If you're new, we recommend taking a look at the [Causal language modeling](https://huggingface.co/docs/transformers/tasks/language_modeling) guide first from the Transformers documentation. When you're ready, come back and see how easy it is to drop PEFT in to your training!
Before you begin, make sure you have all the necessary libraries installed.
```bash
pip install -q peft transformers datasets
```
## Dataset
For this guide, you'll use the `twitter_complaints` subset of the [RAFT](https://huggingface.co/datasets/ought/raft) dataset. The `twitter_complaints` subset contains tweets labeled as `complaint` and `no complaint` and you can check out the [dataset viewer](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) for a better idea of what the data looks like.
Use the [`~datasets.load_dataset`] function to load the dataset and create a new `text_label` column so it is easier to understand what the `Label` values, `1` and `2` mean.
```py
from datasets import load_dataset
ds = load_dataset(
"parquet",
data_files={
"train": "hf://datasets/ought/raft@refs/convert/parquet/twitter_complaints/train/0000.parquet",
"test": "hf://datasets/ought/raft@refs/convert/parquet/twitter_complaints/test/0000.parquet"
}
)
classes = [k.replace("_", " ") for k in ds["train"].features["Label"].names]
ds = ds.map(
lambda x: {"text_label": [classes[label] for label in x["Label"]]},
batched=True,
num_proc=1,
)
ds["train"][0]
{"Tweet text": "@HMRCcustomers No this is my first job", "ID": 0, "Label": 2, "text_label": "no complaint"}
```
Load a tokenizer, define the padding token to use, and determine the maximum length of the tokenized label.
```py
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
target_max_length = max([len(tokenizer(class_label)["input_ids"]) for class_label in classes])
print(target_max_length)
```
Create a preprocessing function that tokenizes the tweet text and labels, pad the inputs and labels in each batch, create an attention mask, and truncate sequences to the `max_length`. Then convert the `input_ids`, `attention_mask`, and `labels` to PyTorch tensors.
```py
import torch
max_length = 64
def preprocess_function(examples, text_column="Tweet text", label_column="text_label"):
batch_size = len(examples[text_column])
inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]]
targets = [str(x) for x in examples[label_column]]
model_inputs = tokenizer(inputs)
labels = tokenizer(targets)
classes = [k.replace("_", " ") for k in ds["train"].features["Label"].names]
for i in range(batch_size):
sample_input_ids = model_inputs["input_ids"][i]
label_input_ids = labels["input_ids"][i]
model_inputs["input_ids"][i] = [tokenizer.pad_token_id] * (
max_length - len(sample_input_ids)
) + sample_input_ids
model_inputs["attention_mask"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[
"attention_mask"
][i]
labels["input_ids"][i] = [-100] * (max_length - len(label_input_ids)) + label_input_ids
model_inputs["input_ids"][i] = torch.tensor(model_inputs["input_ids"][i][:max_length])
model_inputs["attention_mask"][i] = torch.tensor(model_inputs["attention_mask"][i][:max_length])
labels["input_ids"][i] = torch.tensor(labels["input_ids"][i][:max_length])
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
Apply the preprocessing function to the entire dataset with the [`~datasets.Dataset.map`] function, and remove the unprocessed columns because the model won't need them.
```py
processed_ds = ds.map(
preprocess_function,
batched=True,
num_proc=1,
remove_columns=ds["train"].column_names,
load_from_cache_file=False,
desc="Running tokenizer on dataset",
)
```
Finally, create a training and evaluation [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader). You can set `pin_memory=True` to speed up the data transfer to the GPU during training if the samples in your dataset are on a CPU.
```py
from torch.utils.data import DataLoader
from transformers import default_data_collator
train_ds = processed_ds["train"]
eval_ds = processed_ds["test"]
batch_size = 16
train_dataloader = DataLoader(train_ds, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)
eval_dataloader = DataLoader(eval_ds, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)
```
## Model
Now let's load a pretrained model to use as the base model for the soft prompt method. This guide uses the [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) model, but you can use any causal language model you want.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
```
### PEFT configuration and model
For any PEFT method, you'll need to create a configuration which contains all the parameters that specify how the PEFT method should be applied. Once the configuration is setup, pass it to the [`~peft.get_peft_model`] function along with the base model to create a trainable [`PeftModel`].
> [!TIP]
> Call the [`~PeftModel.print_trainable_parameters`] method to compare the number of trainable parameters of [`PeftModel`] versus the number of parameters in the base model!
[P-tuning](../conceptual_guides/prompting#p-tuning) adds a trainable embedding tensor where the prompt tokens can be added anywhere in the input sequence. Create a [`PromptEncoderConfig`] with the task type, the number of virtual tokens to add and learn, and the hidden size of the encoder for learning the prompt parameters.
```py
from peft import PromptEncoderConfig, get_peft_model
peft_config = PromptEncoderConfig(task_type="CAUSAL_LM", num_virtual_tokens=20, encoder_hidden_size=128)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
"trainable params: 300,288 || all params: 559,514,880 || trainable%: 0.05366935013417338"
```
[Prefix tuning](../conceptual_guides/prompting#prefix-tuning) adds task-specific parameters in all of the model layers, which are optimized by a separate feed-forward network. Create a [`PrefixTuningConfig`] with the task type and number of virtual tokens to add and learn.
```py
from peft import PrefixTuningConfig, get_peft_model
peft_config = PrefixTuningConfig(task_type="CAUSAL_LM", num_virtual_tokens=20)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
"trainable params: 983,040 || all params: 560,197,632 || trainable%: 0.1754809274167014"
```
[Prompt tuning](../conceptual_guides/prompting#prompt-tuning) formulates all tasks as a *generation* task and it adds a task-specific prompt to the input which is updated independently. The `prompt_tuning_init_text` parameter specifies how to finetune the model (in this case, it is classifying whether tweets are complaints or not). For the best results, the `prompt_tuning_init_text` should have the same number of tokens that should be predicted. To do this, you can set `num_virtual_tokens` to the number of tokens of the `prompt_tuning_init_text`.
Create a [`PromptTuningConfig`] with the task type, the initial prompt tuning text to train the model with, the number of virtual tokens to add and learn, and a tokenizer.
```py
from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
prompt_tuning_init_text = "Classify if the tweet is a complaint or no complaint.\n"
peft_config = PromptTuningConfig(
task_type="CAUSAL_LM",
prompt_tuning_init=PromptTuningInit.TEXT,
num_virtual_tokens=len(tokenizer(prompt_tuning_init_text)["input_ids"]),
prompt_tuning_init_text=prompt_tuning_init_text,
tokenizer_name_or_path="bigscience/bloomz-560m",
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
"trainable params: 8,192 || all params: 559,222,784 || trainable%: 0.0014648902430985358"
```
### Training
Set up an optimizer and learning rate scheduler.
```py
from transformers import get_linear_schedule_with_warmup
lr = 3e-2
num_epochs = 50
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=(len(train_dataloader) * num_epochs),
)
```
Move the model to the GPU and create a training loop that reports the loss and perplexity for each epoch.
```py
from tqdm import tqdm
device = "cuda"
model = model.to(device)
for epoch in range(num_epochs):
model.train()
total_loss = 0
for step, batch in enumerate(tqdm(train_dataloader)):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
eval_loss = 0
for step, batch in enumerate(tqdm(eval_dataloader)):
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
loss = outputs.loss
eval_loss += loss.detach().float()
eval_epoch_loss = eval_loss / len(eval_dataloader)
eval_ppl = torch.exp(eval_epoch_loss)
train_epoch_loss = total_loss / len(train_dataloader)
train_ppl = torch.exp(train_epoch_loss)
print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}")
```
## Share your model
Once training is complete, you can upload your model to the Hub with the [`~transformers.PreTrainedModel.push_to_hub`] method. You'll need to login to your Hugging Face account first and enter your token when prompted.
```py
from huggingface_hub import notebook_login
account =
peft_model_id = f"{account}/bloomz-560-m-peft-method"
model.push_to_hub(peft_model_id)
```
If you check the model file size in the repository, you’ll see that it is a lot smaller than a full sized model!
For example, the adapter weights for a opt-350m model stored on the Hub are only ~6MB compared to the full model size which can be ~700MB.
## Inference
Let's load the model for inference and test it out on a tweet!
```py
from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained("peft_model_id").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
i = 15
inputs = tokenizer(f'{text_column} : {ds["test"][i]["Tweet text"]} Label : ', return_tensors="pt")
print(ds["test"][i]["Tweet text"])
"@NYTsupport i have complained a dozen times & yet my papers are still thrown FAR from my door. Why is this so hard to resolve?"
```
Call the [`~transformers.GenerationMixin.generate`] method to generate the predicted classification label.
```py
with torch.no_grad():
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
"['Tweet text : @NYTsupport i have complained a dozen times & yet my papers are still thrown FAR from my door. Why is this so hard to resolve? Label : complaint']"
```
================================================
FILE: docs/source/tutorial/peft_integrations.md
================================================
# PEFT integrations
PEFT's practical benefits extends to other Hugging Face libraries like [Diffusers](https://hf.co/docs/diffusers) and [Transformers](https://hf.co/docs/transformers). One of the main benefits of PEFT is that an adapter file generated by a PEFT method is a lot smaller than the original model, which makes it super easy to manage and use multiple adapters. You can use one pretrained base model for multiple tasks by simply loading a new adapter finetuned for the task you're solving. Or you can combine multiple adapters with a text-to-image diffusion model to create new effects.
This tutorial will show you how PEFT can help you manage adapters in Diffusers and Transformers.
## Diffusers
Diffusers is a generative AI library for creating images and videos from text or images with diffusion models. LoRA is an especially popular training method for diffusion models because you can very quickly train and share diffusion models to generate images in new styles. To make it easier to use and try multiple LoRA models, Diffusers uses the PEFT library to help manage different adapters for inference.
For example, load a base model and then load the [artificialguybr/3DRedmond-V1](https://huggingface.co/artificialguybr/3DRedmond-V1) adapter for inference with the [`load_lora_weights`](https://huggingface.co/docs/diffusers/v0.24.0/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.load_lora_weights) method. The `adapter_name` argument in the loading method is enabled by PEFT and allows you to set a name for the adapter so it is easier to reference.
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"peft-internal-testing/artificialguybr__3DRedmond-V1",
weight_name="3DRedmond-3DRenderStyle-3DRenderAF.safetensors",
adapter_name="3d"
)
image = pipeline("sushi rolls shaped like kawaii cat faces").images[0]
image
```
Now let's try another cool LoRA model, [ostris/super-cereal-sdxl-lora](https://huggingface.co/ostris/super-cereal-sdxl-lora). All you need to do is load and name this new adapter with `adapter_name`, and use the [`set_adapters`](https://huggingface.co/docs/diffusers/api/loaders/unet#diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters) method to set it as the currently active adapter.
```py
pipeline.load_lora_weights(
"ostris/super-cereal-sdxl-lora",
weight_name="cereal_box_sdxl_v1.safetensors",
adapter_name="cereal"
)
pipeline.set_adapters("cereal")
image = pipeline("sushi rolls shaped like kawaii cat faces").images[0]
image
```
Finally, you can call the [`disable_lora`](https://huggingface.co/docs/diffusers/api/loaders/unet#diffusers.loaders.UNet2DConditionLoadersMixin.disable_lora) method to restore the base model.
```py
pipeline.disable_lora()
```
Learn more about how PEFT supports Diffusers in the [Inference with PEFT](https://huggingface.co/docs/diffusers/tutorials/using_peft_for_inference) tutorial.
## Transformers
🤗 [Transformers](https://hf.co/docs/transformers) is a collection of pretrained models for all types of tasks in all modalities. You can load these models for training or inference. Many of the models are large language models (LLMs), so it makes sense to integrate PEFT with Transformers to manage and train adapters.
Load a base pretrained model to train.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
```
Next, add an adapter configuration to specify how to adapt the model parameters. Call the [`~PeftModel.add_adapter`] method to add the configuration to the base model.
```py
from peft import LoraConfig
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
task_type="CAUSAL_LM"
)
model.add_adapter(peft_config)
```
Now you can train the model with Transformer's [`~transformers.Trainer`] class or whichever training framework you prefer.
To use the newly trained model for inference, the [`~transformers.AutoModel`] class uses PEFT on the backend to load the adapter weights and configuration file into a base pretrained model.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/opt-350m-lora")
```
Alternatively, you can use transformers [Pipelines](https://huggingface.co/docs/transformers/en/main_classes/pipelines) to load the model for conveniently running inference:
```py
from transformers import pipeline
model = pipeline("text-generation", "peft-internal-testing/opt-350m-lora")
print(model("Hello World"))
```
If you're interested in comparing or using more than one adapter, you can call the [`~PeftModel.add_adapter`] method to add the adapter configuration to the base model. The only requirement is the adapter type must be the same (you can't mix a LoRA and LoHa adapter).
```py
from transformers import AutoModelForCausalLM
from peft import LoraConfig
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
model.add_adapter(lora_config_1, adapter_name="adapter_1")
```
Call [`~PeftModel.add_adapter`] again to attach a new adapter to the base model.
```py
model.add_adapter(lora_config_2, adapter_name="adapter_2")
```
Then you can use [`~PeftModel.set_adapter`] to set the currently active adapter.
```py
model.set_adapter("adapter_1")
output = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
```
To disable the adapter, call the [disable_adapters](https://github.com/huggingface/transformers/blob/4e3490f79b40248c53ee54365a9662611e880892/src/transformers/integrations/peft.py#L313) method.
```py
model.disable_adapters()
```
The [enable_adapters](https://github.com/huggingface/transformers/blob/4e3490f79b40248c53ee54365a9662611e880892/src/transformers/integrations/peft.py#L336) can be used to enable the adapters again.
If you're curious, check out the [Load and train adapters with PEFT](https://huggingface.co/docs/transformers/main/peft) tutorial to learn more.
================================================
FILE: docs/source/tutorial/peft_model_config.md
================================================
# PEFT configurations and models
The sheer size of today's large pretrained models - which commonly have billions of parameters - presents a significant training challenge because they require more storage space and more computational power to crunch all those calculations. You'll need access to powerful GPUs or TPUs to train these large pretrained models which is expensive, not widely accessible to everyone, not environmentally friendly, and not very practical. PEFT methods address many of these challenges. There are several types of PEFT methods (soft prompting, matrix decomposition, adapters), but they all focus on the same thing, reduce the number of trainable parameters. This makes it more accessible to train and store large models on consumer hardware.
The PEFT library is designed to help you quickly train large models on free or low-cost GPUs, and in this tutorial, you'll learn how to setup a configuration to apply a PEFT method to a pretrained base model for training. Once the PEFT configuration is setup, you can use any training framework you like (Transformer's [`~transformers.Trainer`] class, [Accelerate](https://hf.co/docs/accelerate), a custom PyTorch training loop).
## PEFT configurations
> [!TIP]
> Learn more about the parameters you can configure for each PEFT method in their respective API reference page.
A configuration stores important parameters that specify how a particular PEFT method should be applied.
For example, take a look at the following [`LoraConfig`](https://huggingface.co/ybelkada/opt-350m-lora/blob/main/adapter_config.json) for applying LoRA and [`PromptEncoderConfig`](https://huggingface.co/smangrul/roberta-large-peft-p-tuning/blob/main/adapter_config.json) for applying p-tuning (these configuration files are already JSON-serialized). Whenever you load a PEFT adapter, it is a good idea to check whether it has an associated adapter_config.json file which is required.
```json
{
"base_model_name_or_path": "facebook/opt-350m", #base model to apply LoRA to
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"lora_alpha": 32,
"lora_dropout": 0.05,
"modules_to_save": null,
"peft_type": "LORA", #PEFT method type
"r": 16,
"revision": null,
"target_modules": [
"q_proj", #model modules to apply LoRA to (query and value projection layers)
"v_proj"
],
"task_type": "CAUSAL_LM" #type of task to train model on
}
```
You can create your own configuration for training by initializing a [`LoraConfig`].
```py
from peft import LoraConfig, TaskType
lora_config = LoraConfig(
r=16,
target_modules=["q_proj", "v_proj"],
task_type=TaskType.CAUSAL_LM,
lora_alpha=32,
lora_dropout=0.05
)
```
```json
{
"base_model_name_or_path": "roberta-large", #base model to apply p-tuning to
"encoder_dropout": 0.0,
"encoder_hidden_size": 128,
"encoder_num_layers": 2,
"encoder_reparameterization_type": "MLP",
"inference_mode": true,
"num_attention_heads": 16,
"num_layers": 24,
"num_transformer_submodules": 1,
"num_virtual_tokens": 20,
"peft_type": "P_TUNING", #PEFT method type
"task_type": "SEQ_CLS", #type of task to train model on
"token_dim": 1024
}
```
You can create your own configuration for training by initializing a [`PromptEncoderConfig`].
```py
from peft import PromptEncoderConfig, TaskType
p_tuning_config = PromptEncoderConfig(
encoder_reparameterization_type="MLP",
encoder_hidden_size=128,
num_attention_heads=16,
num_layers=24,
num_transformer_submodules=1,
num_virtual_tokens=20,
token_dim=1024,
task_type=TaskType.SEQ_CLS
)
```
## PEFT models
With a PEFT configuration in hand, you can now apply it to any pretrained model to create a [`PeftModel`]. Choose from any of the state-of-the-art models from the [Transformers](https://hf.co/docs/transformers) library, a custom model, and even new and unsupported transformer architectures.
For this tutorial, load a base [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) model to finetune.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
```
Use the [`get_peft_model`] function to create a [`PeftModel`] from the base facebook/opt-350m model and the `lora_config` you created earlier.
```py
from peft import get_peft_model
lora_model = get_peft_model(model, lora_config)
lora_model.print_trainable_parameters()
"trainable params: 1,572,864 || all params: 332,769,280 || trainable%: 0.472659014678278"
```
> [!WARNING]
> When calling [`get_peft_model`], the base model will be modified *in-place*. That means, when calling [`get_peft_model`] on a model that was already modified in the same way before, this model will be further mutated. Therefore, if you would like to modify your PEFT configuration after having called [`get_peft_model()`] before, you would first have to unload the model with [`~LoraModel.unload`] and then call [`get_peft_model()`] with your new configuration. Alternatively, you can re-initialize the model to ensure a fresh, unmodified state before applying a new PEFT configuration.
Now you can train the [`PeftModel`] with your preferred training framework! After training, you can save your model locally with [`~PeftModel.save_pretrained`] or upload it to the Hub with the [`~transformers.PreTrainedModel.push_to_hub`] method.
```py
# save locally
lora_model.save_pretrained("your-name/opt-350m-lora")
# push to Hub
lora_model.push_to_hub("your-name/opt-350m-lora")
```
To load a [`PeftModel`] for inference, you'll need to provide the [`PeftConfig`] used to create it and the base model it was trained from.
```py
from peft import PeftModel, PeftConfig
config = PeftConfig.from_pretrained("ybelkada/opt-350m-lora")
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
lora_model = PeftModel.from_pretrained(model, "ybelkada/opt-350m-lora")
```
> [!TIP]
> By default, the [`PeftModel`] is set for inference, but if you'd like to train the adapter some more you can set `is_trainable=True`.
>
> ```py
> lora_model = PeftModel.from_pretrained(model, "ybelkada/opt-350m-lora", is_trainable=True)
> ```
The [`PeftModel.from_pretrained`] method is the most flexible way to load a [`PeftModel`] because it doesn't matter what model framework was used (Transformers, timm, a generic PyTorch model). Other classes, like [`AutoPeftModel`], are just a convenient wrapper around the base [`PeftModel`], and makes it easier to load PEFT models directly from the Hub or locally where the PEFT weights are stored.
```py
from peft import AutoPeftModelForCausalLM
lora_model = AutoPeftModelForCausalLM.from_pretrained("ybelkada/opt-350m-lora")
```
Take a look at the [AutoPeftModel](package_reference/auto_class) API reference to learn more about the [`AutoPeftModel`] classes.
## Next steps
With the appropriate [`PeftConfig`], you can apply it to any pretrained model to create a [`PeftModel`] and train large powerful models faster on freely available GPUs! To learn more about PEFT configurations and models, the following guide may be helpful:
* Learn how to configure a PEFT method for models that aren't from Transformers in the [Working with custom models](../developer_guides/custom_models) guide.
================================================
FILE: examples/alora_finetuning/README.md
================================================
# Activated LoRA (aLoRA)
## Introduction
Activated LoRA (aLoRA) is an adapter that selectively activates its weights only after a given invocation sequence, ensuring that hidden states match the base model prior to this point. This allows reusing the base model KVs (stored in the KV cache) for tokens before the invocation,
enabling much faster real-world inference (e.g. vLLM) when switching between generation with the base model and generation with adapters.
See the [paper](https://huggingface.co/papers/2504.12397) for more details.
## Quick start (shown for Mistral 7B)
```python
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, DataCollatorForLanguageModeling
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
dataset = load_dataset("Lots-of-LoRAs/task1660_super_glue_question_generation", split="train")
invocation_string = "[/INST]" # End of user turn in Mistral chat template
invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False)
lora_config = LoraConfig(
task_type="CAUSAL_LM",
alora_invocation_tokens=invocation_tokens,
r=32,
target_modules=["q_proj", "k_proj", "v_proj"],
)
peft_model = get_peft_model(model, lora_config)
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
trainer = Trainer(
model=peft_model,
train_dataset=dataset,
dataset_text_field="text",
max_length=2048,
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()
peft_model.save_pretrained("alora-mistral-7b")
```
### Use the training example script directly
Pass the invocation string with `--invocation_string` when running the training example
script. For Mistral 7B, do:
```bash
python examples/alora_finetuning/alora_finetuning.py --base_model mistralai/Mistral-7B-Instruct-v0.3 --data_path Lots-of-LoRAs/task1660_super_glue_question_generation --invocation_string "[/INST]"
```
and similarly for Llama-3.2-3B-Instruct:
```bash
python examples/alora_finetuning/alora_finetuning.py --base_model meta-llama/Llama-3.2-3B-Instruct --data_path Lots-of-LoRAs/task1660_super_glue_question_generation --invocation_string "<|start_header_id|>assistant<|end_header_id|>"
```
### Full example of the script
```bash
python alora_finetuning.py \
--base_model "PATH_TO_MODEL" \
--data_path "PATH_TO_DATASET" \
--output_dir "PATH_TO_OUTPUT_DIR" \
--batch_size 1 \
--num_epochs 3 \
--learning_rate 3e-4 \
--cutoff_len 512 \
--val_set_size 500 \
--invocation_string "[/INST]" \
--quantize \
--eval_step 10 \
--save_step 100 \
--device "auto" \
--lora_r 32 \
--lora_alpha 32 \
--lora_dropout 0.05 \
--lora_target_modules "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj" \
--hub_model_id "YOUR_HF_REPO" \
--push_to_hub
```
================================================
FILE: examples/alora_finetuning/alora_finetuning.py
================================================
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
invocation_string: str,
quantize: bool,
eval_step: int,
save_step: int,
device: str,
lora_r: int,
lora_alpha: int,
lora_dropout: float,
lora_target_modules: str,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
print(f"Using device: {device}")
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
tokenizer.pad_token = tokenizer.unk_token
invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False)
if quantize:
if (torch.cuda.is_available() and torch.cuda.is_bf16_supported()) or torch.xpu.is_available():
bnb_4bit_compute_dtype = torch.bfloat16
else:
bnb_4bit_compute_dtype = torch.float16
model = AutoModelForCausalLM.from_pretrained(
base_model,
token=hf_token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
)
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
lora_config = LoraConfig(
task_type="CAUSAL_LM",
alora_invocation_tokens=invocation_tokens,
r=lora_r,
lora_alpha=lora_alpha,
target_modules=(lora_target_modules.split(",") if lora_target_modules else ["q_proj", "k_proj", "v_proj"]),
lora_dropout=lora_dropout,
bias="none",
)
model = get_peft_model(model, lora_config)
model.to(device)
tokenizer.pad_token = tokenizer.eos_token
dataset = load_dataset(data_path)
def tokenize_function(examples):
formatted_texts = [
tokenizer.apply_chat_template(
[
{"role": "user", "content": user_msg},
{"role": "assistant", "content": assistant_msg},
],
tokenize=False, # get plain text first
add_generation_prompt=False,
)
for user_msg, assistant_msg in zip(examples["input"], examples["output"])
]
# 2) Tokenize those texts
model_inputs = tokenizer(
formatted_texts,
padding="max_length",
truncation=True,
max_length=cutoff_len,
)
labels = []
for ids in model_inputs["input_ids"]:
labels.append([(token_id if token_id != tokenizer.pad_token_id else -100) for token_id in ids])
model_inputs["labels"] = labels
return model_inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
trainer.train()
if push_to_hub:
trainer.push_to_hub(commit_message="Fine-tuned model")
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def model_inference(model_path: str, adapter_path: str, prompt: str = None, data_path: str = None):
"""
Simple inference with the tuned aLoRA adapter. Optionally (reuse_cache = True) demonstrates
that the aLoRA adapter can (but does not need to) use KV cache created by the base model,
perhaps during a prior generation turn.
Purely for demonstration purposes. See the [paper](https://huggingface.co/papers/2504.12397)
for realistic multiturn cache reuse examples.
"""
if prompt is None:
# Use first row of test data
dataset = load_dataset(data_path)
prompt = dataset["test"][0]["input"]
tokenizer = AutoTokenizer.from_pretrained(model_path)
base_model = AutoModelForCausalLM.from_pretrained(model_path)
alora_model = PeftModel.from_pretrained(base_model, adapter_path)
chat = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(base_model.device)
# Generate answer with adapter
output_dict = alora_model.generate(**inputs, return_dict_in_generate=True, max_new_tokens=20)
alora_outputs = output_dict.sequences
# Print results
print(f"Prompt: {text}")
response = tokenizer.decode(alora_outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True)
print(f"Trained adapter response: {response}")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune Mistral with Activated LoRA")
parser.add_argument(
"--base_model", type=str, default="mistralai/Mistral-7B-Instruct-v0.3", help="Base model path or name"
)
parser.add_argument(
"--data_path",
type=str,
default="Lots-of-LoRAs/task1660_super_glue_question_generation",
help="Dataset path or name",
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=2, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=2048, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument(
"--invocation_string",
type=str,
default="[/INST]",
help="String that activates the aLoRA adapter. Model dependent.",
)
parser.add_argument("--quantize", action="store_true", help="Use quantization")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--lora_r", type=int, default=32, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=32, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate")
parser.add_argument(
"--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA"
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
invocation_string=args.invocation_string,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
lora_r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
print("Model trained. Running test inference.")
model_inference(model_path=args.base_model, adapter_path=args.output_dir, data_path=args.data_path)
================================================
FILE: examples/arrow_multitask/arrow_phi3_mini.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This script provides a simple evaluation pipeline for multiple-choice reasoning datasets
(e.g., BoolQ, HellaSwag, ARC, OpenBookQA, Winogrande) with different composition strategies.
Usage examples:
python arrow_phi3_mini.py --strategy base --ds_name arc-challenge
python arrow_phi3_mini.py --strategy arrow --ds_name boolq
python arrow_phi3_mini.py --strategy gks --ds_name hswag
Key features:
- Supports three strategies:
• "base" → Evaluate the quantized base model directly
• "arrow" → Use Arrow modular routing with task-specific adapters
• "gks" → Use Arrow + GenKnowSub (subtracting general-domain knowledge)
- Loads evaluation datasets from the Hugging Face Hub
- Implements a batched evaluation loop that computes per-option likelihoods and selects
the answer with the lowest average loss
- Reports simple accuracy
Implementation details:
- The base model is quantized to 4-bit using `BitsAndBytesConfig` (nf4, bf16 compute).
- For Arrow and GKS, task-specific adapters are loaded from the Hugging Face Hub:
TahaBa/phi3-mini-clustered-flan/ts_expert_i
- Task-specific adapters were trained on 10 clusters of FLAN tasks.
- The clusters were created using Model-Based Clustering (MBC):
1. Train a LoRA adapter for each individual task.
2. Apply k-means clustering to group tasks based on these adapters.
3. Train a LoRA adapter for each resulting cluster.
For more details, see the Arrow paper: https://huggingface.co/papers/2405.11157
- For GKS, general adapters are loaded from:
TahaBa/phi3-mini-general-adapters/...
- These adapters were trained on English, French, and German Wikipedia data
using a causal language modeling objective with (507-token context → 5-token completion) pairs.
- This setup encodes general knowledge into the LoRA space, which can then be
subtracted from task-specific adapters during inference to isolate and purify them.
For more details, see the GenKnowSub paper: https://huggingface.co/papers/2505.10939
- `evaluate_on_multi_choice_batched` handles tokenization, masking context tokens,
and computing per-choice log-likelihoods for fair comparison.
- Accuracy is printed at the end for the selected dataset.
This script is mainly meant for demonstration purposes and lightweight evaluation,
not full-scale benchmarking (batch size / max length can be tuned).
=======================================================================================
Results (evaluated with microsoft/Phi-3-mini-4k-instruct, 4-bit quantization):
| Dataset | Base Acc. | Arrow Acc. | Arrow+GKS Acc. |
|--------------|-----------|------------|----------------|
| ARC-Challenge| 0.4515 | 0.5418 | 0.5585 |
| ARC-Easy | 0.6894 | 0.8404 | 0.8473 |
| Winogrande | 0.5769 | 0.6550 | 0.6724 |
| BoolQ | 0.8146 | 0.8030 | 0.8247 |
| OpenBookQA | 0.43 | 0.448 | 0.472 |
| HellaSwag | 0.7318 | 0.7150 | 0.7376 |
Observations:
- Arrow generally improves over the base model by routing tokens to the most relevant task adapters.
- Applying GKS (general knowledge subtraction) consistently gives further gains compared to Arrow and Base.
These numbers are not meant as leaderboard results, but as a sanity check
to verify that the implementation works as expected and demonstrates
the benefits of Arrow and GenKnowSub.
"""
import argparse
import random
import numpy as np
import torch
from datasets import load_dataset
from sklearn.metrics import accuracy_score
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import ArrowConfig, create_arrow_model
MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
MODEL_MAX_LEN = 2048
def parse_args():
parser = argparse.ArgumentParser(description="Training script with strategy selection")
parser.add_argument(
"--strategy",
type=str,
choices=["base", "arrow", "gks"],
default="base",
help="Training strategy to use: base, arrow, or gks",
)
parser.add_argument(
"--ds_name",
type=str,
choices=["boolq", "hswag", "arc-easy", "arc-challenge", "oqa", "wg"],
default="arc-challenge",
help="Dataset to use: boolq, hswag, arc-easy, arc-challenge, oqa, wg",
)
return parser.parse_args()
def read_test_dataset(ds_name):
if ds_name == "boolq":
ds = load_dataset("google/boolq", split="validation", trust_remote_code=True)
elif ds_name == "hswag":
ds = load_dataset("Rowan/hellaswag", split="validation", trust_remote_code=True)
elif ds_name == "arc-challenge":
ds = load_dataset("allenai/ai2_arc", "ARC-Challenge", split="validation", trust_remote_code=True)
elif ds_name == "arc-easy":
ds = load_dataset("allenai/ai2_arc", "ARC-Easy", split="validation", trust_remote_code=True)
elif ds_name == "oqa":
ds = load_dataset("allenai/openbookqa", split="validation", trust_remote_code=True)
elif ds_name == "wg":
ds = load_dataset("allenai/winogrande", "winogrande_xl", split="validation", trust_remote_code=True)
else:
raise f"Dataset {ds_name} is not supported yet."
return ds
def extract_input_content(ds_name, row):
if ds_name == "boolq":
return f"[passage]{row['passage']}[question]{row['question']}"
if ds_name == "hswag":
return row["ctx"]
if (ds_name == "arc-challenge") or (ds_name == "arc-easy"):
return row["question"]
if ds_name == "oqa":
return row["question_stem"]
if ds_name == "wg":
return row["sentence"]
def create_multi_choice_options(row, ds_name):
options_texts = []
content = extract_input_content(ds_name, row)
if ds_name == "boolq":
choices = ["true", "false"]
if ds_name == "hswag":
choices = row["endings"]
if (ds_name == "arc-challenge") or (ds_name == "arc-easy"):
choices = row["choices"]["text"]
if ds_name == "wg":
choices = [row["option1"], row["option2"]]
if ds_name == "oqa":
choices = row["choices"]["text"]
for choice in choices:
options_texts.append(f"<|user|>\n{content}<|end|>\n<|assistant|>{choice}<|end|>\n")
return options_texts
def extract_multi_choice_target_index(row, ds_name):
if ds_name == "boolq":
return 0 if row["answer"] is True else 1
if ds_name == "hswag":
return int(row["label"])
if (ds_name == "arc-challenge") or (ds_name == "arc-easy"):
return row["choices"]["label"].index(row["answerKey"])
if ds_name == "wg":
return int(row["answer"]) - 1
if ds_name == "oqa":
return row["choices"]["label"].index(row["answerKey"])
def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
elif hasattr(torch, "xpu") and torch.xpu.is_available():
torch.xpu.manual_seed_all(seed)
def compute_loglike_loss(logits, labels, reduction="none"):
bs = logits.size(0)
vocab_size = logits.size(-1)
labels = labels.squeeze(-1)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = torch.nn.CrossEntropyLoss(reduction=reduction)
shift_logits = shift_logits.view(-1, vocab_size)
shift_labels = shift_labels.view(-1)
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
# reshape back
if reduction == "none":
loss = loss.view((bs, -1))
non_zero_loss = (loss != 0).sum(dim=-1)
non_zero_loss[non_zero_loss == 0] = 1
loss = loss.sum(dim=-1) / non_zero_loss
return loss.float() # Convert to float32 before returning
def evaluate_on_multi_choice_batched(
eval_dataset, model, tokenizer, ds_name, labels, predictions, args, batch_size=32, max_length=512, device="auto"
):
# Local import to mirror your original function
model.eval()
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
for start in tqdm(
range(0, len(eval_dataset), batch_size), total=(len(eval_dataset) + batch_size - 1) // batch_size
):
rows = [eval_dataset[i] for i in range(start, min(start + batch_size, len(eval_dataset)))]
# Build the flattened option texts for this batch
all_texts = []
options_per_sample = [] # number of options for each sample
ctx_lens_per_option = [] # context length replicated per option
for row in rows:
# options: ["<|user|>...<|assistant|>choiceA<|end|>", ...]
options = create_multi_choice_options(row, ds_name)
options_per_sample.append(len(options))
# compute context length once per sample (align with your -1 shift)
content = extract_input_content(ds_name, row)
context_prompt = f"<|user|>\n{content}<|end|>\n<|assistant|>"
ctx_len = len(tokenizer.encode(context_prompt)) - 1
all_texts.extend(options)
ctx_lens_per_option.extend([ctx_len] * len(options))
# collect gold label
labels.append(extract_multi_choice_target_index(row, ds_name))
# Tokenize all options in one go
tokenized = tokenizer(
all_texts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_length,
)
tokenized = {k: v.to(device) for k, v in tokenized.items()}
# Create masked labels: ignore context and padding
masked_labels = tokenized["input_ids"].clone()
for i, ctx_len in enumerate(ctx_lens_per_option):
masked_labels[i, :ctx_len] = -100
masked_labels[tokenized["attention_mask"] == 0] = -100
with torch.no_grad():
logits = model(input_ids=tokenized["input_ids"], attention_mask=tokenized["attention_mask"]).logits
# per-sequence losses
losses = compute_loglike_loss(logits, masked_labels, reduction="none").detach().cpu()
# Reduce per sample (argmin across its options)
idx = 0
for n_opt in options_per_sample:
pred = torch.argmin(losses[idx : idx + n_opt]).item()
predictions.append(pred)
idx += n_opt
print(
f"Accuracy for dataset {args.ds_name} and strategy {args.strategy} is: {accuracy_score(labels, predictions)}"
)
if __name__ == "__main__":
args = parse_args()
print(f"Selected strategy: {args.strategy}")
print(f"Dataset name: {args.ds_name}")
# Loading the tokeniser
tokenizer = AutoTokenizer.from_pretrained(
MODEL_NAME,
use_fast=True,
padding_side="right",
model_max_length=MODEL_MAX_LEN,
)
# Quantisation config
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
)
# Loading the model
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
dtype=torch.bfloat16,
device_map="auto",
quantization_config=bnb_config,
)
# Loading the test dataset
test_dataset = read_test_dataset(args.ds_name)
print(f"{args.ds_name} is loaded with size: {len(test_dataset)}.")
labels, predictions = [], []
if args.strategy == "base":
# Batch-wise inference
with torch.no_grad():
evaluate_on_multi_choice_batched(
test_dataset,
base_model,
tokenizer,
args.ds_name,
labels,
predictions,
args,
batch_size=64, # tune this
max_length=512, # tune if options are long
device="auto",
)
else:
general_adapter_paths = []
if args.strategy == "gks":
arrow_config = ArrowConfig(
top_k=3,
router_temperature=1.0,
use_gks=True,
)
# General adapter paths from the hub
general_adapter_paths = [
"TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langen/checkpoint-17",
"TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langfr/checkpoint-35",
"TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langger/checkpoint-17",
]
else:
arrow_config = ArrowConfig(
top_k=3,
router_temperature=1.0,
)
# Task-specific adapter paths from the hub
task_specific_adapter_paths = [f"TahaBa/phi3-mini-clustered-flan/ts_expert_{i}" for i in range(10)]
# Creating the Arrow model
model = create_arrow_model(
base_model=base_model,
task_specific_adapter_paths=task_specific_adapter_paths,
general_adapter_paths=general_adapter_paths,
arrow_config=arrow_config,
)
# Batch-wise inference
with torch.no_grad():
evaluate_on_multi_choice_batched(
test_dataset,
model,
tokenizer,
args.ds_name,
labels,
predictions,
args,
batch_size=32, # tune this
max_length=512, # tune if options are long
device="auto",
)
================================================
FILE: examples/arrow_multitask/requirements.txt
================================================
torch
transformers
accelerate
datasets
scikit-learn
tqdm
numpy
bitsandbytes
================================================
FILE: examples/bdlora_finetuning/README.md
================================================
# BD-LoRA Finetuning
Block-Diagonal LoRA (BD-LoRA) is a LoRA variant in which some LoRA factors are constrained to be block-diagonal.
This allows faster serving by eliminating communication overheads when running inference on multiple GPU, at the same finetuning performance as vanilla LoRA.
To get an overview on how to use BD-LoRA, please view the Python notebook at `peft/examples/bdlora_finetuning/bdlora_peft_demo.ipynb`.
To benefit from inference speed-ups, you need an inference engine that is compatible with BD-LoRA. At the moment, there is an experimental PR at https://github.com/vllm-project/vllm/pull/28136 which allows you to use BD-LoRA in vLLM. If you find this work useful, consider leaving a comment there.
To install, you can clone the GitHub repository connected to the fork at https://github.com/Conzel/vllm/tree/bdlora-bk. Then, install vLLM following the usual instructions: https://docs.vllm.ai/en/stable/getting_started/installation/. We assume that you have a hardware setup with at least 2 available GPUs.
This example folder contains 3 scripts:
- `bdlora_peft_demo.ipynb` Showcases how to instantiate a BD-LoRA model, train it, and save/reload the weights.
- `vllm_server.bash` Spins up a BD-LoRA compatible vLLM server. To use it, you need to run the notebook once to create adapters with the correct format.
- `chat.py` Can be used to query the vLLM server after it has finished booting up. Usage example: `python3 chat.py --target lora1`.
================================================
FILE: examples/bdlora_finetuning/bdlora_peft_demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "e2b69494",
"metadata": {},
"source": [
"# Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving"
]
},
{
"cell_type": "markdown",
"id": "2fb2b221",
"metadata": {},
"source": [
"## Introduction\n",
"Block-Diagonal LoRA (BD-LoRA) is a LoRA variant in which some LoRA factors are constrained to be block-diagonal. This allows faster serving by eliminating communication overheads \n",
"when running inference on multiple GPUs. Despite the block-diagonal constraint, BD-LoRA is similarly performant to vanilla LoRA at similar parameter counts.\n",
"\n",
"BD-LoRA is designed to be used with tensor parallelism, which means sharding the weights of a model among multiple GPUs. A popular sharding strategy is the [Megatron Sharding Strategy](https://arxiv.org/abs/1909.08053). For two linear layers $W_1$, $W_2$ that follow each other (for example the up and down projections in a transformer MLP module), we will shard the first layer in a column-parallel way (which requires LoRA B to be block-diagonal) and the second layer in a row-parallel way (which requires LoRA A to be block-diagonal). For the attention module, this can be similarly achieved by taking the Q, K and V projections together as $W_1$ and the out projection as $W_2$, sharding accordingly. This sharding allows a compatible inference engine to distribute each block-diagonal shard over a a different GPU, cutting the need to communicate partial results among GPUs. In the image below, you can see the exact sharding strategy and how this saves computational efforts.\n",
"\n",
"Paper: https://arxiv.org/html/2510.23346v1\n",
"\n",
"
\n",
"\n",
"
\n",
"\n",
"### Performance, rank and parameter count\n",
"BD-LoRA achieves similar performance to LoRA (see image below, or the `method_comparison` folder in the peft repository root) at the same parameter count. However, as every other factor in BD-LoRA is block-diagonal, a BD-LoRA adapter will have less parameters than a LoRA adapter at the same rank. The performance of BD-LoRA is only competitive when the rank is then increased accordingly. We provide example code for rank-matching at the end of this example notebook.\n",
"\n",
"
\n",
"\n",
"
\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "40eea544",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/efs/aconzel/workspace/peft/bdlora-peft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from peft.tuners import BdLoraConfig, LoraConfig\n",
"from peft import get_peft_model\n",
"from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling, AutoModelForCausalLM, AutoTokenizer\n",
"from datasets import load_dataset\n",
"import torch"
]
},
{
"cell_type": "markdown",
"id": "5959e4ab",
"metadata": {},
"source": [
"## Quick Start\n",
"To use BD-LoRA, we can follow standard LoRA-training procedures. We only need to change the `LoraConfig` to a `BdLoraConfig` and specify which LoRA should be block-diagonal. \n",
"As an example, we will train a LLama-Model in such a way that it can later benefit from inference speed-up as specified in the BD-LoRA paper. However, BD-LoRA can be used with all other models that follow a transformer architecture. \n",
"\n",
"As explained in the introduction, we want to shard each module (MLP and attention) in an alternating fashion, first column-parallel with LoRA-B block-diagonal, then row-parallel with LoRA-A block-diagonal. Different from standard MLP modules, Llama also uses a gate projection, which we can fuse together with the up-projection.\n",
"\n",
"Therefore, we want the following block-diagonal factors (following the naming convention from the Llama architecture):\n",
"\n",
"- LoRA-A Block-Diagonal (Row-parallel sharding): Out (`out_proj`), Down (`down_proj`)\n",
"- LoRA-B Block-Diagonal (Column-parallel sharding): QKV (`q_proj, k_proj, v_proj`), Up+Gate (`up_proj, gate_proj`)\n",
"\n",
"Additionally, we need to know on how many GPUs we want to serve before we start training, as this corresponds to the number of block we will use for each block-diagonal factor. For this experiment, we will use 2 blocks (equivalent to a tensor-parallelism degree of 2). Caveat: For a small model such as Llama 3.2-1B which we are using, one would use a single GPU for serving, and use TP=2 or TP=8 only for larger models, like Llama 3.1-8B or Llama 3.3-70B respectively. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6180c1f9",
"metadata": {},
"outputs": [],
"source": [
"model_name = \"meta-llama/Llama-3.2-1B\"\n",
"model = AutoModelForCausalLM.from_pretrained(model_name)\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6a50e350",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 44,826,624 || all params: 1,280,641,024 || trainable%: 3.5003\n"
]
}
],
"source": [
"target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"up_proj\", \"gate_proj\", \"o_proj\", \"down_proj\"]\n",
"# Set this equal to the number of GPUs you want to serve the model with later\n",
"nblocks = 2\n",
"\n",
"bdlora_config = BdLoraConfig(\n",
" target_modules_bd_a=[\"o_proj\", \"down_proj\"],\n",
" target_modules_bd_b=[\"q_proj\", \"v_proj\", \"k_proj\", \"up_proj\", \"gate_proj\"],\n",
" nblocks=nblocks\n",
")\n",
"\n",
"config = LoraConfig(\n",
" r=96,\n",
" # adjust target modules and the ...target_modules_bd attributes according to model architecture (for example renaming)\n",
" target_modules=target_modules,\n",
" use_bdlora=bdlora_config,\n",
" lora_bias=False\n",
")\n",
"\n",
"peft_model = get_peft_model(model, config)\n",
"peft_model.print_trainable_parameters()"
]
},
{
"cell_type": "markdown",
"id": "c1facfea",
"metadata": {},
"source": [
"## Training\n",
"We train the model for 10 steps, this training block is just intended to showcase how BD-LoRA integrates into other huggingface tools."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "64e4681b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=10, training_loss=3.0836387634277345, metrics={'train_runtime': 38.346, 'train_samples_per_second': 66.761, 'train_steps_per_second': 0.261, 'total_flos': 1954507653120000.0, 'train_loss': 3.0836387634277345, 'epoch': 10.0})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = load_dataset(\"imdb\", split=\"train[:1%]\")\n",
"\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"def tokenize(batch):\n",
" return tokenizer(batch[\"text\"], truncation=True, padding=\"max_length\", max_length=128)\n",
"\n",
"dataset = dataset.map(tokenize, batched=True, remove_columns=[\"text\"])\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./results\",\n",
" per_device_train_batch_size=8,\n",
" gradient_accumulation_steps=4,\n",
" warmup_steps=2,\n",
" max_steps=10,\n",
" learning_rate=2e-4,\n",
" logging_steps=1,\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=peft_model,\n",
" args=training_args,\n",
" train_dataset=dataset,\n",
" data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
")\n",
"\n",
"peft_model.config.use_cache = False\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "3ac31a38",
"metadata": {},
"source": [
"## Saving Model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "28f49ee4",
"metadata": {},
"outputs": [],
"source": [
"peft_model.save_pretrained(\"example_bd_lora_adapter\")"
]
},
{
"cell_type": "markdown",
"id": "1a3d6fab",
"metadata": {},
"source": [
"## Example Output"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "20c36df8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Batman Trilogy by Christopher Nolan\n",
"The Batman Trilogy by Christopher Nolan\n",
"The Batman Trilogy by Christopher Nolan\n",
"The Batman Trilogy by Christopher Nolan\n",
"The Batman Trilogy by Christopher Nolan\n",
"The Batman Trilogy by Christopher Nolan\n",
"The Batman Trilogy by Christopher Nolan\n",
"\n"
]
}
],
"source": [
"text = \"The Batman Trilogy by Christopher Nolan\"\n",
"inputs = tokenizer(text, return_tensors=\"pt\").to(model.device) \n",
"\n",
"outputs = peft_model.generate(**inputs, max_length=50)\n",
"decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
"print(decoded)"
]
},
{
"cell_type": "markdown",
"id": "711eb30d",
"metadata": {},
"source": [
"## Investigating the shapes of LoRA Adapters\n",
"We can check out the adapter shapes to see if they follow the sharding patterns that we have discussed. To make the implementation more memory efficient, \n",
"the block-diagonal matrices are not saved in a block-diagonal manner, but the blocks are stacked along the non-rank dimensions. \n",
"\n",
"For example, if a layer is column sharded, such as the q-proj in Llama, then the LoRA-B factor is block-diagonal. Assume that the q-proj has layer weights (out_features, in_features), \n",
"then LoRA-A will have shape (rank, in_features), and LoRA-B will have shape (out_features, rank / TP), which corresponds to TP blocks of shape (out_features/TP, rank/TP) each. This can be checked by investigating the weight shapes:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3660c4de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Base layer has shape: [512, 2048]\n",
"LoRA-A (vanilla): [96, 2048]\n",
"LoRA-B (block-diagonal): [512, 48 ]\n"
]
}
],
"source": [
"shape_base = list(peft_model.state_dict()['base_model.model.model.layers.0.self_attn.v_proj.base_layer.weight'].shape)\n",
"shape_a = list(peft_model.state_dict()['base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight'].shape)\n",
"shape_b = list(peft_model.state_dict()['base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight'].shape)\n",
"print(f\"Base layer has shape: [{shape_base[0]}, {shape_base[1]}]\\nLoRA-A (vanilla): [{shape_a[0]}, {shape_a[1]}]\\nLoRA-B (block-diagonal): [{shape_b[0]}, {shape_b[1]} ]\")"
]
},
{
"cell_type": "markdown",
"id": "d95118af",
"metadata": {},
"source": [
"## Matching the rank\n",
"Assuming we want to achieve the same performance of a LoRA adapter of a given rank, at which rank would we have to train BD-LoRA? We can find this out by matching the number of trainable parameters. A simple iteration over the ranks of the BD-LoRA adapter is sufficient to do that:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cd4fc45a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BD-LoRA rank to match vanilla LoRA performance at rank 64: 96 at 45088768 vanilla LoRA params and 44826624 BD-LoRA params.\n"
]
}
],
"source": [
"def rank_to_params(r: int, bd_lora: bool, nblocks: int):\n",
" model = AutoModelForCausalLM.from_pretrained(model_name)\n",
" if bd_lora:\n",
" config = LoraConfig(\n",
" r=r,\n",
" target_modules=target_modules,\n",
" use_bdlora=bdlora_config,\n",
" lora_bias=False\n",
" )\n",
" else:\n",
" config = LoraConfig(\n",
" r=r,\n",
" # If you use a model different from Llama, change the settings below\n",
" target_modules=target_modules,\n",
" lora_bias=False\n",
" )\n",
"\n",
"\n",
" peft_model = get_peft_model(model, config)\n",
" return peft_model.get_nb_trainable_parameters()[0]\n",
"\n",
"r_orig = 64\n",
"r = r_orig\n",
"lora_nparams = rank_to_params(r, False, nblocks)\n",
"bdlora_nparams = 0\n",
"while bdlora_nparams < lora_nparams:\n",
" r += nblocks\n",
" bdlora_nparams = rank_to_params(r, True, nblocks)\n",
"# subtract nblocks again to be just under the parameter count of vanilla LoRA, following the original papers methodology\n",
"print(f\"BD-LoRA rank to match vanilla LoRA performance at rank {r_orig}: {r-nblocks} at {lora_nparams} vanilla LoRA params and {rank_to_params(r-nblocks, True, nblocks)} BD-LoRA params.\")"
]
},
{
"cell_type": "markdown",
"id": "d27b9545",
"metadata": {},
"source": [
"# Integration with vLLM\n",
"Currently, vLLM has an experimental PR that allows you to use it with BD-LoRA. Clone the github repository and check out the commit of \n",
"the pull request at https://github.com/vllm-project/vllm/pull/28136#. Then, install vLLM following the usual instructions: https://docs.vllm.ai/en/stable/getting_started/installation/. We assume that you have a hardware setup with at least 2 available GPUs. \n",
"\n",
"We have included a script that starts a vLLM server with two BD-LoRA modules at `vllm_server.bash` (you might have to kill this jupyter server beforehand, as it is likely already using your GPU resources).\n",
"Once the server has started, you can query it via `python3 chat.py \"Please write your message here.\" --target lora1`."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "bdlora-peft",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/bdlora_finetuning/chat.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import json
import time
import aiohttp
import typer
def chat(
msg: str,
target: str = "lora1",
max_tokens: int = 100,
deterministic: bool = True,
record: str = "",
num_requests: int = 32,
):
payload = {
"model": target,
"prompt": msg,
"max_tokens": max_tokens,
}
if deterministic:
payload = payload | {"temperature": 0, "top_p": 1, "top_k": 1}
url = "http://localhost:8000/v1/completions"
async def _request(session):
async with session.post(url, json=payload) as response:
response_json = await response.json()
return response_json["choices"][0]["text"]
async def run_concurrent():
async with aiohttp.ClientSession() as session:
tasks = [_request(session) for _ in range(num_requests)]
return await asyncio.gather(*tasks)
start_time = time.time()
response_texts = asyncio.run(run_concurrent())
end_time = time.time()
print(f"Completed {num_requests} requests in {end_time - start_time:.2f} seconds")
if record:
with open(record, "w") as f:
json.dump({"prompt": msg, "responses": response_texts}, f, indent=2)
else:
print(response_texts[0])
if __name__ == "__main__":
typer.run(chat)
================================================
FILE: examples/bdlora_finetuning/vllm_server.bash
================================================
#!/bin/bash
ADAPTER_PATH="example_bd_lora_adapter"
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3.2-1B \
--enable-lora \
--lora-modules lora1=$ADAPTER_PATH lora2=$ADAPTER_PATH \
--tensor-parallel-size 2 \
--block_diagonal_sharded_loras \
--max-lora-rank 128 \
--enforce-eager \
--port 8000
================================================
FILE: examples/boft_controlnet/__init__.py
================================================
================================================
FILE: examples/boft_controlnet/boft_controlnet.md
================================================
# Fine-tuning for controllable generation with BOFT (ControlNet)
This guide demonstrates how to use BOFT, an orthogonal fine-tuning method, to fine-tune Stable Diffusion with either `stabilityai/stable-diffusion-2-1` or `runwayml/stable-diffusion-v1-5` model for controllable generation.
By using BOFT from 🤗 PEFT, we can significantly reduce the number of trainable parameters while still achieving impressive results in various fine-tuning tasks across different foundation models. BOFT enhances model efficiency by integrating full-rank orthogonal matrices with a butterfly structure into specific model blocks, such as attention blocks, mirroring the approach used in LoRA. During fine-tuning, only these inserted matrices are trained, leaving the original model parameters untouched. During inference, the trainable BOFT parameters can be merged into the original model, eliminating any additional computational costs.
As a member of the **orthogonal finetuning** class, BOFT presents a systematic and principled method for fine-tuning. It possesses several unique properties and has demonstrated superior performance compared to LoRA in a variety of scenarios. For further details on BOFT, please consult the [PEFT's GitHub repo's concept guide OFT](https://https://huggingface.co/docs/peft/index), the [original BOFT paper](https://huggingface.co/papers/2311.06243) and the [original OFT paper](https://huggingface.co/papers/2306.07280).
In this guide we provide a controllable generation (ControlNet) fine-tuning script that is available in [PEFT's GitHub repo examples](https://github.com/huggingface/peft/tree/main/examples/boft_controlnet). This implementation is adapted from [diffusers's ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) and [Hecong Wu's ControlLoRA](https://github.com/HighCWu/ControlLoRA). You can try it out and finetune on your custom images.
## Set up your environment
Start by cloning the PEFT repository:
```bash
git clone https://github.com/huggingface/peft
```
Navigate to the directory containing the training scripts for fine-tuning Dreambooth with BOFT:
```bash
cd peft/examples/boft_controlnet
```
Set up your environment: install PEFT, and all the required libraries. At the time of writing this guide we recommend installing PEFT from source.
```bash
conda create --name peft python=3.10
conda activate peft
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install xformers -c xformers
pip install -r requirements.txt
pip install git+https://github.com/huggingface/peft
```
## Data
We use the [control-celeba-hq](https://huggingface.co/datasets/oftverse/control-celeba-hq) dataset for landmark-to-face controllable generation. We also provide evaluation scripts to evaluate the controllable generation performance. This task can be used to quantitatively compare different fine-tuning techniques.
```bash
export DATASET_NAME="oftverse/control-celeba-hq"
```
## Train controllable generation (ControlNet) with BOFT
Start with setting some hyperparameters for BOFT:
```bash
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=0
```
Here:
Navigate to the directory containing the training scripts for fine-tuning Stable Diffusion with BOFT for controllable generation:
```bash
./train_controlnet.sh
```
or
```bash
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATASET_NAME="oftverse/control-celeba-hq"
export PROJECT_NAME="controlnet_${PEFT_TYPE}"
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export CONTROLNET_PATH=""
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}"
accelerate launch train_controlnet.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--resume_from_checkpoint=$RESUME_PATH \
--controlnet_model_name_or_path=$CONTROLNET_PATH \
--output_dir=$OUTPUT_DIR \
--report_to="wandb" \
--dataset_name=$DATASET_NAME \
--resolution=512 \
--learning_rate=1e-5 \
--checkpointing_steps=5000 \
--max_train_steps=50000 \
--validation_steps=2000 \
--num_validation_images=12 \
--train_batch_size=4 \
--dataloader_num_workers=2 \
--seed="0" \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--wandb_project_name=$PROJECT_NAME \
--wandb_run_name=$RUN_NAME \
--enable_xformers_memory_efficient_attention \
--use_boft \
--boft_block_num=$BLOCK_NUM \
--boft_block_size=$BLOCK_SIZE \
--boft_n_butterfly_factor=$N_BUTTERFLY_FACTOR \
--boft_dropout=0.1 \
--boft_bias="boft_only" \
--report_to="wandb" \
```
Run inference on the saved model to sample new images from the validation set:
```bash
./test_controlnet.sh
```
or
```bash
ITER_NUM=50000
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export DATASET_NAME="oftverse/control-celeba-hq"
export CKPT_NAME="checkpoint-${ITER_NUM}"
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}/${CKPT_NAME}"
export CONTROLNET_PATH="${OUTPUT_DIR}/controlnet/model.safetensors"
export UNET_PATH="${OUTPUT_DIR}/unet/${RUN_NAME}"
export RESULTS_PATH="${OUTPUT_DIR}/results"
accelerate launch test_controlnet.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME \
--controlnet_path=$CONTROLNET_PATH \
--unet_path=$UNET_PATH \
--adapter_name=$RUN_NAME \
--output_dir=$RESULTS_PATH \
--dataset_name=$DATASET_NAME \
```
Run evaluation on the sampled images to evaluate the landmark reprojection error:
```bash
./eval.sh
```
or
```bash
ITER_NUM=50000
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export DATASET_NAME="oftverse/control-celeba-hq"
export CKPT_NAME="checkpoint-${ITER_NUM}"
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}/${CKPT_NAME}"
export CONTROLNET_PATH="${OUTPUT_DIR}/controlnet/model.safetensors"
export UNET_PATH="${OUTPUT_DIR}/unet/${RUN_NAME}"
accelerate launch eval.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME \
--controlnet_path=$CONTROLNET_PATH \
--unet_path=$UNET_PATH \
--adapter_name=$RUN_NAME \
--output_dir=$OUTPUT_DIR \
--dataset_name=$DATASET_NAME \
--vis_overlays \
```
================================================
FILE: examples/boft_controlnet/eval.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The implementation is based on "Parameter-Efficient Orthogonal Finetuning
# via Butterfly Factorization" (https://huggingface.co/papers/2311.06243) in ICLR 2024.
import glob
import os
from pathlib import Path
import cv2
import face_alignment
import numpy as np
import torch
from accelerate import Accelerator
from skimage.io import imread
from torchvision.utils import save_image
from tqdm import tqdm
from transformers import AutoTokenizer
from utils.args_loader import parse_args
from utils.dataset import make_dataset
# Determine the best available device
if torch.cuda.is_available():
device = "cuda:0"
else:
# TODO: xpu support in facealignment will be ready after this PR is merged:https://github.com/1adrianb/face-alignment/pull/371
device = "cpu"
detect_model = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, device=device, flip_input=False)
# with open('./data/celebhq-text/prompt_val_blip_full.json', 'rt') as f: # fill50k, COCO
# for line in f:
# val_data = json.loads(line)
end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype=np.int32) - 1
def count_txt_files(directory):
pattern = os.path.join(directory, "*.txt")
txt_files = glob.glob(pattern)
return len(txt_files)
def plot_kpts(image, kpts, color="g"):
"""Draw 68 key points
Args:
image: the input image
kpt: (68, 3).
"""
if color == "r":
c = (255, 0, 0)
elif color == "g":
c = (0, 255, 0)
elif color == "b":
c = (255, 0, 0)
image = image.copy()
kpts = kpts.copy()
radius = max(int(min(image.shape[0], image.shape[1]) / 200), 1)
for i in range(kpts.shape[0]):
st = kpts[i, :2]
if kpts.shape[1] == 4:
if kpts[i, 3] > 0.5:
c = (0, 255, 0)
else:
c = (0, 0, 255)
image = cv2.circle(image, (int(st[0]), int(st[1])), radius, c, radius * 2)
if i in end_list:
continue
ed = kpts[i + 1, :2]
image = cv2.line(image, (int(st[0]), int(st[1])), (int(ed[0]), int(ed[1])), (255, 255, 255), radius)
return image
def generate_landmark2d(dataset, input_dir, pred_lmk_dir, gt_lmk_dir, vis=False):
print("Generate 2d landmarks ...")
os.makedirs(pred_lmk_dir, exist_ok=True)
imagepath_list = sorted(glob.glob(f"{input_dir}/pred*.png"))
for imagepath in tqdm(imagepath_list):
name = Path(imagepath).stem
idx = int(name.split("_")[-1])
pred_txt_path = os.path.join(pred_lmk_dir, f"{idx}.txt")
gt_lmk_path = os.path.join(gt_lmk_dir, f"{idx}_gt_lmk.jpg")
gt_txt_path = os.path.join(gt_lmk_dir, f"{idx}.txt")
gt_img_path = os.path.join(gt_lmk_dir, f"{idx}_gt_img.jpg")
if (not os.path.exists(pred_txt_path)) or (not os.path.exists(gt_txt_path)):
image = imread(imagepath) # [:, :, :3]
out = detect_model.get_landmarks(image)
if out is None:
continue
pred_kpt = out[0].squeeze()
np.savetxt(pred_txt_path, pred_kpt)
# Your existing code for obtaining the image tensor
gt_lmk_img = dataset[idx]["conditioning_pixel_values"]
save_image(gt_lmk_img, gt_lmk_path)
gt_img = (dataset[idx]["pixel_values"]) * 0.5 + 0.5
save_image(gt_img, gt_img_path)
gt_img = (gt_img.permute(1, 2, 0) * 255).type(torch.uint8).cpu().numpy()
out = detect_model.get_landmarks(gt_img)
if out is None:
continue
gt_kpt = out[0].squeeze()
np.savetxt(gt_txt_path, gt_kpt)
# gt_image = cv2.resize(cv2.imread(gt_lmk_path), (512, 512))
if vis:
gt_lmk_image = cv2.imread(gt_lmk_path)
# visualize predicted landmarks
vis_path = os.path.join(pred_lmk_dir, f"{idx}_overlay.jpg")
image = cv2.imread(imagepath)
image_point = plot_kpts(image, pred_kpt)
cv2.imwrite(vis_path, np.concatenate([image_point, gt_lmk_image], axis=1))
# visualize gt landmarks
vis_path = os.path.join(gt_lmk_dir, f"{idx}_overlay.jpg")
image = cv2.imread(gt_img_path)
image_point = plot_kpts(image, gt_kpt)
cv2.imwrite(vis_path, np.concatenate([image_point, gt_lmk_image], axis=1))
def landmark_comparison(val_dataset, lmk_dir, gt_lmk_dir):
print("Calculating reprojection error")
lmk_err = []
pbar = tqdm(range(len(val_dataset)))
for i in pbar:
# line = val_dataset[i]
# img_name = line["image"].split(".")[0]
lmk1_path = os.path.join(gt_lmk_dir, f"{i}.txt")
lmk1 = np.loadtxt(lmk1_path)
lmk2_path = os.path.join(lmk_dir, f"{i}.txt")
if not os.path.exists(lmk2_path):
print(f"{lmk2_path} not exist")
continue
lmk2 = np.loadtxt(lmk2_path)
lmk_err.append(np.mean(np.linalg.norm(lmk1 - lmk2, axis=1)))
pbar.set_description(f"lmk_err: {np.mean(lmk_err):.5f}")
print("Reprojection error:", np.mean(lmk_err))
np.save(os.path.join(lmk_dir, "lmk_err.npy"), lmk_err)
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=logging_dir,
)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
val_dataset = make_dataset(args, tokenizer, accelerator, "test")
gt_lmk_dir = os.path.join(args.output_dir, "gt_lmk")
if not os.path.exists(gt_lmk_dir):
os.makedirs(gt_lmk_dir, exist_ok=True)
pred_lmk_dir = os.path.join(args.output_dir, "pred_lmk")
if not os.path.exists(pred_lmk_dir):
os.makedirs(pred_lmk_dir, exist_ok=True)
input_dir = os.path.join(args.output_dir, "results")
generate_landmark2d(val_dataset, input_dir, pred_lmk_dir, gt_lmk_dir, args.vis_overlays)
if count_txt_files(pred_lmk_dir) == len(val_dataset) and count_txt_files(gt_lmk_dir) == len(val_dataset):
landmark_comparison(val_dataset, pred_lmk_dir, gt_lmk_dir)
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/boft_controlnet/eval.sh
================================================
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
ITER_NUM=50000
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATASET_NAME="oftverse/control-celeba-hq"
export CKPT_NAME="checkpoint-${ITER_NUM}"
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}/${CKPT_NAME}"
export CONTROLNET_PATH="${OUTPUT_DIR}/controlnet/model.safetensors"
export UNET_PATH="${OUTPUT_DIR}/unet/${RUN_NAME}"
accelerate launch eval.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME \
--controlnet_path=$CONTROLNET_PATH \
--unet_path=$UNET_PATH \
--adapter_name=$RUN_NAME \
--output_dir=$OUTPUT_DIR \
--dataset_name=$DATASET_NAME \
--vis_overlays \
================================================
FILE: examples/boft_controlnet/requirements.txt
================================================
datasets==2.16.1
diffusers==0.34.0
transformers==4.54.0
accelerate==1.9.0
wandb==0.16.1
scikit-image==0.22.0
opencv-python==4.9.0.80
git+https://github.com/1adrianb/face-alignment.git
huggingface_hub==0.34.3
numpy<2.0.0
================================================
FILE: examples/boft_controlnet/test_controlnet.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The implementation is based on "Parameter-Efficient Orthogonal Finetuning
# via Butterfly Factorization" (https://huggingface.co/papers/2311.06243) in ICLR 2024.
import os
import sys
import time
from pathlib import Path
import numpy as np
import torch
from accelerate import Accelerator
from diffusers import DDIMScheduler
from diffusers.utils import check_min_version
from safetensors.torch import load_file
from tqdm import tqdm
from transformers import AutoTokenizer
from utils.args_loader import parse_args
from utils.dataset import make_dataset
from utils.light_controlnet import ControlNetModel
from utils.pipeline_controlnet import LightControlNetPipeline
from utils.unet_2d_condition import UNet2DConditionNewModel
sys.path.append("../../src")
from peft import PeftModel # noqa: E402
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
if torch.xpu.is_available():
device = "xpu:0"
elif torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=logging_dir,
)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
val_dataset = make_dataset(args, tokenizer, accelerator, "test")
controlnet_path = args.controlnet_path
unet_path = args.unet_path
controlnet = ControlNetModel()
controlnet.load_state_dict(load_file(controlnet_path))
unet = UNet2DConditionNewModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
unet = PeftModel.from_pretrained(unet, unet_path, adapter_name=args.adapter_name)
pipe = LightControlNetPipeline.from_pretrained(
args.pretrained_model_name_or_path,
controlnet=controlnet,
unet=unet.model,
dtype=torch.float32,
requires_safety_checker=False,
).to(device)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir, exist_ok=True)
exist_lst = [int(img.split("_")[-1][:-4]) for img in os.listdir(args.output_dir)]
all_lst = np.arange(len(val_dataset))
idx_lst = [item for item in all_lst if item not in exist_lst]
print("Number of images to be processed: ", len(idx_lst))
np.random.seed(seed=int(time.time()))
np.random.shuffle(idx_lst)
for idx in tqdm(idx_lst):
output_path = os.path.join(args.output_dir, f"pred_img_{idx:04d}.png")
if not os.path.exists(output_path):
data = val_dataset[idx.item()]
negative_prompt = "low quality, blurry, unfinished"
with torch.no_grad():
pred_img = pipe(
data["text"],
[data["conditioning_pixel_values"]],
num_inference_steps=50,
guidance_scale=7,
negative_prompt=negative_prompt,
).images[0]
pred_img.save(output_path)
# control_img = Image.fromarray(
# (data["conditioning_pixel_value"] * 255).numpy().transpose(1, 2, 0).astype(np.uint8)
# )
# gt_img = Image.fromarray(
# ((data["pixel_value"] + 1.0) * 0.5 * 255).numpy().transpose(1, 2, 0).astype(np.uint8)
# )
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/boft_controlnet/test_controlnet.sh
================================================
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
ITER_NUM=50000
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATASET_NAME="oftverse/control-celeba-hq"
export CKPT_NAME="checkpoint-${ITER_NUM}"
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}/${CKPT_NAME}"
export CONTROLNET_PATH="${OUTPUT_DIR}/controlnet/model.safetensors"
export UNET_PATH="${OUTPUT_DIR}/unet"
export RESULTS_PATH="${OUTPUT_DIR}/results"
accelerate launch test_controlnet.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME \
--controlnet_path=$CONTROLNET_PATH \
--unet_path=$UNET_PATH \
--adapter_name=$RUN_NAME \
--output_dir=$RESULTS_PATH \
--dataset_name=$DATASET_NAME \
================================================
FILE: examples/boft_controlnet/train_controlnet.py
================================================
#!/usr/bin/env python
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The implementation is based on "Parameter-Efficient Orthogonal Finetuning
# via Butterfly Factorization" (https://huggingface.co/papers/2311.06243) in ICLR 2024.
import itertools
import logging
import math
import os
from pathlib import Path
import datasets
import diffusers
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
AutoencoderKL,
DDIMScheduler,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from packaging import version
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from utils.args_loader import (
import_model_class_from_model_name_or_path,
parse_args,
)
from utils.dataset import collate_fn, log_validation, make_dataset
from utils.light_controlnet import ControlNetModel
from utils.tracemalloc import TorchTracemalloc, b2mb
from utils.unet_2d_condition import UNet2DConditionNewModel
from peft import BOFTConfig, get_peft_model
from peft.peft_model import PeftModel
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.16.0.dev0")
logger = get_logger(__name__)
UNET_TARGET_MODULES = ["to_q", "to_v", "to_k", "query", "value", "key"]
TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
@torch.no_grad()
def save_adaptor(accelerator, output_dir, nets_dict):
for net_key in nets_dict.keys():
net_model = nets_dict[net_key]
unwarpped_net = accelerator.unwrap_model(net_model)
if isinstance(unwarpped_net, PeftModel):
unwarpped_net.save_pretrained(
os.path.join(output_dir, net_key),
state_dict=accelerator.get_state_dict(net_model),
safe_serialization=True,
)
else:
accelerator.save_model(
unwarpped_net,
os.path.join(output_dir, net_key),
safe_serialization=True,
)
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=logging_dir,
)
if args.report_to == "wandb":
wandb_init = {
"wandb": {
"name": args.wandb_run_name,
"mode": "online",
}
}
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionNewModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
controlnet = ControlNetModel()
if args.controlnet_model_name_or_path != "":
logger.info(f"Loading existing controlnet weights from {args.controlnet_model_name_or_path}")
controlnet.load_state_dict(torch.load(args.controlnet_model_name_or_path))
if args.use_boft:
config = BOFTConfig(
boft_block_size=args.boft_block_size,
boft_block_num=args.boft_block_num,
boft_n_butterfly_factor=args.boft_n_butterfly_factor,
target_modules=UNET_TARGET_MODULES,
boft_dropout=args.boft_dropout,
bias=args.boft_bias,
)
unet = get_peft_model(unet, config)
unet.print_trainable_parameters()
vae.requires_grad_(False)
controlnet.requires_grad_(True)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
unet.train()
controlnet.train()
if args.train_text_encoder and args.use_boft:
config = BOFTConfig(
boft_block_size=args.boft_block_size,
boft_block_num=args.boft_block_num,
boft_n_butterfly_factor=args.boft_n_butterfly_factor,
target_modules=TEXT_ENCODER_TARGET_MODULES,
boft_dropout=args.boft_dropout,
bias=args.boft_bias,
)
text_encoder = get_peft_model(text_encoder, config, adapter_name=args.wandb_run_name)
text_encoder.print_trainable_parameters()
if args.train_text_encoder:
text_encoder.train()
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
controlnet.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if accelerator.device.type == "xpu":
logger.warning("XPU doesn't support xformers yet, xformers is not applied.")
elif is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warning(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
controlnet.enable_xformers_memory_efficient_attention()
if args.train_text_encoder and not (args.use_lora or args.use_boft or args.use_oft):
text_encoder.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
controlnet.enable_gradient_checkpointing()
unet.enable_gradient_checkpointing()
if args.train_text_encoder and not (args.use_lora or args.use_boft or args.use_oft):
text_encoder.gradient_checkpointing_enable()
# Check that all trainable models are in full precision
low_precision_error_string = (
" Please make sure to always have all model weights in full float32 precision when starting training - even if"
" doing mixed precision training, copy of the weights should still be float32."
)
if accelerator.unwrap_model(controlnet).dtype != torch.float32:
raise ValueError(
f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet).dtype}. {low_precision_error_string}"
)
if accelerator.unwrap_model(unet).dtype != torch.float32:
raise ValueError(
f"UNet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
)
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
params_to_optimize = [param for param in controlnet.parameters() if param.requires_grad]
params_to_optimize += [param for param in unet.parameters() if param.requires_grad]
if args.train_text_encoder:
params_to_optimize += [param for param in text_encoder.parameters() if param.requires_grad]
# Optimizer creation
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Load the dataset
train_dataset = make_dataset(args, tokenizer, accelerator, "train")
val_dataset = make_dataset(args, tokenizer, accelerator, "test")
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
controlnet, optimizer, train_dataloader, lr_scheduler
)
if args.train_text_encoder:
text_encoder = accelerator.prepare(text_encoder)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers(args.wandb_project_name, config=vars(args), init_kwargs=wandb_init)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
if "checkpoint-current" in dirs:
path = "checkpoint-current"
dirs = [d for d in dirs if d.startswith("checkpoint") and d.endswith("0")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
else:
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
if path.split("-")[1] == "current":
global_step = int(dirs[-1].split("-")[1])
else:
global_step = int(path.split("-")[1])
initial_global_step = global_step
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
disable=not accelerator.is_local_main_process,
)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
with TorchTracemalloc() as tracemalloc:
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
continue
with accelerator.accumulate(controlnet), accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
# Get the guided hint for the UNet (320 dim)
guided_hint = controlnet(
controlnet_cond=controlnet_image,
)
# Predict the noise residual
model_pred = unet(
noisy_latents,
timesteps,
guided_hint=guided_hint,
encoder_hidden_states=encoder_hidden_states,
).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(controlnet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else itertools.chain(
controlnet.parameters(),
)
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
global_step += 1
step_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
if accelerator.is_main_process:
if global_step % args.validation_steps == 0 or global_step == 1:
logger.info(f"Running validation... \n Generating {args.num_validation_images} images.")
logger.info("Running validation... ")
with torch.no_grad():
log_validation(val_dataset, text_encoder, unet, controlnet, args, accelerator)
if global_step % args.checkpointing_steps == 0:
save_adaptor(accelerator, step_save_path, {"controlnet": controlnet, "unet": unet})
# save text_encoder if any
if args.train_text_encoder:
save_adaptor(accelerator, step_save_path, {"text_encoder": text_encoder})
accelerator.save_state(step_save_path)
logger.info(f"Saved {global_step} state to {step_save_path}")
logger.info(f"Saved current state to {step_save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the train : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the train (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/boft_controlnet/train_controlnet.sh
================================================
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
export DATASET_NAME="oftverse/control-celeba-hq"
export PROJECT_NAME="controlnet_${PEFT_TYPE}"
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export CONTROLNET_PATH=""
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}"
accelerate launch train_controlnet.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--resume_from_checkpoint=$RESUME_PATH \
--controlnet_model_name_or_path=$CONTROLNET_PATH \
--output_dir=$OUTPUT_DIR \
--report_to="wandb" \
--dataset_name=$DATASET_NAME \
--resolution=512 \
--learning_rate=1e-5 \
--checkpointing_steps=500 \
--max_train_steps=50000 \
--validation_steps=5000 \
--num_validation_images=12 \
--train_batch_size=4 \
--dataloader_num_workers=2 \
--seed="0" \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--wandb_project_name=$PROJECT_NAME \
--wandb_run_name=$RUN_NAME \
--enable_xformers_memory_efficient_attention \
--use_boft \
--boft_block_num=$BLOCK_NUM \
--boft_block_size=$BLOCK_SIZE \
--boft_n_butterfly_factor=$N_BUTTERFLY_FACTOR \
--boft_dropout=0.1 \
--boft_bias="boft_only" \
================================================
FILE: examples/boft_controlnet/utils/__init__.py
================================================
================================================
FILE: examples/boft_controlnet/utils/args_loader.py
================================================
import argparse
import os
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesModelWithTransformation,
)
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--controlnet_model_name_or_path",
type=str,
default=None,
help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
" If not specified controlnet weights are initialized from unet.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help=(
"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
" float32 precision."
),
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--output_dir",
type=str,
default="controlnet-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
"instructions."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="wandb",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--wandb_run_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing the target image."
)
parser.add_argument(
"--conditioning_image_column",
type=str,
default="conditioning_image",
help="The column of the dataset containing the controlnet conditioning image.",
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--proportion_empty_prompts",
type=float,
default=0,
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
nargs="+",
help=(
"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
" Provide either a matching number of `--validation_image`s, a single `--validation_image`"
" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
),
)
parser.add_argument(
"--validation_image",
type=str,
default=None,
nargs="+",
help=(
"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
" a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
" `--validation_image` that will be used with all `--validation_prompt`s."
),
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run validation every X steps. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="train_controlnet",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
# evaluation arguments
parser.add_argument("--controlnet_path", type=str, default=None, help="Path to pretrained controlnet.")
parser.add_argument("--unet_path", type=str, default=None, help="Path to pretrained unet.")
parser.add_argument("--adapter_name", type=str, default=None, help="Name of the adapter to use.")
parser.add_argument("--vis_overlays", action="store_true", help="Whether to visualize the landmarks.")
# self-invented arguments
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--name",
type=str,
help=("The name of the current experiment run, consists of [data]-[prompt]"),
)
# BOFT args
parser.add_argument("--use_boft", action="store_true", help="Whether to use BOFT for parameter efficient tuning")
parser.add_argument("--boft_block_num", type=int, default=8, help="The number of BOFT blocks")
parser.add_argument("--boft_block_size", type=int, default=0, help="The size of BOFT blocks")
parser.add_argument("--boft_n_butterfly_factor", type=int, default=0, help="The number of butterfly factors")
parser.add_argument("--boft_dropout", type=float, default=0.1, help="BOFT dropout, only used if use_boft is True")
parser.add_argument(
"--boft_bias",
type=str,
default="none",
help="Bias type for BOFT. Can be 'none', 'all' or 'boft_only', only used if use_boft is True",
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
if args.dataset_name is not None and args.train_data_dir is not None:
raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
if args.validation_prompt is not None and args.validation_image is None:
raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
if args.validation_prompt is None and args.validation_image is not None:
raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
if (
args.validation_image is not None
and args.validation_prompt is not None
and len(args.validation_image) != 1
and len(args.validation_prompt) != 1
and len(args.validation_image) != len(args.validation_prompt)
):
raise ValueError(
"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
" or the same number of `--validation_prompt`s and `--validation_image`s"
)
if args.resolution % 8 != 0:
raise ValueError(
"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
)
return args
================================================
FILE: examples/boft_controlnet/utils/dataset.py
================================================
import random
import numpy as np
import torch
import wandb
from datasets import load_dataset
from diffusers import DDIMScheduler
from PIL import Image
from torchvision import transforms
from utils.pipeline_controlnet import LightControlNetPipeline
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def log_validation(val_dataset, text_encoder, unet, controlnet, args, accelerator):
pipeline = LightControlNetPipeline.from_pretrained(
args.pretrained_model_name_or_path,
controlnet=accelerator.unwrap_model(controlnet, keep_fp32_wrapper=True),
unet=accelerator.unwrap_model(unet, keep_fp32_wrapper=True).model,
text_encoder=accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True),
safety_checker=None,
revision=args.revision,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
image_logs = []
for idx in range(args.num_validation_images):
data = val_dataset[idx]
validation_prompt = data["text"]
validation_image = data["conditioning_pixel_values"]
image = pipeline(
validation_prompt,
[validation_image],
num_inference_steps=50,
generator=generator,
)[0][0]
image_logs.append(
{
"validation_image": validation_image,
"image": image,
"validation_prompt": validation_prompt,
}
)
for tracker in accelerator.trackers:
formatted_images = []
for log in image_logs:
image = log["image"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
image = wandb.Image(image, caption=validation_prompt)
formatted_images.append(image)
tracker.log({"validation": formatted_images})
del pipeline
torch.cuda.empty_cache()
def make_dataset(args, tokenizer, accelerator, split="train"):
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
if args.train_data_dir is not None:
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset[split].column_names
# Get the column names for input/target.
if args.image_column is None:
image_column = column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
if args.conditioning_image_column is None:
conditioning_image_column = column_names[2]
else:
conditioning_image_column = args.conditioning_image_column
if conditioning_image_column not in column_names:
raise ValueError(
f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if random.random() < args.proportion_empty_prompts:
captions.append("")
elif isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
return inputs.input_ids
image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
conditioning_image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
images = [image_transforms(image) for image in images]
conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]]
conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
examples["pixel_values"] = images
examples["conditioning_pixel_values"] = conditioning_images
examples["input_ids"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset[split] = dataset[split].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
split_dataset = dataset[split].with_transform(preprocess_train)
return split_dataset
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.stack([example["input_ids"] for example in examples])
return {
"pixel_values": pixel_values,
"conditioning_pixel_values": conditioning_pixel_values,
"input_ids": input_ids,
}
================================================
FILE: examples/boft_controlnet/utils/light_controlnet.py
================================================
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Optional, Union
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unets.unet_2d_blocks import (
CrossAttnDownBlock2D,
DownBlock2D,
)
from diffusers.utils import BaseOutput, logging
from torch import nn
from torch.nn import functional as F
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class ControlNetOutput(BaseOutput):
down_block_res_samples: tuple[torch.Tensor]
mid_block_res_sample: torch.Tensor
class ControlNetConditioningEmbedding(nn.Module):
"""
Quoting from https://huggingface.co/papers/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
(activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
model) to encode image-space conditions ... into feature maps ..."
"""
def __init__(
self,
conditioning_embedding_channels: int,
conditioning_channels: int = 3,
block_out_channels: tuple[int] = (16, 32, 96, 256),
):
super().__init__()
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
channel_out = block_out_channels[i + 1]
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = zero_module(
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
)
def forward(self, conditioning):
embedding = self.conv_in(conditioning)
embedding = F.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = F.silu(embedding)
embedding = self.conv_out(embedding)
return embedding
class ControlNetModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 4,
out_channels: int = 320,
controlnet_conditioning_channel_order: str = "rgb",
conditioning_embedding_out_channels: Optional[tuple[int]] = (16, 32, 96, 256),
):
super().__init__()
# for control image
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=out_channels,
block_out_channels=conditioning_embedding_out_channels,
)
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: dict[str, AttentionProcessor]):
if hasattr(module, "set_processor"):
processors[f"{name}.processor"] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, dict[str, AttentionProcessor]]):
r"""
Parameters:
`processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
of **all** `Attention` layers.
In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
self.set_attn_processor(AttnProcessor())
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
def set_attention_slice(self, slice_size):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = []
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim)
for child in module.children():
fn_recursive_retrieve_sliceable_dims(child)
# retrieve number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: list[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop())
for child in module.children():
fn_recursive_set_attention_slice(child, slice_size)
reversed_slice_size = list(reversed(slice_size))
for module in self.children():
fn_recursive_set_attention_slice(module, reversed_slice_size)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
module.gradient_checkpointing = value
def forward(
self,
controlnet_cond: torch.FloatTensor,
) -> Union[ControlNetOutput, tuple]:
# check channel order
channel_order = self.config.controlnet_conditioning_channel_order
if channel_order == "rgb":
# in rgb order by default
...
elif channel_order == "bgr":
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
else:
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
# 2. pre-process
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
return controlnet_cond
def zero_module(module):
for p in module.parameters():
nn.init.zeros_(p)
return module
================================================
FILE: examples/boft_controlnet/utils/pipeline_controlnet.py
================================================
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any, Optional, Union
import numpy as np
import PIL.Image
import torch
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline
from diffusers.utils import BaseOutput, logging
from torch.nn import functional as F
from utils.light_controlnet import ControlNetModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class LightControlNetPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, or `None` if safety checking could not be performed.
"""
images: Union[list[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[list[bool]]
class LightControlNetPipeline(StableDiffusionControlNetPipeline):
_optional_components = ["safety_checker", "feature_extractor"]
def check_inputs(
self,
prompt,
image,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
controlnet_conditioning_scale=1.0,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
if isinstance(prompt, list):
logger.warning(
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
" prompts. The conditionings will be fixed across the prompts."
)
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
self.check_image(image, prompt, prompt_embeds)
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if not isinstance(image, list):
raise TypeError("For multiple controlnets: `image` must be type `list`")
# When `image` is a nested list:
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
elif any(isinstance(i, list) for i in image):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif len(image) != len(self.controlnet.nets):
raise ValueError(
"For multiple controlnets: `image` must have the same length as the number of controlnets."
)
for image_ in image:
self.check_image(image_, prompt, prompt_embeds)
else:
assert False
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if isinstance(controlnet_conditioning_scale, list):
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
):
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False
@torch.no_grad()
def __call__(
self,
prompt: Union[str, list[str]] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
list[torch.FloatTensor],
list[PIL.Image.Image],
list[np.ndarray],
] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, list[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, list[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, list[float]] = 1.0,
guess_mode: bool = False,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
specified in init, images must be passed as a list such that each element of the list can be correctly
batched for input to a single controlnet.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
corresponding scale as a list.
guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
image,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
controlnet_conditioning_scale,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
controlnet = self.controlnet._orig_mod if hasattr(self.controlnet, "_orig_mod") else self.controlnet
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare image
if isinstance(controlnet, ControlNetModel):
image = self.prepare_image(
image=image,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
height, width = image.shape[-2:]
elif isinstance(controlnet, MultiControlNetModel):
images = []
for image_ in image:
image_ = self.prepare_image(
image=image_,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
images.append(image_)
image = images
height, width = image[0].shape[-2:]
else:
assert False
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 6. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# controlnet(s) inference
if guess_mode and do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
else:
control_model_input = latent_model_input
# Get the guided hint for the UNet (320 dim)
guided_hint = self.controlnet(
controlnet_cond=image,
)
# Predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
guided_hint=guided_hint,
encoder_hidden_states=prompt_embeds,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# If we do sequential model offloading, let's offload unet and controlnet
# manually for max memory savings
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
self.controlnet.to("cpu")
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return LightControlNetPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
================================================
FILE: examples/boft_controlnet/utils/tracemalloc.py
================================================
import gc
import threading
import psutil
import torch
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
self.device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
self.device_module = getattr(torch, self.device_type, torch.cuda)
gc.collect()
self.device_module.empty_cache()
self.device_module.reset_peak_memory_stats() # reset the peak gauge to zero
self.begin = self.device_module.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
"""get resident set size memory for the current process"""
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
# time.sleep(0.001) # 1msec
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
self.device_module.empty_cache()
self.end = self.device_module.memory_allocated()
self.peak = self.device_module.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
================================================
FILE: examples/boft_controlnet/utils/unet_2d_condition.py
================================================
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Any, Optional, Union
import torch
from diffusers.models import UNet2DConditionModel
from diffusers.utils import BaseOutput, logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class UNet2DConditionOutput(BaseOutput):
"""
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: torch.FloatTensor
class UNet2DConditionNewModel(UNet2DConditionModel):
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
guided_hint: Optional[torch.Tensor] = None,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[dict[str, Any]] = None,
added_cond_kwargs: Optional[dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, tuple]:
r"""
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
encoder_attention_mask (`torch.Tensor`):
(batch, sequence_length) cross-attention mask, applied to encoder_hidden_states. True = keep, False =
discard. Mask will be converted into a bias, which adds large negative values to attention scores
corresponding to "discard" tokens.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
added_cond_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified includes additonal conditions that can be used for additonal time
embeddings or encoder hidden states projections. See the configurations `encoder_hid_dim_type` and
`addition_embed_type` for more information.
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None:
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb, timestep_cond)
if self.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
if self.config.class_embed_type == "timestep":
class_labels = self.time_proj(class_labels)
# `Timesteps` does not contain any weights and will always return f32 tensors
# there might be better ways to encapsulate this.
class_labels = class_labels.to(dtype=sample.dtype)
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
if self.config.class_embeddings_concat:
emb = torch.cat([emb, class_emb], dim=-1)
else:
emb = emb + class_emb
if self.config.addition_embed_type == "text":
aug_emb = self.add_embedding(encoder_hidden_states)
emb = emb + aug_emb
elif self.config.addition_embed_type == "text_image":
# Kadinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
aug_emb = self.add_embedding(text_embs, image_embs)
emb = emb + aug_emb
if self.time_embed_act is not None:
emb = self.time_embed_act(emb)
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
# Kadinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
# 2. pre-process and insert conditioning (ControlNet)
# Note: the added "guided_hint" is the only difference between this implementation and the original UNet2DConditionModel
sample = self.conv_in(sample)
sample = guided_hint + sample if guided_hint is not None else sample
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
# 4. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
if mid_block_additional_residual is not None:
sample = sample + mid_block_additional_residual
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
upsample_size=upsample_size,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = upsample_block(
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
================================================
FILE: examples/boft_dreambooth/.gitignore
================================================
data/
================================================
FILE: examples/boft_dreambooth/__init__.py
================================================
================================================
FILE: examples/boft_dreambooth/boft_dreambooth.md
================================================
# DreamBooth fine-tuning with BOFT
This guide demonstrates how to use BOFT, an orthogonal fine-tuning method, to fine-tune Dreambooth with either `stabilityai/stable-diffusion-2-1` or `runwayml/stable-diffusion-v1-5` model.
By using BOFT from 🤗 PEFT, we can significantly reduce the number of trainable parameters while still achieving impressive results in various fine-tuning tasks across different foundation models. BOFT enhances model efficiency by integrating full-rank orthogonal matrices with a butterfly structure into specific model blocks, such as attention blocks, mirroring the approach used in LoRA. During fine-tuning, only these inserted matrices are trained, leaving the original model parameters untouched. During inference, the trainable BOFT parameters can be merged into the original model, eliminating any additional computational costs.
As a member of the **orthogonal finetuning** class, BOFT presents a systematic and principled method for fine-tuning. It possesses several unique properties and has demonstrated superior performance compared to LoRA in a variety of scenarios. For further details on BOFT, please consult the [PEFT's GitHub repo's concept guide OFT](https://https://huggingface.co/docs/peft/index), the [original BOFT paper](https://huggingface.co/papers/2311.06243) and the [original OFT paper](https://huggingface.co/papers/2306.07280).
In this guide we provide a Dreambooth fine-tuning script that is available in [PEFT's GitHub repo examples](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth). This implementation is adapted from [peft's lora_dreambooth](https://github.com/huggingface/peft/tree/main/examples/lora_dreambooth). You can try it out and finetune on your custom images.
## Set up your environment
Start by cloning the PEFT repository:
```bash
git clone --recursive https://github.com/huggingface/peft
```
Navigate to the directory containing the training scripts for fine-tuning Dreambooth with BOFT:
```bash
cd peft/examples/boft_dreambooth
```
Set up your environment: install PEFT, and all the required libraries. At the time of writing this guide we recommend installing PEFT from source. The following environment setup should work on A100 and H100:
### CUDA
```bash
conda create --name peft python=3.10
conda activate peft
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install xformers -c xformers
pip install -r requirements.txt
pip install git+https://github.com/huggingface/peft
```
The follwing environment setuo is validated work on Intel XPU:
### Intel XPU
```bash
conda create --name peft python=3.10
conda activate peft
pip install pip install torch==2.8.0.dev20250615+xpu torchvision==0.23.0.dev20250615+xpu torchaudio==2.8.0.dev20250615+xpu --index-url https://download.pytorch.org/whl/nightly/xpu --no-cache-dir
pip install -r requirements.txt
pip install git+https://github.com/huggingface/peft
```
## Download the data
[dreambooth](https://github.com/google/dreambooth) dataset should have been automatically cloned in the following structure when running the training script.
```
boft_dreambooth
├── data
│ ├── data_dir
│ └── dreambooth
│ └── data
│ ├── backpack
│ └── backpack_dog
│ ...
```
You can also put your custom images into `boft_dreambooth/data/dreambooth`.
## Finetune Dreambooth with BOFT
```bash
./train_dreambooth.sh
```
or using the following script arguments:
```bash
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
```
Here:
- `INSTANCE_DIR`: The directory containing the images that you intend to use for training your model.
- `CLASS_DIR`: The directory containing class-specific images. In this example, we use prior preservation to avoid overfitting and language-drift. For prior preservation, you need other images of the same class as part of the training process. However, these images can be generated and the training script will save them to a local path you specify here.
- `OUTPUT_DIR`: The destination folder for storing the trained model's weights.
To learn more about DreamBooth fine-tuning with prior-preserving loss, check out the [Diffusers documentation](https://huggingface.co/docs/diffusers/training/dreambooth#finetuning-with-priorpreserving-loss).
Launch the training script with `accelerate` and pass hyperparameters, as well as LoRa-specific arguments to it such as:
- `use_boft`: Enables BOFT in the training script.
- `boft_block_size`: the BOFT matrix block size across different layers, expressed in `int`. Smaller block size results in sparser update matrices with fewer trainable parameters. **Note**, please choose it to be dividable to most layer `in_features` dimension, e.g., 4, 8, 16. Also, you can only specify either `boft_block_size` or `boft_block_num`, but not both simultaneously, because `boft_block_size` x `boft_block_num` = layer dimension.
- `boft_block_num`: the number of BOFT matrix blocks across different layers, expressed in `int`. Fewer blocks result in sparser update matrices with fewer trainable parameters. **Note**, please choose it to be dividable to most layer `in_features` dimension, e.g., 4, 8, 16. Also, you can only specify either `boft_block_size` or `boft_block_num`, but not both simultaneously, because `boft_block_size` x `boft_block_num` = layer dimension.
- `boft_n_butterfly_factor`: the number of butterfly factors. **Note**, for `boft_n_butterfly_factor=1`, BOFT is the same as vanilla OFT, for `boft_n_butterfly_factor=2`, the effective block size of OFT becomes twice as big and the number of blocks becomes half.
- `bias`: specify if the `bias` parameters should be trained. Can be `none`, `all` or `boft_only`.
- `boft_dropout`: specify the probability of multiplicative dropout.
Here's what the full set of script arguments may look like:
```bash
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
VALIDATION_PROMPT=${PROMPT_LIST[@]}
INSTANCE_PROMPT="a photo of ${UNIQUE_TOKEN} ${CLASS_TOKEN}"
CLASS_PROMPT="a photo of ${CLASS_TOKEN}"
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export PROJECT_NAME="dreambooth_${PEFT_TYPE}"
export RUN_NAME="${SELECTED_SUBJECT}_${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export INSTANCE_DIR="./data/dreambooth/dataset/${SELECTED_SUBJECT}"
export CLASS_DIR="./data/class_data/${CLASS_TOKEN}"
export OUTPUT_DIR="./data/output/${PEFT_TYPE}"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir="$CLASS_DIR" \
--output_dir=$OUTPUT_DIR \
--wandb_project_name=$PROJECT_NAME \
--wandb_run_name=$RUN_NAME \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="$INSTANCE_PROMPT" \
--validation_prompt="$VALIDATION_PROMPT" \
--class_prompt="$CLASS_PROMPT" \
--resolution=512 \
--train_batch_size=1 \
--num_dataloader_workers=2 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--use_boft \
--boft_block_num=$BLOCK_NUM \
--boft_block_size=$BLOCK_SIZE \
--boft_n_butterfly_factor=$N_BUTTERFLY_FACTOR \
--boft_dropout=0.1 \
--boft_bias="boft_only" \
--learning_rate=3e-5 \
--max_train_steps=1010 \
--checkpointing_steps=200 \
--validation_steps=200 \
--enable_xformers_memory_efficient_attention \
--report_to="wandb" \
```
or use this training script:
```bash
./train_dreambooth.sh $idx
```
with the `$idx` corresponds to different subjects.
If you are running this script on Windows, you may need to set the `--num_dataloader_workers` to 0.
## Inference with a single adapter
To run inference with the fine-tuned model, simply run the jupyter notebook `dreambooth_inference.ipynb` for visualization with `jupyter notebook` under `./examples/boft_dreambooth`.
================================================
FILE: examples/boft_dreambooth/dreambooth_inference.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "acab479f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import torch\n",
"from accelerate.logging import get_logger\n",
"from diffusers import StableDiffusionPipeline\n",
"from diffusers.utils import check_min_version\n",
"\n",
"from peft import PeftModel\n",
"\n",
"# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n",
"check_min_version(\"0.10.0.dev0\")\n",
"\n",
"logger = get_logger(__name__)\n",
"\n",
"MODEL_NAME = \"stabilityai/stable-diffusion-2-1\"\n",
"# MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\n",
"\n",
"PEFT_TYPE=\"boft\"\n",
"BLOCK_NUM=8\n",
"BLOCK_SIZE=0\n",
"N_BUTTERFLY_FACTOR=1\n",
"SELECTED_SUBJECT=\"backpack\"\n",
"EPOCH_IDX = 200\n",
"\n",
"PROJECT_NAME=f\"dreambooth_{PEFT_TYPE}\"\n",
"RUN_NAME=f\"{SELECTED_SUBJECT}_{PEFT_TYPE}_{BLOCK_NUM}{BLOCK_SIZE}{N_BUTTERFLY_FACTOR}\"\n",
"OUTPUT_DIR=f\"./data/output/{PEFT_TYPE}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06cfd506",
"metadata": {},
"outputs": [],
"source": [
"def get_boft_sd_pipeline(\n",
" ckpt_dir, base_model_name_or_path=None, epoch=int, dtype=torch.float32, device=\"auto\", adapter_name=\"default\"\n",
"):\n",
" if device == \"auto\":\n",
" device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"\n",
" if base_model_name_or_path is None:\n",
" raise ValueError(\"Please specify the base model name or path\")\n",
"\n",
" pipe = StableDiffusionPipeline.from_pretrained(\n",
" base_model_name_or_path, torch_dtype=dtype, requires_safety_checker=False\n",
" ).to(device)\n",
" \n",
" load_adapter(pipe, ckpt_dir, epoch, adapter_name)\n",
"\n",
" if dtype in (torch.float16, torch.bfloat16):\n",
" pipe.unet.half()\n",
" pipe.text_encoder.half()\n",
"\n",
" pipe.to(device)\n",
" return pipe\n",
"\n",
"\n",
"def load_adapter(pipe, ckpt_dir, epoch, adapter_name=\"default\"):\n",
" \n",
" unet_sub_dir = os.path.join(ckpt_dir, f\"unet/{epoch}\", adapter_name)\n",
" text_encoder_sub_dir = os.path.join(ckpt_dir, f\"text_encoder/{epoch}\", adapter_name)\n",
" \n",
" if isinstance(pipe.unet, PeftModel):\n",
" pipe.unet.load_adapter(unet_sub_dir, adapter_name=adapter_name)\n",
" else:\n",
" pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)\n",
" \n",
" if os.path.exists(text_encoder_sub_dir):\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.load_adapter(text_encoder_sub_dir, adapter_name=adapter_name)\n",
" else:\n",
" pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)\n",
" \n",
"\n",
"def set_adapter(pipe, adapter_name):\n",
" pipe.unet.set_adapter(adapter_name)\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.set_adapter(adapter_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98a0d8ac",
"metadata": {},
"outputs": [],
"source": [
"prompt = \"a photo of sks backpack on a wooden floor\"\n",
"negative_prompt = \"low quality, blurry, unfinished\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4e888d2",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"pipe = get_boft_sd_pipeline(OUTPUT_DIR, MODEL_NAME, EPOCH_IDX, adapter_name=RUN_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1c1a1c0",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a1aafdf-8cf7-4e47-9471-26478034245e",
"metadata": {},
"outputs": [],
"source": [
"# load and reset another adapter\n",
"# WARNING: requires training DreamBooth with `boft_bias=None`\n",
"\n",
"SELECTED_SUBJECT=\"dog\"\n",
"EPOCH_IDX = 200\n",
"RUN_NAME=f\"{SELECTED_SUBJECT}_{PEFT_TYPE}_{BLOCK_NUM}{BLOCK_SIZE}{N_BUTTERFLY_FACTOR}\"\n",
"\n",
"load_adapter(pipe, OUTPUT_DIR, epoch=EPOCH_IDX, adapter_name=RUN_NAME)\n",
"set_adapter(pipe, adapter_name=RUN_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7091ad0-2005-4528-afc1-4f9d70a9a535",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"prompt = \"a photo of sks dog running on the beach\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:peft] *",
"language": "python",
"name": "conda-env-peft-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/boft_dreambooth/requirements.txt
================================================
transformers==4.54.0
accelerate==1.9.0
evaluate
tqdm
datasets==4.0.0
diffusers==0.34.0
Pillow
huggingface_hub
safetensors
nb_conda_kernels
ipykernel
ipywidgets
wandb==0.21.0
================================================
FILE: examples/boft_dreambooth/train_dreambooth.py
================================================
#!/usr/bin/env python
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The implementation is based on "Parameter-Efficient Orthogonal Finetuning
# via Butterfly Factorization" (https://huggingface.co/papers/2311.06243) in ICLR 2024.
import hashlib
import itertools
import logging
import math
import os
from contextlib import nullcontext
from pathlib import Path
import datasets
import diffusers
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from huggingface_hub import Repository
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from utils.args_loader import (
get_full_repo_name,
import_model_class_from_model_name_or_path,
parse_args,
)
from utils.dataset import DreamBoothDataset, PromptDataset, collate_fn
from utils.tracemalloc import TorchTracemalloc, b2mb
from peft import BOFTConfig, get_peft_model
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.16.0.dev0")
logger = get_logger(__name__)
UNET_TARGET_MODULES = ["to_q", "to_v", "to_k", "query", "value", "key", "to_out.0", "add_k_proj", "add_v_proj"]
TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
def save_adaptor(accelerator, step, unet, text_encoder, args):
unwarpped_unet = accelerator.unwrap_model(unet)
unwarpped_unet.save_pretrained(
os.path.join(args.output_dir, f"unet/{step}"), state_dict=accelerator.get_state_dict(unet)
)
if args.train_text_encoder:
unwarpped_text_encoder = accelerator.unwrap_model(text_encoder)
unwarpped_text_encoder.save_pretrained(
os.path.join(args.output_dir, f"text_encoder/{step}"),
state_dict=accelerator.get_state_dict(text_encoder),
)
def main(args):
validation_prompts = list(filter(None, args.validation_prompt[0].split(".")))
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=accelerator_project_config,
)
if args.report_to == "wandb":
import wandb
wandb_init = {
"wandb": {
"name": args.wandb_run_name,
"mode": "online",
}
}
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
global_seed = hash(args.wandb_run_name) % (2**32)
set_seed(global_seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
dtype = torch.float16 if accelerator.device.type in ["cuda", "xpu"] else torch.float32
if args.prior_generation_precision == "fp32":
dtype = torch.float32
elif args.prior_generation_precision == "fp16":
dtype = torch.float16
elif args.prior_generation_precision == "bf16":
dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
dtype=dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name) # noqa: F841
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
if args.use_boft:
config = BOFTConfig(
boft_block_size=args.boft_block_size,
boft_block_num=args.boft_block_num,
boft_n_butterfly_factor=args.boft_n_butterfly_factor,
target_modules=UNET_TARGET_MODULES,
boft_dropout=args.boft_dropout,
bias=args.boft_bias,
)
unet = get_peft_model(unet, config, adapter_name=args.wandb_run_name)
unet.print_trainable_parameters()
vae.requires_grad_(False)
unet.train()
if args.train_text_encoder and args.use_boft:
config = BOFTConfig(
boft_block_size=args.boft_block_size,
boft_block_num=args.boft_block_num,
boft_n_butterfly_factor=args.boft_n_butterfly_factor,
target_modules=TEXT_ENCODER_TARGET_MODULES,
boft_dropout=args.boft_dropout,
bias=args.boft_bias,
)
text_encoder = get_peft_model(text_encoder, config, adapter_name=args.wandb_run_name)
text_encoder.print_trainable_parameters()
text_encoder.train()
else:
text_encoder.requires_grad_(False)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if accelerator.device.type == "xpu":
logger.warn("XPU hasn't support xformers yet, ignore it.")
elif is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# below fails when using boft so commenting it out
if args.train_text_encoder and not args.use_boft:
text_encoder.gradient_checkpointing_enable()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = [param for param in unet.parameters() if param.requires_grad]
if args.train_text_encoder:
params_to_optimize += [param for param in text_encoder.parameters() if param.requires_grad]
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Download the official dreambooth dataset from the official repository: https://github.com/google/dreambooth.git
data_path = os.path.join(os.getcwd(), "data", "dreambooth")
if not os.path.exists(data_path):
os.makedirs(os.path.join(os.getcwd(), "data"), exist_ok=True)
os.system(f"git clone https://github.com/google/dreambooth.git '{data_path}'")
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.num_dataloader_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and text_encoder to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers(args.wandb_project_name, config=vars(args), init_kwargs=wandb_init)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = resume_global_step // num_update_steps_per_epoch
resume_step = resume_global_step % num_update_steps_per_epoch
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
if args.train_text_encoder:
text_encoder.train()
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
with TorchTracemalloc() if not args.no_tracemalloc else nullcontext() as tracemalloc:
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
global_step += 1
if global_step % args.checkpointing_steps == 0 and global_step != 0:
if accelerator.is_main_process:
save_adaptor(accelerator, global_step, unet, text_encoder, args)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if (
args.validation_prompt is not None
and (step + num_update_steps_per_epoch * epoch) % args.validation_steps == 0
and global_step > 10
):
unet.eval()
logger.info(
f"Running validation... \n Generating {len(validation_prompts)} images with prompt:"
f" {validation_prompts[0]}, ......"
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
safety_checker=None,
revision=args.revision,
)
# set `keep_fp32_wrapper` to True because we do not want to remove
# mixed precision hooks while we are still training
pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
if args.seed is not None:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
else:
generator = None
# images = []
# for _ in range(args.num_validation_images):
# image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
# images.append(image)
images = []
val_img_dir = os.path.join(
args.output_dir,
f"validation/{global_step}",
args.wandb_run_name,
)
os.makedirs(val_img_dir, exist_ok=True)
for val_promot in validation_prompts:
image = pipeline(val_promot, num_inference_steps=50, generator=generator).images[0]
image.save(os.path.join(val_img_dir, f"{'_'.join(val_promot.split(' '))}.png"[1:]))
images.append(image)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
import wandb
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
if global_step >= args.max_train_steps:
break
# Printing the accelerator memory usage details such as allocated memory, peak memory, and total memory usage
if not args.no_tracemalloc:
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the train : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the train (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/boft_dreambooth/train_dreambooth.sh
================================================
IDX=$1
PROMPT_IDX=$((IDX % 25))
CLASS_IDX=$((IDX % 30))
# Define the UNIQUE_TOKEN, CLASS_TOKENs, and SUBJECT_NAMES
UNIQUE_TOKEN="qwe"
SUBJECT_NAMES=(
"backpack" "backpack_dog" "bear_plushie" "berry_bowl" "can"
"candle" "cat" "cat2" "clock" "colorful_sneaker"
"dog" "dog2" "dog3" "dog5" "dog6"
"dog7" "dog8" "duck_toy" "fancy_boot" "grey_sloth_plushie"
"monster_toy" "pink_sunglasses" "poop_emoji" "rc_car" "red_cartoon"
"robot_toy" "shiny_sneaker" "teapot" "vase" "wolf_plushie"
)
CLASS_TOKENs=(
"backpack" "backpack" "stuffed animal" "bowl" "can"
"candle" "cat" "cat" "clock" "sneaker"
"dog" "dog" "dog" "dog" "dog"
"dog" "dog" "toy" "boot" "stuffed animal"
"toy" "glasses" "toy" "toy" "cartoon"
"toy" "sneaker" "teapot" "vase" "stuffed animal"
)
CLASS_TOKEN=${CLASS_TOKENs[$CLASS_IDX]}
SELECTED_SUBJECT=${SUBJECT_NAMES[$CLASS_IDX]}
if [[ $CLASS_IDX =~ ^(0|1|2|3|4|5|8|9|17|18|19|20|21|22|23|24|25|26|27|28|29)$ ]]; then
PROMPT_LIST=(
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the jungle."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the snow."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on the beach."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on a cobblestone street."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of pink fabric."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a wooden floor."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a city in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a mountain in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a blue house in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a purple rug in a forest."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a wheat field in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a tree and autumn leaves in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with the Eiffel Tower in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} floating on top of water."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} floating in an ocean of milk."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of green grass with sunflowers around it."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a mirror."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of the sidewalk in a crowded street."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a dirt road."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a white rug."
"a red ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a purple ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a shiny ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a wet ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a cube shaped ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
)
prompt_test_list=(
"a ${CLASS_TOKEN} in the jungle"
"a ${CLASS_TOKEN} in the snow"
"a ${CLASS_TOKEN} on the beach"
"a ${CLASS_TOKEN} on a cobblestone street"
"a ${CLASS_TOKEN} on top of pink fabric"
"a ${CLASS_TOKEN} on top of a wooden floor"
"a ${CLASS_TOKEN} with a city in the background"
"a ${CLASS_TOKEN} with a mountain in the background"
"a ${CLASS_TOKEN} with a blue house in the background"
"a ${CLASS_TOKEN} on top of a purple rug in a forest"
"a ${CLASS_TOKEN} with a wheat field in the background"
"a ${CLASS_TOKEN} with a tree and autumn leaves in the background"
"a ${CLASS_TOKEN} with the Eiffel Tower in the background"
"a ${CLASS_TOKEN} floating on top of water"
"a ${CLASS_TOKEN} floating in an ocean of milk"
"a ${CLASS_TOKEN} on top of green grass with sunflowers around it"
"a ${CLASS_TOKEN} on top of a mirror"
"a ${CLASS_TOKEN} on top of the sidewalk in a crowded street"
"a ${CLASS_TOKEN} on top of a dirt road"
"a ${CLASS_TOKEN} on top of a white rug"
"a red ${CLASS_TOKEN}"
"a purple ${CLASS_TOKEN}"
"a shiny ${CLASS_TOKEN}"
"a wet ${CLASS_TOKEN}"
"a cube shaped ${CLASS_TOKEN}"
)
else
PROMPT_LIST=(
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the jungle."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the snow."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on the beach."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on a cobblestone street."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of pink fabric."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a wooden floor."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a city in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a mountain in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a blue house in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a purple rug in a forest."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a red hat."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a santa hat."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a rainbow scarf."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a black top hat and a monocle."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a chef outfit."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a firefighter outfit."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a police outfit."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing pink glasses."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a yellow shirt."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a purple wizard outfit."
"a red ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a purple ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a shiny ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a wet ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a cube shaped ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
)
prompt_test_list=(
"a ${CLASS_TOKEN} in the jungle"
"a ${CLASS_TOKEN} in the snow"
"a ${CLASS_TOKEN} on the beach"
"a ${CLASS_TOKEN} on a cobblestone street"
"a ${CLASS_TOKEN} on top of pink fabric"
"a ${CLASS_TOKEN} on top of a wooden floor"
"a ${CLASS_TOKEN} with a city in the background"
"a ${CLASS_TOKEN} with a mountain in the background"
"a ${CLASS_TOKEN} with a blue house in the background"
"a ${CLASS_TOKEN} on top of a purple rug in a forest"
"a ${CLASS_TOKEN} wearing a red hat"
"a ${CLASS_TOKEN} wearing a santa hat"
"a ${CLASS_TOKEN} wearing a rainbow scarf"
"a ${CLASS_TOKEN} wearing a black top hat and a monocle"
"a ${CLASS_TOKEN} in a chef outfit"
"a ${CLASS_TOKEN} in a firefighter outfit"
"a ${CLASS_TOKEN} in a police outfit"
"a ${CLASS_TOKEN} wearing pink glasses"
"a ${CLASS_TOKEN} wearing a yellow shirt"
"a ${CLASS_TOKEN} in a purple wizard outfit"
"a red ${CLASS_TOKEN}"
"a purple ${CLASS_TOKEN}"
"a shiny ${CLASS_TOKEN}"
"a wet ${CLASS_TOKEN}"
"a cube shaped ${CLASS_TOKEN}"
)
fi
VALIDATION_PROMPT=${PROMPT_LIST[@]}
INSTANCE_PROMPT="a photo of ${UNIQUE_TOKEN} ${CLASS_TOKEN}"
CLASS_PROMPT="a photo of ${CLASS_TOKEN}"
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
export PROJECT_NAME="dreambooth_${PEFT_TYPE}"
export RUN_NAME="${SELECTED_SUBJECT}_${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export INSTANCE_DIR="./data/dreambooth/dataset/${SELECTED_SUBJECT}"
export CLASS_DIR="./data/class_data/${CLASS_TOKEN}"
export OUTPUT_DIR="./data/output/${PEFT_TYPE}"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir="$CLASS_DIR" \
--output_dir=$OUTPUT_DIR \
--wandb_project_name=$PROJECT_NAME \
--wandb_run_name=$RUN_NAME \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="$INSTANCE_PROMPT" \
--validation_prompt="$VALIDATION_PROMPT" \
--class_prompt="$CLASS_PROMPT" \
--resolution=512 \
--train_batch_size=1 \
--num_dataloader_workers=2 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--use_boft \
--boft_block_num=$BLOCK_NUM \
--boft_block_size=$BLOCK_SIZE \
--boft_n_butterfly_factor=$N_BUTTERFLY_FACTOR \
--boft_dropout=0.1 \
--boft_bias="boft_only" \
--learning_rate=3e-5 \
--max_train_steps=1010 \
--checkpointing_steps=200 \
--validation_steps=200 \
--enable_xformers_memory_efficient_attention \
--report_to="wandb" \
================================================
FILE: examples/boft_dreambooth/utils/__init__.py
================================================
================================================
FILE: examples/boft_dreambooth/utils/args_loader.py
================================================
import argparse
import os
import warnings
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a Dreambooth training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--validation_prompt",
nargs="+",
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=500,
help=(
"Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
# boft args
parser.add_argument("--use_boft", action="store_true", help="Whether to use BOFT for parameter efficient tuning")
parser.add_argument("--boft_block_num", type=int, default=4, help="The number of BOFT blocks")
parser.add_argument("--boft_block_size", type=int, default=0, help="The size of BOFT blocks")
parser.add_argument("--boft_n_butterfly_factor", type=int, default=2, help="The number of butterfly factors")
parser.add_argument("--boft_dropout", type=float, default=0.1, help="BOFT dropout, only used if use_boft is True")
parser.add_argument(
"--boft_bias",
type=str,
default="none",
help="Bias type for BOFT. Can be 'none', 'all' or 'boft_only', only used if use_boft is True",
)
parser.add_argument(
"--num_dataloader_workers", type=int, default=1, help="Num of workers for the training dataloader."
)
parser.add_argument(
"--no_tracemalloc",
default=False,
action="store_true",
help="Flag to stop memory allocation tracing during training. This could speed up training on Windows.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="wandb",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--wandb_run_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
# if args.dataset_name is None and args.train_data_dir is None:
# raise ValueError("Need either a dataset name or a training folder.")
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
================================================
FILE: examples/boft_dreambooth/utils/dataset.py
================================================
from pathlib import Path
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def collate_fn(examples, with_prior_preservation=False):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
================================================
FILE: examples/boft_dreambooth/utils/tracemalloc.py
================================================
import gc
import threading
import psutil
import torch
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
self.device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
self.device_module = getattr(torch, self.device_type, torch.cuda)
gc.collect()
self.device_module.empty_cache()
self.device_module.reset_peak_memory_stats() # reset the peak gauge to zero
self.begin = self.device_module.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
"""get resident set size memory for the current process"""
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
# time.sleep(0.001) # 1msec
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
self.device_module.empty_cache()
self.end = self.device_module.memory_allocated()
self.peak = self.device_module.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
================================================
FILE: examples/cartridge_self_study/README.md
================================================
# CARTRIDGE self-study distillation (example)
This folder shows an **example** workflow for training a `CARTRIDGE` adapter via a SELF‑STUDY‑style
context-distillation objective (see the [Cartridges paper](https://huggingface.co/papers/2506.06266)).
PEFT intentionally keeps this training logic out of the core library; treat this as a starting point you can adapt.
## Installation
```bash
pip install -r requirements.txt
```
## Files
- `synthesize.py`: generates synthetic QA pairs about a corpus using vLLM with prefix caching.
- `train_distill.py`: trains a `CARTRIDGE` adapter via self-study distillation.
- `arxiv_synthesize.py`: like `synthesize.py`, with defaults for the Cartridges paper LaTeX.
- `arxiv_train.py`: like `train_distill.py`, with arxiv-specific defaults.
## How it works
1. **Synthesize**: Generate QA pairs where the model has access to the full document context
2. **Train**: Distill knowledge from teacher to student using a single model in memory:
- Teacher (adapter disabled): document + question → logits
- Student (adapter enabled): question + cartridge KV cache → logits
3. **Inference**: The trained cartridge provides compressed document knowledge as a KV cache prefix
## Run
### 1. Synthesize training data
```bash
python synthesize.py \
--model Qwen/Qwen3-4B \
--corpus_path /path/to/document.txt \
--out_jsonl distill.jsonl \
--num_samples 1024 \
--use_vllm
```
With `--use_vllm`, the document is cached and reused across all samples via automatic prefix caching.
### 2. Train cartridge
```bash
python train_distill.py \
--model Qwen/Qwen3-4B \
--document /path/to/document.txt \
--distill_jsonl distill.jsonl \
--output_dir cartridge_adapter \
--num_virtual_tokens 256 \
--num_frozen_tokens 1 \
--max_steps 500
```
If you want to follow the arXiv paper example locally, you can use the LaTeX source included in this repo at
`examples/cartridge_self_study/data/cartridges.tex` (download it first):
```bash
mkdir -p examples/cartridge_self_study/data
curl -L -o examples/cartridge_self_study/data/cartridges.tex \
https://raw.githubusercontent.com/HazyResearch/cartridges/refs/heads/main/examples/arxiv/cartridges.tex
```
### 3. Load and use cartridge
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(model, "cartridge_adapter")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")
inputs = tokenizer("What is the document about?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## arXiv example
Convenience wrappers for training on the Cartridges paper LaTeX:
```bash
# From the repo root:
# Synthesize QA pairs (uses vLLM with prefix caching)
python examples/cartridge_self_study/arxiv_synthesize.py \
--model Qwen/Qwen3-4B \
--corpus_path examples/cartridge_self_study/data/cartridges.tex \
--num_samples 1024 \
--use_vllm
# Train cartridge
python examples/cartridge_self_study/arxiv_train.py \
--model Qwen/Qwen3-4B \
--document examples/cartridge_self_study/data/cartridges.tex \
--distill_jsonl distill.jsonl \
--output_dir cartridge_adapter \
--max_steps 500
```
================================================
FILE: examples/cartridge_self_study/arxiv_synthesize.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from pathlib import Path
from synthesize import synthesize_self_study_jsonl
from transformers import AutoTokenizer
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="Qwen/Qwen2.5-0.5B-Instruct")
parser.add_argument(
"--corpus_path",
type=str,
default=str(Path(__file__).resolve().parent / "data/cartridges.tex"),
)
parser.add_argument("--out_jsonl", type=str, default="distill.jsonl")
parser.add_argument("--num_samples", type=int, default=256)
parser.add_argument("--seed_prompts", type=str, default="structuring,summarization,question,use_cases,creative")
parser.add_argument("--max_new_tokens", type=int, default=512)
parser.add_argument("--temperature", type=float, default=0.7)
parser.add_argument("--top_p", type=float, default=0.95)
parser.add_argument("--max_corpus_tokens", type=int, default=2048)
parser.add_argument(
"--use_vllm",
action="store_true",
help="Use vLLM for faster generation with automatic prefix caching.",
)
parser.add_argument("--seed", type=int, default=0, help="Seed for deterministic prompt-type shuffling.")
parser.add_argument(
"--tensor_parallel_size",
type=int,
default=1,
help="Tensor parallel size for vLLM (number of GPUs).",
)
args = parser.parse_args()
corpus_text = Path(args.corpus_path).read_text(encoding="utf-8")
tokenizer = AutoTokenizer.from_pretrained(args.model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if args.max_corpus_tokens is not None:
ids = tokenizer(
corpus_text,
add_special_tokens=False,
truncation=True,
max_length=args.max_corpus_tokens,
)["input_ids"]
corpus_text = tokenizer.decode(ids, skip_special_tokens=True)
if args.use_vllm:
from vllm import LLM
model = LLM(
model=args.model,
tensor_parallel_size=args.tensor_parallel_size,
enable_prefix_caching=True,
)
else:
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(args.model, dtype=torch.bfloat16, device_map="auto")
synthesize_self_study_jsonl(
output_path=Path(args.out_jsonl),
model=model,
tokenizer=tokenizer,
corpus_text=corpus_text,
num_samples=args.num_samples,
seed_prompt_types=[s.strip() for s in args.seed_prompts.split(",") if s.strip()],
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
top_p=args.top_p,
use_vllm=args.use_vllm,
seed=args.seed,
)
if __name__ == "__main__":
main()
================================================
FILE: examples/cartridge_self_study/arxiv_train.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from pathlib import Path
import torch
from train_distill import DistillationCollator, DistillationTrainer, DistillJsonlDataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import CartridgeConfig, get_peft_model
from peft.tuners.cartridge.utils import initialize_kv_prefix_from_text
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, required=True, help="Model to use for both teacher and student")
parser.add_argument("--document", type=str, required=True, help="Path to text file for KV cache initialization")
parser.add_argument("--distill_jsonl", type=str, default="distill.jsonl")
parser.add_argument("--output_dir", type=str, default="cartridge_adapter")
parser.add_argument("--num_virtual_tokens", type=int, default=256)
parser.add_argument("--num_frozen_tokens", type=int, default=1)
parser.add_argument("--top_k", type=int, default=20)
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--learning_rate", type=float, default=1e-3)
parser.add_argument("--max_steps", type=int, default=1000)
parser.add_argument("--device", type=str, default="cuda", choices=["cpu", "mps", "cuda", "xpu"])
parser.add_argument(
"--max_init_length", type=int, default=2048, help="Max tokens for text initialization (truncate long docs)"
)
args = parser.parse_args()
if args.device == "mps" and not (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()):
raise ValueError("Requested device 'mps' but MPS is not available.")
if args.device == "cuda" and not torch.cuda.is_available():
raise ValueError("Requested device 'cuda' but CUDA is not available.")
model_dtype = torch.float16 if args.device in {"cuda", "mps"} else None
device_map = args.device if args.device != "cpu" else None
tokenizer = AutoTokenizer.from_pretrained(args.model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
base_model = AutoModelForCausalLM.from_pretrained(args.model, dtype=model_dtype, device_map=device_map)
model = get_peft_model(
base_model,
CartridgeConfig(
task_type="CAUSAL_LM",
num_virtual_tokens=args.num_virtual_tokens,
num_frozen_tokens=args.num_frozen_tokens,
),
)
print(f"Initializing cartridge from document: {args.document}", flush=True)
document_text = Path(args.document).read_text()
initialize_kv_prefix_from_text(
model,
tokenizer,
text=document_text,
use_chat_template=False,
max_length=args.max_init_length,
)
print(f"Cartridge initialized with {args.num_virtual_tokens} tokens from text", flush=True)
ds = DistillJsonlDataset(args.distill_jsonl)
collator = DistillationCollator(tokenizer)
train_args = TrainingArguments(
output_dir=args.output_dir,
per_device_train_batch_size=args.per_device_train_batch_size,
learning_rate=args.learning_rate,
max_steps=args.max_steps,
logging_steps=10,
save_steps=100,
report_to=[],
remove_unused_columns=False,
use_cpu=args.device == "cpu",
dataloader_pin_memory=False,
)
trainer = DistillationTrainer(
model=model,
top_k=args.top_k,
args=train_args,
train_dataset=ds,
data_collator=collator,
)
trainer.train()
model.save_pretrained(args.output_dir)
if __name__ == "__main__":
main()
================================================
FILE: examples/cartridge_self_study/requirements.txt
================================================
torch
transformers
peft
vllm
================================================
FILE: examples/cartridge_self_study/synthesize.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
import random
from pathlib import Path
from transformers import AutoTokenizer
SEED_PROMPTS = {
"structuring": (
"Generate a single instruction asking an LLM to structure information from the document above. "
"Be specific about what section or topic to structure. "
"Output only the instruction, nothing else."
),
"summarization": (
"Generate a single instruction asking an LLM to summarize part of the document above. "
"Be explicit about which section to summarize. "
"Output only the instruction, nothing else."
),
"question": (
"Generate a question that tests knowledge of the document above. "
"Include specific details (names, dates, numbers) so the question is unambiguous. "
"Output only the question, nothing else."
),
"use_cases": (
"Think of a practical real-world task someone could accomplish using knowledge from the document. "
"Generate a single question or instruction reflecting that use case. "
"Output only the question/instruction, nothing else."
),
"creative": (
"Generate a creative question inspired by the document above. Output only the question, nothing else."
),
}
# Chat template kwargs to disable thinking mode for models like Qwen3
CHAT_TEMPLATE_KWARGS = {"enable_thinking": False}
MAX_NEW_TOKENS_FOR_QUESTIONS = 256
def synthesize_self_study_jsonl(
*,
output_path: Path,
model,
tokenizer,
corpus_text: str,
num_samples: int,
seed_prompt_types: list[str],
max_new_tokens: int,
temperature: float,
top_p: float,
use_vllm: bool = False,
seed: int = 0,
):
"""
Synthesize self-study data for cartridge training.
Uses the full corpus as context for all samples, varying only the seed prompt.
With vLLM's prefix caching, the document KV cache is computed once and reused.
If use_vllm=True, `model` should be a vllm.LLM instance.
Otherwise, `model` should be a HuggingFace model.
"""
output_path.parent.mkdir(parents=True, exist_ok=True)
if output_path.exists():
output_path.unlink()
for t in seed_prompt_types:
if t not in SEED_PROMPTS:
raise ValueError(f"Unknown seed prompt type '{t}', expected one of: {sorted(SEED_PROMPTS)}")
# Pre-generate prompt indices (cycling through seed prompt types).
prompt_indices = [i % len(seed_prompt_types) for i in range(num_samples)]
rng = random.Random(seed)
rng.shuffle(prompt_indices)
if use_vllm:
_synthesize_vllm(
output_path=output_path,
model=model,
tokenizer=tokenizer,
corpus_text=corpus_text,
seed_prompt_types=seed_prompt_types,
prompt_indices=prompt_indices,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
)
else:
_synthesize_hf(
output_path=output_path,
model=model,
tokenizer=tokenizer,
corpus_text=corpus_text,
seed_prompt_types=seed_prompt_types,
prompt_indices=prompt_indices,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
)
def _synthesize_vllm(
*,
output_path: Path,
model,
tokenizer,
corpus_text: str,
seed_prompt_types: list[str],
prompt_indices: list[int],
max_new_tokens: int,
temperature: float,
top_p: float,
):
"""Synthesize using vLLM with prefix caching (two-stage like original cartridges).
Stage 1: Generate questions using meta-prompts (all share document prefix)
Stage 2: Generate answers to those questions (all share document prefix)
"""
from vllm import SamplingParams
# Stage 1: Generate questions
question_messages = [
[
{"role": "system", "content": corpus_text},
{"role": "user", "content": SEED_PROMPTS[seed_prompt_types[prompt_idx]]},
]
for prompt_idx in prompt_indices
]
question_params = SamplingParams(
max_tokens=MAX_NEW_TOKENS_FOR_QUESTIONS,
temperature=temperature if temperature > 0 else 0.0,
top_p=top_p if temperature > 0 else 1.0,
)
print("Stage 1: Generating questions...")
question_outputs = model.chat(
question_messages,
question_params,
use_tqdm=True,
chat_template_kwargs=CHAT_TEMPLATE_KWARGS,
)
questions = [out.outputs[0].text.strip() for out in question_outputs]
# Stage 2: Generate answers
answer_messages = [
[
{"role": "system", "content": corpus_text},
{"role": "user", "content": question},
]
for question in questions
]
answer_params = SamplingParams(
max_tokens=max_new_tokens,
temperature=0.0,
top_p=1.0,
)
print("Stage 2: Generating answers...")
answer_outputs = model.chat(
answer_messages,
answer_params,
use_tqdm=True,
chat_template_kwargs=CHAT_TEMPLATE_KWARGS,
)
# Build training records
for i, (question, answer_out) in enumerate(zip(questions, answer_outputs)):
# Get the answer token IDs directly from vLLM output (avoids decode/re-encode mismatch)
answer_ids = list(answer_out.outputs[0].token_ids)
teacher_prompt_ids = tokenizer.apply_chat_template(
[{"role": "system", "content": corpus_text}, {"role": "user", "content": question}],
tokenize=True,
add_generation_prompt=True,
**CHAT_TEMPLATE_KWARGS,
)
student_prompt_ids = tokenizer.apply_chat_template(
[{"role": "user", "content": question}],
tokenize=True,
add_generation_prompt=True,
**CHAT_TEMPLATE_KWARGS,
)
record = {
"teacher_input_ids": teacher_prompt_ids + answer_ids,
"student_input_ids": student_prompt_ids + answer_ids,
"ctx_len": len(teacher_prompt_ids) - len(student_prompt_ids),
}
with output_path.open("a", encoding="utf-8") as f:
f.write(json.dumps(record) + "\n")
def _synthesize_hf(
*,
output_path: Path,
model,
tokenizer,
corpus_text: str,
seed_prompt_types: list[str],
prompt_indices: list[int],
max_new_tokens: int,
temperature: float,
top_p: float,
):
"""Synthesize using HuggingFace transformers (two-stage, one sample at a time)."""
import torch
from tqdm import tqdm
device = getattr(model, "device", torch.device("cpu"))
model.eval()
for prompt_idx in tqdm(prompt_indices, desc="Generating samples"):
meta_prompt = SEED_PROMPTS[seed_prompt_types[prompt_idx]]
# Stage 1: Generate question
question_input = tokenizer.apply_chat_template(
[{"role": "system", "content": corpus_text}, {"role": "user", "content": meta_prompt}],
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=False,
**CHAT_TEMPLATE_KWARGS,
).to(device)
gen_kwargs = {
"max_new_tokens": MAX_NEW_TOKENS_FOR_QUESTIONS,
"do_sample": temperature > 0,
"pad_token_id": tokenizer.pad_token_id or tokenizer.eos_token_id,
}
if temperature > 0:
gen_kwargs["temperature"] = max(temperature, 1e-5)
gen_kwargs["top_p"] = top_p
with torch.no_grad():
question_out = model.generate(question_input, **gen_kwargs)
question_tokens = question_out[0, question_input.shape[1] :].tolist()
question = tokenizer.decode(question_tokens, skip_special_tokens=True).strip()
# Stage 2: Generate answer
teacher_input = tokenizer.apply_chat_template(
[{"role": "system", "content": corpus_text}, {"role": "user", "content": question}],
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=False,
**CHAT_TEMPLATE_KWARGS,
).to(device)
student_input = tokenizer.apply_chat_template(
[{"role": "user", "content": question}],
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=False,
**CHAT_TEMPLATE_KWARGS,
).to(device)
with torch.no_grad():
answer_out = model.generate(
teacher_input,
max_new_tokens=max_new_tokens,
do_sample=False,
pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
)
answer_tokens = answer_out[0, teacher_input.shape[1] :].tolist()
record = {
"teacher_input_ids": answer_out[0].tolist(),
"student_input_ids": student_input[0].tolist() + answer_tokens,
"ctx_len": int(teacher_input.shape[1]) - int(student_input.shape[1]),
}
with output_path.open("a", encoding="utf-8") as f:
f.write(json.dumps(record) + "\n")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="Qwen/Qwen2.5-0.5B-Instruct")
parser.add_argument("--corpus_path", type=str, required=True)
parser.add_argument("--out_jsonl", type=str, required=True)
parser.add_argument("--num_samples", type=int, default=1024)
parser.add_argument("--seed_prompts", type=str, default="structuring,summarization,question,use_cases,creative")
parser.add_argument("--max_new_tokens", type=int, default=512)
parser.add_argument("--temperature", type=float, default=0.7)
parser.add_argument("--top_p", type=float, default=0.95)
parser.add_argument(
"--max_corpus_tokens",
type=int,
default=None,
help="Optional cap on the number of tokens used from the corpus.",
)
parser.add_argument(
"--use_vllm",
action="store_true",
help="Use vLLM for faster generation with automatic prefix caching.",
)
parser.add_argument("--seed", type=int, default=0, help="Seed for deterministic prompt-type shuffling.")
parser.add_argument(
"--tensor_parallel_size",
type=int,
default=1,
help="Tensor parallel size for vLLM (number of GPUs).",
)
args = parser.parse_args()
corpus_text = Path(args.corpus_path).read_text(encoding="utf-8")
tokenizer = AutoTokenizer.from_pretrained(args.model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if args.max_corpus_tokens is not None:
ids = tokenizer(
corpus_text,
add_special_tokens=False,
truncation=True,
max_length=args.max_corpus_tokens,
)["input_ids"]
corpus_text = tokenizer.decode(ids, skip_special_tokens=True)
if args.use_vllm:
from vllm import LLM
model = LLM(
model=args.model,
tensor_parallel_size=args.tensor_parallel_size,
enable_prefix_caching=True,
)
else:
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(args.model, dtype=torch.bfloat16, device_map="auto")
synthesize_self_study_jsonl(
output_path=Path(args.out_jsonl),
model=model,
tokenizer=tokenizer,
corpus_text=corpus_text,
num_samples=args.num_samples,
seed_prompt_types=[s.strip() for s in args.seed_prompts.split(",") if s.strip()],
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
top_p=args.top_p,
use_vllm=args.use_vllm,
seed=args.seed,
)
if __name__ == "__main__":
main()
================================================
FILE: examples/cartridge_self_study/train_distill.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
from pathlib import Path
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from peft import CartridgeConfig, get_peft_model
from peft.tuners.cartridge.utils import initialize_kv_prefix_from_text
class DistillJsonlDataset(Dataset):
def __init__(self, path: str | Path):
self.rows = []
with Path(path).open("r", encoding="utf-8") as f:
for line in f:
if line.strip():
self.rows.append(json.loads(line))
def __len__(self) -> int:
return len(self.rows)
def __getitem__(self, idx: int):
r = self.rows[idx]
return {
"teacher_input_ids": r["teacher_input_ids"],
"student_input_ids": r["student_input_ids"],
"ctx_len": r["ctx_len"],
}
class DistillationCollator:
def __init__(self, tokenizer):
self.tokenizer = tokenizer
def __call__(self, features):
teacher_ids = [{"input_ids": f["teacher_input_ids"]} for f in features]
student_ids = [{"input_ids": f["student_input_ids"]} for f in features]
teacher_batch = self.tokenizer.pad(teacher_ids, return_tensors="pt")
student_batch = self.tokenizer.pad(student_ids, return_tensors="pt")
ctx_len = torch.tensor([int(f["ctx_len"]) for f in features], dtype=torch.long)
return {
"teacher_input_ids": teacher_batch["input_ids"],
"teacher_attention_mask": teacher_batch["attention_mask"],
"student_input_ids": student_batch["input_ids"],
"student_attention_mask": student_batch["attention_mask"],
"ctx_len": ctx_len,
}
class DistillationTrainer(Trainer):
def __init__(self, *args, top_k: int = 20, teacher_temperature: float = 1.0, **kwargs):
super().__init__(*args, **kwargs)
self.top_k = int(top_k)
self.teacher_temperature = float(teacher_temperature)
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
teacher_input_ids = inputs["teacher_input_ids"].to(model.device)
teacher_attention_mask = inputs["teacher_attention_mask"].to(model.device)
student_input_ids = inputs["student_input_ids"].to(model.device)
student_attention_mask = inputs["student_attention_mask"].to(model.device)
ctx_len = inputs["ctx_len"].to(model.device)
with torch.no_grad():
with model.disable_adapter():
teacher_out = model(
input_ids=teacher_input_ids,
attention_mask=teacher_attention_mask,
use_cache=False,
)
teacher_logits = teacher_out.logits / max(self.teacher_temperature, 1e-5)
student_out = model(
input_ids=student_input_ids,
attention_mask=student_attention_mask,
use_cache=False,
)
student_logits = student_out.logits
# Vectorized distillation loss (avoids Python `.item()` in per-example indexing).
# Align teacher logits to student positions via the per-example `ctx_len` offset.
student_logits = student_logits[:, :-1, :] # [B, Ls-1, V]
seq_len = student_logits.shape[1]
pos = torch.arange(seq_len, device=student_logits.device)[None, :] # [1, Ls-1]
student_len = student_attention_mask.sum(dim=1).to(torch.long) # [B]
valid = pos < (student_len - 1).clamp(min=0)[:, None] # [B, Ls-1]
teacher_pos = ctx_len[:, None] + pos # [B, Ls-1]
in_bounds = teacher_pos < teacher_logits.shape[1]
valid = valid & in_bounds
teacher_pos = teacher_pos.clamp(min=0, max=teacher_logits.shape[1] - 1)
teacher_slice = teacher_logits.gather(
dim=1, index=teacher_pos[:, :, None].expand(-1, -1, teacher_logits.shape[-1])
) # [B, Ls-1, V]
k = min(self.top_k, teacher_slice.shape[-1])
topk_ids = torch.topk(teacher_slice, k=k, dim=-1).indices # [B, Ls-1, K]
teacher_logprobs = F.log_softmax(teacher_slice, dim=-1).gather(-1, topk_ids)
student_logprobs = F.log_softmax(student_logits, dim=-1).gather(-1, topk_ids)
loss_by_pos = -(teacher_logprobs.exp() * student_logprobs).sum(dim=-1) # [B, Ls-1]
loss_by_pos = loss_by_pos.masked_fill(~valid, 0.0)
denom = valid.sum(dim=1).clamp(min=1)
per_example = loss_by_pos.sum(dim=1) / denom
if valid.any():
loss = per_example[valid.any(dim=1)].mean()
else:
loss = student_logits.new_zeros(())
return (loss, student_out) if return_outputs else loss
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, required=True, help="Model to use for both teacher and student")
parser.add_argument("--distill_jsonl", type=str, required=True)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument("--document", type=str, required=True, help="Path to text file for KV cache initialization")
parser.add_argument("--num_virtual_tokens", type=int, default=256)
parser.add_argument("--num_frozen_tokens", type=int, default=1)
parser.add_argument("--top_k", type=int, default=20)
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--learning_rate", type=float, default=1e-3)
parser.add_argument("--max_steps", type=int, default=1000)
parser.add_argument("--device", type=str, default="cuda", choices=["cpu", "mps", "cuda", "xpu"])
parser.add_argument(
"--max_init_length", type=int, default=2048, help="Max tokens for text initialization (truncate long docs)"
)
args = parser.parse_args()
if args.device == "mps" and not (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()):
raise ValueError("Requested device 'mps' but MPS is not available.")
if args.device == "xpu" and not torch.xpu.is_available():
raise ValueError("Requested device 'xpu' but XPU is not available.")
if args.device == "cuda" and not torch.cuda.is_available():
raise ValueError("Requested device 'cuda' but CUDA is not available.")
model_dtype = torch.float16 if args.device in {"cuda", "mps", "xpu"} else None
device_map = args.device if args.device != "cpu" else None
tokenizer = AutoTokenizer.from_pretrained(args.model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
base_model = AutoModelForCausalLM.from_pretrained(args.model, dtype=model_dtype, device_map=device_map)
model = get_peft_model(
base_model,
CartridgeConfig(
task_type="CAUSAL_LM",
num_virtual_tokens=args.num_virtual_tokens,
num_frozen_tokens=args.num_frozen_tokens,
),
)
print(f"Initializing cartridge from document: {args.document}", flush=True)
document_text = Path(args.document).read_text()
initialize_kv_prefix_from_text(
model,
tokenizer,
text=document_text,
use_chat_template=False,
max_length=args.max_init_length,
)
print(f"Cartridge initialized with {args.num_virtual_tokens} tokens from text", flush=True)
ds = DistillJsonlDataset(args.distill_jsonl)
collator = DistillationCollator(tokenizer)
train_args = TrainingArguments(
output_dir=args.output_dir,
per_device_train_batch_size=args.per_device_train_batch_size,
learning_rate=args.learning_rate,
max_steps=args.max_steps,
logging_steps=10,
save_steps=100,
report_to=[],
remove_unused_columns=False,
use_cpu=args.device == "cpu",
dataloader_pin_memory=False,
)
trainer = DistillationTrainer(
model=model,
top_k=args.top_k,
args=train_args,
train_dataset=ds,
data_collator=collator,
)
trainer.train()
model.save_pretrained(args.output_dir)
if __name__ == "__main__":
main()
================================================
FILE: examples/causal_language_modeling/accelerate_ds_zero3_cpu_offload_config.yaml
================================================
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 1.0
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config: {}
machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: 'no'
num_machines: 1
num_processes: 1
rdzv_backend: static
same_network: true
use_cpu: false
================================================
FILE: examples/causal_language_modeling/peft_ln_tuning_clm.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "71fbfca2",
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM\n",
"from peft import get_peft_config, get_peft_model, LNTuningConfig, TaskType, PeftType\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"\n",
"# Hyper-parameters\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"model_name_or_path = \"bigscience/bloomz-560m\"\n",
"tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n",
"peft_config = LNTuningConfig(\n",
" task_type=TaskType.CAUSAL_LM,\n",
")\n",
"\n",
"dataset_name = \"twitter_complaints\"\n",
"checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\n",
" \"/\", \"_\"\n",
")\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
"max_length = 64\n",
"lr = 5e-2\n",
"num_epochs = 50\n",
"batch_size = 8"
]
},
{
"cell_type": "markdown",
"id": "a617882d",
"metadata": {},
"source": [
"## Load and Process Dataset for LM Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1a3648b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Unlabeled', 'complaint', 'no complaint']\n",
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
" num_rows: 50\n",
" })\n",
" test: Dataset({\n",
" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
" num_rows: 3399\n",
" })\n",
"})\n"
]
},
{
"data": {
"text/plain": [
"{'Tweet text': '@HMRCcustomers No this is my first job',\n",
" 'ID': 0,\n",
" 'Label': 2,\n",
" 'text_label': 'no complaint'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = load_dataset(\n",
" \"parquet\",\n",
" data_files={\n",
" \"train\": f\"hf://datasets/ought/raft@refs/convert/parquet/{dataset_name}/train/0000.parquet\",\n",
" \"test\": f\"hf://datasets/ought/raft@refs/convert/parquet/{dataset_name}/test/0000.parquet\"\n",
" }\n",
")\n",
"\n",
"classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"Label\"].names]\n",
"print(classes)\n",
"dataset = dataset.map(\n",
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fe12d4d3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Running tokenizer on dataset: 100%|██████████| 50/50 [00:00<00:00, 3551.43 examples/s]\n",
"Running tokenizer on dataset: 100%|██████████| 3399/3399 [00:00<00:00, 8558.01 examples/s]\n"
]
}
],
"source": [
"# data preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
"if tokenizer.pad_token_id is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
"print(target_max_length)\n",
"\n",
"\n",
"def preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" targets = [str(x) for x in examples[label_column]]\n",
" model_inputs = tokenizer(inputs)\n",
" labels = tokenizer(targets, add_special_tokens=False) # don't add bos token because we concatenate with inputs\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i] + [tokenizer.eos_token_id]\n",
" # print(i, sample_input_ids, label_input_ids)\n",
" model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n",
" labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
" # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i]\n",
" model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
" max_length - len(sample_input_ids)\n",
" ) + sample_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
" \"attention_mask\"\n",
" ][i]\n",
" labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
" return model_inputs\n",
"\n",
"\n",
"processed_datasets = dataset.map(\n",
" preprocess_function,\n",
" batched=True,\n",
" num_proc=1,\n",
" remove_columns=dataset[\"train\"].column_names,\n",
" load_from_cache_file=False,\n",
" desc=\"Running tokenizer on dataset\",\n",
")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"eval_dataset = processed_datasets[\"train\"]\n",
"\n",
"\n",
"train_dataloader = DataLoader(\n",
" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
")\n",
"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "641b21fe",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Running tokenizer on dataset: 100%|██████████| 3399/3399 [00:00<00:00, 17380.64 examples/s]\n"
]
},
{
"data": {
"text/plain": [
"{'input_ids': tensor([[ 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
" 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
" 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
" 227985, 5484, 915, 2566, 74757, 64626, 12384, 44639, 613,\n",
" 52282, 2670, 79920, 3344, 1002, 368, 17646, 14472, 8348,\n",
" 664, 718, 4, 19036, 17, 31849, 17, 6312, 76,\n",
" 44, 62470, 56, 91, 50, 14839, 21, 77658, 915,\n",
" 210],\n",
" [ 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
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" 2256, 664, 2550, 18833, 18607, 162467, 4, 1387, 6199,\n",
" 3291, 23405, 613, 4657, 17082, 566, 3432, 368, 78851,\n",
" 1185, 61273, 23181, 1553, 15596, 212, 116057, 77658, 915,\n",
" 210],\n",
" [ 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
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" 1320, 368, 10029, 632, 49666, 92, 34, 77658, 915,\n",
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" 3, 3, 3, 3, 3, 227985, 5484, 915, 2566,\n",
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" 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
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" 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
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" 210],\n",
" [ 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
" 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
" 227985, 5484, 915, 2566, 80, 2068, 479, 2566, 80,\n",
" 1376, 878, 147587, 3904, 632, 368, 6084, 65673, 78851,\n",
" 11736, 15527, 19082, 33151, 461, 17, 45575, 17887, 632,\n",
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" 14512, 27, 71, 8184, 19, 290, 63748, 77658, 915,\n",
" 210]]),\n",
" 'attention_mask': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def test_preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" model_inputs = tokenizer(inputs)\n",
" # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
" max_length - len(sample_input_ids)\n",
" ) + sample_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
" \"attention_mask\"\n",
" ][i]\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" return model_inputs\n",
"\n",
"\n",
"test_dataset = dataset[\"test\"].map(\n",
" test_preprocess_function,\n",
" batched=True,\n",
" num_proc=1,\n",
" remove_columns=dataset[\"train\"].column_names,\n",
" load_from_cache_file=False,\n",
" desc=\"Running tokenizer on dataset\",\n",
")\n",
"\n",
"test_dataloader = DataLoader(test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
"next(iter(test_dataloader))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "218df807",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"425"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# show dataset size\n",
"len(test_dataloader)"
]
},
{
"cell_type": "markdown",
"id": "aa55f803",
"metadata": {},
"source": [
"## Train the LM with LNTuning\n",
"1. Create the base LM.\n",
"2. Only activate the LayerNorm layers in the LM for training.\n",
"3. Train the LM on the training dataset."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a773e092",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 100,352 || all params: 559,314,944 || trainable%: 0.017941948642087417\n"
]
}
],
"source": [
"# 1. creating the base LM\n",
"model = AutoModelForCausalLM.from_pretrained(model_name_or_path)\n",
"# 2. Only activate the LayerNorm layers in the Attention blocks in the LM for training\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b2f91568",
"metadata": {},
"outputs": [],
"source": [
"# setup the optimizer and lr scheduler\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0,\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e4fb69fc",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 7/7 [00:00<00:00, 7.09it/s]\n",
"100%|██████████| 7/7 [00:00<00:00, 23.05it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=0: train_ppl=tensor(8.1918, device='cuda:0') train_epoch_loss=tensor(2.1031, device='cuda:0') eval_ppl=tensor(2.1760, device='cuda:0') eval_epoch_loss=tensor(0.7775, device='cuda:0')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 7/7 [00:00<00:00, 10.88it/s]\n",
"100%|██████████| 7/7 [00:00<00:00, 23.11it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=1: train_ppl=tensor(1.8009, device='cuda:0') train_epoch_loss=tensor(0.5883, device='cuda:0') eval_ppl=tensor(2.1198, device='cuda:0') eval_epoch_loss=tensor(0.7513, device='cuda:0')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=2: train_ppl=tensor(2.0387, device='cuda:0') train_epoch_loss=tensor(0.7123, device='cuda:0') eval_ppl=tensor(1.6793, device='cuda:0') eval_epoch_loss=tensor(0.5184, device='cuda:0')\n"
]
},
{
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=3: train_ppl=tensor(1.4885, device='cuda:0') train_epoch_loss=tensor(0.3978, device='cuda:0') eval_ppl=tensor(1.2918, device='cuda:0') eval_epoch_loss=tensor(0.2561, device='cuda:0')\n"
]
},
{
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"text": [
"epoch=4: train_ppl=tensor(1.3062, device='cuda:0') train_epoch_loss=tensor(0.2671, device='cuda:0') eval_ppl=tensor(1.3259, device='cuda:0') eval_epoch_loss=tensor(0.2821, device='cuda:0')\n"
]
},
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]
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"output_type": "stream",
"text": [
"epoch=5: train_ppl=tensor(1.3129, device='cuda:0') train_epoch_loss=tensor(0.2722, device='cuda:0') eval_ppl=tensor(1.2315, device='cuda:0') eval_epoch_loss=tensor(0.2082, device='cuda:0')\n"
]
},
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]
},
{
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"text": [
"epoch=6: train_ppl=tensor(1.2605, device='cuda:0') train_epoch_loss=tensor(0.2315, device='cuda:0') eval_ppl=tensor(1.2705, device='cuda:0') eval_epoch_loss=tensor(0.2394, device='cuda:0')\n"
]
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]
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"epoch=7: train_ppl=tensor(1.2452, device='cuda:0') train_epoch_loss=tensor(0.2193, device='cuda:0') eval_ppl=tensor(1.2103, device='cuda:0') eval_epoch_loss=tensor(0.1909, device='cuda:0')\n"
]
},
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]
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"output_type": "stream",
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"epoch=8: train_ppl=tensor(1.2185, device='cuda:0') train_epoch_loss=tensor(0.1976, device='cuda:0') eval_ppl=tensor(1.2127, device='cuda:0') eval_epoch_loss=tensor(0.1929, device='cuda:0')\n"
]
},
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"epoch=9: train_ppl=tensor(1.1868, device='cuda:0') train_epoch_loss=tensor(0.1713, device='cuda:0') eval_ppl=tensor(1.1765, device='cuda:0') eval_epoch_loss=tensor(0.1625, device='cuda:0')\n"
]
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"epoch=10: train_ppl=tensor(1.1905, device='cuda:0') train_epoch_loss=tensor(0.1744, device='cuda:0') eval_ppl=tensor(1.1539, device='cuda:0') eval_epoch_loss=tensor(0.1431, device='cuda:0')\n"
]
},
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"epoch=11: train_ppl=tensor(1.1475, device='cuda:0') train_epoch_loss=tensor(0.1376, device='cuda:0') eval_ppl=tensor(1.1238, device='cuda:0') eval_epoch_loss=tensor(0.1167, device='cuda:0')\n"
]
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]
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"epoch=13: train_ppl=tensor(1.0798, device='cuda:0') train_epoch_loss=tensor(0.0768, device='cuda:0') eval_ppl=tensor(1.1151, device='cuda:0') eval_epoch_loss=tensor(0.1089, device='cuda:0')\n"
]
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"epoch=14: train_ppl=tensor(1.0622, device='cuda:0') train_epoch_loss=tensor(0.0604, device='cuda:0') eval_ppl=tensor(1.0347, device='cuda:0') eval_epoch_loss=tensor(0.0341, device='cuda:0')\n"
]
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]
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]
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]
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"epoch=18: train_ppl=tensor(1.0010, device='cuda:0') train_epoch_loss=tensor(0.0010, device='cuda:0') eval_ppl=tensor(1.0010, device='cuda:0') eval_epoch_loss=tensor(0.0010, device='cuda:0')\n"
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"\n"
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}
],
"source": [
"# 3. train the LM on the training dataset\n",
"model = model.to(device)\n",
"\n",
"for epoch in range(num_epochs):\n",
" model.train()\n",
" total_loss = 0\n",
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
" # print(batch)\n",
" # print(batch[\"input_ids\"].shape)\n",
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" total_loss += loss.detach().float()\n",
" loss.backward()\n",
" optimizer.step()\n",
" lr_scheduler.step()\n",
" optimizer.zero_grad()\n",
"\n",
" model.eval()\n",
" eval_loss = 0\n",
" eval_preds = []\n",
" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
" with torch.no_grad():\n",
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" eval_loss += loss.detach().float()\n",
" eval_preds.extend(\n",
" tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
" )\n",
"\n",
" eval_epoch_loss = eval_loss / len(eval_dataloader)\n",
" eval_ppl = torch.exp(eval_epoch_loss)\n",
" train_epoch_loss = total_loss / len(train_dataloader)\n",
" train_ppl = torch.exp(train_epoch_loss)\n",
" print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
]
},
{
"cell_type": "markdown",
"id": "fbf339a2",
"metadata": {},
"source": [
"## Test the LM"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "53752a7b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@TommyHilfiger Dramatic shopping exp. ordered 6 jeans same size (30/32) 2 fits / 2 too large / 2 too slim : same brand > different sizing\n",
"{'input_ids': tensor([[227985, 5484, 915, 2566, 226154, 126015, 5385, 259, 239364,\n",
" 3396, 70823, 5853, 17, 57247, 1231, 191040, 5025, 7869,\n",
" 375, 2324, 149349, 12, 415, 122321, 897, 415, 10136,\n",
" 10021, 897, 415, 10136, 6497, 381, 915, 5025, 51950,\n",
" 66869, 5955, 272, 20311, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"tensor([[227985, 5484, 915, 2566, 226154, 126015, 5385, 259, 239364,\n",
" 3396, 70823, 5853, 17, 57247, 1231, 191040, 5025, 7869,\n",
" 375, 2324, 149349, 12, 415, 122321, 897, 415, 10136,\n",
" 10021, 897, 415, 10136, 6497, 381, 915, 5025, 51950,\n",
" 66869, 5955, 272, 20311, 77658, 915, 210, 1936, 106863,\n",
" 2, 1936, 106863, 2, 1936, 106863, 2, 1936]],\n",
" device='cuda:0')\n",
"['Tweet text : @TommyHilfiger Dramatic shopping exp. ordered 6 jeans same size (30/32) 2 fits / 2 too large / 2 too slim : same brand > different sizing Label : no complaintno complaintno complaintno']\n"
]
}
],
"source": [
"model.eval()\n",
"i = 33\n",
"inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n",
"print(dataset[\"test\"][i][\"Tweet text\"])\n",
"print(inputs)\n",
"\n",
"with torch.no_grad():\n",
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
" outputs = model.generate(\n",
" input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
" )\n",
" print(outputs)\n",
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
"cell_type": "markdown",
"id": "c8f35152",
"metadata": {},
"source": [
"## Save the trainable LM weights (LayerNorm layers)\n",
"You can push model to hub or save model locally. \n",
"\n",
"- Option1: Push the model to Hugging Face Hub:\n",
"\n",
" ```python\n",
" model.push_to_hub(\n",
" f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\"/\", \"_\"),\n",
" token = \"hf_...\"\n",
" )\n",
" ```\n",
" token (`bool` or `str`, *optional*):\n",
" `token` is to be used for HTTP Bearer authorization when accessing remote files. If `True`, will use the token generated\n",
" when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`\n",
" is not specified.\n",
" Or you can get your token from https://huggingface.co/settings/token\n",
" ```\n",
"- Option2: Save model locally:\n",
"\n",
" ```python\n",
" peft_model_id = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\"/\", \"_\")\n",
" model.save_pretrained(peft_model_id)\n",
" ```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d8ba1f8c",
"metadata": {},
"outputs": [],
"source": [
"# saving model\n",
"peft_model_id = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\n",
" \"/\", \"_\"\n",
")\n",
"model.save_pretrained(peft_model_id)"
]
},
{
"cell_type": "markdown",
"id": "4dd7ab9c",
"metadata": {},
"source": [
"## Test the LM using LNTuning loaded from saved weights\n",
"1. load the LNTuning configuration\n",
"2. load the base LM\n",
"3. merge the LNTuning weights into the base LM using the PEFT config"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "4d9476e1",
"metadata": {},
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
"\n",
"# load the LNTuning config\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"# load the base LM\n",
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)\n",
"# merge LNTuning weights into the base LM\n",
"model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ebe174a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@greateranglia Ok thanks...\n",
"{'input_ids': tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n",
" 49337, 1369, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n",
" 49337, 1369, 77658, 915, 210, 1936, 106863, 2, 1936,\n",
" 106863, 2, 1936, 106863, 2, 1936]], device='cuda:0')\n",
"['Tweet text : @greateranglia Ok thanks... Label : no complaintno complaintno complaintno']\n"
]
}
],
"source": [
"model.to(device)\n",
"model.eval()\n",
"i = 4\n",
"inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n",
"print(dataset[\"test\"][i][\"Tweet text\"])\n",
"print(inputs)\n",
"\n",
"with torch.no_grad():\n",
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
" outputs = model.generate(\n",
" input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
" )\n",
" print(outputs)\n",
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"vscode": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py
================================================
import gc
import os
import sys
import threading
import psutil
import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
default_data_collator,
get_linear_schedule_with_warmup,
set_seed,
)
from peft import LoraConfig, TaskType, get_peft_model
def levenshtein_distance(str1, str2):
# TC: O(N^2)
# SC: O(N)
if str1 == str2:
return 0
num_rows = len(str1) + 1
num_cols = len(str2) + 1
dp_matrix = list(range(num_cols))
for i in range(1, num_rows):
prev = dp_matrix[0]
dp_matrix[0] = i
for j in range(1, num_cols):
temp = dp_matrix[j]
if str1[i - 1] == str2[j - 1]:
dp_matrix[j] = prev
else:
dp_matrix[j] = min(prev, dp_matrix[j], dp_matrix[j - 1]) + 1
prev = temp
return dp_matrix[num_cols - 1]
def get_closest_label(eval_pred, classes):
min_id = sys.maxsize
min_edit_distance = sys.maxsize
for i, class_label in enumerate(classes):
edit_distance = levenshtein_distance(eval_pred.strip(), class_label)
if edit_distance < min_edit_distance:
min_id = i
min_edit_distance = edit_distance
return classes[min_id]
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
gc.collect()
self.device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
self.device_module = getattr(torch, self.device_type, torch.cuda)
self.device_module.empty_cache()
self.device_module.reset_peak_memory_stats() # reset the peak gauge to zero
self.begin = self.device_module.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
"""get resident set size memory for the current process"""
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
# time.sleep(0.001) # 1msec
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
self.device_module.empty_cache()
self.end = self.device_module.memory_allocated()
self.peak = self.device_module.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def main():
accelerator = Accelerator()
model_name_or_path = "bigscience/bloomz-7b1"
dataset_name = "twitter_complaints"
peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)
text_column = "Tweet text"
label_column = "text_label"
lr = 3e-3
num_epochs = 20
batch_size = 8
seed = 42
max_length = 64
do_test = False
set_seed(seed)
dataset = load_dataset(
"parquet",
data_files={
"train": f"hf://datasets/ought/raft@refs/convert/parquet/{dataset_name}/train/0000.parquet",
"test": f"hf://datasets/ought/raft@refs/convert/parquet/{dataset_name}/test/0000.parquet",
},
)
classes = [k.replace("_", " ") for k in dataset["train"].features["Label"].names]
dataset = dataset.map(
lambda x: {"text_label": [classes[label] for label in x["Label"]]},
batched=True,
num_proc=1,
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
def preprocess_function(examples):
batch_size = len(examples[text_column])
inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]]
targets = [str(x) for x in examples[label_column]]
model_inputs = tokenizer(inputs)
labels = tokenizer(targets, add_special_tokens=False) # don't add bos token because we concatenate with inputs
for i in range(batch_size):
sample_input_ids = model_inputs["input_ids"][i]
label_input_ids = labels["input_ids"][i] + [tokenizer.eos_token_id]
model_inputs["input_ids"][i] = sample_input_ids + label_input_ids
labels["input_ids"][i] = [-100] * len(sample_input_ids) + label_input_ids
model_inputs["attention_mask"][i] = [1] * len(model_inputs["input_ids"][i])
for i in range(batch_size):
sample_input_ids = model_inputs["input_ids"][i]
label_input_ids = labels["input_ids"][i]
model_inputs["input_ids"][i] = [tokenizer.pad_token_id] * (
max_length - len(sample_input_ids)
) + sample_input_ids
model_inputs["attention_mask"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[
"attention_mask"
][i]
labels["input_ids"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids
model_inputs["input_ids"][i] = torch.tensor(model_inputs["input_ids"][i][:max_length])
model_inputs["attention_mask"][i] = torch.tensor(model_inputs["attention_mask"][i][:max_length])
labels["input_ids"][i] = torch.tensor(labels["input_ids"][i][:max_length])
model_inputs["labels"] = labels["input_ids"]
return model_inputs
def test_preprocess_function(examples):
batch_size = len(examples[text_column])
inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]]
model_inputs = tokenizer(inputs)
for i in range(batch_size):
sample_input_ids = model_inputs["input_ids"][i]
model_inputs["input_ids"][i] = [tokenizer.pad_token_id] * (
max_length - len(sample_input_ids)
) + sample_input_ids
model_inputs["attention_mask"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[
"attention_mask"
][i]
model_inputs["input_ids"][i] = torch.tensor(model_inputs["input_ids"][i][:max_length])
model_inputs["attention_mask"][i] = torch.tensor(model_inputs["attention_mask"][i][:max_length])
return model_inputs
with accelerator.main_process_first():
processed_datasets = dataset.map(
preprocess_function,
batched=True,
num_proc=1,
remove_columns=dataset["train"].column_names,
load_from_cache_file=True,
desc="Running tokenizer on dataset",
)
accelerator.wait_for_everyone()
train_dataset = processed_datasets["train"]
with accelerator.main_process_first():
processed_datasets = dataset.map(
test_preprocess_function,
batched=True,
num_proc=1,
remove_columns=dataset["train"].column_names,
load_from_cache_file=False,
desc="Running tokenizer on dataset",
)
eval_dataset = processed_datasets["train"]
test_dataset = processed_datasets["test"]
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
)
eval_dataloader = DataLoader(
eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
)
test_dataloader = DataLoader(
test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
)
print(next(iter(train_dataloader)))
# creating model
model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
# lr scheduler
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=(len(train_dataloader) * num_epochs),
)
model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler = accelerator.prepare(
model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler
)
accelerator.print(model)
is_ds_zero_3 = False
if getattr(accelerator.state, "deepspeed_plugin", None):
is_ds_zero_3 = accelerator.state.deepspeed_plugin.zero_stage == 3
for epoch in range(num_epochs):
with TorchTracemalloc() as tracemalloc:
model.train()
total_loss = 0
for step, batch in enumerate(tqdm(train_dataloader)):
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Printing the memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the train : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the train (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
train_epoch_loss = total_loss / len(train_dataloader)
train_ppl = torch.exp(train_epoch_loss)
accelerator.print(f"{epoch=}: {train_ppl=} {train_epoch_loss=}")
model.eval()
eval_preds = []
with TorchTracemalloc() as tracemalloc:
for _, batch in enumerate(tqdm(eval_dataloader)):
batch = {k: v for k, v in batch.items() if k != "labels"}
with torch.no_grad():
outputs = accelerator.unwrap_model(model).generate(
**batch, synced_gpus=is_ds_zero_3, max_new_tokens=10
) # synced_gpus=True for DS-stage 3
outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id)
preds = accelerator.gather_for_metrics(outputs)
preds = preds[:, max_length:].detach().cpu().numpy()
eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
# Printing the memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the eval : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the eval (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the eval (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the eval (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the eval : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the eval (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
correct = 0
total = 0
assert len(eval_preds) == len(dataset["train"][label_column]), (
f"{len(eval_preds)} != {len(dataset['train'][label_column])}"
)
for pred, true in zip(eval_preds, dataset["train"][label_column]):
if pred.strip() == true.strip():
correct += 1
total += 1
accuracy = correct / total * 100
accelerator.print(f"{accuracy=}")
accelerator.print(f"{eval_preds[:10]=}")
accelerator.print(f"{dataset['train'][label_column][:10]=}")
if do_test:
model.eval()
test_preds = []
for _, batch in enumerate(tqdm(test_dataloader)):
batch = {k: v for k, v in batch.items() if k != "labels"}
with torch.no_grad():
outputs = accelerator.unwrap_model(model).generate(
**batch, synced_gpus=is_ds_zero_3, max_new_tokens=10
) # synced_gpus=True for DS-stage 3
outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id)
preds = accelerator.gather(outputs)
preds = preds[:, max_length:].detach().cpu().numpy()
test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
test_preds_cleaned = []
for _, pred in enumerate(test_preds):
test_preds_cleaned.append(get_closest_label(pred, classes))
test_df = dataset["test"].to_pandas()
assert len(test_preds_cleaned) == len(test_df), f"{len(test_preds_cleaned)} != {len(test_df)}"
test_df[label_column] = test_preds_cleaned
test_df["text_labels_orig"] = test_preds
accelerator.print(test_df[[text_column, label_column]].sample(20))
pred_df = test_df[["ID", label_column]]
pred_df.columns = ["ID", "Label"]
os.makedirs(f"data/{dataset_name}", exist_ok=True)
pred_df.to_csv(f"data/{dataset_name}/predictions.csv", index=False)
accelerator.wait_for_everyone()
# Option1: Pushing the model to Hugging Face Hub
# model.push_to_hub(
# f"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace("/", "_"),
# token = "hf_..."
# )
# token (`bool` or `str`, *optional*):
# `token` is to be used for HTTP Bearer authorization when accessing remote files. If `True`, will use the token generated
# when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`
# is not specified.
# Or you can get your token from https://huggingface.co/settings/token
# Option2: Saving the model locally
peft_model_id = f"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace(
"/", "_"
)
model.save_pretrained(peft_model_id)
accelerator.wait_for_everyone()
if __name__ == "__main__":
main()
================================================
FILE: examples/causal_language_modeling/peft_lora_clm_with_additional_tokens.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "5f239612-620e-4430-8685-9fdc6b179b41",
"metadata": {},
"source": [
"# Training PEFT models with new tokens being added to the embedding layers and tokenizer\n",
"\n",
"In this example, we will learn how to train a LoRA model when adding new tokens to the tokenizer and model. \n",
"This is a common usecase when doing the following:\n",
"1. Instruction finetuning with new tokens beind added such as `<|user|>`, `<|assistant|>`, `<|system|>`, ``, `` to properly format the conversations\n",
"2. Finetuning on a specific language wherein language spoecific tokens are added, e.g., korean tokens being added to vocabulary for finetuning LLM on Korean datasets.\n",
"3. Instruction finetuning to return outputs in certain format to enable agent behaviour new tokens such as `<|FUNCTIONS|>`, `<|BROWSE|>`, `<|TEXT2IMAGE|>`, `<|ASR|>`, `<|TTS|>`, `<|GENERATECODE|>`, `<|RAG|>`.\n",
"\n",
"In such cases, you add the Embedding modules to the LORA `target_modules`. PEFT will take care of saving the embedding layers with the new added tokens along with the adapter weights that were trained on the specific initialization of the embeddings weights of the added tokens."
]
},
{
"cell_type": "markdown",
"id": "b27c55e8-edaa-4059-90bc-d6096d596902",
"metadata": {},
"source": [
"Let's import the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f864c90",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"WANDB_PROJECT\"] = \"PeftExamples\"\n",
"import transformers\n",
"from peft import (\n",
" LoraConfig,\n",
" PeftConfig,\n",
" PeftModel,\n",
" get_peft_model,\n",
" prepare_model_for_kbit_training,\n",
")\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" HfArgumentParser,\n",
" TrainingArguments,\n",
" Trainer,\n",
" default_data_collator,\n",
")\n",
"import torch\n",
"from dataclasses import dataclass, field\n",
"from typing import Optional\n",
"from dataclass_csv import DataclassReader\n",
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"from enum import Enum"
]
},
{
"cell_type": "markdown",
"id": "74950a3f-bb63-4ce5-9e2b-1b83f92b13a2",
"metadata": {},
"source": [
"## Prepare Model and Tokenizer"
]
},
{
"cell_type": "markdown",
"id": "76763f5e-64b2-409b-8845-ae5589f8a4e0",
"metadata": {},
"source": [
"Now, we will be adding 27 new tokens as well as replace the existing pad, bos and eos tokens of the model."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fd0498ea-547e-418d-bf13-c9abafdd5476",
"metadata": {},
"outputs": [],
"source": [
"class SpecialTokens(str, Enum):\n",
" begin_target = \"<|begintarget|>\"\n",
" end_target = \"<|endtarget|>\"\n",
" begin_context = \"<|begincontext|>\"\n",
" end_context = \"<|endcontext|>\"\n",
" system = \"<|system|>\"\n",
" user = \"<|user|>\"\n",
" begin_last_user_utterance = \"<|beginlastuserutterance|>\"\n",
" end_last_user_utterance = \"<|endlastuserutterance|>\"\n",
" begin_dsts = \"<|begindsts|>\"\n",
" end_dsts = \"<|enddsts|>\"\n",
" begin_dst = \"<|begindst|>\"\n",
" end_dst = \"<|enddst|>\"\n",
" begin_belief = \"<|beginbelief|>\"\n",
" end_belief = \"<|endbelief|>\"\n",
" begin_response = \"<|beginresponse|>\"\n",
" end_response = \"<|endresponse|>\"\n",
" begin_action = \"<|beginaction|>\"\n",
" end_action = \"<|endaction|>\"\n",
" begin_user_action = \"<|beginuseraction|>\"\n",
" end_user_action = \"<|enduseraction|>\"\n",
" sys_actions = \"<|sysactions|>\"\n",
" begin_intent = \"<|beginintent|>\"\n",
" end_intent = \"<|endintent|>\"\n",
" begin_requested_slots = \"<|beginrequestedslots|>\"\n",
" end_requested_slots = \"<|endrequestedslots|>\"\n",
" pad_token = \"<|pad|>\"\n",
" bos_token = \"<|startoftext|>\"\n",
"\n",
" @classmethod\n",
" def list(cls):\n",
" return [c.value for c in cls]"
]
},
{
"cell_type": "markdown",
"id": "ae4a4255-5f13-4eef-a024-4f1de0f2173b",
"metadata": {},
"source": [
"We will be finetuning Mistral-7B model. Let's load the tokenizer and add the special tokens followed by loading the base model and resizzing the embedding layers to accomodate the newly added tokens."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f0eedef9",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "91c67b6377fc4dd7977bf544de784d51",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|><|begincontext|><|user|> Can you find me place to eat?<|system|> What kind of food would you like to have and where would you like me to search in?<|user|> Food kind of California will be perfect in SF.<|system|> There are 10 restaurants, Al's Place is one of the good restaurant in San Francisco.<|user|> Can you look for any other restaurant?<|system|> Alta Msp is one of the good restaurant in San Francisco.<|beginlastuserutterance|> Can you find me the address?<|endlastuserutterance|><|endcontext|><|begintarget|><|begindsts|><|begindst|><|beginintent|> FindRestaurants<|endintent|><|beginrequestedslots|> Restaurants^street_address<|endrequestedslots|><|beginbelief|> Restaurants^city->SF~San Francisco|Restaurants^cuisine->California<|endbelief|><|enddst|><|enddsts|><|beginuseraction|> REQUEST->Restaurants^street_address~<|enduseraction|><|beginaction|> INFORM->Restaurants^street_address~1275 Minnesota Street<|endaction|><|beginresponse|> The street address of the restaurant is 1275 Minnesota Street.<|endresponse|><|endtarget|><|endtarget|>\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenizer.decode(train_dataset[0][\"input_ids\"])"
]
},
{
"cell_type": "markdown",
"id": "239d1c83-196d-471e-9bf7-5f36dafa9894",
"metadata": {},
"source": [
"# Train the model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ec80d6ee",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n",
"Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msmangrul\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
]
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.16.0"
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"text/plain": [
""
]
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"data": {
"text/html": [
"Run data is saved locally in /raid/sourab/temp/wandb/run-20231128_230934-edod21gq"
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""
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"output_type": "display_data"
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{
"data": {
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"Syncing run ethereal-eon-1 to Weights & Biases (docs) "
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""
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"metadata": {},
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{
"data": {
"text/html": [
" View project at https://wandb.ai/smangrul/PeftExamples"
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""
]
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{
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" View run at https://wandb.ai/smangrul/PeftExamples/runs/edod21gq"
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
]
},
{
"data": {
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"\n",
"
"
],
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""
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"metadata": {},
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{
"data": {
"text/plain": [
"TrainOutput(global_step=1275, training_loss=0.8170911183076747, metrics={'train_runtime': 213.5513, 'train_samples_per_second': 47.693, 'train_steps_per_second': 5.97, 'total_flos': 344546979840000.0, 'train_loss': 0.8170911183076747, 'epoch': 5.0})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# training and evaluation\n",
"\n",
"\n",
"def compute_metrics(eval_preds):\n",
" preds, labels = eval_preds\n",
" preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
" labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
"\n",
" correct = 0\n",
" total = 0\n",
" for pred, true in zip(preds, labels):\n",
" if pred.strip() == true.strip():\n",
" correct += 1\n",
" total += 1\n",
" accuracy = correct / total\n",
" return {\"accuracy\": accuracy}\n",
"\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" \"out\",\n",
" per_device_train_batch_size=batch_size,\n",
" learning_rate=lr,\n",
" num_train_epochs=num_epochs,\n",
" eval_strategy=\"epoch\",\n",
" logging_strategy=\"epoch\",\n",
" save_strategy=\"no\",\n",
" report_to=[],\n",
" predict_with_generate=True,\n",
" generation_config=GenerationConfig(max_length=max_length),\n",
")\n",
"trainer = Seq2SeqTrainer(\n",
" model=model,\n",
" processing_class=tokenizer,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
" data_collator=default_data_collator,\n",
" compute_metrics=compute_metrics,\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a8de6005",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:13.045146Z",
"start_time": "2023-05-30T09:53:13.035612Z"
}
},
"outputs": [],
"source": [
"# saving model\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"model.save_pretrained(peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd20cd4c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:15.240763Z",
"start_time": "2023-05-30T09:53:15.059304Z"
}
},
"outputs": [],
"source": [
"ckpt = f\"{peft_model_id}/adapter_model.safetensors\"\n",
"!du -h $ckpt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "76c2fc29",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:25.055105Z",
"start_time": "2023-05-30T09:53:17.797989Z"
}
},
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
"\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
"model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d997f1cc",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:26.777030Z",
"start_time": "2023-05-30T09:53:26.013697Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPS grew to 0.04 eur from 0.02 eur .\n",
"{'input_ids': tensor([[ 3, 24935, 3, 4774, 12, 4097, 6348, 3, 1238, 45,\n",
" 4097, 4305, 3, 1238, 3, 5, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"tensor([[ 0, 1465, 1]])\n",
"['positive']\n"
]
}
],
"source": [
"model.eval()\n",
"i = 107\n",
"inputs = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"pt\")\n",
"print(dataset[\"validation\"][text_column][i])\n",
"print(inputs)\n",
"\n",
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
" print(outputs)\n",
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb746c1e",
"metadata": {},
"outputs": [],
"source": []
}
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================================================
FILE: examples/conditional_generation/requirements.txt
================================================
transformers
accelerate
evaluate
deepspeed
tqdm
datasets
safetensors
scikit-learn
================================================
FILE: examples/corda_finetuning/README.md
================================================
# CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning
## Introduction
Existing PEFT methods are mostly agnostic of the context of a task of concern, e.g., a downstream task to learn or some pre-trained world knowledge to maintain.
[CorDA](https://openreview.net/pdf?id=Gi00NVru6n) builds task-aware LoRA adapters from weight decomposition oriented by the context of the task concerned.
Concretely, CorDA randomly collects a few (by default 256 in our `preprocess.py`) data samples from a target task, e.g. questions from a QA dataset or instructions to write a code or solve a math problem, and feeds these samples into a pre-trained LLM. We can obtain the covariance matrix of the input activation of each linear layer, i.e., $C=XX^T\in\mathcal{R}^{d_{in}\times d_{in}}$.
We then perform singular value decomposition (SVD) for the weight $W\in \mathcal{R}^{d_{out}\times d_{in}}$ multiplied by the covariance matrix, i.e., $\verb|SVD|(WC) = U\Sigma V^T$. In this way, the context expressed by these representative covariance matrices is able to orientate the decomposition, such that the principal components (the singular vectors with the largest singular values) are most associated with the task of concern (please refer to Fig.2 of our paper for the advantage of our decomposition over the plain SVD). To ensure the same inference result with the pre-trained model at the start of adaptation, we multiply the inverse of these covariance matrices with the decomposed components, i.e., $\hat{W}=U\Sigma V^T C^{-1}$.
Thanks to the task-awareness, you can choose how to utilize the task-specific principal components. For examples, if you want to adapt a model to a new task without losing the knowledge of a question-answering dataset, e.g., TriviaQA and NQopen, you can sample questions from this dataset to collect covariance matrices, and keep the principal components frozen because they compact the ability of this dataset, while using the lowest components with the smallest $r$ singular values to initialize the learnable LoRA adapters. This is achieved by the **knowledge-preserved mode (KPM)** of CorDA, which learns new tasks effectively while keeping the world knowledge you are concerned about as sound as possible. Alternatively, when your primary objective is to maximize performance on the finetuning task, disregarding the preservation of world knowledge, the **instruction-previewed mode (IPM**) will be favored. In this mode, CorDA uses the instruction and response from the fine-tuning task (e.g., Math or Code) to produce the covariance matrices. The principal components with the largest $r$ singular values, capturing the characteristics of the finetuning task in advance, can better adapt to the new ability, so they are used to initialize the LoRA adapters, with the remaining components frozen. IPM can further accelerate convergence to enhance the fine-tuning performance on downstream tasks.
The implementations of KPM and IPM are compared as follows:
| Mode | Collect covariance from | LoRA $A$ | LoRA $B$ |
|---|---|---|---
|KPM | questions from the knowledge benchmark to maintain | $A=\sqrt{\Sigma}\_{[-r:]}(V^T C^{-1})\_{[-r:,:]}$ | $B=U_{[:,-r:]}\sqrt{\Sigma}_{[-r:]}$ |
IPM | instructions and responses from the downstream task to learn | $A= \sqrt{\Sigma}\_{[:r]} (V^T C^{-1})\_{[:r,:]}$ | $B =U_{[:,:r]} \sqrt{\Sigma}_{[:r]}$ |
### Comparison with alternative methods
The distinction between CorDA with other similar LoRA initialization methods is summarized as follows:
| Method | Initialization for | SVD on | Data-driven | Supports knowledge maintenance |
| - | - | - | - | - |
| PiSSA | $A$ and $B$ | weights | no | no |
| EVA | $A$ | activations | yes | no |
|CorDA | $A$ and $B$ | weights (oriented by covariance) | yes | yes |
"Supports knowledge maintenance" denotes the ability of explicitly associating a knowledge benchmark with some components of the pre-trained weights after decomposition, and keeping these components frozen during fine-tuning.
### Some Results
- Performance with knowledge-preserved mode (sample from NQopen, fine-tune on Math)
| Method | Model | NQ open | GSM8k | Math | Avg. |
|---|---|---|---|---|---|
|Pre-trained|Llama-2-7b| 14.99 | -| - | - |
|LoRA|Llama-2-7b|1.27| 42.68 | 5.88 | 16.61 |
|**CorDA (KPM)** |Llama-2-7b| **8.20** | **46.32** | **7.00** | **20.51** |
|Pre-trained|Llama-2-13b|23.63|-|-|-|
|LoRA|Llama-2-13b| 16.26 | 57.24 | 8.92 | 27.47 |
|**CorDA (KPM)** |Llama-2-13b| **19.86** | **59.29** | **9.62** | **29.59** |
|Pre-trained|Llama-3-8b|13.41|-|-|-|
|LoRA|Llama-3-8b| 8.75 | 72.33 | 24.04| 35.04 |
|**CorDA (KPM)** |Llama-3-8b| **9.61** | **74.68** | **25.34** | **36.54** |
|Pre-trained|Gemma-2-9b|12.85|-|-|-|
|LoRA|Gemma-2-9b| 9.28 | 83.47 | 42.30| 45.02 |
|**CorDA (KPM)** |Gemma-2-9b|**10.17** | **84.08** | **42.64** | **45.63** |
- Performance with instruction-previewed mode (sample from Math, fine-tune on Math)
| Method | Model | GSM8k | Math |
| --- | --- | --- | ---|
|LoRA| Llama-2-7b | 42.68 | 5.88 |
|PiSSA | Llama-2-7b | 51.63 | 7.32 |
| **CorDA (IPM)** | Llama-2-7b | **53.45** | **8.64** |
|LoRA| Llama-2-13b | 57.24 | 8.92 |
|PiSSA | Llama-2-13b |60.88 | 11.08|
| **CorDA (IPM)** | Llama-2-13b | **62.47** |**11.54** |
|LoRA| Gemma-2-9b | 83.47 | 42.30 |
|PiSSA | Gemma-2-9b | 84.23 | 43.52|
| **CorDA (IPM)** | Gemma-2-9b | **84.45** | **43.88** |
## Quick Start
### Knowledge-preserved adaptation mode
```py
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft.tuners.lora.config import CordaConfig
from peft.tuners.lora.corda import preprocess_corda
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer.pad_token_id = tokenizer.eos_token_id
sampled_dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:256]")
dataset = load_dataset("imdb", split="train[:256]")
def run_model():
for batch in sampled_dataset:
input_ids = batch["text"]
input_ids = input_ids.to(model.device)
with torch.no_grad():
model(input_ids)
corda_config = CordaConfig(
corda_method="kpm",
)
lora_config = LoraConfig(
init_lora_weights="corda",
corda_config=corda_config,
)
# Call `preprocess_corda` first to collect covariance matrix and build SVD result for model
# For more details, please refer to documentation of `preprocess_corda`
preprocess_corda(model, lora_config, run_model=run_model)
# Call `get_peft_model` after preprocessing, or else you'll encounter error
peft_model = get_peft_model(model, lora_config)
peft_model.print_trainable_parameters()
training_args = SFTConfig(dataset_text_field="text", max_length=128)
trainer = SFTTrainer(
model=peft_model,
args=training_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("corda-llama-2-7b")
```
### Instruction-previewed adaptation mode
```py
# Get model and dataset identically as KPM...
# Different from KPM, we run the model on dataset of the downstream task to collect covariance matrices
def run_model():
for batch in dataset:
input_ids = batch["text"]
input_ids = input_ids.to(model.device)
with torch.no_grad():
model(input_ids)
# Different from KPM, we set `corda_method` to `"ipm"`
corda_config = CordaConfig(
corda_method="ipm",
)
# The rest of training process is identical to KPM...
```
## Advanced Usage
### Preprocessing
`preprocess.py`: This script builds CorDA adapters for a model, and saves the adapters initial weights and residual model weights to a specified directory. Example usage:
#### Knowledge-preserved adaptation mode
```bash
export CUDA_VISIBLE_DEVICES=0 # force to use device 0 of CUDA GPU
export ZE_AFFINITY_MASK=0 # force to use device 0 of Intel XPU
python -u preprocess.py --model_id="meta-llama/Llama-2-7b-hf" \
--r 128 --seed 233 \
--save_model --save_path {path_to_residual_model} \
--calib_dataset "nqopen"
```
Arguments:
- `--model_id` is the pre-trained model for decomposition.
- `--r` is the low rank of LoRA, e.g. 128.
- `--calib_dataset` specifies the dataset to sample data to obtain covariance matrices. KPA mode uses QA datasets such as `"nqopen"`, `"traivia_qa"`, or other choices.
- `--save_model` saves the initialized model in `--save_path`.
#### Instruction-previewed adaptation mode
```bash
export CUDA_VISIBLE_DEVICES=0 # force to use device 0 of CUDA GPU
export ZE_AFFINITY_MASK=0 # force to use device 0 of Intel XPU
python -u preprocess.py --model_id="meta-llama/Llama-2-7b-hf" \
--r 128 --seed 233 \
--save_model --save_path {path_to_residual_model} \
--first_eigen --calib_dataset "MetaMATH"
```
Arguments:
- `--first_eigen` uses the largest $r$ singular values and vectors to initialize the learnable adapter for the instruction-previewed adaptation mode.
- `--calib_dataset` specifies the dataset to sample data to obtain covariance matrices. Instruction-previewed mode uses the downstream task dataset you are learning, such as `"MetaMATH"`, `"codefeedback"`, `"WizLMinstruct"`, `"alpaca"`, or other choices.
#### Note about memory consumption
The process of collecting covariance matrices is performed in `torch.float32` by default. If you would like to reduce the memory consumption of preprocessing, you can specify `use_float16_for_covariance=True` in `CordaConfig` to collect covariance matrices in `torch.float16`. But this may cause numerical instability only in a few cases, such that the initialized model does not ensure the exact same inference result as the original model. So it is suggested to check, e.g., comparing the inference result of Wiki/PTB perplexity before and after preprocessing, if you choose to perform in `torch.float16`.
### Fine-tuning
`corda_finetuning.py`: This script fine-tunes the preprocessed model built above on a downstream task.
Example usage:
```bash
python corda_finetuning.py \
--model_name_or_path {path_to_residual_model} \
--output_dir {path_to_output_model} \
--corda_mode True \
--data_path meta-math/MetaMathQA \
--dataset_split "train[:100000]" \
--dataset_field query response \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 32 \
--save_strategy "steps" \
--save_steps 100 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_steps 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--bf16 True \
--tf32 True \
--report_to none
```
### Convert CorDA to LoRA
The main advantage of CorDA is concentrated during the training phase. For a trained CorDA adapter, we recommend converting it equivalently to the LoRA adapter for using and sharing.
```python
# The fine-tuned matrices $A$ and $B$ in CorDA adapter is saved and should be combined with the residual model.
peft_model.save_pretrained(output_dir)
# Given the matrices $A_0$ and $B_0$, initialized by CorDA and untrained, and the trained matrices $A$ and $B$,
# we can convert these to LoRA by setting $\Delta W = A \times B - A_0 \times B_0 = [A \mid A_0] \times [B \mid -B_0]^T = A'B'$.
peft_model.save_pretrained(output_dir, path_initial_model_for_weight_conversion="corda_init")
```
This conversion enables the loading of LoRA on top of a standard base model:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto"
)
# No SVD is performed during this step, and the base model remains unaltered.
peft_model = PeftModel.from_pretrained(model, "corda-llama-2-7b-lora")
```
Utilizing the converted LoRA does not require modifying the parameters of the base model. When multiple converted LoRAs are needed simultaneously, each adapter operates independently without interference, allowing for the adapters to be freely deleted or added.
Note that this conversion is not supported if `rslora` is used in combination with `rank_pattern` or `alpha_pattern`.
## Citation
```
@inproceedings{yangcorda,
title={CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning},
author={Yang, Yibo and Li, Xiaojie and Zhou, Zhongzhu and Song, Shuaiwen Leon and Wu, Jianlong and Nie, Liqiang and Ghanem, Bernard},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
}
```
================================================
FILE: examples/corda_finetuning/corda_finetuning.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os
from collections.abc import Sequence
from dataclasses import dataclass, field
from typing import Optional
import torch
import transformers
from datasets import load_dataset
from transformers import Trainer
from peft import LoraConfig, PeftModel, get_peft_model
IGNORE_INDEX = -100
PROMPT = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
)
def get_nb_trainable_parameters(model) -> tuple[int, int]:
r"""
Returns the number of trainable parameters and the number of all parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
# Due to the design of 4bit linear layers from bitsandbytes
# one needs to multiply the number of parameters by 2 to get
# the correct number of parameters
if param.__class__.__name__ == "Params4bit":
num_bytes = param.quant_storage.itemsize if hasattr(param, "quant_storage") else 1
num_params = num_params * 2 * num_bytes
all_param += num_params
if param.requires_grad:
trainable_params += num_params
return trainable_params, all_param
@dataclass
class TrainingArguments(transformers.TrainingArguments):
model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
data_path: str = field(default=None, metadata={"help": "Path to the training data."})
dataset_split: str = field(default="train[:100000]", metadata={"help": "(`['train', 'test', 'eval']`):"})
dataset_field: list[str] = field(default=None, metadata={"help": "Fields of dataset input and output."})
dataloader_num_proc: int = field(default=16, metadata={"help": "Number of processes to load dataset"})
dataloader_batch_size: int = field(
default=3000,
metadata={
"help": "batch size to load dataset. To set the batch size for training, you should pass --batch_size argument instead."
},
)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
lora_r: int = field(
default=None,
metadata={"help": "The rank of LoRA adapter. When passing `None`, CorDA or full fine-tuning is used."},
)
corda_mode: bool = field(default=True, metadata={"help": "True for CorDA mode"})
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
"""Collects the state dict and dump to disk."""
state_dict = trainer.model.state_dict()
if trainer.args.should_save:
cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
del state_dict
trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
def smart_tokenizer_and_embedding_resize(
special_tokens_dict: dict,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
"""
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = model.get_input_embeddings().weight.data
output_embeddings = model.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> dict:
"""Tokenize a list of strings."""
tokenized_list = [
tokenizer(
text,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
)
for text in strings
]
input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
input_ids_lens = labels_lens = [
tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
]
return {
"input_ids": input_ids,
"labels": labels,
"input_ids_lens": input_ids_lens,
"labels_lens": labels_lens,
}
def preprocess(
sources: Sequence[str],
targets: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
) -> dict:
"""Preprocess the data by tokenizing."""
examples = [s + t for s, t in zip(sources, targets)]
examples_tokenized, sources_tokenized = (_tokenize_fn(strings, tokenizer) for strings in (examples, sources))
input_ids = examples_tokenized["input_ids"]
labels = copy.deepcopy(input_ids)
for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]):
label[:source_len] = IGNORE_INDEX
return {
"input_ids": input_ids,
"labels": labels,
}
@dataclass
class DataCollatorForSupervisedDataset:
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[dict]) -> dict[str, torch.Tensor]:
input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
input_ids = [torch.tensor(x) for x in input_ids]
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
)
labels = [torch.tensor(x) for x in labels]
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": input_ids.ne(self.tokenizer.pad_token_id),
}
def train_tokenize_function(examples, tokenizer, query, response):
sources = [
PROMPT.format_map(
{
"instruction": instruction,
}
)
for instruction in examples[query]
]
targets = [f"{output}{tokenizer.eos_token}" for output in examples[response]]
data_dict = preprocess(sources, targets, tokenizer)
return data_dict
def train():
parser = transformers.HfArgumentParser(TrainingArguments)
script_args = parser.parse_args_into_dataclasses()[0]
print(script_args)
if script_args.corda_mode:
print("Train in CorDA mode")
res_model = transformers.AutoModelForCausalLM.from_pretrained(
script_args.model_name_or_path,
device_map="auto",
)
model = PeftModel.from_pretrained(
res_model, script_args.model_name_or_path, subfolder="corda_init", is_trainable=True
)
elif script_args.lora_r is not None:
print("Train in LoRA mode")
model = transformers.AutoModelForCausalLM.from_pretrained(
script_args.model_name_or_path,
device_map="auto",
)
lora_config = LoraConfig(
r=script_args.lora_r,
lora_alpha=script_args.lora_r,
init_lora_weights=True, # script_args.init_lora_weights,
target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
lora_dropout=0,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
else:
print("Train in Full Finetuning mode")
model = transformers.AutoModelForCausalLM.from_pretrained(
script_args.model_name_or_path,
dtype=torch.bfloat16,
device_map="auto",
)
trainable_params, all_param = get_nb_trainable_parameters(model)
print(
f"trainable params: {trainable_params:,d} || all params: {all_param:,d} || trainable%: {100 * trainable_params / all_param}"
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
script_args.model_name_or_path,
model_max_length=script_args.model_max_length,
padding_side="right",
use_fast=True,
trust_remote_code=True,
)
tokenizer.pad_token_id = tokenizer.eos_token_id
raw_train_datasets = load_dataset(script_args.data_path, split=script_args.dataset_split)
train_dataset = raw_train_datasets.map(
train_tokenize_function,
batched=True,
batch_size=script_args.dataloader_batch_size,
num_proc=script_args.dataloader_num_proc,
remove_columns=raw_train_datasets.column_names,
load_from_cache_file=True,
desc="Running tokenizer on train dataset",
fn_kwargs={
"tokenizer": tokenizer,
"query": script_args.dataset_field[0],
"response": script_args.dataset_field[1],
},
)
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
data_module = {
"train_dataset": train_dataset,
"data_collator": data_collator,
}
trainer = Trainer(model=model, processing_class=tokenizer, args=script_args, **data_module)
trainer.train()
trainer.save_state()
model.save_pretrained(os.path.join(script_args.output_dir, "ft"))
if __name__ == "__main__":
train()
================================================
FILE: examples/corda_finetuning/datautils.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import numpy as np
import torch
from datasets import load_dataset
"""
doc https://huggingface.co/docs/datasets/loading
doc https://huggingface.co/docs/datasets/process
doc https://huggingface.co/blog/llama2#how-to-prompt-llama-2
"""
def set_seed(seed):
np.random.seed(seed)
torch.random.manual_seed(seed)
def sample_train_loaders(name, tokenizer, nsamples=128, seed=0, seqlen=2048):
set_seed(seed)
if "wikitext2" in name:
traindata = load_dataset(
"wikitext",
"wikitext-2-raw-v1",
split="train",
)
traindata = "\n\n".join(traindata["text"])
elif "c4" in name:
traindata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
)
traindata = "\n\n".join(traindata["text"])
else:
raise NotImplementedError
trainloader = []
for _ in range(nsamples):
i = random.randint(0, len(traindata) - seqlen * 2 - 1)
j = i + seqlen * 2
# breakpoint()
trainenc = tokenizer(traindata[i:j], return_tensors="pt")
inp = trainenc.input_ids[:, :seqlen]
trainloader.append(inp)
return trainloader
def get_redpajama_train(tokenizer, percent=10, seed=3, batch_size=128, max_length=2048):
def tokenization(example):
return tokenizer(example["text"], truncation=True, max_length=max_length)
if percent != 100:
split = f"train[:{int(850000 * percent / 100)}]"
else:
split = "train"
dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", split=split)
processed_dataset = dataset.map(tokenization, batched=True, batch_size=batch_size, num_proc=os.cpu_count())
return processed_dataset
def get_english_quote(dataset_name, tokenizer):
data = load_dataset(dataset_name)
data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
return data["train"]
def get_qat_dataset(name, tokenizer, data_percent):
if name == "red_pajama":
data = get_redpajama_train(tokenizer, data_percent)
elif name == "Abirate/english_quotes":
data = get_english_quote(name, tokenizer)
else:
raise NotImplementedError
data = data.shuffle()
return data
llama_chat_format = """[INST] <>
"Below is an instruction that describes a task. Write a response that appropriately completes the request."
<>
{instruction} [/INST] {response}
"""
def get_calib_data(name, tokenizer, model_id, nsamples, seqlen=2048, seed=3):
print(f" get_data_from: {name}, nsamples={nsamples}, seqlen={seqlen}, {seed}")
cache_file = f"cache/{name}_{model_id.replace('/', '_')}_{nsamples}_{seqlen}_{seed}.pt"
traindataset = []
if not os.path.exists("cache"):
os.makedirs("cache")
if os.path.exists(cache_file):
print(f"found data file: {cache_file}")
traindataset = torch.load(cache_file)
print("loaded ...")
return traindataset
if name == "c4":
traindata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
)
tot_text = "\n\n".join(traindata["text"])
elif name == "wikitext2":
traindata = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
tot_text = "\n\n".join(traindata["text"])
elif name == "ptb":
traindata = load_dataset(
"ptb_text_only",
"penn_treebank",
split="train",
)
tot_text = "\n\n".join(traindata["sentence"])
elif name == "traivia_qa":
traindata = load_dataset("trivia_qa", "rc", split="train")
tot_text = "\n\n".join(traindata["question"])
elif name == "nqopen":
traindata = load_dataset("nq_open", split="train")
tot_text = "\n\n".join(traindata["question"])
elif name == "alpaca":
selected_data_dict = load_dataset("iboing/alpaca_data", split="train").shuffle(seed=seed).take(nsamples)
for example in selected_data_dict:
if example.get("input", "") == "":
s = llama_chat_format.format(instruction=example["instruction"], response=example["output"])
trainenc = tokenizer(s, return_tensors="pt")
inp = trainenc.input_ids[:, :seqlen]
attention_mask = torch.ones_like(inp)
traindataset.append({"input_ids": inp, "attention_mask": attention_mask})
print("example instruction:", s)
torch.save(traindataset, cache_file)
return traindataset
elif name == "MetaMATH":
selected_data_dict = load_dataset("iboing/MetaMathQA-395K", split="train").shuffle(seed=seed).take(nsamples)
for example in selected_data_dict:
if example.get("input", "") == "":
s = llama_chat_format.format(instruction=example["query"], response=example["response"])
trainenc = tokenizer(s, return_tensors="pt")
inp = trainenc.input_ids[:, :seqlen]
attention_mask = torch.ones_like(inp)
traindataset.append({"input_ids": inp, "attention_mask": attention_mask})
print("example instruction:", s)
torch.save(traindataset, cache_file)
return traindataset
elif name == "codefeedback":
selected_data_dict = (
load_dataset("iboing/CodeFeedback-Filtered-Instruction", split="train").shuffle(seed=seed).take(nsamples)
)
for example in selected_data_dict:
if example.get("input", "") == "":
s = llama_chat_format.format(instruction=example["query"], response=example["answer"])
trainenc = tokenizer(s, return_tensors="pt")
inp = trainenc.input_ids[:, :seqlen]
attention_mask = torch.ones_like(inp)
traindataset.append({"input_ids": inp, "attention_mask": attention_mask})
print("example instruction:", s)
torch.save(traindataset, cache_file)
return traindataset
elif name == "WizLMinstruct":
selected_data_dict = (
load_dataset("iboing/WizardLM_evol_instruct_V2_143k", split="train").shuffle(seed=seed).take(nsamples)
)
for example in selected_data_dict:
if example.get("input", "") == "":
s = llama_chat_format.format(
instruction=example["conversation"][0]["human"], response=example["conversation"][0]["assistant"]
)
trainenc = tokenizer(s, return_tensors="pt")
inp = trainenc.input_ids[:, :seqlen]
attention_mask = torch.ones_like(inp)
traindataset.append({"input_ids": inp, "attention_mask": attention_mask})
print("example instruction:", s)
torch.save(traindataset, cache_file)
return traindataset
else:
raise NotImplementedError
print(f"tot_text={len(tot_text)}")
for _ in range(nsamples):
i = random.randint(0, len(tot_text) - seqlen - 1)
j = i + seqlen * 10
trainenc = tokenizer(tot_text[i:j], return_tensors="pt")
inp = trainenc.input_ids[:, :seqlen]
attention_mask = torch.ones_like(inp)
traindataset.append({"input_ids": inp, "attention_mask": attention_mask})
torch.save(traindataset, cache_file)
return traindataset
def get_eval_loaders(name, tokenizer):
if "wikitext2" in name:
testdata = load_dataset(
"wikitext",
"wikitext-2-raw-v1",
split="test",
)
testenc = tokenizer("\n\n".join(testdata["text"]), return_tensors="pt")
return testenc
if "ptb" in name:
valdata = load_dataset(
"ptb_text_only",
"penn_treebank",
split="validation",
)
testenc = tokenizer("\n\n".join(valdata["sentence"]), return_tensors="pt")
return testenc
if "c4" in name:
testdata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
split="validation",
)
testenc = tokenizer("\n\n".join(testdata["text"]), return_tensors="pt")
return testenc
raise NotImplementedError
================================================
FILE: examples/corda_finetuning/preprocess.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import numpy as np
import torch
from datautils import get_calib_data
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import get_peft_model
from peft.tuners.lora.config import CordaConfig, LoraConfig
from peft.tuners.lora.corda import preprocess_corda
@torch.no_grad()
def run_model(model, calib_loader):
model.eval()
for batch in tqdm(calib_loader):
batch = {k: v.to(model.device) for k, v in batch.items()}
model(**batch)
def main(args):
# Setting random seed of numpy and torch
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
elif torch.xpu.is_available():
torch.xpu.manual_seed_all(args.seed)
torch.use_deterministic_algorithms(True)
# Load model
model_id = args.model_id
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id, device_map="auto", dtype=torch.float16, trust_remote_code=True
)
# Collect data
calib_loader = get_calib_data(args.calib_dataset, tokenizer, model_id, args.calib_loader_size, seed=args.seed)
# Evaluate the original model
print("\n---- model before svd ---\n")
print(model)
# Perform decomposition
corda_config = CordaConfig(
corda_method="ipm" if args.first_eigen else "kpm",
)
lora_config = LoraConfig(
init_lora_weights="corda",
target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
r=args.r,
lora_alpha=args.r,
corda_config=corda_config,
)
preprocess_corda(
model,
lora_config,
run_model=lambda: run_model(model, calib_loader),
)
model = get_peft_model(model, lora_config)
# Evaluate again to check if the model is consistent
# Using `model.model` here because `get_peft_model` wraps a layer to the model
print("\n---- model after svd ---\n")
print(model)
# Save as hugging face model
if args.save_model:
assert args.save_path is not None
save_path = args.save_path
# Save CorDA modules
model.peft_config["default"].init_lora_weights = True
model.save_pretrained(os.path.join(save_path, "corda_init"))
# Save residual model
model = model.unload()
model.save_pretrained(save_path)
# Save tokenizer
tokenizer.save_pretrained(save_path)
print(f"Done building CorDA huggingface model in {save_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_id",
type=str,
default="meta-llama/Llama-2-7b-hf",
help="Pretrained model ID",
)
parser.add_argument(
"--calib_loader_size",
type=int,
default=256,
help="number of samples used for covariance matrices",
)
parser.add_argument(
"--calib_dataset",
type=str,
default="wikitext2",
choices=[
"wikitext2",
"c4",
"ptb",
"traivia_qa",
"nqopen",
"MetaMATH",
"codefeedback",
"WizLMinstruct",
"alpaca",
],
help="calibration dataset",
)
parser.add_argument(
"--eval_mmlu",
action="store_true",
help="evaluate mmlu",
)
parser.add_argument(
"--seed",
type=int,
default=233,
help="random seed",
)
parser.add_argument(
"--r",
type=int,
default=None,
)
parser.add_argument(
"--first_eigen",
action="store_true",
)
parser.add_argument(
"--save_model",
action="store_true",
)
parser.add_argument(
"--save_path",
type=str,
default=None,
)
args = parser.parse_args()
main(args)
================================================
FILE: examples/cpt_finetuning/README.md
================================================
# Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods
## Introduction ([Paper](https://huggingface.co/papers/2410.17222), [Code](https://github.com/tsachiblau/Context-aware-Prompt-Tuning-Advancing-In-Context-Learning-with-Adversarial-Methods), [Notebook](cpt_train_and_inference.ipynb), [Colab](https://colab.research.google.com/drive/1UhQDVhZ9bDlSk1551SuJV8tIUmlIayta?usp=sharing))
> Large Language Models (LLMs) can perform few-shot learning using either optimization-based approaches or In-Context Learning (ICL). Optimization-based methods often suffer from overfitting, as they require updating a large number of parameters with limited data. In contrast, ICL avoids overfitting but typically underperforms compared to optimization-based methods and is highly sensitive to the selection, order, and format of demonstration examples. To overcome these challenges, we introduce Context-aware Prompt Tuning (CPT), a method inspired by ICL, Prompt Tuning (PT), and adversarial attacks. CPT builds on the ICL strategy of concatenating examples before the input, extending it by incorporating PT-like learning to refine the context embedding through iterative optimization, extracting deeper insights from the training examples. Our approach carefully modifies specific context tokens, considering the unique structure of the examples within the context. In addition to updating the context with PT-like optimization, CPT draws inspiration from adversarial attacks, adjusting the input based on the labels present in the context while preserving the inherent value of the user-provided data. To ensure robustness and stability during optimization, we employ a projected gradient descent algorithm, constraining token embeddings to remain close to their original values and safeguarding the quality of the context. Our method has demonstrated superior accuracy across multiple classification tasks using various LLM models, outperforming existing baselines and effectively addressing the overfitting challenge in few-shot learning.
CPT optimizing only specific token embeddings while keeping the rest of the model frozen (image source).
---
## Dataset Creation and Collation for CPT
This document explains how to prepare datasets for CPT, linking the dataset preparation processes in the code to the methods and principles described in the CPT paper, specifically in **Sections 3.1**, **3.2**, and **3.3**.
---
### Template-Based Tokenization
#### The Role of Templates
Templates define the structure of the input-output pairs, enabling the model to interpret the task within a unified context.
- **Input Templates**:
Templates like `"input: {sentence}"` structure raw input sentences. The `{sentence}` placeholder is replaced with the actual input text.
- **Output Templates**:
Templates such as `"output: {label}"` format the labels (e.g., `positive`, `negative`, etc.).
- **Separator Tokens**:
Separators distinguish different parts of the input, such as the input text and labels, as well as separate examples within the context.
#### How CPT Utilizes Context Structure
CPT leverages the context structure, encoded within the `cpt_tokens_type_mask`, to optimize the context effectively. to optimize the context effectively. By treating different token types based on their roles, the model updates some tokens while using others solely for optimization:
1. **Refrain from Updating Label Tokens**:
Some context tokens represent label tokens, which contain valuable, unmodifiable information. By excluding these tokens from updates during training, CPT ensures that the labels remain fixed, preserving their integrity.
2. **Apply Type-Specific Projection Norms**:
CPT employs Projected Gradient Descent (PGD) to update context embeddings, applying tailored norms to different context parts. This approach reduces overfitting while maintaining robustness and generalization by preserving the integrity of user-provided examples.
#### Limitations
CPT is designed for few-shot scenarios, as concatenating more examples increases memory usage due to the self-attention mechanism and additional loss terms. For larger datasets, users can limit the number of context examples and use the remaining samples solely for optimization to manage memory efficiently.
## Citation
```bib
@article{
blau2025cpt,
title={Context-Aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods},
author={Tsachi Blau, Moshe Kimhi, Yonatan Belinkov, Alexander Bronstein, Chaim Baskin},
journal={arXiv preprint arXiv:2410.17222}},
year={2025}
}
```
================================================
FILE: examples/cpt_finetuning/cpt_train_and_inference.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# CPT Training and Inference\n",
"This notebook demonstrates the training and evaluation process of Context-Aware Prompt Tuning (CPT) using the Hugging Face Trainer. For more details, refer to the [Paper](https://huggingface.co/papers/2410.17222).\n",
"\n",
"\n",
"## Sections Overview:\n",
"1. **Setup**: Import libraries and configure the environment.\n",
"2. **Data Preparation**: Load and preprocess the dataset.\n",
"3. **Model Training**: Configure and train the model.\n",
"4. **Evaluation**: Test the model's performance and visualize results."
],
"metadata": {
"id": "R_byvXT9lpTU"
},
"id": "R_byvXT9lpTU"
},
{
"cell_type": "markdown",
"source": [
"# Setup\n",
"\n",
"---\n",
"\n",
"\n"
],
"metadata": {
"collapsed": false,
"id": "11b07b07ac5e472b"
},
"id": "11b07b07ac5e472b"
},
{
"cell_type": "markdown",
"source": [
"## Installation"
],
"metadata": {
"id": "O8DWZb8ZrGRU"
},
"id": "O8DWZb8ZrGRU"
},
{
"cell_type": "code",
"source": [
"!pip install datasets\n",
"!pip install git+https://github.com/huggingface/peft"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d6KZ5REDrFiM",
"outputId": "e505bc0e-082a-4720-9117-b730d9fd67fa"
},
"id": "d6KZ5REDrFiM",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Collecting git+https://github.com/huggingface/peft\n",
" Cloning https://github.com/huggingface/peft to /tmp/pip-req-build-0mbyx_z_\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft /tmp/pip-req-build-0mbyx_z_\n",
" Resolved https://github.com/huggingface/peft to commit 131efba5d48753a3355ecd4f3833ae010a0510d6\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Imports"
],
"metadata": {
"id": "5BerCvfkq_jp"
},
"id": "5BerCvfkq_jp"
},
{
"cell_type": "code",
"source": [
"from typing import Any, Dict, List, Union\n",
"\n",
"import numpy as np\n",
"import torch\n",
"from datasets import load_dataset\n",
"from torch.utils.data import Dataset\n",
"from tqdm import tqdm\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" DataCollatorForLanguageModeling,\n",
" Trainer,\n",
" TrainingArguments,\n",
")\n",
"\n",
"from peft import CPTConfig, TaskType, get_peft_model\n",
"\n",
"\n",
"MAX_INPUT_LENGTH = 1024\n",
"MAX_ICL_SAMPLES = 10\n",
"NUM_TRAINING_SAMPLES = 100\n",
"model_id = 'bigscience/bloom-1b7'"
],
"metadata": {
"id": "Y0pETNFBl963"
},
"id": "Y0pETNFBl963",
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Data Preparation\n",
"---"
],
"metadata": {
"id": "9hO_I3aDmCQu"
},
"id": "9hO_I3aDmCQu"
},
{
"cell_type": "code",
"source": [
"# Initialize the tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\n",
" model_id, # The name or path of the pre-trained tokenizer (e.g., \"bert-base-uncased\").\n",
" cache_dir='.', # Directory to cache the tokenizer files locally.\n",
" padding_side='right', # Specifies that padding should be added to the right side of sequences.\n",
" trust_remote_code=True # Allows loading tokenizer implementations from external sources.\n",
")"
],
"metadata": {
"id": "STK5N0LJrZmA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4c5c3dda-07ae-4f67-df29-4a2ff499e5ad"
},
"id": "STK5N0LJrZmA",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Load the SST-2 dataset from the GLUE benchmark\n",
"dataset = load_dataset('glue', 'sst2')\n",
"\n",
"def add_string_labels(example):\n",
" \"\"\"\n",
" Converts numerical labels into human-readable string labels.\n",
"\n",
" Args:\n",
" example (dict): A single example from the dataset with a numerical 'label'.\n",
"\n",
" Returns:\n",
" dict: The example augmented with a 'label_text' field.\n",
" \"\"\"\n",
" # Map numerical label to string label\n",
" example['label_text'] = \"positive\" if example['label'] == 1 else \"negative\"\n",
" return example\n",
"\n",
"# Subset and process the training dataset\n",
"context_dataset = dataset['train'].select(range(MAX_ICL_SAMPLES)).map(add_string_labels)\n",
"train_dataset = dataset['train'].select(range(MAX_ICL_SAMPLES, NUM_TRAINING_SAMPLES + MAX_ICL_SAMPLES)).map(add_string_labels)"
],
"metadata": {
"id": "C3oq4lDDrcUf",
"colab": {
"base_uri": "https://localhost:8080/",
"referenced_widgets": [
"72a5be4b77ec4d5994bcace9d462da84",
"bed78529ff2c4d08befca97c50cb5efc",
"cf7077acfce04aff8af0a2483dbf094c",
"910462d70d944d00ba54958d77bee755",
"a899818bdad0415b860eaac4afe31f30",
"3d78a6c8923547cf8c75bc8c10125eda",
"8083f95a673a423286ade63051de757d",
"13fc203ab1b44c83b6cfcc1e171d26ad",
"663a0196d2b547fd8a6890b8a86080c2",
"72be01164e974d59b05bee716e9bc978",
"4cedaf37e79e4ff1a10ffb96ec543e81"
],
"height": 49
},
"outputId": "5ae1ff54-d726-4f07-e6d7-cd53145b5d6f"
},
"id": "C3oq4lDDrcUf",
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/100 [00:00 0 else 0 # Increment type indices dynamically\n",
" for i in cpt_context_dataset[i]['input_type_mask']\n",
" ]\n",
"\n",
" # Increment the type mask offset after processing the sample\n",
" first_type_mask += 4"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:24:58.894814Z",
"start_time": "2024-10-22T09:24:58.893841Z"
},
"id": "aef03bbd5d86d3d8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1bb1343b-b5f8-4998-e34b-6a8ae8063381"
},
"id": "aef03bbd5d86d3d8",
"execution_count": 7
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{
"cell_type": "markdown",
"source": [
"# Model Training\n",
"\n",
"---"
],
"metadata": {
"collapsed": false,
"id": "2c40f24774d83372"
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"id": "2c40f24774d83372"
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{
"cell_type": "markdown",
"source": [
"## Load model"
],
"metadata": {
"id": "p0jFTzkisMgN"
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"id": "p0jFTzkisMgN"
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{
"cell_type": "code",
"outputs": [],
"source": [
"# Load a pre-trained causal language model\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_id,\n",
" cache_dir='.',\n",
" dtype=torch.float16,\n",
" device_map='auto'\n",
")\n",
"\n",
"# Initialize the CPT configuration\n",
"config = CPTConfig(\n",
" task_type=TaskType.CAUSAL_LM,\n",
" cpt_token_ids=context_ids,\n",
" cpt_mask=context_attention_mask,\n",
" cpt_tokens_type_mask=context_input_type_mask,\n",
"\n",
" opt_weighted_loss_type='decay',\n",
" opt_loss_decay_factor=0.95, # we choose the exponential decay factor applied to the loss\n",
" opt_projection_epsilon=0.2, # we choose the projection over the input tokens\n",
" opt_projection_format_epsilon=0.1, # we choose the projection over input and output templates\n",
"\n",
" tokenizer_name_or_path=model_id,\n",
")\n",
"\n",
"# Initialize the CPT model with PEFT\n",
"model = get_peft_model(base_model, config)"
],
"metadata": {
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"end_time": "2024-10-22T09:25:08.941945Z",
"start_time": "2024-10-22T09:25:04.393323Z"
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"id": "17ac445134919a39",
"execution_count": 8
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{
"cell_type": "markdown",
"source": [
"## Setting Collate Function"
],
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"id": "4e49660c50d98741"
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"source": [
"class CPTDataCollatorForLanguageModeling(DataCollatorForLanguageModeling):\n",
" def __init__(self, tokenizer, training=True, mlm=False):\n",
" \"\"\"\n",
" Custom collator for CPT-style language modeling.\n",
"\n",
" Args:\n",
" tokenizer: The tokenizer to handle tokenization and special tokens.\n",
" training (bool): If True, operates in training mode; otherwise in evaluation mode.\n",
" mlm (bool): If True, enables masked language modeling.\n",
" \"\"\"\n",
"\n",
" super().__init__(tokenizer, mlm=mlm) # Initialize the parent class\n",
" self.training = training\n",
"\n",
" # Add a special padding token if not already defined\n",
" self.tokenizer.add_special_tokens({\"pad_token\": \"[PAD]\"})\n",
"\n",
" def torch_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:\n",
" \"\"\"\n",
" Process a batch of examples for language modeling.\n",
"\n",
" Args:\n",
" examples (List): A batch of examples with tokenized inputs and optional sample masks.\n",
"\n",
" Returns:\n",
" Dict: A dictionary containing padded and tensor-converted inputs, attention masks,\n",
" input type masks, and optional sample masks and labels.\n",
" \"\"\"\n",
"\n",
" # Initialize a list to collect sample masks if provided\n",
" list_sample_mask = []\n",
" for i in range(len(examples)):\n",
" if \"sample_mask\" in examples[i].keys():\n",
" list_sample_mask.append(examples[i].pop(\"sample_mask\"))\n",
"\n",
" # Define a helper function for padding sequences to the maximum length\n",
" max_len = max(len(ex[\"input_ids\"]) for ex in examples)\n",
"\n",
" # Define a helper function for padding sequences to the maximum length\n",
" def pad_sequence(sequence, max_len, pad_value=0):\n",
" return sequence + [pad_value] * (max_len - len(sequence))\n",
"\n",
" # Pad and convert `input_ids`, `attention_mask`, and `input_type_mask` to tensors\n",
" input_ids = torch.tensor([pad_sequence(ex[\"input_ids\"], max_len) for ex in examples])\n",
" attention_mask = torch.tensor([pad_sequence(ex[\"attention_mask\"], max_len) for ex in examples])\n",
" input_type_mask = torch.tensor([pad_sequence(ex[\"input_type_mask\"], max_len) for ex in examples])\n",
"\n",
" # Create the initial batch dictionary\n",
" batch = {\"input_ids\": input_ids, \"attention_mask\": attention_mask, \"input_type_mask\": input_type_mask}\n",
"\n",
" # Create a tensor to store sample masks\n",
" tensor_sample_mask = batch[\"input_ids\"].clone().long()\n",
" tensor_sample_mask[:, :] = 0 # Initialize with zeros\n",
"\n",
" # Populate the tensor with the provided sample masks\n",
" for i in range(len(list_sample_mask)):\n",
" tensor_sample_mask[i, : len(list_sample_mask[i])] = list_sample_mask[i]\n",
"\n",
" # Copy `input_ids` to use as `labels`\n",
" batch[\"labels\"] = batch[\"input_ids\"].clone()\n",
"\n",
" # If in evaluation mode, include the `sample_mask` in the batch\n",
" if not self.training:\n",
" batch[\"sample_mask\"] = tensor_sample_mask\n",
"\n",
" return batch"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:25:08.953199Z",
"start_time": "2024-10-22T09:25:08.945689Z"
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"id": "b0fac840f060e3aa",
"execution_count": 9
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{
"cell_type": "markdown",
"source": [
"## Training"
],
"metadata": {
"collapsed": false,
"id": "48f535d74e6602b"
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"id": "48f535d74e6602b"
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{
"cell_type": "code",
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
"\n",
"
"
]
},
"metadata": {}
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=500, training_loss=0.09815525007247924, metrics={'train_runtime': 90.6767, 'train_samples_per_second': 5.514, 'train_steps_per_second': 5.514, 'total_flos': 79477977907200.0, 'train_loss': 0.09815525007247924, 'epoch': 5.0})"
]
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"training_args = TrainingArguments(\n",
" output_dir='../.',\n",
" use_cpu=False,\n",
" auto_find_batch_size=False,\n",
" learning_rate=1e-4,\n",
" logging_steps=100,\n",
" per_device_train_batch_size=1,\n",
" save_total_limit=1,\n",
" remove_unused_columns=False,\n",
" num_train_epochs=5,\n",
" fp16=True,\n",
" save_strategy='no',\n",
" report_to=\"none\"\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=cpt_train_dataset, # Custom CPT training dataset.\n",
" data_collator=CPTDataCollatorForLanguageModeling(tokenizer, training=True, mlm=False)\n",
")\n",
"\n",
"trainer.train()"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:25:27.599132Z",
"start_time": "2024-10-22T09:25:13.906685Z"
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 268
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"id": "1a865c2ad2dc7218",
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"id": "1a865c2ad2dc7218",
"execution_count": 10
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{
"cell_type": "markdown",
"source": [
"# Model Evaluation\n",
"\n",
"---"
],
"metadata": {
"collapsed": false,
"id": "b799ea89a567590f"
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"id": "b799ea89a567590f"
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"cell_type": "code",
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 100/100 [00:00<00:00, 1972.82it/s]\n"
]
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"name": "stdout",
"text": [
"Sentence: input: it 's a charming and often affecting journey . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: unflinchingly bleak and desperate output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's slow -- very , very slow . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: a sometimes tedious film . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: or doing last year 's taxes with your ex-wife . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the mesmerizing performances of the leads keep the film grounded and keep the audience riveted . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it takes a strange kind of laziness to waste the talents of robert forster , anne meara , eugene levy , and reginald veljohnson all in the same movie . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: ... the film suffers from a lack of humor ( something needed to balance out the violence ) ... output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: we root for ( clara and paul ) , even like them , though perhaps it 's an emotion closer to pity . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: even horror fans will most likely not find what they 're seeking with trouble every day ; the movie lacks both thrills and humor . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a gorgeous , high-spirited musical from india that exquisitely blends music , dance , song , and high drama . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the emotions are raw and will strike a nerve with anyone who 's ever had family trauma . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: audrey tatou has a knack for picking roles that magnify her outrageous charm , and in this literate french comedy , she 's as morning-glory exuberant as she was in amélie . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ... the movie is just a plain old monster . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: in its best moments , resembles a bad high school production of grease , without benefit of song . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: pumpkin takes an admirable look at the hypocrisy of political correctness , but it does so with such an uneven tone that you never know when humor ends and tragedy begins . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the iditarod lasts for days - this just felt like it did . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: holden caulfield did it better . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: a delectable and intriguing thriller filled with surprises , read my lips is an original . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: seldom has a movie so closely matched the spirit of a man and his work . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: nicks , seemingly uncertain what 's going to make people laugh , runs the gamut from stale parody to raunchy sex gags to formula romantic comedy . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the action switches between past and present , but the material link is too tenuous to anchor the emotional connections that purport to span a 125-year divide . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it 's an offbeat treat that pokes fun at the democratic exercise while also examining its significance for those who take part . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's a cookie-cutter movie , a cut-and-paste job . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: i had to look away - this was god awful . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: thanks to scott 's charismatic roger and eisenberg 's sweet nephew , roger dodger is one of the most compelling variations on in the company of men . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ... designed to provide a mix of smiles and tears , `` crossroads '' instead provokes a handful of unintentional howlers and numerous yawns . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a gorgeous , witty , seductive movie . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: if the movie succeeds in instilling a wary sense of ` there but for the grace of god , ' it is far too self-conscious to draw you deeply into its world . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it does n't believe in itself , it has no sense of humor ... it 's just plain bored . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a sequence of ridiculous shoot - 'em - up scenes . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the weight of the piece , the unerring professionalism of the chilly production , and the fascination embedded in the lurid topic prove recommendation enough . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ( w ) hile long on amiable monkeys and worthy environmentalism , jane goodall 's wild chimpanzees is short on the thrills the oversize medium demands . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: as surreal as a dream and as detailed as a photograph , as visually dexterous as it is at times imaginatively overwhelming . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: escaping the studio , piccoli is warmly affecting and so is this adroitly minimalist movie . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: there 's ... tremendous energy from the cast , a sense of playfulness and excitement that seems appropriate . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this illuminating documentary transcends our preconceived vision of the holy land and its inhabitants , revealing the human complexities beneath . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the subtle strength of `` elling '' is that it never loses touch with the reality of the grim situation . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: holm ... embodies the character with an effortlessly regal charisma . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the title not only describes its main characters , but the lazy people behind the camera as well . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: it offers little beyond the momentary joys of pretty and weightless intellectual entertainment . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: a synthesis of cliches and absurdities that seems positively decadent in its cinematic flash and emptiness . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a subtle and well-crafted ( for the most part ) chiller . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: has a lot of the virtues of eastwood at his best . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's hampered by a lifetime-channel kind of plot and a lead actress who is out of her depth . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it feels like an after-school special gussied up with some fancy special effects , and watching its rote plot points connect is about as exciting as gazing at an egg timer for 93 minutes . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: for the most part , director anne-sophie birot 's first feature is a sensitive , extraordinarily well-acted drama . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: mr. tsai is a very original artist in his medium , and what time is it there ? output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: sade is an engaging look at the controversial eponymous and fiercely atheistic hero . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: so devoid of any kind of intelligible story that it makes films like xxx and collateral damage seem like thoughtful treatises output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a tender , heartfelt family drama . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ... a hollow joke told by a cinematic gymnast having too much fun embellishing the misanthropic tale to actually engage it . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the cold turkey would 've been a far better title . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: manages to be both repulsively sadistic and mundane . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it 's just disappointingly superficial -- a movie that has all the elements necessary to be a fascinating , involving character study , but never does more than scratch the surface . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: this is a story of two misfits who do n't stand a chance alone , but together they are magnificent . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: schaeffer has to find some hook on which to hang his persistently useless movies , and it might as well be the resuscitation of the middle-aged character . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the primitive force of this film seems to bubble up from the vast collective memory of the combatants . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: on this tricky topic , tadpole is very much a step in the right direction , with its blend of frankness , civility and compassion . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the script kicks in , and mr. hartley 's distended pace and foot-dragging rhythms follow . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: you wonder why enough was n't just a music video rather than a full-length movie . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: if you 're hard up for raunchy college humor , this is your ticket right here . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: a fast , funny , highly enjoyable movie . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: good old-fashioned slash-and-hack is back ! output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this one is definitely one to skip , even for horror movie fanatics . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: for all its impressive craftsmanship , and despite an overbearing series of third-act crescendos , lily chou-chou never really builds up a head of emotional steam . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: exquisitely nuanced in mood tics and dialogue , this chamber drama is superbly acted by the deeply appealing veteran bouquet and the chilling but quite human berling . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: uses high comedy to evoke surprising poignance . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: one of creepiest , scariest movies to come along in a long , long time , easily rivaling blair witch or the others . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: a string of rehashed sight gags based in insipid vulgarity . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: among the year 's most intriguing explorations of alientation . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the movie fails to live up to the sum of its parts . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the son 's room is a triumph of gentility that earns its moments of pathos . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: there is nothing outstanding about this film , but it is good enough and will likely be appreciated most by sailors and folks who know their way around a submarine . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this is a train wreck of an action film -- a stupefying attempt by the filmmakers to force-feed james bond into the mindless xxx mold and throw 40 years of cinematic history down the toilet in favor of bright flashes and loud bangs . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the draw ( for `` big bad love '' ) is a solid performance by arliss howard . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: green might want to hang onto that ski mask , as robbery may be the only way to pay for his next project . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: it 's one pussy-ass world when even killer-thrillers revolve around group therapy sessions . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: though it 's become almost redundant to say so , major kudos go to leigh for actually casting people who look working-class . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the band 's courage in the face of official repression is inspiring , especially for aging hippies ( this one included ) . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the movie achieves as great an impact by keeping these thoughts hidden as ... ( quills ) did by showing them . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the film flat lines when it should peak and is more missed opportunity and trifle than dark , decadent truffle . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: jaglom ... put ( s ) the audience in the privileged position of eavesdropping on his characters output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: fresnadillo 's dark and jolting images have a way of plying into your subconscious like the nightmare you had a week ago that wo n't go away . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: we know the plot 's a little crazy , but it held my interest from start to finish . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's a scattershot affair , but when it hits its mark it 's brilliant . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: hardly a masterpiece , but it introduces viewers to a good charitable enterprise and some interesting real people . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: you wo n't like roger , but you will quickly recognize him . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: if steven soderbergh 's ` solaris ' is a failure it is a glorious failure . output: positive \n",
" \t The prediction is: negative\n",
" \t The GT is positive\n",
"Sentence: input: byler reveals his characters in a way that intrigues and even fascinates us , and he never reduces the situation to simple melodrama . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this riveting world war ii moral suspense story deals with the shadow side of american culture : racial prejudice in its ugly and diverse forms . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: it 's difficult to imagine the process that produced such a script , but here 's guessing that spray cheese and underarm noises played a crucial role . output: negative \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: no sophomore slump for director sam mendes , who segues from oscar winner to oscar-winning potential with a smooth sleight of hand . output: positive \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: on the whole , the movie lacks wit , feeling and believability to compensate for its incessant coarseness and banality . output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: why make a documentary about these marginal historical figures ? output: negative \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"The model Acc is 90.0%\n"
]
}
],
"source": [
"model.eval()\n",
"\n",
"# Select relevant columns from the test dataset\n",
"test_dataset = test_dataset.select_columns(['sentence', 'label_text'])\n",
"\n",
"# Convert the test dataset to a CPT-compatible format\n",
"cpt_test_dataset = CPTDataset(test_dataset, tokenizer, templates)\n",
"\n",
"# Get the device where the model is loaded (CPU, GPU or XPU)\n",
"device = model.device\n",
"list_bool_predictions = []\n",
"\n",
"for i in range(len(test_dataset)):\n",
" input_ids, input_type_mask = cpt_test_dataset[i]['input_ids'], cpt_test_dataset[i]['input_type_mask']\n",
"\n",
" # Pass the inputs through the model\n",
" outputs = model(\n",
" input_ids=torch.Tensor(input_ids).long().to(device=device).view(1, -1),\n",
" labels=torch.Tensor(input_ids).long().to(device=device).view(1, -1),\n",
" input_type_mask=torch.Tensor(input_type_mask).long().to(device=device).view(1, -1)\n",
" )\n",
"\n",
" # Shift logits to exclude the last token and match the labels\n",
" shifted_logits = outputs.logits[..., :-1, :].contiguous().to(model.dtype)[0, -len(input_ids) + 1:]\n",
" shift_labels = torch.Tensor(input_ids).long().to(device=device).view(1, -1)[0, 1:].contiguous().to(device)\n",
" shifted_input_type_mask = torch.Tensor(input_type_mask).long().to(device=device).view(1, -1)[..., 1:].contiguous().to(device)\n",
"\n",
" # Create a mask for the type `4` tokens (label tokens)\n",
" mask = torch.Tensor(shifted_input_type_mask).long().to(device=device).view(-1,) == 4\n",
"\n",
" # Extract logits and labels corresponding to the mask\n",
" logit = shifted_logits[mask]\n",
" label = shift_labels[mask]\n",
"\n",
" # All possible label tokens for `negative` and `positive`\n",
" all_labels = torch.Tensor([tokenizer(i, add_special_tokens=False)[\"input_ids\"] for i in ['negative', 'positive']]).long().to(device).view(-1,)\n",
"\n",
" # Compare logits with label tokens and infer prediction\n",
" prediction = logit[0, torch.Tensor([tokenizer(i, add_special_tokens=False)[\"input_ids\"] for i in ['negative', 'positive']]).long().to(device).view(-1,)].argmax()\n",
" prediction_text = 'negative' if prediction == 0 else 'positive'\n",
" print(f\"Sentence: {tokenizer.decode(input_ids)} \\n \\t The prediction is: {prediction_text}\\n \\t The GT is {tokenizer.decode(label)}\")\n",
" list_bool_predictions.append(prediction_text == tokenizer.decode(label))\n",
"\n",
"print(f'The model Acc is {100 * np.mean(list_bool_predictions)}%')"
],
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================================================
FILE: examples/delora_finetuning/README.md
================================================
# DeLoRA: Decoupled Low-Rank Adaptation
## Introduction
[DeLoRA](https://huggingface.co/papers/2503.18225) tackles finetuning in a Frobenius-norm bounded setup: this allows to prevent divergence from the pretrained model, effectively decoupling the learning of angles and magnitudes.
This is done by (i) normalization of the BA low-rank matrices, which bound the updates' Frobenius norm, (ii) learnable scaling lambda, which controls the update's boundary/magnitude, (iii) layer-wise scaling of ||W||, to adapt each update's norm to the original weights' norm.
## Quick start
With respect to your standard PEFT training procedure with LoRA, simply swap your `LoraConfig` for a `DeloraConfig`. Note however that `lora_alpha` parameter is replaced by `delora_lambda` parameter which sets an upper bound to the Frobenius norm of the weight change.
```python
import torch
from peft import DeloraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
tokenizer.pad_token_id = tokenizer.eos_token_id
delora_config = DeloraConfig(r=32, delora_lambda=15)
peft_model = get_peft_model(model, delora_config)
peft_model.print_trainable_parameters()
dataset = load_dataset("imdb", split="train[:1%]")
training_args = SFTConfig(dataset_text_field="text", max_length=128)
trainer = SFTTrainer(
model=peft_model,
args=training_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("delora-llama-3-8b")
```
To utilize the fine-tuned DeLoRA modules, simply run the following command:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3-8B", dtype=torch.bfloat16, device_map="auto"
)
peft_model = PeftModel.from_pretrained(model, "delora-llama-3-8b")
```
## Advanced Usage
In this script the default DeLoRA layers are the query and value layers of the Llama model. Adding adapters on more layers will increase memory usage. If you wish to choose a different set of layers for DeLoRA to be applied on, you can simply define it using:
```bash
python examples/delora_finetuning/delora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --target_modules "q_proj,k_proj,v_proj,o_proj"
```
Using different lambdas for different layers is also possible by setting `lambda_pattern`.
### Fine-tune
```bash
python delora_finetuning.py \
--base_model "PATH_TO_MODEL" \
--data_path "PATH_TO_DATASET" \
--output_dir "PATH_TO_OUTPUT_DIR" \
--batch_size 1 \
--num_epochs 3 \
--learning_rate 3e-3 \
--cutoff_len 512 \
--val_set_size 500 \
--eval_step 10 \
--save_step 100 \
--device "auto" \
--rank 32 \
--delora_lambda 15 \
--module_dropout 0.1 \
--target_modules "q_proj,v_proj" \
--hub_model_id "YOUR_HF_REPO" \
--push_to_hub
```
## Additional Notes
### Best practices
- use 10-100x larger learning rate than standard LoRA variants (typical values from 1e-3/1e-2/..)
- do not set a too small initial boundary parameter lambda (typical values are around 10/15/..)
### DeLoRA vs DoRA
DeLoRA might feel quite similar to DoRA (given the similar target of decoupling angular from magnitude learning), however it presents key differences: (i) DoRA applies normalization and scaling operations on the fully finetuned weights ($W + \Delta W$), (ii) DoRA's normalization operation is performed on the column space of the weight matrices.
Conversely DeLoRA (i) introduces the normalization and scaling operations directly on the weight updates $\Delta W$, better preventing divergence from the pretrained model, and (ii) normalizes the inner low-dimensional space, which enforces a Frobenius-norm boundary to the weight updates.
## Citation
```
@inproceedings{bini2025decouplinganglesstrengthlowrank,
title={Decoupling Angles and Strength in Low-rank Adaptation},
author={Massimo Bini and Leander Girrbach and Zeynep Akata},
year={2025},
booktitle={International Conference on Learning Representations (ICLR)},
}
```
================================================
FILE: examples/delora_finetuning/delora_finetuning.py
================================================
# This script is based on examples/randlora_finetuning/randlora_finetuning.py
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import DeloraConfig, get_peft_model
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
eval_step: int,
save_step: int,
device: str,
rank: int,
delora_lambda: int,
module_dropout: float,
target_modules: str,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
# Compute type
device_type = device.type
device_module = getattr(torch, device_type, torch.cuda)
bf16_supported = device_module.is_available() and device_module.is_bf16_supported()
dtype = torch.bfloat16 if bf16_supported else torch.float32
# Load the base model
model = AutoModelForCausalLM.from_pretrained(
base_model,
dtype=dtype,
)
# DeLoRA config for the PEFT model
peft_config = DeloraConfig(
r=rank,
delora_lambda=delora_lambda,
target_modules=(target_modules.split(",") if target_modules else None),
module_dropout=module_dropout,
bias="none",
)
# get the peft model with DeLoRA config
model = get_peft_model(model, peft_config)
model.to(device) # MODEL TO ACCELERATOR
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Compute the total amount of training step for warmup
max_steps = int((len(dataset) // batch_size) * num_epochs)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=int(max_steps * 0.1), # 10% of total trainig steps
weight_decay=0.0,
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
learning_rate=learning_rate,
hub_token=hf_token,
label_names=["labels"],
)
# Clear accelerator cache to free memory
device_module.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
# Push the main model to the hub
trainer.push_to_hub(commit_message="Fine-tuned model")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DeLoRA")
parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=3e-3, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--rank", type=int, default=32, help="DeLoRA basis rank")
parser.add_argument("--delora_lambda", type=int, default=640, help="DeLoRA alpha")
parser.add_argument("--module_dropout", type=float, default=0.05, help="DeLoRA dropout rate")
parser.add_argument(
"--target_modules", type=str, default=None, help="Comma-separated list of target modules for DeLoRA"
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
if args.device == "auto":
args.device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
rank=args.rank,
delora_lambda=args.delora_lambda,
module_dropout=args.module_dropout,
target_modules=args.target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
================================================
FILE: examples/dna_language_models/dna_lm.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "db4dc272-88fe-47ad-98fd-b94d4f840dca",
"metadata": {
"id": "db4dc272-88fe-47ad-98fd-b94d4f840dca"
},
"source": [
"# PEFT with DNA Language Models"
]
},
{
"cell_type": "markdown",
"id": "d381f473-0d37-4b5b-ae9e-d2b32bab7c04",
"metadata": {
"id": "d381f473-0d37-4b5b-ae9e-d2b32bab7c04"
},
"source": [
"This notebook demonstrates how to utilize parameter-efficient fine-tuning techniques (PEFT) from the PEFT library to fine-tune a DNA Language Model (DNA-LM). The fine-tuned DNA-LM will be applied to solve a task from the nucleotide benchmark dataset. Parameter-efficient fine-tuning (PEFT) techniques are crucial for adapting large pre-trained models to specific tasks with limited computational resources."
]
},
{
"cell_type": "markdown",
"id": "23f460c3-d7e5-437f-a5e9-d029cd225bf8",
"metadata": {
"id": "23f460c3-d7e5-437f-a5e9-d029cd225bf8"
},
"source": [
"### 1. Import relevant libraries"
]
},
{
"cell_type": "markdown",
"id": "29a35f95-738a-4f5e-88ce-dc5f8f9be5dc",
"metadata": {
"id": "29a35f95-738a-4f5e-88ce-dc5f8f9be5dc"
},
"source": [
"We'll start by importing the required libraries, including the PEFT library and other dependencies."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0a40abdf-ca1c-436f-a2af-603cd67a45a4",
"metadata": {
"id": "0a40abdf-ca1c-436f-a2af-603cd67a45a4"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/homebrew/anaconda3/envs/peft/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import torch\n",
"import transformers\n",
"import peft\n",
"import tqdm\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "a445f8be-545d-4085-a5f9-c64983655224",
"metadata": {
"id": "a445f8be-545d-4085-a5f9-c64983655224"
},
"source": [
"### 2. Load models\n"
]
},
{
"cell_type": "markdown",
"id": "63782b55-1c38-4e44-b003-e57daa813bed",
"metadata": {
"id": "63782b55-1c38-4e44-b003-e57daa813bed"
},
"source": [
"We'll load a pre-trained DNA Language Model, \"SpeciesLM\", that serves as the base for fine-tuning. This is done using the transformers library from HuggingFace.\n",
"\n",
"The tokenizer and the model comes from the paper, \"Species-aware DNA language models capture regulatory elements and their evolution\". [Paper Link](https://www.biorxiv.org/content/10.1101/2023.01.26.525670v2), [Code Link](https://github.com/gagneurlab/SpeciesLM). They introduce a species-aware DNA language model, which is trained on more than 800 species spanning over 500 million years of evolution."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dac961f4-c450-4124-923e-f4ba9bbd5e07",
"metadata": {
"id": "dac961f4-c450-4124-923e-f4ba9bbd5e07"
},
"outputs": [],
"source": [
"from transformers import AutoTokenizer, AutoModelForMaskedLM"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e73fae58-03e9-4acc-b0fc-9bc810c7d366",
"metadata": {
"id": "e73fae58-03e9-4acc-b0fc-9bc810c7d366"
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"gagneurlab/SpeciesLM\", revision = \"downstream_species_lm\")\n",
"lm = AutoModelForMaskedLM.from_pretrained(\"gagneurlab/SpeciesLM\", revision = \"downstream_species_lm\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca43b893-2d66-4e93-a08f-b17a92040709",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ca43b893-2d66-4e93-a08f-b17a92040709",
"outputId": "ccbac964-a329-414d-f537-3cae7da66cf2"
},
"outputs": [
{
"data": {
"text/plain": [
"BertForMaskedLM(\n",
" (bert): BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(5504, 768, padding_idx=0)\n",
" (position_embeddings): Embedding(512, 768)\n",
" (token_type_embeddings): Embedding(2, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSdpaSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (cls): BertOnlyMLMHead(\n",
" (predictions): BertLMPredictionHead(\n",
" (transform): BertPredictionHeadTransform(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (transform_act_fn): GELUActivation()\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" )\n",
" (decoder): Linear(in_features=768, out_features=5504, bias=True)\n",
" )\n",
" )\n",
")"
]
},
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"lm.eval()\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"lm.to(device);"
]
},
{
"cell_type": "markdown",
"id": "c1bda6f2-34bb-4ce2-aa3f-3013548b0a28",
"metadata": {
"id": "c1bda6f2-34bb-4ce2-aa3f-3013548b0a28"
},
"source": [
"### 2. Prepare datasets"
]
},
{
"cell_type": "markdown",
"id": "f4c61e59-457c-47d9-8929-5e8cd32d3125",
"metadata": {
"id": "f4c61e59-457c-47d9-8929-5e8cd32d3125"
},
"source": [
"We'll load the `nucleotide_transformer_downstream_tasks` dataset, which contains 18 downstream tasks from the Nucleotide Transformer paper. This dataset provides a consistent genomics benchmark with binary classification tasks."
]
},
{
"cell_type": "code",
"execution_count": null,
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Copy a token from your Hugging Face\ntokens page and paste it below. Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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================================================
FILE: examples/dora_finetuning/README.md
================================================
# DoRA: Weight-Decomposed Low-Rank Adaptation
## Introduction
[DoRA](https://huggingface.co/papers/2402.09353) is a novel approach that leverages low rank adaptation through weight decomposition analysis to investigate the inherent differences between full fine-tuning and LoRA. DoRA initially decomposes the pretrained weight into its magnitude and directional components and finetunes both of them. Because the directional component is large in terms of parameter numbers, we further decompose it with LoRA for efficient finetuning. This results in enhancing both the learning capacity and training stability of LoRA while avoiding any additional inference overhead.
## Quick start
```python
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
lora_config = LoraConfig(
use_dora=True
)
peft_model = get_peft_model(model, lora_config)
trainer = transformers.Trainer(
model=peft_model,
train_dataset=dataset,
dataset_text_field="text",
max_length=2048,
tokenizer=tokenizer,
)
trainer.train()
peft_model.save_pretrained("dora-llama-3-8b")
```
There is no additional change needed to your standard LoRA procedure, except for specifying `use_dora = True` option in your lora configuration.
Run the finetuning script simply by running:
```bash
python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco
```
This 👆🏻 by default will load the model in peft set up with LoRA config. Now if you wanna quickly compare it with Dora, all you need to do is to input ` --use_dora` in the command line. So same above example would be 👇🏻;
```bash
python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco --use_dora
```
DoRA also supports quantization. To use 4-bit quantization try:
```bash
python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --quantize
```
Similarly, by default the LoRA layers are the attention and MLP layers of LLama model, if you get to choose a different set of layers for LoRA to be applied on, you can simply define it using:
```bash
python examples/dora_finetuning/dora_finetuning.py --lora_target_modules "q_proj,k_proj,v_proj,o_proj"
```
### Full example of the script
```bash
python dora_finetuning.py \
--base_model "PATH_TO_MODEL" \
--data_path "PATH_TO_DATASET" \
--output_dir "PATH_TO_OUTPUT_DIR" \
--batch_size 1 \
--num_epochs 3 \
--learning_rate 3e-4 \
--cutoff_len 512 \
--val_set_size 500 \
--use_dora \
--quantize \
--eval_step 10 \
--save_step 100 \
--lora_r 16 \
--lora_alpha 32 \
--lora_dropout 0.05 \
--lora_target_modules "q_proj,k_proj,v_proj,o_proj" \
--hub_model_id "YOUR_HF_REPO" \
--push_to_hub
```
## Use the model on 🤗
You can load and use the model as any other 🤗 models.
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("ShirinYamani/huggyllama-llama-7b-finetuned")
```
## DoRA vs. LoRA
In general, DoRA finetuning on diffusion models is still experimental and is likely to require different hyperparameter values to perform best compared to LoRA.
Specifically, people have noticed 2 differences to take into account in your training:
1. LoRA seem to converge faster than DoRA (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA)
2. DoRA quality superior to LoRA especially in lower ranks: The difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example.
## Citation
```
@article{liu2024dora,
title={DoRA: Weight-Decomposed Low-Rank Adaptation},
author={Liu, Shih-Yang and Wang, Chien-Yi and Yin, Hongxu and Molchanov, Pavlo and Wang, Yu-Chiang Frank and Cheng, Kwang-Ting and Chen, Min-Hung},
journal={arXiv preprint arXiv:2402.09353},
year={2024}
}
```
================================================
FILE: examples/dora_finetuning/dora-caching.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Small script to measure DoRA caching efficiency
"""
import argparse
import time
from contextlib import contextmanager
import torch
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model
from peft.helpers import DoraCaching
from peft.utils import infer_device
device = infer_device()
# check for CPU
if device == "cpu":
raise ValueError("This benchmark requires a hardware accelerator, only found CPU")
torch_accelerator_module = getattr(torch, device, torch.cuda)
@contextmanager
def timeit(logs):
start = time.perf_counter()
yield
end = time.perf_counter()
dur = end - start
logs["time"].append(dur)
def run_benchmark(model, num_runs):
logs = {
"time": [],
}
mem_start = torch_accelerator_module.max_memory_reserved()
for _ in range(num_runs + 1):
with timeit(logs):
for i in range(3):
x = torch.randint(10, 100, (1, 50)).to(device)
model(x)
mem_end = torch_accelerator_module.max_memory_reserved()
logs["memory"] = (mem_end - mem_start) / 1024**2
# remove the first run (warm up)
del logs["time"][0]
return logs
def main(model_id, num_runs):
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map=device)
base_memory = torch_accelerator_module.max_memory_reserved() / 1024**2
# LORA
config = LoraConfig(init_lora_weights=False, use_dora=False)
model = get_peft_model(model, config)
model.eval()
torch_accelerator_module.reset_peak_memory_stats()
logs_lora = run_benchmark(model, num_runs)
avg_duration_lora = sum(logs_lora["time"]) / num_runs
max_memory_lora = logs_lora["memory"] + base_memory
# DORA
del model
torch_accelerator_module.empty_cache()
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map=device)
base_memory = torch_accelerator_module.max_memory_reserved() / 1024**2
config = LoraConfig(init_lora_weights=False, use_dora=True)
model = get_peft_model(model, config)
model.eval()
# WITHOUT CACHING
torch_accelerator_module.reset_peak_memory_stats()
logs_dora_no_caching = run_benchmark(model, num_runs)
avg_duration_no_caching = sum(logs_dora_no_caching["time"]) / num_runs
max_memory_no_caching = logs_dora_no_caching["memory"] + base_memory
# WITH CACHING
torch_accelerator_module.reset_peak_memory_stats()
with DoraCaching():
logs_dora_caching = run_benchmark(model, num_runs)
avg_duration_caching = sum(logs_dora_caching["time"]) / num_runs
max_memory_caching = logs_dora_caching["memory"] + base_memory
print(
f"Benchmark results for model {model_id} with {num_runs} runs:\n\n"
f"avg time LoRA: {avg_duration_lora:.4f} sec\n"
f"avg time DoRA no caching: {avg_duration_no_caching:.4f} sec\n"
f"avg time DoRA with caching: {avg_duration_caching:.4f} sec\n"
f"\n"
f"memory LoRA: {max_memory_lora:.2f} MB\n"
f"memory DoRA no caching: {max_memory_no_caching:.2f} MB\n"
f"memory DoRA with caching: {max_memory_caching:.2f} MB\n"
f"\n"
f"DoRA time overhead no caching: {(avg_duration_no_caching - avg_duration_lora) / avg_duration_lora * 100:.2f}%\n"
f"DoRA time overhead with caching: {(avg_duration_caching - avg_duration_lora) / avg_duration_lora * 100:.2f}%\n"
f"\n"
f"DoRA memory overhead no caching: {(max_memory_no_caching - max_memory_lora) / max_memory_lora * 100:.2f}%\n"
f"DoRA memory overhead with caching: {(max_memory_caching - max_memory_lora) / max_memory_lora * 100:.2f}%"
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Benchmark DoRA caching efficiency")
parser.add_argument("--model_id", type=str, default="meta-llama/Llama-3.1-8B", help="Model ID to benchmark")
parser.add_argument("--num_runs", type=int, default=10, help="Number of runs for the benchmark")
args = parser.parse_args()
main(args.model_id, args.num_runs)
================================================
FILE: examples/dora_finetuning/dora_finetuning.py
================================================
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
use_dora: bool,
quantize: bool,
eval_step: int,
save_step: int,
device: str,
lora_r: int,
lora_alpha: int,
lora_dropout: float,
lora_target_modules: str,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
# QDoRA (quantized dora): IF YOU WANNA QUANTIZE THE MODEL
if quantize:
if (torch.cuda.is_available() and torch.cuda.is_bf16_supported()) or torch.xpu.is_available():
bnb_4bit_compute_dtype = torch.bfloat16
else:
bnb_4bit_compute_dtype = torch.float16
model = AutoModelForCausalLM.from_pretrained(
base_model,
token=hf_token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
)
# setup for quantized training
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
# LoRa config for the PEFT model
lora_config = LoraConfig(
use_dora=use_dora, # to use Dora OR compare to Lora just set the --use_dora
r=lora_r, # Rank of matrix
lora_alpha=lora_alpha,
target_modules=(
lora_target_modules.split(",")
if lora_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
),
lora_dropout=lora_dropout,
bias="none",
)
# get the peft model with LoRa config
model = get_peft_model(model, lora_config)
model.to(device) # MODEL TO GPU/CUDA
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
# Clear device cache to free memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
# Push the main model to the hub
trainer.push_to_hub(commit_message="Fine-tuned model")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DoRA and PEFT")
parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=3e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--use_dora", action="store_true", help="Apply Dora")
parser.add_argument("--quantize", action="store_true", help="Use quantization")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--lora_r", type=int, default=8, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate")
parser.add_argument(
"--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA"
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
use_dora=args.use_dora,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
lora_r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
================================================
FILE: examples/ephemeral_gpu_offloading/load_with_dora.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Example script demonstrating the time difference loading a model with a DoRA using ephemeral GPU offloading vs doing it purely on the CPU.
Example outputs:
$ python load_with_dora.py
--- Loading model ---
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:04<00:00, 1.03s/it]
--- Loading PeftModel ---
--- Done ---
Model loading time: 4.83s
PeftModel loading time: 28.14s
Use ephemeral GPU offloading: False
(Note: if this was the first time you ran the script, or if your cache was cleared, the times shown above are invalid, due to the time taken to download the model and DoRA files. Just re-run the script in this case.)
$ python load_with_dora.py --ephemeral_gpu_offload
--- Loading model ---
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:03<00:00, 1.11it/s]
--- Loading PeftModel ---
--- Done ---
Model loading time: 4.28s
PeftModel loading time: 16.59s
Use ephemeral GPU offloading: True
(Note: if this was the first time you ran the script, or if your cache was cleared, the times shown above are invalid, due to the time taken to download the model and DoRA files. Just re-run the script in this case.)
"""
import argparse
import time
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM
from peft import PeftModel
def main():
parser = argparse.ArgumentParser(description="Load a model with DoRA using ephemeral GPU offloading")
parser.add_argument("--model", type=str, default="NousResearch/Hermes-2-Pro-Mistral-7B", help="Model to load")
parser.add_argument(
"--dora",
type=str,
default="peft-internal-testing/DoRA-Hermes-2-Pro-Mistral-7B",
help="DoRA to use",
)
parser.add_argument("--ephemeral_gpu_offload", action="store_true", help="Use ephemeral GPU offloading")
parser.add_argument(
"--merge_model_path", type=str, help="Merge the model with the DoRA model and save to the given path"
)
args = parser.parse_args()
peft_model_kwargs = {
"ephemeral_gpu_offload": args.ephemeral_gpu_offload,
"max_memory": {"cpu": "256GiB"},
"device_map": {"": "cpu"},
}
# Predownload
try:
snapshot_download(repo_id=args.model)
except Exception as e:
print(f"Failed to download model: {e}")
# We continue anyway as this might be e.g. a local directory or something
try:
snapshot_download(repo_id=args.dora)
except Exception as e:
print(f"Failed to download DoRA: {e}")
# We continue anyway as this might be e.g. a local directory or something
start = time.perf_counter()
print("--- Loading model ---")
model = AutoModelForCausalLM.from_pretrained(args.model)
model_time = time.perf_counter() - start
print("--- Loading PeftModel ---")
peft_model = PeftModel.from_pretrained(model, args.dora, **peft_model_kwargs)
print("--- Done ---")
peft_model_time = time.perf_counter() - start
print(f"Model loading time: {model_time:.2f}s")
print(f"PeftModel loading time: {peft_model_time:.2f}s")
print(f"Use ephemeral GPU offloading: {args.ephemeral_gpu_offload}")
if args.merge_model_path is not None:
merged_model = peft_model.merge_and_unload(progressbar=True)
merged_model.save_pretrained(args.merge_model_path)
if __name__ == "__main__":
main()
================================================
FILE: examples/eva_finetuning/README.md
================================================
# EVA: Explained Variance Adaptation
## Introduction ([Paper](https://huggingface.co/papers/2410.07170), [code](https://github.com/ml-jku/EVA))
Explained Variance Adaptation (EVA) is a novel initialization method for LoRA style adapters which initializes adapter weights in a data driven manner and adaptively allocates ranks according to the variance they explain. EVA improves average performance on a multitude of tasks across various domains, such as Language generation and understanding, Image classification, and Decision Making.
The abstract from the paper is:
*Foundation models (FMs) are pre-trained on large-scale datasets and then fine-tuned on a downstream task for a specific application. The most successful and most commonly used fine-tuning method is to update the pre-trained weights via a low-rank adaptation (LoRA). LoRA introduces new weight matrices that are usually initialized at random with a uniform rank distribution across model weights. Recent works focus on weight-driven initialization or learning of adaptive ranks during training. Both approaches have only been investigated in isolation, resulting in slow convergence or a uniform rank distribution, in turn leading to sub-optimal performance. We propose to enhance LoRA by initializing the new weights in a data-driven manner by computing singular value decomposition on minibatches of activation vectors. Then, we initialize the LoRA matrices with the obtained right-singular vectors and re-distribute ranks among all weight matrices to explain the maximal amount of variance and continue the standard LoRA fine-tuning procedure. This results in our new method **E**xplained **V**ariance **A**daptation (EVA). We apply EVA to a variety of fine-tuning tasks ranging from language generation and understanding to image classification and reinforcement learning. EVA exhibits faster convergence than competitors and attains the highest average score across a multitude of tasks per domain.*
## Quick Start
Below is an example of how to use EVA with a causal language model. For a more detailed example see [eva_finetuning.py](https://github.com/huggingface/peft/blob/main/examples/eva_finetuning/eva_finetuning.py).
```python
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import EvaConfig, LoraConfig, get_peft_model, initialize_lora_eva_weights
# config
model_name = "meta-llama/Llama-3.1-8B"
max_seq_len = 512
rank = 16
alpha = 1
rho = 2.0
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"]
svd_batch_size = 4 # can be different from the batch size used in finetuning
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
# load dataset
dataset = load_dataset("Rowan/hellaswag")
dataset = dataset.map(
lambda x: tokenizer(x["ctx"], padding="max_length", truncation=True, max_length=max_seq_len),
batched=True,
remove_columns=dataset["train"].column_names,
)
dataset.set_format(type="torch")
# create dataloader for SVD
# typically this is the same as the dataloader used for finetuning
dataloader = DataLoader(
dataset["train"],
batch_size=svd_batch_size,
collate_fn=lambda examples: {k: torch.stack([v[k] for v in examples], dim=0) for k in examples[0].keys()},
)
# setup peft config
eva_config = EvaConfig(
rho=rho
)
peft_config = LoraConfig(
r=rank,
lora_alpha=alpha,
target_modules=target_modules,
init_lora_weights="eva",
eva_config=eva_config
)
# move model to accelerator
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model = model.to(device)
# to optimize memory usage during EVA initialization, set low_cpu_mem_usage=True
peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
initialize_lora_eva_weights(peft_model, dataloader)
```
`initialize_lora_eva_weights` will compute the SVD and load the components into the model. After this continue with standard LoRA finetuning.
## Using EVA with Bitsandbytes
EVA is fully compatible with bitsandbytes. Simply initialize the pretrained model with a BitsAndBytesConfig and then use the peft model with EVA.
```python
from transformers import BitsAndBytesConfig
from peft import prepare_model_for_kbit_training
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=BitsAndBytesConfig(load_in_4bit=True)
)
model = prepare_model_for_kbit_training(model)
peft_model = get_peft_model(model, peft_config)
initialize_lora_eva_weights(peft_model, dataloader)
```
## Getting the EVA state_dict without loading the adapter weights
In some cases you might just want to get the state_dict after EVA initialization without loading the adapter weights. This can be useful for example if:
- you want to precompute and store the state_dict for different downstream tasks.
- you need to quantize the model for finetuning but want to perform EVA initialization with model weights in full/half precision.
- you do not intend to use a peft model for LoRA finetuning.
- you would like to leverage multiple accelerators for EVA initialization. (At the moment this is not directly supported by `initialize_lora_eva_weights`)
You can do this by calling `get_eva_state_dict` directly (you only need to pass `peft_config` if `model` is not a PeftModel):
```python
from peft import get_eva_state_dict
eva_state_dict = get_eva_state_dict(model, dataloader, peft_config)
```
Later you can load the state_dict into a `PeftModel` by using the `eva_state_dict` argument in `initialize_lora_eva_weights`:
```python
initialize_lora_eva_weights(peft_model, eva_state_dict=eva_state_dict)
```
## Leveraging multiple accelerators
EVA initialization can be parallelized across multiple accelerators. In this case inputs from multiple accelerators are gathered before computing the SVD for the batch. This requires that the model is wrapped in a `torch.nn.DataParallel` or `torch.nn.DistributedDataParallel` class. An example of how to use this can be found in [eva_finetuning_multi_accelerator.py](https://github.com/huggingface/peft/blob/main/examples/eva_finetuning/eva_finetuning_multi_accelerator.py).
## Customizing EVA
By default, EVA is designed to work with standard transformer language models. However we integrated three different parameters which can be used to customize EVA for other types of models.
1. `forward_fn`: Defines how the forward pass during EVA initialization should be computed.
2. `prepare_model_inputs_fn`: Can be used if it is necessary to use information contained in the original model_input to prepare the input for SVD in individual layers.
3. `prepare_layer_inputs_fn`: Defines how layer inputs should be prepared for SVD.
All three parameters can be passed to `initialize_lora_eva_weights` and `get_eva_state_dict`.
### forward_fn
`forward_fn` defines how the forward pass during EVA initialization should be computed. `forward_fn` receives two arguments: `model` and `inputs`. By default this is set to `forward_fn_dict` which simply returns `model(**inputs)`.
### prepare_model_inputs_fn
`prepare_model_inputs_fn` can be used if it is necessary to use information contained in the original model_input to prepare the input for SVD in individual layers. `prepare_model_inputs_fn` receives two arguments: `model_input` and `peft_config`. This component is separate from `prepare_layer_inputs_fn` as the output only needs to be computed once per batch. By default this parameter is set to `prepare_model_inputs_fn_language_modeling` which is used get a subset of indices based on attention and label mask to avoid including padding tokens in the SVD computation. If you would like to not use this component set `prepare_model_inputs_fn` to None. The default logic is:
```python
def prepare_model_inputs_fn_language_modeling(model_input, peft_config: LoraConfig):
mask = model_input.get("attention_mask", torch.ones_like(model_input["input_ids"])).bool()
if peft_config.eva_config.use_label_mask and hasattr(model_input, "labels"):
mask = torch.logical_and(mask, model_input["labels"] != peft_config.eva_config.label_mask_value)
return mask.nonzero()
```
### prepare_layer_inputs_fn
`prepare_layer_inputs_fn` can be used to preprocess the layer inputs before passing them to the SVD algorithm. `prepare_layer_inputs_fn` receives three arguments: `layer_input`, `model_input` and `layer_name`. It can either be a callable or a dictionary where the keys are the layer names and the values are callables. If it is a dictionary, functions are assigned to adapter layers based on the layer names. By default a language modeling setting is assumed where model_inputs are the outputs of `prepare_model_inputs_fn_language_modeling` which is a mask of indices. If this parameter is set to None, only two modifications are made to the layer inputs
- take the first element incase of a tuple or list.
- if the input has more than 2 dimensions, we flatten all but the last dimension.
Must always return a tensor. The default logic is:
```python
def prepare_layer_inputs_fn_default(layer_input, model_input, layer_name) -> torch.Tensor:
if isinstance(layer_input, (tuple, list)):
layer_input = layer_input[0]
return layer_input[model_input.T.unbind()]
```
## Citation
In case you find our work useful, please consider citing it.
```
@article{paischer2024eva,
title={One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation},
author={Fabian Paischer, Lukas Hauzenberger, Thomas Schmied, Benedikt Alkin, Marc Peter Deisenroth, Sepp Hochreiter},
journal={arXiv preprint arXiv:2410.07170},
year={2024}
}
```
================================================
FILE: examples/eva_finetuning/eva_finetuning.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from utils import DataCollator, TokenizerMetaMath
from peft import EvaConfig, LoraConfig, get_peft_model, initialize_lora_eva_weights
DEVICE = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
# config
model_name = "meta-llama/Llama-3.1-8B"
max_seq_len = 512
rank = 16
alpha = 1
rho = 2.0
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"]
svd_batch_size = 4 # can be different from the batch size used in finetuning
batch_size = 4
learning_rate = 5e-4
gradient_accumulation_steps = 8
num_epochs = 1
output_dir = "outputs"
bf16 = True
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# load dataset
dataset = load_dataset("meta-math/MetaMathQA")
dataset = dataset.map(
TokenizerMetaMath(model_name),
batched=True,
remove_columns=dataset["train"].column_names,
)
dataset.set_format(type="torch")
# data collator
data_collator = DataCollator(tokenizer.eos_token_id, max_length=max_seq_len)
# dataloader
dataloader = DataLoader(
dataset["train"],
batch_size=svd_batch_size,
collate_fn=data_collator,
)
# setup peft config
eva_config = EvaConfig(rho=rho)
peft_config = LoraConfig(
r=rank, lora_alpha=alpha, target_modules=target_modules, init_lora_weights="eva", eva_config=eva_config
)
# move model to accelerator
model = model.to(DEVICE)
# to optimize memory usage during eva initialization, set low_cpu_mem_usage=True
peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
initialize_lora_eva_weights(peft_model, dataloader)
# setup training arguments
training_args = TrainingArguments(
per_device_train_batch_size=batch_size,
learning_rate=learning_rate,
gradient_accumulation_steps=gradient_accumulation_steps,
num_train_epochs=num_epochs,
output_dir=output_dir,
remove_unused_columns=False,
bf16=bf16,
)
# continue with standard finetuning
trainer = Trainer(
model=peft_model,
args=training_args,
train_dataset=dataset["train"],
data_collator=data_collator,
)
trainer.train()
================================================
FILE: examples/eva_finetuning/eva_finetuning_multi_accelerator.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torch.distributed as dist
from datasets import load_dataset
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from utils import DataCollator, TokenizerMetaMath
from peft import EvaConfig, LoraConfig, get_eva_state_dict, get_peft_model, initialize_lora_eva_weights
# run this script e.g. with: torchrun --nproc_per_node=4 eva_finetuning_multi_gpu.py
# config
model_name = "meta-llama/Llama-2-7b-hf"
max_seq_len = 512
rank = 16
alpha = 1
rho = 2.0
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"]
svd_batch_size = 4 # can be different from the batch size used in finetuning
batch_size = 4
learning_rate = 5e-4
gradient_accumulation_steps = 8
num_epochs = 1
output_dir = "outputs"
bf16 = True
# Initialize distributed environment
if torch.cuda.is_available():
local_rank = int(os.environ.get("LOCAL_RANK", -1))
torch.cuda.set_device(local_rank)
dist.init_process_group("nccl")
world_size = dist.get_world_size()
elif torch.xpu.is_available():
local_rank = int(os.environ.get("LOCAL_RANK", -1))
torch.xpu.set_device(local_rank)
dist.init_process_group("xccl")
world_size = dist.get_world_size()
else:
local_rank = -1
world_size = 1
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# load dataset
dataset = load_dataset("meta-math/MetaMathQA")
dataset = dataset.map(
TokenizerMetaMath(model_name),
batched=True,
remove_columns=dataset["train"].column_names,
)
dataset.set_format(type="torch")
# data collator
data_collator = DataCollator(tokenizer.eos_token_id, max_length=max_seq_len)
# Create sampler for distributed training
sampler = DistributedSampler(dataset["train"], num_replicas=world_size, rank=local_rank)
# dataloader
dataloader = DataLoader(
dataset["train"],
batch_size=svd_batch_size,
collate_fn=data_collator,
sampler=sampler,
shuffle=False,
)
sampler.set_epoch(0)
# Wrap model in DDP
model = model.to(local_rank)
model = DDP(model, device_ids=[local_rank], output_device=local_rank)
# setup peft config
eva_config = EvaConfig(rho=rho)
peft_config = LoraConfig(
r=rank, lora_alpha=alpha, target_modules=target_modules, init_lora_weights="eva", eva_config=eva_config
)
# EVA initialization
eva_state_dict = get_eva_state_dict(model, dataloader, peft_config)
eva_state_dict = {".".join(["base_model.model"] + k.split(".")[1:]): v for k, v in eva_state_dict.items()}
# cleanup ddp
model = model.module
# initialize peft model
peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
initialize_lora_eva_weights(peft_model, eva_state_dict=eva_state_dict)
# setup training arguments
training_args = TrainingArguments(
per_device_train_batch_size=batch_size,
learning_rate=learning_rate,
gradient_accumulation_steps=gradient_accumulation_steps,
num_train_epochs=num_epochs,
output_dir=output_dir,
remove_unused_columns=False,
bf16=bf16,
)
# continue with standard finetuning
trainer = Trainer(
model=peft_model,
args=training_args,
train_dataset=dataset["train"],
data_collator=data_collator,
)
trainer.train()
================================================
FILE: examples/eva_finetuning/utils.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from transformers import AutoTokenizer
class TokenizerMetaMath:
PROMPT_NO_INPUT = (
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{query}\n\n### Response: "
)
PROMPT = (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{query}\n\n### Input:\n{input}\n\n### Response: "
)
def format_prompt(self, query):
query = query.split("\n", 1)
if len(query) == 1 or query[1].strip("\n") == "":
return self.PROMPT_NO_INPUT.format(query=query[0])
else:
return self.PROMPT.format(query=query[0], input=query[1])
def __init__(self, tokenizer_path):
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
def __call__(self, examples):
prompts = [self.format_prompt(text) for text in examples["query"]]
completions = examples["response"]
return self._tokenize_fn(prompts, completions)
def _tokenize_fn(self, prompts, completions):
prompt_tokens = self.tokenizer(prompts, add_special_tokens=False)["input_ids"]
input_tokens = self.tokenizer([x + y for x, y in zip(prompts, completions)], add_special_tokens=False)[
"input_ids"
]
input_tokens = [[self.tokenizer.bos_token_id] + x + [self.tokenizer.eos_token_id] for x in input_tokens]
prompt_length = [len(x) + 1 for x in prompt_tokens] # +1 for the bos token
input_length = [len(x) for x in input_tokens]
return {"input_ids": input_tokens, "prompt_length": prompt_length, "input_length": input_length}
class DataCollator:
def __init__(self, eos_token_id, max_length=None):
self.eos_token_id = eos_token_id
self.max_length = max_length
def __call__(self, batch):
batch = {k: [item[k] for item in batch] for k in batch[0]}
input_lengths = torch.stack(batch["input_length"])
prompt_lengths = torch.stack(batch["prompt_length"])
input_ids = torch.nn.utils.rnn.pad_sequence(
batch["input_ids"], batch_first=True, padding_value=self.eos_token_id
)
col_indices = torch.arange(input_ids.size(1)).unsqueeze(0)
attention_mask = col_indices < input_lengths.unsqueeze(1)
label_mask = torch.logical_or(col_indices < prompt_lengths.unsqueeze(1), ~attention_mask)
labels = input_ids.masked_fill(label_mask, -100)
if self.max_length is not None:
input_ids = input_ids[:, : self.max_length]
attention_mask = attention_mask[:, : self.max_length]
labels = labels[:, : self.max_length]
return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
================================================
FILE: examples/evaluation/lora-lm-eval.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "qAkXdLL2D25p"
},
"source": [
"## Peft model evaluation using [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness)\n",
"\n",
"In this notebook, we are going to learn how to evaluate the finetuned lora model on the hellaswag task using lm-eval-harness toolkit."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "o52TJHcYD25q",
"outputId": "c5482c79-ff56-4ffa-d20c-46c3d30d2cd5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m DEPRECATION: Building 'rouge-score' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'rouge-score'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m DEPRECATION: Building 'sqlitedict' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'sqlitedict'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m DEPRECATION: Building 'word2number' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'word2number'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"# Install LM-Eval\n",
"!pip install -q datasets evaluate lm_eval"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uhUflrJXD25q"
},
"source": [
"### First we will check the accuracy score on the hellaswag task for the base bert without finetuning"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hwJIYD5KD25q",
"outputId": "51e69f81-d048-46b2-9699-658d3ffc5f08"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7b1ea8948a0747bc98795d6459270044",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"README.md: 0.00B [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ec51e06812446899b66826c41697f8d",
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},
"text/plain": [
"data/train-00000-of-00001.parquet: 0%| | 0.00/24.4M [00:00\n",
" \n",
" \n",
" [3910/3910 40:13, Epoch 5/5]\n",
" \n",
"
RamPro 10\" All Purpose Utility Air Tires/Wheel...
\n",
"
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"
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\n",
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\n",
"
MaxAuto 2-Pack 13x5.00-6 2PLY Turf Mower Tract...
\n",
"
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"
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"
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\n",
"
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\n",
"
NEIKO 20601A 14.5 inch Steel Tire Spoon Lever ...
\n",
"
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"
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"
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"
2PK 13x5.00-6 13x5.00x6 13x5x6 13x5-6 2PLY Tur...
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"
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"
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"
(Set of 2) 15x6.00-6 Husqvarna/Poulan Tire Whe...
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"
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"
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"
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"
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\n",
"
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\n",
"
\n",
"
\n",
"
476273
\n",
"
476273
\n",
"
Chanel No.5 Eau Premiere Spray 50ml/1.7oz
\n",
"
\n",
"
\n",
"
476274
\n",
"
476274
\n",
"
Steve Madden Designer 15 Inch Carry on Suitcas...
\n",
"
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"
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"
476275
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"
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\n",
"
CHANEL Le Lift Creme Yeux, Black, 0.5 Ounce
\n",
"
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"
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"
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"
Coco Mademoiselle by Chanel for Women - 3.4 oz...
\n",
"
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"
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"
Chânél No. 5 by Chânél Eau De Parfum Premiere ...
\n",
"
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" \n",
"
\n",
"
476278 rows × 2 columns
\n",
""
],
"text/plain": [
" index product_title\n",
"0 0 RamPro 10\" All Purpose Utility Air Tires/Wheel...\n",
"1 1 MaxAuto 2-Pack 13x5.00-6 2PLY Turf Mower Tract...\n",
"2 2 NEIKO 20601A 14.5 inch Steel Tire Spoon Lever ...\n",
"3 3 2PK 13x5.00-6 13x5.00x6 13x5x6 13x5-6 2PLY Tur...\n",
"4 4 (Set of 2) 15x6.00-6 Husqvarna/Poulan Tire Whe...\n",
"... ... ...\n",
"476273 476273 Chanel No.5 Eau Premiere Spray 50ml/1.7oz\n",
"476274 476274 Steve Madden Designer 15 Inch Carry on Suitcas...\n",
"476275 476275 CHANEL Le Lift Creme Yeux, Black, 0.5 Ounce\n",
"476276 476276 Coco Mademoiselle by Chanel for Women - 3.4 oz...\n",
"476277 476277 Chânél No. 5 by Chânél Eau De Parfum Premiere ...\n",
"\n",
"[476278 rows x 2 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product_dataset_for_indexing"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "85840ec6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
index
\n",
"
product_title
\n",
"
\n",
" \n",
" \n",
"
\n",
"
34710
\n",
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34710
\n",
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ROK 4-1/2 inch Diamond Saw Blade Set, Pack of 3
\n",
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277590
\n",
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277590
\n",
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WSGG Medical Goggles, FDA registered, Safety Goggles, Fit Over Glasses, Anti-Fog, Anti-Splash (1 pack)
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474000
\n",
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474000
\n",
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iJDMTOY 15W CREE High Power LED Angel Eye Bulbs Compatible With BMW 5 6 7 Series X3 X5 (E39 E60 E63 E65 E53), 7000K Xenon White Headlight Ring Marker Lights
\n",
"
\n",
"
\n",
"
18997
\n",
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18997
\n",
"
USB Charger, Anker Elite Dual Port 24W Wall Charger, PowerPort 2 with PowerIQ and Foldable Plug, for iPhone 11/Xs/XS Max/XR/X/8/7/6/Plus, iPad Pro/Air 2/Mini 3/Mini 4, Samsung S4/S5, and More
\n",
"
\n",
"
\n",
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208666
\n",
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208666
\n",
"
AOGGY Compatible with MacBook Air 13 inch Case A1466/A1369 (2010-2017 Release) Glitter Fluorescent Color Plastic Hard Case， with Older Version MacBook Air 13 inch Keyboard Cover - Gold
\n",
"
\n",
"
\n",
"
326614
\n",
"
326614
\n",
"
CUTE STONE Little Kitchen Playset, Kitchen Toy Set with Realistic Sound &Light, Play Sink, Cooking Stove with Steam, Play Food and Kitchen Accessories, Great Kitchen Toys for Toddlers Kids
chouyatou Women's Short Sleeve/Strap Open Bust Bodysuit Shapewear Firm Control Body Shaper (X-Small, Nude Sleeve)
\n",
"
\n",
"
\n",
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319970
\n",
"
319970
\n",
"
AMT 256 Hz Medical-Grade Tuning Fork Instrument with Fixed Weights, Non-Magnetic Aluminum Alloy (C 256)
\n",
"
\n",
"
\n",
"
416956
\n",
"
416956
\n",
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Timberland HIKER-ROUND 54 BROWN
\n",
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\n",
" \n",
"
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"34710 34710 \n",
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"319970 319970 \n",
"416956 416956 \n",
"\n",
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"34710 ROK 4-1/2 inch Diamond Saw Blade Set, Pack of 3 \n",
"277590 WSGG Medical Goggles, FDA registered, Safety Goggles, Fit Over Glasses, Anti-Fog, Anti-Splash (1 pack) \n",
"474000 iJDMTOY 15W CREE High Power LED Angel Eye Bulbs Compatible With BMW 5 6 7 Series X3 X5 (E39 E60 E63 E65 E53), 7000K Xenon White Headlight Ring Marker Lights \n",
"18997 USB Charger, Anker Elite Dual Port 24W Wall Charger, PowerPort 2 with PowerIQ and Foldable Plug, for iPhone 11/Xs/XS Max/XR/X/8/7/6/Plus, iPad Pro/Air 2/Mini 3/Mini 4, Samsung S4/S5, and More \n",
"208666 AOGGY Compatible with MacBook Air 13 inch Case A1466/A1369 (2010-2017 Release) Glitter Fluorescent Color Plastic Hard Case， with Older Version MacBook Air 13 inch Keyboard Cover - Gold \n",
"326614 CUTE STONE Little Kitchen Playset, Kitchen Toy Set with Realistic Sound &Light, Play Sink, Cooking Stove with Steam, Play Food and Kitchen Accessories, Great Kitchen Toys for Toddlers Kids \n",
"105637 Milwaukee Electric Tool 2470-21 M12 Cordless Shear Kit, 12 V, Li-Ion \n",
"342392 chouyatou Women's Short Sleeve/Strap Open Bust Bodysuit Shapewear Firm Control Body Shaper (X-Small, Nude Sleeve) \n",
"319970 AMT 256 Hz Medical-Grade Tuning Fork Instrument with Fixed Weights, Non-Magnetic Aluminum Alloy (C 256) \n",
"416956 Timberland HIKER-ROUND 54 BROWN "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option(\"max_colwidth\", 300)\n",
"product_dataset_for_indexing.sample(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "408b6e00",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Running tokenizer on dataset: 0%| | 0/476278 [00:00 k\n",
"\n",
" # Query dataset, k - number of the closest elements (returns 2 numpy arrays)\n",
" labels, distances = search_index.knn_query(query_embeddings, k=k)\n",
"\n",
" return [\n",
" (ids_to_products_dict[label], (1 - distance))\n",
" for label, distance in zip(labels[0], distances[0])\n",
" if (1 - distance) >= threshold\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "1c47f12d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"query='NLP and ML books'\n",
"cosine_sim_score=0.92 product='Machine Learning: A Journey from Beginner to Advanced Including Deep Learning, Scikit-learn and Tensorflow'\n",
"cosine_sim_score=0.91 product='Mastering Machine Learning with scikit-learn'\n",
"cosine_sim_score=0.91 product='Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems'\n",
"cosine_sim_score=0.91 product='Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems'\n",
"cosine_sim_score=0.91 product='Practical Deep Learning: A Python-Based Introduction'\n",
"cosine_sim_score=0.9 product='Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlow'\n",
"cosine_sim_score=0.9 product='Mastering Machine Learning with scikit-learn - Second Edition: Apply effective learning algorithms to real-world problems using scikit-learn'\n",
"cosine_sim_score=0.9 product='Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow'\n",
"cosine_sim_score=0.9 product='Machine Learning Algorithms: Naive Bayes'\n",
"cosine_sim_score=0.9 product='Fundamentals of Machine Learning for Predictive Data Anayltics: Algorithms, Worked Examples, and Case Studies'\n"
]
}
],
"source": [
"query = \"NLP and ML books\"\n",
"k = 10\n",
"query_embeddings = get_query_embeddings(query, model, tokenizer, device)\n",
"search_results = get_nearest_neighbours(k, product_search_index, query_embeddings, ids_to_products_dict, threshold=0.7)\n",
"\n",
"print(f\"{query=}\")\n",
"for product, cosine_sim_score in search_results:\n",
" print(f\"cosine_sim_score={round(cosine_sim_score,2)} {product=}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9e2dd2c",
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/feature_extraction/requirements.txt
================================================
peft
accelerate
transformers
datasets==2.18.0
evaluate
hnswlib
pandas
tqdm
huggingface_hub
wandb
================================================
FILE: examples/fp4_finetuning/finetune_fp4_opt_bnb_peft.py
================================================
import os
import torch
import torch.nn as nn
import transformers
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # force to use CUDA GPU device 0
os.environ["ZE_AFFINITY_MASK"] = "0" # force to use Intel XPU device 0
# -*- coding: utf-8 -*-
"""Finetune-opt-bnb-peft.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o
## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`
In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.
The fine-tuning method will rely on a recent method called "Low Rank Adapters" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model.
After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!
### Install requirements
First, run the cells below to install the requirements:
"""
"""### Model loading
Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead.
"""
device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
device_module = getattr(torch, device_type, torch.cuda)
free_in_GB = int(device_module.mem_get_info()[0] / 1024**3)
max_memory = f"{free_in_GB - 2}GB"
n_gpus = device_module.device_count()
max_memory = {i: max_memory for i in range(n_gpus)}
model = AutoModelForCausalLM.from_pretrained(
"facebook/opt-350m",
max_memory=max_memory,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
"""### Post-processing on the model
Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons.
"""
print(model)
for param in model.parameters():
param.requires_grad = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
param.data = param.data.to(torch.float32)
# model.gradient_checkpointing_enable() # reduce number of stored activations
# model.model.decoder.project_in = lambda x: x.requires_grad_(True)
class CastOutputToFloat(nn.Sequential):
def forward(self, x):
return super().forward(x).to(torch.float32)
model.lm_head = CastOutputToFloat(model.lm_head)
"""### Apply LoRA
Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`.
"""
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
config = LoraConfig(
r=64,
lora_alpha=32,
target_modules=["q_proj", "v_proj", "out_proj", "fc1", "fc2"],
lora_dropout=0.01,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
print_trainable_parameters(model)
# Verifying the datatypes.
dtypes = {}
for _, p in model.named_parameters():
dtype = p.dtype
if dtype not in dtypes:
dtypes[dtype] = 0
dtypes[dtype] += p.numel()
total = 0
for k, v in dtypes.items():
total += v
for k, v in dtypes.items():
print(k, v, v / total)
"""### Training"""
data = load_dataset("Abirate/english_quotes")
data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
trainer = transformers.Trainer(
model=model,
train_dataset=data["train"],
args=transformers.TrainingArguments(
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
warmup_steps=10,
max_steps=20,
learning_rate=3e-4,
fp16=True,
logging_steps=1,
output_dir="outputs",
),
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
trainer.train()
# from huggingface_hub import notebook_login
# notebook_login()
# model.push_to_hub("ybelkada/opt-6.7b-lora", use_auth_token=True)
"""## Load adapters from the Hub
You can also directly load adapters from the Hub using the commands below:
"""
# import torch
# from peft import PeftModel, PeftConfig
# from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
#
# peft_model_id = "ybelkada/opt-6.7b-lora"
# config = PeftConfig.from_pretrained(peft_model_id)
# model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map='auto')
# tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
#
## Load the Lora model
# model = PeftModel.from_pretrained(model, peft_model_id)
#
# """## Inference
#
# You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`.
# """
#
batch = tokenizer("Two things are infinite: ", return_tensors="pt").to(model.device)
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
model.eval()
with torch.amp.autocast(device_type=device_type):
output_tokens = model.generate(**batch, max_new_tokens=50)
print("\n\n", tokenizer.decode(output_tokens[0], skip_special_tokens=True))
# model.save('./test.pt')
# """As you can see by fine-tuning for few steps we have almost recovered the quote from Albert Einstein that is present in the [training data](https://huggingface.co/datasets/Abirate/english_quotes)."""
================================================
FILE: examples/gralora_finetuning/README.md
================================================
# GraLoRA: Granular Low-Rank Adaptation
## Introduction
[**Granular Low-Rank Adaptation (GraLoRA)**](https://huggingface.co/papers/2505.20355) is a PEFT method designed to enhance the **expressivity** of low-rank adaptation while improving **robustness to outlier** activations, based on insights from well-known issues in quantization.
GraLoRA introduces a structured and fine-grained adaptation scheme. It divides the adaptation space into a grid of $𝑘^2$ smaller, independent adapter pairs, each responsible for a localized subset of the input and output dimensions.
## Quick start
With respect to your standard PEFT training procedure with LoRA, simply swap your `LoraConfig` for a `GraloraConfig`.
```python
import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTTrainer, SFTConfig
from peft import GraloraConfig
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
gralora_config = GraloraConfig()
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
processing_class=tokenizer,
peft_config=gralora_config,
args=SFTConfig(
max_length=2048,
dataset_text_field="text",
per_device_train_batch_size=2,
),
)
trainer.train()
trainer.model.save_pretrained("gralora-llama-3.2-3b")
```
Run the finetuning script simply by running:
```sh
python examples/gralora_finetuning/gralora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco
```
## Use the model on 🤗
You can load and use the model as any other 🤗 models.
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3-8B", dtype=torch.bfloat16, device_map="auto"
)
peft_model = PeftModel.from_pretrained(model, "gralora-llama-3-8b")
```
## Additional Notes
While `gralora_k` is set to 2 for default, you can increase this value to create more fine-grained adapters. `gralora_k` of 4 is recommended when the total rank (`r + hybrid_r`) is 64 or higher.
## Citation
```
@misc{jung2025graloragranularlowrankadaptation,
title={GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning},
author={Yeonjoon Jung and Daehyun Ahn and Hyungjun Kim and Taesu Kim and Eunhyeok Park},
year={2025},
eprint={2505.20355},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.20355},
}
```
================================================
FILE: examples/gralora_finetuning/gralora_finetuning.py
================================================
# This script is based on examples/dora_finetuning/dora_finetuning.py
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import GraloraConfig, get_peft_model
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
eval_step: int,
save_step: int,
device: str,
gralora_r: int,
gralora_alpha: int,
gralora_dropout: float,
gralora_target_modules: str,
gralora_k: int,
hybrid_r: int,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
# GraLoRA config for the PEFT model
gralora_config = GraloraConfig(
r=gralora_r, # Rank of matrix
alpha=gralora_alpha,
target_modules=(
gralora_target_modules.split(",")
if gralora_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
),
gralora_dropout=gralora_dropout,
gralora_k=gralora_k,
hybrid_r=hybrid_r,
bias="none",
)
# get the peft model with GraLoRA config
model = get_peft_model(model, gralora_config)
model.to(device) # MODEL TO GPU/CUDA
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
# Clear device cache to free memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
# Push the main model to the hub
trainer.push_to_hub(commit_message="Fine-tuned model")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with GraLoRA and PEFT")
parser.add_argument("--base_model", type=str, default="meta-llama/Llama-3.2-3B", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--gralora_r", type=int, default=8, help="LoRA rank")
parser.add_argument("--gralora_alpha", type=int, default=16, help="LoRA alpha")
parser.add_argument("--gralora_dropout", type=float, default=0.05, help="LoRA dropout rate")
parser.add_argument(
"--gralora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA"
)
parser.add_argument("--gralora_k", type=int, default=2, help="GraLoRA k")
parser.add_argument("--hybrid_r", type=int, default=0, help="Hybrid rank")
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
gralora_r=args.gralora_r,
gralora_alpha=args.gralora_alpha,
gralora_dropout=args.gralora_dropout,
gralora_target_modules=args.gralora_target_modules,
gralora_k=args.gralora_k,
hybrid_r=args.hybrid_r,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
================================================
FILE: examples/hra_dreambooth/README.md
================================================
# DreamBooth fine-tuning with HRA
This guide demonstrates how to use Householder reflection adaptation (HRA) method, to fine-tune Dreambooth with `stabilityai/stable-diffusion-2-1` model.
HRA provides a new perspective connecting LoRA to OFT and achieves encouraging performance in various downstream tasks.
HRA adapts a pre-trained model by multiplying each frozen weight matrix with a chain of r learnable Householder reflections (HRs).
HRA can be interpreted as either an OFT adapter or an adaptive LoRA.
Consequently, it harnesses the advantages of both strategies, reducing parameters and computation costs while penalizing the loss of pre-training knowledge.
For further details on HRA, please consult the [original HRA paper](https://huggingface.co/papers/2405.17484).
In this guide we provide a Dreambooth fine-tuning script that is available in [PEFT's GitHub repo examples](https://github.com/huggingface/peft/tree/main/examples/hra_dreambooth). This implementation is adapted from [peft's boft_dreambooth](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth).
You can try it out and fine-tune on your custom images.
## Set up your environment
Start by cloning the PEFT repository:
```bash
git clone --recursive https://github.com/huggingface/peft
```
Navigate to the directory containing the training scripts for fine-tuning Dreambooth with HRA:
```bash
cd peft/examples/hra_dreambooth
```
Set up your environment: install PEFT, and all the required libraries. At the time of writing this guide we recommend installing PEFT from source. The following environment setup should work on A100 and H100:
```bash
conda create --name peft python=3.10
conda activate peft
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install xformers -c xformers
pip install -r requirements.txt
pip install git+https://github.com/huggingface/peft
```
## Download the data
[dreambooth](https://github.com/google/dreambooth) dataset should have been automatically cloned in the following structure when running the training script.
```
hra_dreambooth
├── data
│ └── dreambooth
│ └── dataset
│ ├── backpack
│ └── backpack_dog
│ ...
```
You can also put your custom images into `hra_dreambooth/data/dreambooth/dataset`.
## Fine-tune Dreambooth with HRA
```bash
class_idx=0
bash ./train_dreambooth.sh $class_idx
```
where the `$class_idx` corresponds to different subjects ranging from 0 to 29.
Launch the training script with `accelerate` and pass hyperparameters, as well as LoRa-specific arguments to it such as:
- `use_hra`: Enables HRA in the training script.
- `hra_r`: the number of HRs (i.e., r) across different layers, expressed in `int`.
As r increases, the number of trainable parameters increases, which generally leads to improved performance.
However, this also results in higher memory consumption and longer computation times.
Therefore, r is usually set to 8.
**Note**, please set r to an even number to avoid potential issues during initialization.
- `hra_apply_GS`: Applies Gram-Schmidt orthogonalization. Default is `false`.
- `hra_bias`: specify if the `bias` parameters should be trained. Can be `none`, `all` or `hra_only`.
If you are running this script on Windows, you may need to set the `--num_dataloader_workers` to 0.
To learn more about DreamBooth fine-tuning with prior-preserving loss, check out the [Diffusers documentation](https://huggingface.co/docs/diffusers/training/dreambooth#finetuning-with-priorpreserving-loss).
## Generate images with the fine-tuned model
To generate images with the fine-tuned model, simply run the jupyter notebook `dreambooth_inference.ipynb` for visualization with `jupyter notebook` under `./examples/hra_dreambooth`.
================================================
FILE: examples/hra_dreambooth/dreambooth_inference.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"id": "acab479f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from PIL import Image\n",
"\n",
"import torch\n",
"from accelerate.logging import get_logger\n",
"from diffusers import StableDiffusionPipeline\n",
"from diffusers.utils import check_min_version\n",
"\n",
"from peft import PeftModel\n",
"\n",
"\n",
"# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n",
"check_min_version(\"0.10.0.dev0\")\n",
"\n",
"logger = get_logger(__name__)\n",
"\n",
"MODEL_NAME = \"stabilityai/stable-diffusion-2-1\"\n",
"\n",
"PEFT_TYPE=\"hra\"\n",
"HRA_R=8\n",
"SELECTED_SUBJECT=\"backpack\"\n",
"EPOCH_IDX = 1000\n",
"\n",
"PROJECT_NAME=f\"dreambooth_{PEFT_TYPE}\"\n",
"RUN_NAME=f\"{SELECTED_SUBJECT}_{PEFT_TYPE}_{HRA_R}\"\n",
"OUTPUT_DIR=f\"./data/output/{PEFT_TYPE}\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "06cfd506",
"metadata": {},
"outputs": [],
"source": [
"def get_hra_sd_pipeline(\n",
" ckpt_dir, base_model_name_or_path=None, epoch=int, dtype=torch.float32, device=\"cuda\", adapter_name=\"default\"\n",
"):\n",
"\n",
" if base_model_name_or_path is None:\n",
" raise ValueError(\"Please specify the base model name or path\")\n",
"\n",
" pipe = StableDiffusionPipeline.from_pretrained(\n",
" base_model_name_or_path, torch_dtype=dtype, requires_safety_checker=False\n",
" ).to(device)\n",
" \n",
" load_adapter(pipe, ckpt_dir, epoch, adapter_name)\n",
"\n",
" if dtype in (torch.float16, torch.bfloat16):\n",
" pipe.unet.half()\n",
" pipe.text_encoder.half()\n",
"\n",
" pipe.to(device)\n",
" return pipe\n",
"\n",
"\n",
"def load_adapter(pipe, ckpt_dir, epoch, adapter_name=\"default\"):\n",
" \n",
" unet_sub_dir = os.path.join(ckpt_dir, f\"unet/{epoch}\", adapter_name)\n",
" text_encoder_sub_dir = os.path.join(ckpt_dir, f\"text_encoder/{epoch}\", adapter_name)\n",
" \n",
" if isinstance(pipe.unet, PeftModel):\n",
" pipe.unet.load_adapter(unet_sub_dir, adapter_name=adapter_name)\n",
" else:\n",
" pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)\n",
" \n",
" if os.path.exists(text_encoder_sub_dir):\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.load_adapter(text_encoder_sub_dir, adapter_name=adapter_name)\n",
" else:\n",
" pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)\n",
" \n",
"\n",
"def set_adapter(pipe, adapter_name):\n",
" pipe.unet.set_adapter(adapter_name)\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.set_adapter(adapter_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98a0d8ac",
"metadata": {},
"outputs": [],
"source": [
"prompt = \"a purple qwe backpack.\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4e888d2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 14.47it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.72 s, sys: 495 ms, total: 2.22 s\n",
"Wall time: 2.28 s\n"
]
}
],
"source": [
"%%time\n",
"pipe = get_hra_sd_pipeline(OUTPUT_DIR, MODEL_NAME, EPOCH_IDX, adapter_name=RUN_NAME, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f1c1a1c0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/50 [00:00"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "60fa38d2",
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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",
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",
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is an example.\n",
"example_image = Image.open(\"./a_purple_qwe_backpack.png\")\n",
"example_image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llama",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/hra_dreambooth/requirements.txt
================================================
transformers==4.55.0
accelerate==1.9.0
evaluate
tqdm
datasets==4.0.0
diffusers==0.34.0
Pillow
huggingface_hub
safetensors
ipykernel
ipywidgets
wandb==0.21.0
================================================
FILE: examples/hra_dreambooth/train_dreambooth.py
================================================
#!/usr/bin/env python
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The implementation is based on "Bridging The Gap between Low-rank and Orthogonal
# Adaptation via Householder Reflection Adaptation" (https://huggingface.co/papers/2405.17484).
import hashlib
import itertools
import logging
import math
import os
from contextlib import nullcontext
from pathlib import Path
import datasets
import diffusers
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from huggingface_hub import Repository
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from utils.args_loader import (
get_full_repo_name,
import_model_class_from_model_name_or_path,
parse_args,
)
from utils.dataset import DreamBoothDataset, PromptDataset, collate_fn
from utils.tracemalloc import TorchTracemalloc, b2mb
from peft import HRAConfig, get_peft_model
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.16.0.dev0")
logger = get_logger(__name__)
UNET_TARGET_MODULES = ["to_q", "to_v", "to_k", "query", "value", "key", "to_out.0", "add_k_proj", "add_v_proj"]
TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
def save_adaptor(accelerator, step, unet, text_encoder, args):
unwarpped_unet = accelerator.unwrap_model(unet)
unwarpped_unet.save_pretrained(
os.path.join(args.output_dir, f"unet/{step}"), state_dict=accelerator.get_state_dict(unet)
)
if args.train_text_encoder:
unwarpped_text_encoder = accelerator.unwrap_model(text_encoder)
unwarpped_text_encoder.save_pretrained(
os.path.join(args.output_dir, f"text_encoder/{step}"),
state_dict=accelerator.get_state_dict(text_encoder),
)
def main(args):
validation_prompts = list(filter(None, args.validation_prompt[0].split(".")))
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to if args.report_to != "none" else None,
project_dir=accelerator_project_config,
)
if args.report_to == "wandb":
import wandb
args.wandb_project_name = args.project_name
args.wandb_run_name = args.run_name
wandb_init = {
"wandb": {
"name": args.wandb_run_name,
"mode": "online",
}
}
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
global_seed = hash(args.run_name) % (2**32)
set_seed(global_seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
dtype = torch.float16 if accelerator.device.type in ["cuda", "xpu"] else torch.float32
if args.prior_generation_precision == "fp32":
dtype = torch.float32
elif args.prior_generation_precision == "fp16":
dtype = torch.float16
elif args.prior_generation_precision == "bf16":
dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
dtype=dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name) # noqa: F841
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
if args.use_hra:
config = HRAConfig(
r=args.hra_r,
apply_GS=args.hra_apply_GS,
target_modules=UNET_TARGET_MODULES,
bias=args.hra_bias,
)
unet = get_peft_model(unet, config, adapter_name=args.run_name)
unet.print_trainable_parameters()
vae.requires_grad_(False)
unet.train()
if args.train_text_encoder and args.use_hra:
config = HRAConfig(
r=args.hra_r,
apply_GS=args.hra_apply_GS,
target_modules=UNET_TARGET_MODULES,
bias=args.hra_bias,
)
text_encoder = get_peft_model(text_encoder, config, adapter_name=args.run_name)
text_encoder.print_trainable_parameters()
text_encoder.train()
else:
text_encoder.requires_grad_(False)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if accelerator.device.type == "xpu":
logger.warning("XPU hasn't support xformers yet, ignore it.")
elif is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# below fails when using hra so commenting it out
if args.train_text_encoder and not args.use_hra:
text_encoder.gradient_checkpointing_enable()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = [param for param in unet.parameters() if param.requires_grad]
if args.train_text_encoder:
params_to_optimize += [param for param in text_encoder.parameters() if param.requires_grad]
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Download the official dreambooth dataset from the official repository: https://github.com/google/dreambooth.git
data_path = os.path.join(os.getcwd(), "data", "dreambooth")
if not os.path.exists(data_path):
os.makedirs(os.path.join(os.getcwd(), "data"), exist_ok=True)
os.system(f"git clone https://github.com/google/dreambooth.git '{data_path}'")
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.num_dataloader_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and text_encoder to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
if args.report_to == "wandb":
accelerator.init_trackers(args.wandb_project_name, config=vars(args), init_kwargs=wandb_init)
else:
accelerator.init_trackers(args.project_name, config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = resume_global_step // num_update_steps_per_epoch
resume_step = resume_global_step % num_update_steps_per_epoch
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
if args.train_text_encoder:
text_encoder.train()
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
with TorchTracemalloc() if not args.no_tracemalloc else nullcontext() as tracemalloc:
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
global_step += 1
if global_step % args.checkpointing_steps == 0 and global_step != 0:
if accelerator.is_main_process:
save_adaptor(accelerator, global_step, unet, text_encoder, args)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if (
args.validation_prompt is not None
and (step + num_update_steps_per_epoch * epoch) % args.validation_steps == 0
and global_step > 10
):
unet.eval()
logger.info(
f"Running validation... \n Generating {len(validation_prompts)} images with prompt:"
f" {validation_prompts[0]}, ......"
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
safety_checker=None,
revision=args.revision,
)
# set `keep_fp32_wrapper` to True because we do not want to remove
# mixed precision hooks while we are still training
pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
if args.seed is not None:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
else:
generator = None
images = []
val_img_dir = os.path.join(
args.output_dir,
f"validation/{global_step}",
args.run_name,
)
os.makedirs(val_img_dir, exist_ok=True)
for val_promot in validation_prompts:
image = pipeline(val_promot, num_inference_steps=50, generator=generator).images[0]
image.save(os.path.join(val_img_dir, f"{'_'.join(val_promot.split(' '))}.png"[1:]))
images.append(image)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
import wandb
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
if global_step >= args.max_train_steps:
break
# Printing the device memory usage details such as allocated memory, peak memory, and total memory usage
if not args.no_tracemalloc:
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the train : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the train (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/hra_dreambooth/train_dreambooth.sh
================================================
CLASS_IDX=$1
# Define the UNIQUE_TOKEN, CLASS_TOKENs, and SUBJECT_NAMES
UNIQUE_TOKEN="qwe"
SUBJECT_NAMES=(
"backpack" "backpack_dog" "bear_plushie" "berry_bowl" "can"
"candle" "cat" "cat2" "clock" "colorful_sneaker"
"dog" "dog2" "dog3" "dog5" "dog6"
"dog7" "dog8" "duck_toy" "fancy_boot" "grey_sloth_plushie"
"monster_toy" "pink_sunglasses" "poop_emoji" "rc_car" "red_cartoon"
"robot_toy" "shiny_sneaker" "teapot" "vase" "wolf_plushie"
)
CLASS_TOKENs=(
"backpack" "backpack" "stuffed animal" "bowl" "can"
"candle" "cat" "cat" "clock" "sneaker"
"dog" "dog" "dog" "dog" "dog"
"dog" "dog" "toy" "boot" "stuffed animal"
"toy" "glasses" "toy" "toy" "cartoon"
"toy" "sneaker" "teapot" "vase" "stuffed animal"
)
CLASS_TOKEN=${CLASS_TOKENs[$CLASS_IDX]}
SELECTED_SUBJECT=${SUBJECT_NAMES[$CLASS_IDX]}
if [[ $CLASS_IDX =~ ^(0|1|2|3|4|5|8|9|17|18|19|20|21|22|23|24|25|26|27|28|29)$ ]]; then
PROMPT_LIST=(
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the jungle."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the snow."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on the beach."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on a cobblestone street."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of pink fabric."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a wooden floor."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a city in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a mountain in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a blue house in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a purple rug in a forest."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a wheat field in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a tree and autumn leaves in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with the Eiffel Tower in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} floating on top of water."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} floating in an ocean of milk."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of green grass with sunflowers around it."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a mirror."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of the sidewalk in a crowded street."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a dirt road."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a white rug."
"a red ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a purple ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a shiny ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a wet ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a cube shaped ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
)
prompt_test_list=(
"a ${CLASS_TOKEN} in the jungle"
"a ${CLASS_TOKEN} in the snow"
"a ${CLASS_TOKEN} on the beach"
"a ${CLASS_TOKEN} on a cobblestone street"
"a ${CLASS_TOKEN} on top of pink fabric"
"a ${CLASS_TOKEN} on top of a wooden floor"
"a ${CLASS_TOKEN} with a city in the background"
"a ${CLASS_TOKEN} with a mountain in the background"
"a ${CLASS_TOKEN} with a blue house in the background"
"a ${CLASS_TOKEN} on top of a purple rug in a forest"
"a ${CLASS_TOKEN} with a wheat field in the background"
"a ${CLASS_TOKEN} with a tree and autumn leaves in the background"
"a ${CLASS_TOKEN} with the Eiffel Tower in the background"
"a ${CLASS_TOKEN} floating on top of water"
"a ${CLASS_TOKEN} floating in an ocean of milk"
"a ${CLASS_TOKEN} on top of green grass with sunflowers around it"
"a ${CLASS_TOKEN} on top of a mirror"
"a ${CLASS_TOKEN} on top of the sidewalk in a crowded street"
"a ${CLASS_TOKEN} on top of a dirt road"
"a ${CLASS_TOKEN} on top of a white rug"
"a red ${CLASS_TOKEN}"
"a purple ${CLASS_TOKEN}"
"a shiny ${CLASS_TOKEN}"
"a wet ${CLASS_TOKEN}"
"a cube shaped ${CLASS_TOKEN}"
)
else
PROMPT_LIST=(
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the jungle."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in the snow."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on the beach."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on a cobblestone street."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of pink fabric."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a wooden floor."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a city in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a mountain in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} with a blue house in the background."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} on top of a purple rug in a forest."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a red hat."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a santa hat."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a rainbow scarf."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a black top hat and a monocle."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a chef outfit."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a firefighter outfit."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a police outfit."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing pink glasses."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} wearing a yellow shirt."
"a ${UNIQUE_TOKEN} ${CLASS_TOKEN} in a purple wizard outfit."
"a red ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a purple ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a shiny ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a wet ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
"a cube shaped ${UNIQUE_TOKEN} ${CLASS_TOKEN}."
)
prompt_test_list=(
"a ${CLASS_TOKEN} in the jungle"
"a ${CLASS_TOKEN} in the snow"
"a ${CLASS_TOKEN} on the beach"
"a ${CLASS_TOKEN} on a cobblestone street"
"a ${CLASS_TOKEN} on top of pink fabric"
"a ${CLASS_TOKEN} on top of a wooden floor"
"a ${CLASS_TOKEN} with a city in the background"
"a ${CLASS_TOKEN} with a mountain in the background"
"a ${CLASS_TOKEN} with a blue house in the background"
"a ${CLASS_TOKEN} on top of a purple rug in a forest"
"a ${CLASS_TOKEN} wearing a red hat"
"a ${CLASS_TOKEN} wearing a santa hat"
"a ${CLASS_TOKEN} wearing a rainbow scarf"
"a ${CLASS_TOKEN} wearing a black top hat and a monocle"
"a ${CLASS_TOKEN} in a chef outfit"
"a ${CLASS_TOKEN} in a firefighter outfit"
"a ${CLASS_TOKEN} in a police outfit"
"a ${CLASS_TOKEN} wearing pink glasses"
"a ${CLASS_TOKEN} wearing a yellow shirt"
"a ${CLASS_TOKEN} in a purple wizard outfit"
"a red ${CLASS_TOKEN}"
"a purple ${CLASS_TOKEN}"
"a shiny ${CLASS_TOKEN}"
"a wet ${CLASS_TOKEN}"
"a cube shaped ${CLASS_TOKEN}"
)
fi
VALIDATION_PROMPT=${PROMPT_LIST[@]}
INSTANCE_PROMPT="a photo of ${UNIQUE_TOKEN} ${CLASS_TOKEN}"
CLASS_PROMPT="a photo of ${CLASS_TOKEN}"
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
PEFT_TYPE="hra"
HRA_R=8
export PROJECT_NAME="dreambooth_${PEFT_TYPE}"
export RUN_NAME="${SELECTED_SUBJECT}_${PEFT_TYPE}_${HRA_R}"
export INSTANCE_DIR="./data/dreambooth/dataset/${SELECTED_SUBJECT}"
export CLASS_DIR="./data/class_data/${CLASS_TOKEN}"
export OUTPUT_DIR="./data/output/${PEFT_TYPE}"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir="$CLASS_DIR" \
--output_dir=$OUTPUT_DIR \
--project_name=$PROJECT_NAME \
--run_name=$RUN_NAME \
--with_prior_preservation \
--prior_loss_weight=1.0 \
--instance_prompt="$INSTANCE_PROMPT" \
--validation_prompt="$VALIDATION_PROMPT" \
--class_prompt="$CLASS_PROMPT" \
--resolution=512 \
--train_batch_size=1 \
--num_dataloader_workers=2 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--use_hra \
--hra_r=$HRA_R \
--hra_bias="hra_only" \
--learning_rate=5e-3 \
--max_train_steps=510 \
--checkpointing_steps=200 \
--validation_steps=200 \
--enable_xformers_memory_efficient_attention \
--report_to="none" \
================================================
FILE: examples/hra_dreambooth/utils/__init__.py
================================================
================================================
FILE: examples/hra_dreambooth/utils/args_loader.py
================================================
# adapted from [peft's boft_dreambooth](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth)
import argparse
import os
import warnings
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a Dreambooth training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--validation_prompt",
nargs="+",
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=500,
help=(
"Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
# hra args
parser.add_argument("--use_hra", action="store_true", help="Whether to use HRA for parameter efficient tuning.")
parser.add_argument("--hra_r", type=int, default=8, help="The rank of HRA across different layers.")
parser.add_argument(
"--hra_apply_GS", default=False, action="store_true", help="Whether to apply Gram-Schmidt orthogonalization."
)
parser.add_argument(
"--hra_bias",
type=str,
default="none",
help="Bias type for HRA. Can be 'none', 'all' or 'hra_only', only used if use_hra is True.",
)
parser.add_argument(
"--num_dataloader_workers", type=int, default=1, help="Num of workers for the training dataloader."
)
parser.add_argument(
"--no_tracemalloc",
default=False,
action="store_true",
help="Flag to stop memory allocation tracing during training. This could speed up training on Windows.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--project_name",
type=str,
default=None,
help=("The project name for log tracking"),
)
parser.add_argument(
"--run_name",
type=str,
default=None,
help=("The run name for log tracking"),
)
parser.add_argument(
"--report_to",
type=str,
default="wandb",
help=(
'The integration to report the results and logs to. Supported platforms are `"wandb"`'
' (default), `"tensorboard"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--wandb_run_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
# if args.dataset_name is None and args.train_data_dir is None:
# raise ValueError("Need either a dataset name or a training folder.")
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
================================================
FILE: examples/hra_dreambooth/utils/dataset.py
================================================
# adapted from [peft's boft_dreambooth](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth)
from pathlib import Path
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def collate_fn(examples, with_prior_preservation=False):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
================================================
FILE: examples/hra_dreambooth/utils/tracemalloc.py
================================================
# adapted from [peft's boft_dreambooth](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth)
import gc
import threading
import psutil
import torch
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
self.device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
self.device_module = getattr(torch, self.device_type, torch.cuda)
gc.collect()
self.device_module.empty_cache()
self.device_module.reset_peak_memory_stats() # reset the peak gauge to zero
self.begin = self.device_module.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
"""get resident set size memory for the current process"""
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
# time.sleep(0.001) # 1msec
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
self.device_module.empty_cache()
self.end = self.device_module.memory_allocated()
self.peak = self.device_module.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
================================================
FILE: examples/image_classification/README.md
================================================
# Fine-tuning for image classification using LoRA and 🤗 PEFT
## Vision Transformer model from transformers
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipynb)
We provide a notebook (`image_classification_peft_lora.ipynb`) where we learn how to use [LoRA](https://huggingface.co/papers/2106.09685) from 🤗 PEFT to fine-tune an image classification model by ONLY using **0.7%** of the original trainable parameters of the model.
LoRA adds low-rank "update matrices" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://huggingface.co/papers/2106.09685).
## PoolFormer model from timm
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_timm_peft_lora.ipynb)
The notebook `image_classification_timm_peft_lora.ipynb` showcases fine-tuning an image classification model using from the [timm](https://huggingface.co/docs/timm/index) library. Again, LoRA is used to reduce the numberof trainable parameters to a fraction of the total.
================================================
FILE: examples/image_classification/image_classification_peft_lora.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "71GTxOD71mEn"
},
"source": [
"## Introduction\n",
"\n",
"In this notebook, we will learn how to use [LoRA](https://huggingface.co/papers/2106.09685) from 🤗 PEFT to fine-tune an image classification model by ONLY using **0.77%** of the original trainable parameters of the model. \n",
"\n",
"LoRA adds low-rank \"update matrices\" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://huggingface.co/papers/2106.09685). \n",
"\n",
"Let's get started by installing the dependencies. \n",
"\n",
"__*Note that this notebook builds on top the [official image classification example notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb).*__"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0a_bETbqv4P7"
},
"source": [
"## Install dependencies\n",
"\n",
"Here we're installing `peft` from source to ensure we have access to all the bleeding edge features of `peft`. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z0_5BYt8hobv",
"outputId": "aafcbc39-b972-493a-8922-2141b1621926"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m53.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m102.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip install transformers accelerate evaluate datasets git+https://github.com/huggingface/peft -q"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y8dSVHoIv7HC"
},
"source": [
"## Authentication\n",
"\n",
"We will share our fine-tuned model at the end of training. So, to do that we just authenticate using our 🤗 token. This token is available from [here](https://huggingface.co/settings/tokens). If you don't have a 🤗 account already, we highly encourage you to do so; it's free!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359,
"referenced_widgets": [
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},
"id": "31Zv6rFYr37d",
"outputId": "6476ebcf-6d71-4b7d-ee38-dc4f8e8d024e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Token is valid.\n",
"Your token has been saved in your configured git credential helpers (store).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AX7aJaIKjbCF"
},
"source": [
"## Check the library versions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ejkn8GBzh_DB",
"outputId": "777afbdf-e026-43d8-8efa-80bb958d0ca3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===================================BUG REPORT===================================\n",
"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
"================================================================================\n"
]
}
],
"source": [
"import transformers\n",
"import accelerate\n",
"import peft"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "A833xxo3ir28",
"outputId": "da71ef1c-b6d7-43e2-a78b-23556785ef02"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transformers version: 4.26.0\n",
"Accelerate version: 0.16.0\n",
"PEFT version: 0.1.0.dev0\n"
]
}
],
"source": [
"print(f\"Transformers version: {transformers.__version__}\")\n",
"print(f\"Accelerate version: {accelerate.__version__}\")\n",
"print(f\"PEFT version: {peft.__version__}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Po1Ve9u5v_Ul"
},
"source": [
"## Select a model checkpoint to fine-tune"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "vhvCQpP-isJr"
},
"outputs": [],
"source": [
"model_checkpoint = \"google/vit-base-patch16-224-in21k\" # pre-trained model from which to fine-tune"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UKN3rMAsjgEz"
},
"source": [
"## Load a dataset\n",
"\n",
"We're only loading the first 5000 instances from the training set of the [Food-101 dataset](https://huggingface.co/datasets/food101) to keep this example runtime short. "
]
},
{
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"execution_count": 6,
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"id": "rI0d2_liitUr",
"outputId": "4ae986eb-6cbb-4d9f-bb99-1ffbb05ee835"
},
"outputs": [
{
"data": {
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"text/plain": [
"Downloading builder script: 0%| | 0.00/6.21k [00:00\n",
" \n",
" \n",
" [45/45 04:44, Epoch 5/5]\n",
" \n",
"
\n",
" \n",
"
\n",
"
Epoch
\n",
"
Training Loss
\n",
"
Validation Loss
\n",
"
Accuracy
\n",
"
\n",
" \n",
" \n",
"
\n",
"
1
\n",
"
No log
\n",
"
0.506871
\n",
"
0.896000
\n",
"
\n",
"
\n",
"
2
\n",
"
2.162700
\n",
"
0.189141
\n",
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0.946000
\n",
"
\n",
"
\n",
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3
\n",
"
0.345100
\n",
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0.144759
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0.960000
\n",
"
\n",
"
\n",
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4
\n",
"
0.211600
\n",
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0.150886
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0.958000
\n",
"
\n",
"
\n",
"
5
\n",
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0.171100
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0.149751
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0.958000
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"
"
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""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 500\n",
" Batch size = 128\n",
"Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9\n",
"Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/config.json\n",
"Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/pytorch_model.bin\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/preprocessor_config.json\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 500\n",
" Batch size = 128\n",
"Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18\n",
"Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/config.json\n",
"Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/pytorch_model.bin\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/preprocessor_config.json\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 500\n",
" Batch size = 128\n",
"Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27\n",
"Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/config.json\n",
"Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/pytorch_model.bin\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/preprocessor_config.json\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 500\n",
" Batch size = 128\n",
"Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36\n",
"Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/config.json\n",
"Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/pytorch_model.bin\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/preprocessor_config.json\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 500\n",
" Batch size = 128\n",
"Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45\n",
"Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/config.json\n",
"Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/pytorch_model.bin\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/preprocessor_config.json\n",
"Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27 (score: 0.96).\n"
]
}
],
"source": [
"trainer = Trainer(\n",
" lora_model,\n",
" args,\n",
" train_dataset=train_ds,\n",
" eval_dataset=val_ds,\n",
" processing_class=image_processor,\n",
" compute_metrics=compute_metrics,\n",
" data_collator=collate_fn,\n",
")\n",
"train_results = trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b2NENHxHCejv"
},
"source": [
"In just a few minutes, we have a fine-tuned model with 96% validation accuracy. Also, note that we used a very small subset of the training dataset which is definitely impacting the results. "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 198
},
"id": "_MAd2906jQKG",
"outputId": "7b825531-4e8d-4666-d6fc-eaa1accf3bb6"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 500\n",
" Batch size = 128\n"
]
},
{
"data": {
"text/html": [
"\n",
"
Copy a token from your Hugging Face\ntokens page and paste it below. Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
Copy a token from your Hugging Face\ntokens page and paste it below. Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
Copy a token from your Hugging Face\ntokens page and paste it below. Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
"
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================================================
FILE: examples/int8_training/config.yaml
================================================
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: MULTI_XPU
downcast_bf16: 'no'
enable_cpu_affinity: false
gpu_ids: all
ipex_config:
ipex: false
machine_rank: 0
main_training_function: main
mixed_precision: 'no'
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
================================================
FILE: examples/int8_training/fine_tune_blip2_int8.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader, Dataset
from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
# Let's define the LoraConfig
config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
)
# We load our model and processor using `transformers`
model = AutoModelForVision2Seq.from_pretrained(
"Salesforce/blip2-opt-2.7b", quantization_config=BitsAndBytesConfig(load_in_8bit=True)
)
processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
# Get our peft model and print the number of trainable parameters
model = get_peft_model(model, config)
model.print_trainable_parameters()
# Let's load the dataset here!
dataset = load_dataset("ybelkada/football-dataset", split="train")
class ImageCaptioningDataset(Dataset):
def __init__(self, dataset, processor):
self.dataset = dataset
self.processor = processor
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
item = self.dataset[idx]
encoding = self.processor(images=item["image"], padding="max_length", return_tensors="pt")
# remove batch dimension
encoding = {k: v.squeeze() for k, v in encoding.items()}
encoding["text"] = item["text"]
return encoding
def collator(batch):
# pad the input_ids and attention_mask
processed_batch = {}
for key in batch[0].keys():
if key != "text":
processed_batch[key] = torch.stack([example[key] for example in batch])
else:
text_inputs = processor.tokenizer(
[example["text"] for example in batch], padding=True, return_tensors="pt"
)
processed_batch["input_ids"] = text_inputs["input_ids"]
processed_batch["attention_mask"] = text_inputs["attention_mask"]
return processed_batch
train_dataset = ImageCaptioningDataset(dataset, processor)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=2, collate_fn=collator)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model.train()
for epoch in range(50):
print("Epoch:", epoch)
for idx, batch in enumerate(train_dataloader):
input_ids = batch.pop("input_ids").to(device)
pixel_values = batch.pop("pixel_values").to(device, torch.float16)
outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids)
loss = outputs.loss
print("Loss:", loss.item())
loss.backward()
optimizer.step()
optimizer.zero_grad()
if idx % 10 == 0:
generated_output = model.generate(pixel_values=pixel_values)
print(processor.batch_decode(generated_output, skip_special_tokens=True))
================================================
FILE: examples/int8_training/peft_adalora_whisper_large_training.py
================================================
import argparse
import gc
import json
import logging
import math
import os
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from random import randint
from typing import Any, Union
# datasets imports
import datasets
# metric imports
import evaluate
import numpy as np
import torch
import transformers
import wandb
# accelerate imports
from accelerate import Accelerator, dispatch_model
from accelerate.logging import get_logger
from datasets import Audio, DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
# hf imports
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
BitsAndBytesConfig,
SchedulerType,
WhisperForConditionalGeneration,
WhisperProcessor,
get_scheduler,
set_seed,
)
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
# peft imports
from peft import AdaLoraConfig, LoraConfig, PeftModel, get_peft_model
logger = get_logger(__name__, log_level="INFO")
def parse_args():
parser = argparse.ArgumentParser(description="Whisper Fine-Tuning with AdaLora")
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument("--language", type=str, help="Language to use for training; e.g., 'Hindi' ", required=True)
parser.add_argument("--language_abbr", type=str, help="Language to use for training; e.g., 'hi' ", required=True)
parser.add_argument(
"--task", type=str, default="transcribe", help="Task to use for training; e.g., 'transcribe' ", required=False
)
parser.add_argument(
"--dataset_name",
type=str,
default="mozilla-foundation/common_voice_11_0",
help="Dataset to use for training; e.g., 'whisper' ",
required=False,
)
parser.add_argument(
"--dataset_in_streaming_mode",
action="store_true",
help="Whether to use streaming mode for the dataset.",
)
parser.add_argument(
"--do_lower_case", action="store_true", help="lowercase the transcribed text before tokenizing"
)
parser.add_argument(
"--do_remove_punctuation", action="store_true", help="remove punctuation from the transcribed text"
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
)
parser.add_argument("--max_audio_input_length", type=float, default=30.0, help="Maximum audio length in seconds.")
parser.add_argument(
"--preprocessing_num_workers",
type=int,
default=None,
help="The number of processes to use for the preprocessing.",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--buffer_size",
type=int,
default=5000,
help="Number of samples to prefetch in the streaming mode.",
)
parser.add_argument(
"--dataloader_pin_memory",
action="store_true",
help="Whether or not to pin memory for the DataLoader.",
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help="Number of subprocesses to use for data loading.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--load_best_model",
action="store_true",
help="Whether to load the best model at the end of training",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--logging_steps",
type=int,
default=100,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--evaluation_steps",
type=int,
default=500,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
# lora/adalora specific args
parser.add_argument(
"--use_peft",
action="store_true",
help="Whether to use PEFT",
)
parser.add_argument(
"--use_adalora",
action="store_true",
help="Whether to use AdaLoRA or LoRA. If set, uses AdaLoRA instead of the default LoRA.",
)
parser.add_argument(
"--init_r",
type=int,
default=12,
help="Initial AdaLoRA rank",
)
parser.add_argument(
"--target_r",
type=int,
default=4,
help="Target AdaLoRA rank",
)
parser.add_argument(
"--tinit",
type=int,
default=200,
help="number of warmup steps for AdaLoRA wherein no pruning is performed",
)
parser.add_argument(
"--tfinal",
type=int,
default=1000,
help=" fix the resulting budget distribution and fine-tune the model for tfinal steps when using AdaLoRA ",
)
parser.add_argument(
"--delta_t",
type=int,
default=10,
help="interval of steps for AdaLoRA to update rank",
)
parser.add_argument(
"--lora_alpha",
type=int,
default=32,
help="LORA alpha",
)
parser.add_argument(
"--r",
type=int,
default=8,
help="LORA rank",
)
parser.add_argument(
"--lora_dropout",
type=float,
default=0.1,
help="LORA dropout",
)
parser.add_argument(
"--orth_reg_weight",
type=float,
default=0.5,
help="Orthogonal regularization weight",
)
parser.add_argument(
"--debug_mode",
action="store_true",
help="Whether to use debug mode",
)
args = parser.parse_args()
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
def load_streaming_dataset(dataset_name, dataset_config_name, split, **kwargs):
if "+" in split:
# load multiple splits separated by the `+` symbol *with* streaming mode
dataset_splits = [
load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs)
for split_name in split.split("+")
]
# interleave multiple splits to form one dataset
interleaved_dataset = interleave_datasets(dataset_splits)
return interleaved_dataset
else:
# load a single split *with* streaming mode
dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=True, **kwargs)
return dataset
def prepare_dataset_wrapper(do_lower_case, do_remove_punctuation, processor, normalizer):
def prepare_dataset(batch):
# load and (possibly) resample audio data to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = processor.feature_extractor(
audio["array"], sampling_rate=audio["sampling_rate"]
).input_features[0]
# compute input length of audio sample in seconds
batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]
# optional pre-processing steps
transcription = batch["sentence"]
if do_lower_case:
transcription = transcription.lower()
if do_remove_punctuation:
transcription = normalizer(transcription).strip()
# encode target text to label ids
batch["labels"] = processor.tokenizer(transcription).input_ids
return batch
return prepare_dataset
def save_model_hook(models, weights, output_dir):
for model in models:
model.save_pretrained(output_dir)
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
while len(models) > 0:
model = models.pop()
# pop models so that they are not loaded again
PeftModel.from_pretrained(model.base_model.model, input_dir)
@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
processor: Any
def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need different padding methods
# first treat the audio inputs by simply returning torch tensors
input_features = [{"input_features": feature["input_features"]} for feature in features]
batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
# get the tokenized label sequences
label_features = [{"input_ids": feature["labels"]} for feature in features]
# pad the labels to max length
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
# replace padding with -100 to ignore loss correctly
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
# if bos token is appended in previous tokenization step,
# cut bos token here as it's append later anyways
if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():
labels = labels[:, 1:]
batch["labels"] = labels
return batch
def get_audio_length_processor(max_input_length):
def is_audio_in_length_range(length):
return length < max_input_length
return is_audio_in_length_range
def evaluation_loop(model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator):
model.eval()
predictions = []
references = []
normalized_predictions = []
normalized_references = []
device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
for _, batch in enumerate(tqdm(eval_dataloader)):
with torch.amp.autocast(device_type=device_type):
with torch.no_grad():
generated_tokens = (
model.generate(
input_features=batch["input_features"],
forced_decoder_ids=forced_decoder_ids,
max_new_tokens=255,
)
.cpu()
.numpy()
)
labels = batch["labels"].cpu().numpy()
labels = np.where(labels != -100, labels, processor.tokenizer.pad_token_id)
decoded_preds = processor.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
decoded_labels = processor.tokenizer.batch_decode(labels, skip_special_tokens=True)
predictions.extend(decoded_preds)
references.extend(decoded_labels)
normalized_predictions.extend([normalizer(pred).strip() for pred in decoded_preds])
normalized_references.extend([normalizer(label).strip() for label in decoded_labels])
del generated_tokens, labels, batch
gc.collect()
wer = 100 * metric.compute(predictions=predictions, references=references)
normalized_wer = 100 * metric.compute(predictions=normalized_predictions, references=normalized_references)
eval_metrics = {"eval/wer": wer, "eval/normalized_wer": normalized_wer}
if accelerator.get_tracker("wandb"):
sample_size = min(len(predictions), 256)
ids = [randint(0, len(predictions) - 1) for p in range(0, sample_size)]
sample_predictions = [predictions[i] for i in ids]
sample_references = [references[i] for i in ids]
sample_normalized_predictions = [normalized_predictions[i] for i in ids]
sample_normalized_references = [normalized_references[i] for i in ids]
table_rows = [
list(r)
for r in zip(
sample_predictions, sample_references, sample_normalized_predictions, sample_normalized_references
)
]
eval_metrics["eval_samples"] = wandb.Table(
columns=["predictions", "references", "normalized_predictions", "normalized_references"],
rows=table_rows,
)
return eval_metrics
def main():
args = parse_args()
accelerator_kwargs = {"gradient_accumulation_steps": args.gradient_accumulation_steps}
if args.with_tracking:
accelerator_kwargs["log_with"] = args.report_to
accelerator_kwargs["project_dir"] = args.output_dir
accelerator = Accelerator(**accelerator_kwargs)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
api = HfApi(token=args.hub_token)
# Create repo (repo_name from args or inferred)
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# load dataset either in streaming mode or not
processor = WhisperProcessor.from_pretrained(args.model_name_or_path, language=args.language, task=args.task)
normalizer = BasicTextNormalizer()
prepare_dataset = prepare_dataset_wrapper(args.do_lower_case, args.do_remove_punctuation, processor, normalizer)
is_audio_in_length_range = get_audio_length_processor(args.max_audio_input_length)
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
if args.dataset_in_streaming_mode:
raw_datasets = IterableDatasetDict()
loading_method = load_streaming_dataset
else:
raw_datasets = DatasetDict()
loading_method = load_dataset
if args.debug_mode:
train_split = "train[:100]"
test_split = "test[:10]"
else:
train_split = "train+validation"
test_split = "test"
raw_datasets["train"] = loading_method(args.dataset_name, args.language_abbr, split=train_split)
raw_datasets["test"] = loading_method(args.dataset_name, args.language_abbr, split=test_split)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
logger.info("Dataset loaded: %s", raw_datasets)
logger.info(f"{raw_datasets['train'][0]}")
vectorized_datasets = raw_datasets.map(
prepare_dataset,
remove_columns=list(next(iter(raw_datasets.values())).features),
num_proc=args.preprocessing_num_workers,
).with_format("torch")
if args.dataset_in_streaming_mode:
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
buffer_size=args.buffer_size,
seed=args.seed,
)
# filter out audio files that are too long from the training set
is_audio_in_length_range = get_audio_length_processor(args.max_audio_input_length)
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
is_audio_in_length_range, input_columns=["input_length"]
)
# get dataloaders
train_dataloader = DataLoader(
vectorized_datasets["train"],
batch_size=args.per_device_train_batch_size,
shuffle=True,
collate_fn=data_collator,
num_workers=args.dataloader_num_workers,
pin_memory=args.dataloader_pin_memory,
)
eval_dataloader = DataLoader(
vectorized_datasets["test"],
batch_size=args.per_device_eval_batch_size,
collate_fn=data_collator,
num_workers=args.dataloader_num_workers,
pin_memory=args.dataloader_pin_memory,
)
# metric
metric = evaluate.load("wer")
# model
model = WhisperForConditionalGeneration.from_pretrained(
args.model_name_or_path, quantization_config=BitsAndBytesConfig(load_in_8bit=True)
)
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
if hasattr(model, "hf_device_map") and len(set(model.hf_device_map.values()).intersection({"cpu", "disk"})) > 0:
raise ValueError("Training on CPU or disk is not supported.")
if hasattr(model, "hf_device_map") and len(set(model.hf_device_map.values())) > 1:
device_map = model.hf_device_map.copy()
# required because `labels` are on main execution device (0) while the output of `proj_out` is on other device.
# So, this leads to device mismatch error when calculation cross-entropy between logits and labels.
# Won't arise during inference as `labels` aren't supplied during that time
# instead of changing device of one of the tied modules, I have to do this for all tied modules
# else the execution device of remaining tied modules isn't changed
device_map["model.decoder.embed_tokens"] = model._hf_hook.execution_device
device_map["model.decoder.embed_positions"] = model._hf_hook.execution_device
device_map["proj_out"] = model._hf_hook.execution_device
dispatch_model(model, device_map=device_map)
# preparing peft model
if args.use_peft:
from peft import prepare_model_for_kbit_training
model = prepare_model_for_kbit_training(model)
# as Whisper model uses Conv layer in encoder, checkpointing disables grad computation
# to avoid this, make the inputs trainable
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.model.encoder.conv1.register_forward_hook(make_inputs_require_grad)
# Calculate total steps first for AdaLoRA
if args.max_train_steps is None:
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
total_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
total_steps = args.max_train_steps
# wrapping model with adalora tuner
if args.use_adalora:
config = AdaLoraConfig(
init_r=args.init_r,
target_r=args.target_r,
beta1=0.85,
beta2=0.85,
tinit=args.tinit,
tfinal=args.tfinal,
deltaT=args.delta_t,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
orth_reg_weight=args.orth_reg_weight,
total_step=total_steps,
)
else:
config = LoraConfig(
r=args.r,
lora_alpha=args.lora_alpha,
target_modules=["q_proj", "v_proj"],
lora_dropout=args.lora_dropout,
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
# optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
if args.max_train_steps is None:
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# scheduler
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
accelerator.print(model)
# Note here that the max steps is adjusted by the accelerator's num_processes
args.max_train_steps = math.ceil(args.max_train_steps / accelerator.num_processes)
if args.use_peft and args.use_adalora:
# Update the total_step in the config to reflect the adjusted max_train_steps
# Handle DDP case where model is wrapped
if hasattr(model, "module"):
# DDP case
model.module.base_model.peft_config["default"].total_step = args.max_train_steps
else:
# Non-DDP case
model.base_model.peft_config["default"].total_step = args.max_train_steps
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
run_name = f"run-{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}"
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers(
"Whisper PEFT Fine-Tuning", config=experiment_config, init_kwargs={"wandb": {"name": run_name}}
)
# saving and loading checkpoints for resuming training
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
global_step = 0
starting_epoch = 0
best_metric = None
resume_step = 0
forced_decoder_ids = processor.get_decoder_prompt_ids(language=args.language, task=args.task)
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint)
path = os.path.basename(args.resume_from_checkpoint)
training_difference = os.path.splitext(path)[0]
global_step = resume_step = int(training_difference.replace("step_", ""))
starting_epoch = resume_step // len(train_dataloader)
resume_step -= starting_epoch * len(train_dataloader)
# We need to adjust the progress bar to the current step
progress_bar.update(resume_step)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
running_loss = 0
for step, batch in enumerate(accelerator.skip_first_batches(train_dataloader, num_batches=resume_step)):
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
# Update the importance of low-rank matrices
# and allocate the budget accordingly.
# This is only needed for AdaLora.
# Note that this requires parameter gradients.
# Hence being called before optimizer.zero_grad().
if args.use_peft and args.use_adalora:
# Handle DDP case where model is wrapped
if hasattr(model, "module"):
# DDP case
peft_model = model.module
else:
# Non-DDP case
peft_model = model
# Check if rank_pattern exists before calling update_and_allocate
if (
hasattr(peft_model, "peft_config")
and peft_model.peft_config["default"].rank_pattern is not None
and global_step >= args.tinit # Only start updating after tinit steps
):
peft_model.update_and_allocate(global_step)
optimizer.zero_grad()
global_step += 1
progress_bar.update(1)
if args.with_tracking:
step_loss = accelerator.reduce(loss.detach().clone()).item()
total_loss += step_loss
running_loss += step_loss
if global_step % args.checkpointing_steps == 0:
output_dir = os.path.join(args.output_dir, f"step_{global_step}")
accelerator.save_state(output_dir)
if global_step % args.logging_steps == 0:
if args.with_tracking:
accelerator.log({"train/running_loss": running_loss / args.logging_steps}, step=global_step)
running_loss = 0
if global_step % args.evaluation_steps == 0:
eval_metrics = evaluation_loop(
model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator
)
if args.with_tracking:
logger.info(f"Step {global_step} eval metrics: {eval_metrics}")
accelerator.log(eval_metrics, step=global_step)
if best_metric is None or eval_metrics["eval/wer"] < best_metric:
best_metric = eval_metrics["eval/wer"]
accelerator.save_state(os.path.join(args.output_dir, "best_checkpoint"))
model.train()
if global_step >= args.max_train_steps:
break
if args.with_tracking:
train_epoch_loss = total_loss / (step + 1)
logger.info(f"Epoch {epoch} train loss: {train_epoch_loss}")
accelerator.log({"epoch/train_loss": train_epoch_loss}, step=epoch)
if args.push_to_hub and epoch <= args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, is_main_process=accelerator.is_main_process)
# evaluate the model at the end of training
eval_metrics = evaluation_loop(
model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator
)
if args.with_tracking:
logger.info(f"Step {global_step} eval metrics: {eval_metrics}")
accelerator.log(eval_metrics, step=global_step)
if best_metric is None or eval_metrics["eval/wer"] < best_metric:
best_metric = eval_metrics["eval/wer"]
accelerator.save_state(os.path.join(args.output_dir, "best_checkpoint"))
if accelerator.is_main_process:
processor.tokenizer.save_pretrained(args.output_dir)
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message=f"Training in progress epoch {epoch}",
run_as_future=True,
)
if args.load_best_model:
# load the best model
accelerator.load_state(os.path.join(args.output_dir, "best_checkpoint"))
# Handle DDP case where model is wrapped
if hasattr(model, "module"):
# DDP case
peft_model = model.module
else:
# Non-DDP case
peft_model = model
# Only resize if rank_pattern exists
if hasattr(peft_model, "peft_config") and peft_model.peft_config["default"].rank_pattern is not None:
peft_model.resize_modules_by_rank_pattern(peft_model.peft_config["default"].rank_pattern, "default")
eval_metrics = evaluation_loop(
model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator
)
if args.with_tracking:
best_metrics = {"best_" + k: v for k, v in eval_metrics.items()}
accelerator.log(best_metrics, step=global_step)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, is_main_process=accelerator.is_main_process)
if accelerator.is_main_process:
processor.tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
)
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
eval_metrics.pop("eval_samples")
json.dump(eval_metrics, f)
if __name__ == "__main__":
main()
================================================
FILE: examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "5cefac89",
"metadata": {},
"source": [
"# Finetuning Whisper-large-V2 on Colab using PEFT-Lora + BNB INT8 training"
]
},
{
"cell_type": "markdown",
"id": "090fa3ed",
"metadata": {},
"source": [
"In this Colab, we present a step-by-step guide on how to fine-tune Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers and 🤗 PEFT. Using 🤗 PEFT and `bitsandbytes`, you can train the `whisper-large-v2` seamlessly on a colab with T4 GPU (16 GB VRAM). In this notebook, with most parts from [fine_tune_whisper.ipynb](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb#scrollTo=BRdrdFIeU78w) is adapted to train using PEFT LoRA+BNB INT8.\n",
"\n",
"For more details on model, datasets and metrics, refer blog [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "625e47a0",
"metadata": {},
"source": [
"## initial Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eJrPyQM5Xhv5",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eJrPyQM5Xhv5",
"outputId": "cfd6d8c9-964c-492b-b641-8e80e337f783"
},
"outputs": [],
"source": [
"!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n",
"!apt update\n",
"!apt install -y ffmpeg"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "r_Ivl7qlX0dz",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r_Ivl7qlX0dz",
"outputId": "2caa9eed-f01a-4603-a527-fe3b0b58b6e2"
},
"outputs": [],
"source": [
"!pip install datasets==3.6.0\n",
"!pip install git+https://github.com/huggingface/transformers\n",
"!pip install librosa\n",
"!pip install evaluate>=0.30\n",
"!pip install jiwer\n",
"!pip install gradio\n",
"!pip install -q datasets accelerate\n",
"!pip install -q git+https://github.com/bitsandbytes-foundation/bitsandbytes.git\n",
"!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
]
},
{
"cell_type": "markdown",
"id": "8a528c1a",
"metadata": {},
"source": [
"Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed0OpduhX2JF",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303,
"referenced_widgets": [
"c60690c2aee74763bf23115553f4e640",
"7d3d6c198e794219ab5db59f0228c8ab",
"5d5ea0207c6148769ad9f15b7b3dd92d",
"642e28d258ca4c30a5df94c5cf7e0471",
"170ee581427d4f30925dc393d124c1be",
"d5d5aa24182a4e04b3fdae1ca7fad52a",
"6dba643113a547ac9b6e121d008791d6",
"cd9bda1053a14890ad9091c63c0a0acf",
"4522666ebbcf4647b06ce81a6316fbbb",
"989b3df296504f34a62d31ca0d6d88bb",
"e8a7a34c6fb146f0b38a40f389a617fa",
"960553d142c446cd8852523887a5cc04",
"441820fb176048109e0f8f7e9519d735",
"0bb38c654e18429a8396466ebab84504",
"1acfc4a2809e41dd995817c3526650dd",
"6d5801774beb4b529b227ef2f098614e",
"dfdf23cde48c421caebb573060641d6a"
]
},
"id": "ed0OpduhX2JF",
"outputId": "ecc2048a-b46a-4b20-b94a-5912924feb3d"
},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1da5fff",
"metadata": {
"id": "e1da5fff"
},
"outputs": [],
"source": [
"# Select CUDA device index\n",
"import os\n",
"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
"model_name_or_path = \"openai/whisper-large-v2\"\n",
"language = \"Marathi\"\n",
"language_abbr = \"mr\"\n",
"task = \"transcribe\"\n",
"dataset_name = \"mozilla-foundation/common_voice_11_0\""
]
},
{
"cell_type": "markdown",
"id": "805b1c56",
"metadata": {},
"source": [
"## Load Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
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},
"id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
"outputId": "b1729004-591e-41c2-c206-9d0e572eb8e5"
},
"outputs": [],
"source": [
"from datasets import load_dataset, DatasetDict\n",
"\n",
"common_voice = DatasetDict()\n",
"\n",
"common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train+validation\")\n",
"common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test\")\n",
"\n",
"print(common_voice)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
"outputId": "dd81bced-f544-4d55-9669-5babb901f842"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['audio', 'sentence'],\n",
" num_rows: 3927\n",
" })\n",
" test: Dataset({\n",
" features: ['audio', 'sentence'],\n",
" num_rows: 1816\n",
" })\n",
"})\n"
]
}
],
"source": [
"common_voice = common_voice.remove_columns(\n",
" [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"]\n",
")\n",
"\n",
"print(common_voice)"
]
},
{
"cell_type": "markdown",
"id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
"metadata": {
"id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
},
"source": [
"## Prepare Feature Extractor, Tokenizer and Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"4d25d9919acf44a19f1b6f8fd625f808",
"b041efb27b6149fcaf206590f1c1b961",
"4668602d021a400a8b0ec88599abc85c",
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"c3ea17f7dd94462986d30b751664d77b",
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"e9e10f1e53b74509bfc9c0bf11502c5e"
]
},
"id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
"outputId": "7abb2062-e755-4f1a-e88b-9b7bf2986dbf"
},
"outputs": [],
"source": [
"from transformers import WhisperFeatureExtractor\n",
"\n",
"feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 209,
"referenced_widgets": [
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"41faf55c8878475f8a986b02ad73e8ce",
"b5c8221a09df4dfdb74017d4af544b95",
"65e4b8dae1e4435cb7737a8d5dd13d91",
"6d932eadfd6a448a9a71d741bb64f428",
"dce0d285c7d947dfba9ee5bc1a6ebece",
"bde010029c374a0eb2bb942f380f0e8b",
"a91f37ce798d411ea2a33cfdb1f01251"
]
},
"id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
"outputId": "4c094075-3f95-42db-9f4b-4db0c2fbeb91"
},
"outputs": [],
"source": [
"from transformers import WhisperTokenizer\n",
"\n",
"tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, language=language, task=task)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
"metadata": {
"id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
},
"outputs": [],
"source": [
"from transformers import WhisperProcessor\n",
"\n",
"processor = WhisperProcessor.from_pretrained(model_name_or_path, language=language, task=task)"
]
},
{
"cell_type": "markdown",
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
"metadata": {
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
},
"source": [
"### Prepare Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
"outputId": "1f1fe2d1-3ad2-42d4-e6f0-f0929785ae8e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-1.3727526e-15, -1.2400461e-13, -1.5159097e-13, ...,\n",
" 4.7928120e-06, 3.5631349e-06, 1.6352631e-06], dtype=float32), 'sampling_rate': 48000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
]
}
],
"source": [
"print(common_voice[\"train\"][0])"
]
},
{
"cell_type": "markdown",
"id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
"metadata": {
"id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
},
"source": [
"Since \n",
"our input audio is sampled at 48kHz, we need to _downsample_ it to \n",
"16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
"\n",
"We'll set the audio inputs to the correct sampling rate using dataset's \n",
"[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
"method. This operation does not change the audio in-place, \n",
"but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
"first time that they are loaded:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f12e2e57-156f-417b-8cfb-69221cc198e8",
"metadata": {
"id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
},
"outputs": [],
"source": [
"from datasets import Audio\n",
"\n",
"common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
]
},
{
"cell_type": "markdown",
"id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707",
"metadata": {
"id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707"
},
"source": [
"Re-loading the first audio sample in the Common Voice dataset will resample \n",
"it to the desired sampling rate:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87122d71-289a-466a-afcf-fa354b18946b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "87122d71-289a-466a-afcf-fa354b18946b",
"outputId": "727a709a-2b21-4c54-807f-efd40ea1719c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-4.4097186e-14, -9.4153831e-14, 3.4645775e-13, ...,\n",
" -7.6018655e-06, -1.8617659e-06, 4.4520480e-06], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
]
}
],
"source": [
"print(common_voice[\"train\"][0])"
]
},
{
"cell_type": "markdown",
"id": "91edc72d-08f8-4f01-899d-74e65ce441fc",
"metadata": {
"id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
},
"source": [
"Now we can write a function to prepare our data ready for the model:\n",
"1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
"2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
"3. We encode the transcriptions to label ids through the use of the tokenizer."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6525c478-8962-4394-a1c4-103c54cce170",
"metadata": {
"id": "6525c478-8962-4394-a1c4-103c54cce170"
},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" # load and resample audio data from 48 to 16kHz\n",
" audio = batch[\"audio\"]\n",
"\n",
" # compute log-Mel input features from input audio array\n",
" batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
"\n",
" # encode target text to label ids\n",
" batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
" return batch"
]
},
{
"cell_type": "markdown",
"id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
"metadata": {
"id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
},
"source": [
"We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 197,
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},
"id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
"outputId": "eecac4c8-c5f8-427b-a2ba-0d51fae825ac"
},
"outputs": [],
"source": [
"common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c4be572c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c4be572c",
"outputId": "0383124a-d4b1-4abe-a8a1-868ce6b3884e"
},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['input_features', 'labels'],\n",
" num_rows: 3927\n",
"})"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"common_voice[\"train\"]"
]
},
{
"cell_type": "markdown",
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
"metadata": {
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
},
"source": [
"## Training and Evaluation"
]
},
{
"cell_type": "markdown",
"id": "8d230e6d-624c-400a-bbf5-fa660881df25",
"metadata": {
"id": "8d230e6d-624c-400a-bbf5-fa660881df25"
},
"source": [
"### Define a Data Collator"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
"metadata": {
"id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass\n",
"from typing import Any, Dict, List, Union\n",
"\n",
"\n",
"@dataclass\n",
"class DataCollatorSpeechSeq2SeqWithPadding:\n",
" processor: Any\n",
"\n",
" def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lengths and need different padding methods\n",
" # first treat the audio inputs by simply returning torch tensors\n",
" input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
" batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
"\n",
" # get the tokenized label sequences\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
" # pad the labels to max length\n",
" labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
"\n",
" # if bos token is appended in previous tokenization step,\n",
" # cut bos token here as it's append later anyways\n",
" if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
" labels = labels[:, 1:]\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
},
{
"cell_type": "markdown",
"id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
"metadata": {
"id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
},
"source": [
"Let's initialise the data collator we've just defined:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
"metadata": {
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
},
"outputs": [],
"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
]
},
{
"cell_type": "markdown",
"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
"metadata": {
"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
},
"source": [
"### Evaluation Metrics"
]
},
{
"cell_type": "markdown",
"id": "66fee1a7-a44c-461e-b047-c3917221572e",
"metadata": {
"id": "66fee1a7-a44c-461e-b047-c3917221572e"
},
"source": [
"We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
"ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b22b4011-f31f-4b57-b684-c52332f92890",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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},
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"outputId": "b0a08086-69b9-4ab4-97ac-dbed295f2e15"
},
"outputs": [],
"source": [
"import evaluate\n",
"\n",
"metric = evaluate.load(\"wer\")"
]
},
{
"cell_type": "markdown",
"id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508",
"metadata": {
"id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
},
"source": [
"We then simply have to define a function that takes our model \n",
"predictions and returns the WER metric. This function, called\n",
"`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
"in the `label_ids` (undoing the step we applied in the \n",
"data collator to ignore padded tokens correctly in the loss).\n",
"It then decodes the predicted and label ids to strings. Finally,\n",
"it computes the WER between the predictions and reference labels:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52",
"metadata": {
"id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
},
"outputs": [],
"source": [
"def compute_metrics(pred):\n",
" pred_ids = pred.predictions\n",
" label_ids = pred.label_ids\n",
"\n",
" # replace -100 with the pad_token_id\n",
" label_ids[label_ids == -100] = tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
" label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
"\n",
" wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" return {\"wer\": wer}"
]
},
{
"cell_type": "markdown",
"id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
"metadata": {
"id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
},
"source": [
"### Load a Pre-Trained Checkpoint"
]
},
{
"cell_type": "markdown",
"id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
"metadata": {
"id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
},
"source": [
"Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
"is trivial through use of 🤗 Transformers!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 220,
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},
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
"outputId": "163d4b39-7e5d-4126-8d78-846c5d94dca6"
},
"outputs": [],
"source": [
"from transformers import WhisperForConditionalGeneration, BitsAndBytesConfig\n",
"\n",
"model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, quantization_config=BitsAndBytesConfig(load_in_8bit=True))\n",
"\n",
"# model.hf_device_map - this should be {\" \": 0}"
]
},
{
"cell_type": "markdown",
"id": "a15ead5f-2277-4a39-937b-585c2497b2df",
"metadata": {
"id": "a15ead5f-2277-4a39-937b-585c2497b2df"
},
"source": [
"Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)):"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
"metadata": {
"id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
},
"outputs": [],
"source": [
"model.config.forced_decoder_ids = None\n",
"model.config.suppress_tokens = []"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "bR-_yaEOPsfQ",
"metadata": {
"id": "bR-_yaEOPsfQ"
},
"source": [
"### Post-processing on the model\n",
"\n",
"Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast all non `int8` layers in `float32` for stability."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "Cl_ZQualPt9R",
"metadata": {
"id": "Cl_ZQualPt9R"
},
"outputs": [],
"source": [
"from peft import prepare_model_for_kbit_training\n",
"\n",
"model = prepare_model_for_kbit_training(model)"
]
},
{
"cell_type": "markdown",
"id": "Vjl4j4RJPmPR",
"metadata": {
"id": "Vjl4j4RJPmPR"
},
"source": [
"### Apply LoRA\n",
"\n",
"Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "DQtpDPRHPyOL",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DQtpDPRHPyOL",
"outputId": "1effcbde-7acc-4f62-f24b-e6236a43f833"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203\n"
]
}
],
"source": [
"from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n",
"\n",
"config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n",
"\n",
"model = get_peft_model(model, config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "markdown",
"id": "3906d436",
"metadata": {},
"source": [
"We are ONLY using **1%** of the total trainable parameters, thereby performing **Parameter-Efficient Fine-Tuning**"
]
},
{
"cell_type": "markdown",
"id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
"metadata": {
"id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
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"source": [
"### Define the Training Configuration"
]
},
{
"cell_type": "markdown",
"id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
"metadata": {
"id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
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"source": [
"In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
"metadata": {
"id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
},
"outputs": [],
"source": [
"from transformers import Seq2SeqTrainingArguments\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=\"temp\", # change to a repo name of your choice\n",
" per_device_train_batch_size=8,\n",
" gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n",
" learning_rate=1e-3,\n",
" warmup_steps=50,\n",
" num_train_epochs=3,\n",
" eval_strategy=\"epoch\",\n",
" fp16=True,\n",
" per_device_eval_batch_size=8,\n",
" generation_max_length=128,\n",
" logging_steps=25,\n",
" remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n",
" label_names=[\"labels\"], # same reason as above\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b3a944d8-3112-4552-82a0-be25988b3857",
"metadata": {
"id": "b3a944d8-3112-4552-82a0-be25988b3857"
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"source": [
"**Few Important Notes:**\n",
"1. `remove_unused_columns=False` and `label_names=[\"labels\"]` are required as the PeftModel's forward doesn't have the signature of the base model's forward.\n",
"\n",
"2. INT8 training required autocasting. `predict_with_generate` can't be passed to Trainer because it internally calls transformer's `generate` without autocasting leading to errors. \n",
"\n",
"3. Because of point 2, `compute_metrics` shouldn't be passed to `Seq2SeqTrainer` as seen below. (commented out)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d546d7fe-0543-479a-b708-2ebabec19493",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d546d7fe-0543-479a-b708-2ebabec19493",
"outputId": "e2fabe64-2c50-42ff-a7ca-7773813e9408"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check the examples in https://github.com/huggingface/peft for more details.\n",
"Using cuda_amp half precision backend\n"
]
}
],
"source": [
"from transformers import Seq2SeqTrainer, TrainerCallback, TrainingArguments, TrainerState, TrainerControl\n",
"from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR\n",
"\n",
"\n",
"class SavePeftModelCallback(TrainerCallback):\n",
" def on_save(\n",
" self,\n",
" args: TrainingArguments,\n",
" state: TrainerState,\n",
" control: TrainerControl,\n",
" **kwargs,\n",
" ):\n",
" checkpoint_folder = os.path.join(args.output_dir, f\"{PREFIX_CHECKPOINT_DIR}-{state.global_step}\")\n",
"\n",
" peft_model_path = os.path.join(checkpoint_folder, \"adapter_model\")\n",
" kwargs[\"model\"].save_pretrained(peft_model_path)\n",
"\n",
" pytorch_model_path = os.path.join(checkpoint_folder, \"pytorch_model.bin\")\n",
" if os.path.exists(pytorch_model_path):\n",
" os.remove(pytorch_model_path)\n",
" return control\n",
"\n",
"\n",
"trainer = Seq2SeqTrainer(\n",
" args=training_args,\n",
" model=model,\n",
" train_dataset=common_voice[\"train\"],\n",
" eval_dataset=common_voice[\"test\"],\n",
" data_collator=data_collator,\n",
" # compute_metrics=compute_metrics,\n",
" processing_class=processor.feature_extractor,\n",
" callbacks=[SavePeftModelCallback],\n",
")\n",
"model.config.use_cache = False # silence the warnings. Please re-enable for inference!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
"outputId": "cdea5268-f33a-4d48-ea4a-a9c71576f81d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"***** Running training *****\n",
" Num examples = 3927\n",
" Num Epochs = 3\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 1473\n",
" Number of trainable parameters = 15728640\n",
"/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
"/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
" warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
]
},
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"\n",
"
\n",
" "
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""
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"name": "stderr",
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"text": [
"***** Running Evaluation *****\n",
" Num examples = 1816\n",
" Batch size = 8\n",
"Saving model checkpoint to ./whisper-small-hi/checkpoint-500\n",
"Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
"Feature extractor saved in ./whisper-small-hi/checkpoint-500/preprocessor_config.json\n",
"/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
"/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
" warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
"***** Running Evaluation *****\n",
" Num examples = 1816\n",
" Batch size = 8\n",
"Saving model checkpoint to ./whisper-small-hi/checkpoint-1000\n",
"Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
"Feature extractor saved in ./whisper-small-hi/checkpoint-1000/preprocessor_config.json\n",
"/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
"/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
" warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
"***** Running Evaluation *****\n",
" Num examples = 1816\n",
" Batch size = 8\n"
]
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"
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"text": [
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=1473, training_loss=0.20080567288382556, metrics={'train_runtime': 13136.6638, 'train_samples_per_second': 0.897, 'train_steps_per_second': 0.112, 'total_flos': 2.52799085113344e+19, 'train_loss': 0.20080567288382556, 'epoch': 3.0})"
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================================================
FILE: examples/int8_training/requirements.txt
================================================
accelerate
git+https://github.com/bitsandbytes-foundation/bitsandbytes.git
datasets==3.6.0
evaluate
jiwer
librosa
soundfile
transformers==4.52.4
wandb
================================================
FILE: examples/int8_training/run_adalora_whisper_int8.sh
================================================
accelerate launch --config_file config.yaml peft_adalora_whisper_large_training.py \
--model_name_or_path "openai/whisper-large-v2" \
--language "Marathi" \
--language_abbr "mr" \
--task "transcribe" \
--dataset_name "mozilla-foundation/common_voice_11_0" \
--push_to_hub \
--preprocessing_num_workers 2 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--dataloader_pin_memory \
--dataloader_num_workers 2 \
--learning_rate 1e-3 \
--weight_decay 1e-4 \
--num_train_epochs 3 \
--gradient_accumulation_steps 1 \
--lr_scheduler_type "linear" \
--num_warmup_steps 50 \
--output_dir "adalora_whisper_large_marathi_multi_adapter" \
--seed 42 \
--load_best_model \
--with_tracking \
--report_to "wandb" \
--hub_token $HUB_TOKEN \
--checkpointing_steps 2000 \
--evaluation_steps 2000 \
--logging_steps 25 \
--use_peft \
--use_adalora \
--init_r 12 \
--target_r 8 \
--tinit 100 \
--tfinal 800 \
--delta_t 10 \
--lora_alpha 32 \
--lora_dropout 0.1 \
--orth_reg_weight 0.5
================================================
FILE: examples/lily_finetuning/README.md
================================================
# Lily: Low-Rank Interconnected Adaptation Across Layers
## Introduction
[**Lily**](https://arxiv.org/abs/2407.09946) is a PEFT method that introduces **cross-layer parameter sharing** to improve parameter efficiency. Unlike LoRA, which assigns independent adapter pairs to each layer, Lily shares adapter components across layers in two ways:
- **A sharing**: consecutive blocks of `stride_A` layers share the same A adapter, reducing the number of distinct input projections.
- **B sharing**: a small pool of `num_B` B adapters is shared globally across all layers. For each forward pass, a lightweight **router** computes a softmax-weighted combination of all B adapters to produce a layer-specific output projection.
This design allows Lily to cover more layers with fewer parameters, making it possible to use larger rank for each adapter without increasing parameter count and enabling information sharing across layers.
## Quick start
With respect to your standard PEFT training procedure with LoRA, simply swap your `LoraConfig` for a `LilyConfig`.
```python
import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTTrainer, SFTConfig
from peft import LilyConfig
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
lily_config = LilyConfig()
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
processing_class=tokenizer,
peft_config=lily_config,
args=SFTConfig(
max_length=2048,
dataset_text_field="text",
per_device_train_batch_size=2,
),
)
trainer.train()
trainer.model.save_pretrained("lily-llama-3.2-3b")
```
Run the finetuning script simply by running:
```sh
python examples/lily_finetuning/lily_finetuning.py --base_model meta-llama/Llama-3.2-3B --data_path timdettmers/openassistant-guanaco
```
## Use the model on 🤗
You can load and use the model as any other 🤗 models.
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.2-3B", dtype=torch.bfloat16, device_map="auto"
)
peft_model = PeftModel.from_pretrained(model, "lily-llama-3.2-3b")
```
## Additional Notes
- `r` controls the rank (inner hidden dimension). Since Lily typically uses fewer adapter instances than LoRA, it is recommended to use a **larger `r`** — typically `2x`–`4x` the rank you would use in LoRA.
- `stride_A` controls how many consecutive layers share one A adapter. Larger `stride_A` means fewer distinct A adapters and fewer trainable parameters. Suggested values: `2`, `3`, or `4`. Make sure that `total_layers` is divisible by `stride_A` to ensure even sharing.
- `num_B` controls the size of the shared B adapter pool. It is recommended to set `num_B` to roughly `total_layers / stride_A`. Note that `num_B >= 2` is required.
- `scaling` is a direct scalar multiplier on the adapter output (analogous to `alpha / r` in LoRA). It is recommended to start with `2.0` and treat it as a hyperparameter.
- The general rule of thumb: **prefer larger `r` with larger `stride_A` and smaller `num_B`** over smaller `r` with smaller `stride_A` and larger `num_B`.
## Citation
```
@inproceedings{zhong-etal-2025-low,
title = "Low-Rank Interconnected Adaptation across Layers",
author = "Zhong, Yibo and
Zhao, Jinman and
Zhou, Yao",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.874/",
doi = "10.18653/v1/2025.findings-acl.874",
pages = "17005--17029",
ISBN = "979-8-89176-256-5",
abstract = "Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method that learns weight updates $\Delta W = AB$ for pretrained weights $W$ through low-rank adapters $A$ and $B$. While LoRA ensures hardware efficiency, its low-rank weight updates limit adaptation performance. In this paper, we propose low-rank interconnected adaptation across layers (Lily), a novel PEFT method that introduces an interconnected framework with locally shared $A$ and globally shared $B$ experts. This structure eliminates redundant per-layer $AB$ pairs, enabling higher-rank $\Delta W$ with equal or fewer parameters. To enhance expressiveness, we use data-dependent routers to determine $A$-$B$ interconnections, preventing $B$ experts from converging to the same behavior and improving representational power across domains. Experiments across modalities, architectures, and model sizes demonstrate Lily{'}s superior performance and efficiency."
}
```
================================================
FILE: examples/lily_finetuning/lily_finetuning.py
================================================
# This script is based on examples/gralora_finetuning/gralora_finetuning.py
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import LilyConfig, get_peft_model
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
eval_step: int,
save_step: int,
device: str,
lily_r: int,
lily_scaling: float,
lily_stride_A: int,
lily_num_B: int,
lily_target_modules: str,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
# Lily config for the PEFT model
lily_config = LilyConfig(
r=lily_r,
scaling=lily_scaling,
stride_A=lily_stride_A,
num_B=lily_num_B,
target_modules=(
lily_target_modules.split(",")
if lily_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
),
)
# get the peft model with Lily config
model = get_peft_model(model, lily_config)
model.print_trainable_parameters()
model.to(device)
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
# Clear device cache to free memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
trainer.push_to_hub(commit_message="Fine-tuned model with Lily")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with Lily and PEFT")
parser.add_argument("--base_model", type=str, default="meta-llama/Llama-3.2-3B", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--lily_r", type=int, default=32, help="Lily rank")
parser.add_argument(
"--lily_scaling", type=float, default=2.0, help="Lily scaling factor applied to adapter output"
)
parser.add_argument(
"--lily_stride_A", type=int, default=4, help="Number of consecutive layers sharing one A adapter"
)
parser.add_argument("--lily_num_B", type=int, default=7, help="Number of shared B adapters (must be >= 2)")
parser.add_argument(
"--lily_target_modules", type=str, default=None, help="Comma-separated list of target modules for Lily"
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
lily_r=args.lily_r,
lily_scaling=args.lily_scaling,
lily_stride_A=args.lily_stride_A,
lily_num_B=args.lily_num_B,
lily_target_modules=args.lily_target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
================================================
FILE: examples/loftq_finetuning/LoftQ_weight_replacement.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "546b6c6d-f949-4387-9c41-6989223911f8",
"metadata": {},
"source": [
"# Initializing weights with LoftQ by replacing LoRA weights in-place"
]
},
{
"cell_type": "markdown",
"id": "d041ecb4-6957-467e-8f3e-d4a12c674e9f",
"metadata": {},
"source": [
"This notebook shows how to apply [LoftQ](https://huggingface.co/papers/2310.08659) initialization on our QLoRA model.\n",
"\n",
"In short, the idea behind LoftQ is the following. When we use QLoRA, i.e. we quantize the base model with bitsandbytes to save memory, and then train LoRA weights on top of this base model, we expect a certain performance gap. This is partly due to the fact that quantization is onyl an approximation of the \"real\" weights and thus introduces a quantization error. By default, LoRA weights are initialized such that they are a no-op at the start of the training. However, we can instead initialize them so that they minimize the quantization error. This is the idea behind LoftQ.\n",
"\n",
"Note that this only influences the initialization of the model. Everything that follows stays the same as always."
]
},
{
"cell_type": "markdown",
"id": "90d5420f-de32-42fa-8792-247f60e3647d",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a2c69b7c-c922-405f-aae1-ccc4f6911155",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "22be0432-8798-44a2-9014-d929525e3059",
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f087ce0f-71b4-45ec-b2f9-197677bbc1ee",
"metadata": {},
"outputs": [],
"source": [
"from peft import get_peft_model, LoraConfig, replace_lora_weights_loftq"
]
},
{
"cell_type": "markdown",
"id": "63fdf18e-4ac4-409e-8475-88147cf85067",
"metadata": {},
"source": [
"## Functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "af14bd0a-597e-446c-800b-619fc0599ee0",
"metadata": {},
"outputs": [],
"source": [
"def get_mae(x, y):\n",
" return (x - y).abs().mean()\n",
"\n",
"\n",
"def get_mse(x, y):\n",
" return torch.pow(x - y, 2).mean()\n",
"\n",
"\n",
"def error_report(x, y):\n",
" mae = get_mae(x, y)\n",
" mse = get_mse(x, y)\n",
" print(\n",
" f\"Mean absolute error: {mae:>8.5f}\\n\"\n",
" f\"Mean squared error: {mse:>8.5f}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "1bc01a5f-7ee8-400f-8e80-3f2b7df29882",
"metadata": {},
"source": [
"## Base model"
]
},
{
"cell_type": "markdown",
"id": "fdc447d9-2f4f-4d0f-afdb-1cf5c4237321",
"metadata": {},
"source": [
"First, let's load a base model and calculate some logits. These logits are the baseline, i.e. we try to match their values as best as possible. We only need these logits for demonstration purposes. In practice, it is not necessary to load the non-quantized weights to apply LoftQ initialization.\n",
"\n",
"**Note**: We have to choose a model with a `model.safetensors` file. As PyTorch checkpoints (pickle) cannot be loaded lazily, we have to use [safetensors](https://huggingface.co/docs/safetensors/index). If those don't exist for your model, save the pretrained model as a safetensors file using `safe_pretrained` and pass the model path to `replace_lora_weights_loftq`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0cb29074-d180-4fdc-8a47-27d2b9857264",
"metadata": {},
"outputs": [],
"source": [
"model_id = \"bigscience/bloomz-560m\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e7ddd6a2-04dd-42ec-9f48-100a3946ae04",
"metadata": {},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_id)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1f5b27db-51cc-41da-a21d-049ff747a149",
"metadata": {},
"outputs": [],
"source": [
"model = AutoModelForCausalLM.from_pretrained(model_id)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "51548b6a-945c-4797-b02a-0e3fc77d1242",
"metadata": {},
"outputs": [],
"source": [
"s = \"\"\"Beautiful is better than ugly.\n",
"Explicit is better than implicit.\n",
"Simple is better than complex.\n",
"Complex is better than complicated.\n",
"Flat is better than nested.\n",
"Sparse is better than dense.\n",
"Readability counts.\n",
"Special cases aren't special enough to break the rules.\n",
"Although practicality beats purity.\n",
"Errors should never pass silently.\n",
"Unless explicitly silenced.\n",
"In the face of ambiguity, refuse the temptation to guess.\n",
"There should be one-- and preferably only one --obvious way to do it.\n",
"Although that way may not be obvious at first unless you're Dutch.\n",
"Now is better than never.\n",
"Although never is often better than *right* now.\n",
"If the implementation is hard to explain, it's a bad idea.\n",
"If the implementation is easy to explain, it may be a good idea.\n",
"Namespaces are one honking great idea -- let's do more of those!\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ce72d923-5283-48ba-96ef-7f859309ad84",
"metadata": {},
"outputs": [],
"source": [
"inputs = tokenizer(s.splitlines(), return_tensors=\"pt\", padding=True)"
]
},
{
"cell_type": "markdown",
"id": "3bfe54cb-76ef-4981-ba25-3e544d264c62",
"metadata": {},
"source": [
"Our baseline logits:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "04bebcaa-3a05-4621-9a03-e25de72fa27c",
"metadata": {},
"outputs": [],
"source": [
"logits_base = model(**inputs).logits"
]
},
{
"cell_type": "markdown",
"id": "fa9c9001-8ade-422d-92f8-bcafa50917c7",
"metadata": {},
"source": [
"## Normal LoRA model"
]
},
{
"cell_type": "markdown",
"id": "8024390b-736a-4b21-848b-aa4f30951d51",
"metadata": {},
"source": [
"Now we load the model quantized with bitsandbytes. For now, only 4bit is supported."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "01d1912a-646e-42d2-8292-6702b77d1948",
"metadata": {},
"outputs": [],
"source": [
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.float16,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b1218717-4db4-48ce-978d-c05dc190fa91",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
]
}
],
"source": [
"model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)"
]
},
{
"cell_type": "markdown",
"id": "a0b4e4c5-3932-4d9a-9457-41a05f24d556",
"metadata": {},
"source": [
"Next we create a LoRA model using PEFT and compute the logits of that model."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4741bce0-cd2b-4f05-a50c-4f9e56b43e72",
"metadata": {},
"outputs": [],
"source": [
"lora_config = LoraConfig(task_type=\"CAUSAL_LM\", target_modules=\"all-linear\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cf55cc48-b55d-4806-b6ab-e9b8035ed526",
"metadata": {},
"outputs": [],
"source": [
"peft_model = get_peft_model(model, lora_config)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f2f11e25-4a1e-485b-be4c-65aec62ac207",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
".../bitsandbytes/nn/modules.py:391: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
" warnings.warn('Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.')\n"
]
}
],
"source": [
"logits_lora = peft_model(**inputs).logits"
]
},
{
"cell_type": "markdown",
"id": "5bc0cde7-0b9f-4305-ac0e-e3a6d2cfa401",
"metadata": {},
"source": [
"Let's check the influence of the quantization error on our logits:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6f404c0d-f428-4923-9122-7b830410f089",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 3.61113\n",
"Mean squared error: 36.53259\n"
]
}
],
"source": [
"error_report(logits_base, logits_lora)"
]
},
{
"cell_type": "markdown",
"id": "58c437e1-4fae-4a2f-9c42-ada6bedb9a4d",
"metadata": {},
"source": [
"## LoftQ"
]
},
{
"cell_type": "markdown",
"id": "1af05376-c8b0-48ec-8d80-7d7f4d32bbd7",
"metadata": {},
"source": [
"Next, let's use LoftQ initialization and see if it helps reduce the error."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "890e6108-3f02-469c-9e7d-f2144448227c",
"metadata": {},
"outputs": [],
"source": [
"replace_lora_weights_loftq(peft_model)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b452db0e-a510-42d3-bef5-f567186e26c2",
"metadata": {},
"outputs": [],
"source": [
"logits_loftq = peft_model(**inputs).logits"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "456dc564-f268-4cf3-9d59-a6942d3733ad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 3.24111\n",
"Mean squared error: 31.13725\n"
]
}
],
"source": [
"error_report(logits_base, logits_loftq)"
]
},
{
"cell_type": "markdown",
"id": "1ddf9e0f-3f78-426c-be59-77c6481674ec",
"metadata": {},
"source": [
"We can see that LoftQ initialization helped a little bit, but the difference is not huge."
]
},
{
"cell_type": "markdown",
"id": "0dd344f2-249c-4fe9-8357-7fe3bcd1e82f",
"metadata": {},
"source": [
"## LoftQ with callback"
]
},
{
"cell_type": "markdown",
"id": "e2fd7dd5-88b3-40b8-95c2-3f3895d8093d",
"metadata": {},
"source": [
"To help with this, let's write a small callback function and pass it to `replace_lora_weights_loftq`. What this function does is that each time one weight is being replaced with LoftQ-initialized weights, we perform a test if the quantization error is actually reduced. If it it is not, we roll back the replacement. This way, we keep only those replacements that improve the results."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "1f882802-22b7-4969-919e-120b1f2893d2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
]
}
],
"source": [
"# Since PEFT has modified the base model, we should reload it\n",
"model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c6438363-b66e-4507-8667-5a6df379a03f",
"metadata": {},
"outputs": [],
"source": [
"peft_model = get_peft_model(model, lora_config)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7b93d082-0fcb-4b20-982e-c1aaf0c71d13",
"metadata": {},
"outputs": [],
"source": [
"current_mse = float(\"inf\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e22eb18d-b06e-47fe-91ba-ff34cbf62f60",
"metadata": {},
"outputs": [],
"source": [
"def my_callback(model, module_name):\n",
" \"\"\"Callable to replace weights with LoFTQ if the mse is lower than the current best one.\"\"\"\n",
" global current_mse\n",
"\n",
" logits = model(**inputs).logits\n",
" mse = get_mse(logits_base, logits)\n",
" if mse < current_mse:\n",
" current_mse = mse\n",
" print(f\"MSE improved for module {module_name}\")\n",
" return True\n",
" print(f\"MSE did not improve for module {module_name}\")\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "44ee90d1-e15a-4740-a39d-ebf9e7adb79c",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE improved for module transformer.h.0.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.0.self_attention.dense\n",
"MSE improved for module transformer.h.0.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.0.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.1.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.1.self_attention.dense\n",
"MSE did not improve for module transformer.h.1.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.1.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.2.self_attention.query_key_value\n",
"MSE improved for module transformer.h.2.self_attention.dense\n",
"MSE improved for module transformer.h.2.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.2.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.3.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.3.self_attention.dense\n",
"MSE improved for module transformer.h.3.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.3.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.4.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.4.self_attention.dense\n",
"MSE improved for module transformer.h.4.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.4.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.5.self_attention.query_key_value\n",
"MSE improved for module transformer.h.5.self_attention.dense\n",
"MSE improved for module transformer.h.5.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.5.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.6.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.6.self_attention.dense\n",
"MSE improved for module transformer.h.6.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.6.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.7.self_attention.query_key_value\n",
"MSE improved for module transformer.h.7.self_attention.dense\n",
"MSE did not improve for module transformer.h.7.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.7.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.8.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.8.self_attention.dense\n",
"MSE improved for module transformer.h.8.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.8.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.9.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.9.self_attention.dense\n",
"MSE did not improve for module transformer.h.9.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.9.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.10.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.10.self_attention.dense\n",
"MSE did not improve for module transformer.h.10.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.10.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.11.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.11.self_attention.dense\n",
"MSE did not improve for module transformer.h.11.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.11.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.12.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.12.self_attention.dense\n",
"MSE improved for module transformer.h.12.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.12.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.13.self_attention.query_key_value\n",
"MSE improved for module transformer.h.13.self_attention.dense\n",
"MSE did not improve for module transformer.h.13.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.13.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.14.self_attention.query_key_value\n",
"MSE improved for module transformer.h.14.self_attention.dense\n",
"MSE did not improve for module transformer.h.14.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.14.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.15.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.15.self_attention.dense\n",
"MSE did not improve for module transformer.h.15.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.15.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.16.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.16.self_attention.dense\n",
"MSE improved for module transformer.h.16.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.16.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.17.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.17.self_attention.dense\n",
"MSE improved for module transformer.h.17.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.17.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.18.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.18.self_attention.dense\n",
"MSE did not improve for module transformer.h.18.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.18.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.19.self_attention.query_key_value\n",
"MSE improved for module transformer.h.19.self_attention.dense\n",
"MSE improved for module transformer.h.19.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.19.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.20.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.20.self_attention.dense\n",
"MSE did not improve for module transformer.h.20.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.20.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.21.self_attention.query_key_value\n",
"MSE improved for module transformer.h.21.self_attention.dense\n",
"MSE did not improve for module transformer.h.21.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.21.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.22.self_attention.query_key_value\n",
"MSE improved for module transformer.h.22.self_attention.dense\n",
"MSE improved for module transformer.h.22.mlp.dense_h_to_4h\n",
"MSE improved for module transformer.h.22.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.23.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.23.self_attention.dense\n",
"MSE improved for module transformer.h.23.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.23.mlp.dense_4h_to_h\n"
]
}
],
"source": [
"replace_lora_weights_loftq(peft_model, callback=my_callback)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "e31adc81-a090-49b2-90f6-9906743c76ae",
"metadata": {},
"outputs": [],
"source": [
"logits_loftq_callback = peft_model(**inputs).logits"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "7c640092-1f26-48be-bea4-487511205440",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 1.79576\n",
"Mean squared error: 8.47075\n"
]
}
],
"source": [
"error_report(logits_base, logits_loftq_callback)"
]
},
{
"cell_type": "markdown",
"id": "1896857e-3d87-44a9-887f-90c765bc8d91",
"metadata": {},
"source": [
"We can see that applying LoftQ with the help of the callback reduced the error quite significantly."
]
},
{
"cell_type": "markdown",
"id": "8eaf86cf-4fb4-455d-ab07-892591564303",
"metadata": {},
"source": [
"## Applying LoftQ multiple times"
]
},
{
"cell_type": "markdown",
"id": "70836a75-5c6d-4b7b-9175-f395aef8383b",
"metadata": {},
"source": [
"It is possible to run `replace_lora_weights_loftq` multiple times on the same model when using the callback."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "8e5ee38c-007c-4c75-9248-005d94b19445",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE did not improve for module transformer.h.0.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.0.self_attention.dense\n",
"MSE did not improve for module transformer.h.0.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.0.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.1.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.1.self_attention.dense\n",
"MSE did not improve for module transformer.h.1.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.1.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.2.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.2.self_attention.dense\n",
"MSE did not improve for module transformer.h.2.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.2.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.3.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.3.self_attention.dense\n",
"MSE did not improve for module transformer.h.3.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.3.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.4.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.4.self_attention.dense\n",
"MSE did not improve for module transformer.h.4.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.4.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.5.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.5.self_attention.dense\n",
"MSE did not improve for module transformer.h.5.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.5.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.6.self_attention.query_key_value\n",
"MSE improved for module transformer.h.6.self_attention.dense\n",
"MSE did not improve for module transformer.h.6.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.6.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.7.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.7.self_attention.dense\n",
"MSE did not improve for module transformer.h.7.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.7.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.8.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.8.self_attention.dense\n",
"MSE did not improve for module transformer.h.8.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.8.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.9.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.9.self_attention.dense\n",
"MSE did not improve for module transformer.h.9.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.9.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.10.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.10.self_attention.dense\n",
"MSE improved for module transformer.h.10.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.10.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.11.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.11.self_attention.dense\n",
"MSE did not improve for module transformer.h.11.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.11.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.12.self_attention.query_key_value\n",
"MSE improved for module transformer.h.12.self_attention.dense\n",
"MSE did not improve for module transformer.h.12.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.12.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.13.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.13.self_attention.dense\n",
"MSE did not improve for module transformer.h.13.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.13.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.14.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.14.self_attention.dense\n",
"MSE did not improve for module transformer.h.14.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.14.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.15.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.15.self_attention.dense\n",
"MSE did not improve for module transformer.h.15.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.15.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.16.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.16.self_attention.dense\n",
"MSE did not improve for module transformer.h.16.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.16.mlp.dense_4h_to_h\n",
"MSE improved for module transformer.h.17.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.17.self_attention.dense\n",
"MSE did not improve for module transformer.h.17.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.17.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.18.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.18.self_attention.dense\n",
"MSE did not improve for module transformer.h.18.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.18.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.19.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.19.self_attention.dense\n",
"MSE did not improve for module transformer.h.19.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.19.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.20.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.20.self_attention.dense\n",
"MSE did not improve for module transformer.h.20.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.20.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.21.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.21.self_attention.dense\n",
"MSE did not improve for module transformer.h.21.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.21.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.22.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.22.self_attention.dense\n",
"MSE did not improve for module transformer.h.22.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.22.mlp.dense_4h_to_h\n",
"MSE did not improve for module transformer.h.23.self_attention.query_key_value\n",
"MSE did not improve for module transformer.h.23.self_attention.dense\n",
"MSE did not improve for module transformer.h.23.mlp.dense_h_to_4h\n",
"MSE did not improve for module transformer.h.23.mlp.dense_4h_to_h\n"
]
}
],
"source": [
"replace_lora_weights_loftq(peft_model, callback=my_callback)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "2abe2702-9510-4814-b5f2-63140a102c17",
"metadata": {},
"outputs": [],
"source": [
"logits_loftq_callback_twice = peft_model(**inputs).logits"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e908de14-01f9-4fdc-91b5-61118a3ce6cb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 1.76357\n",
"Mean squared error: 8.33938\n"
]
}
],
"source": [
"error_report(logits_base, logits_loftq_callback_twice)"
]
},
{
"cell_type": "markdown",
"id": "5b8b09fe-d369-4444-b6e2-cd514e775637",
"metadata": {},
"source": [
"There are further gains, but they are not very big."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/loftq_finetuning/README.md
================================================
# LoftQ: LoRA-fine-tuning-aware Quantization
## Introduction
LoftQ finds quantized LoRA initialization: quantized backbone Q and LoRA adapters A and B, given a pre-trained weight W.
## Quick Start
Steps:
1. Apply LoftQ to a full-precision pre-trained weight and save.
2. Load LoftQ initialization and train.
For step 1, we have provided off-the-shelf LoftQ initializations (see [supported model list](#appendix-off-the-shelf-model-list))
in [Huggingface Hub LoftQ](https://huggingface.co/LoftQ).
If you want to do it yourself, jump to [LoftQ DIY](#loftq-diy).
For step 2, below is an example of loading 4bit Mistral-7B with 64rank LoRA adapters from Huggingface Hub.
```python
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
MODEL_ID = "LoftQ/Mistral-7B-v0.1-4bit-64rank"
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
dtype=torch.bfloat16, # you may change it with different models
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type='nf4',
),
)
peft_model = PeftModel.from_pretrained(
base_model,
MODEL_ID,
subfolder="loftq_init",
is_trainable=True,
)
# Do training with peft_model ...
```
## LoftQ DIY
### Apply LoftQ and save
We provide [quantize_save_load.py](quantize_save_load.py) as an example to apply LoftQ with
different bits(`--bits`), ranks(`--rank`), and alternating steps (`--iter`, a hyper-parameter in LoftQ, see Algorithm 1 in [LoftQ paper](https://huggingface.co/papers/2310.08659)). Currently, this example supports
`llama-2`, `falcon`, `mistral`, `bart`, `t5`, `deberta`, `bert`, `roberta`.
Below is an example of obtaining 4bit LLAMA-2-7b with 16-rank LoRA adapters by 5 alternating steps.
```sh
SAVE_DIR="model_zoo/loftq/"
python quantize_save_load.py \
--model_name_or_path meta-llama/Llama-2-7b-hf \ # high-precision model id in HF
--token HF_TOKEN \ # your HF token if the model is private, e.g., llama-2
--bits 4 \
--iter 5 \
--rank 16 \
--save_dir $SAVE_DIR
```
The above commands end up with creating the model directory under `$SAVE_DIR`.
Specifically, the model directory is named as
`MODEL_DIR = SAVE_DIR + f"{args.model_name_or_path.split('/')[-1]}-{args.bits}bits-{args.rank}rank"`
In this example, `MODEL_DIR="model_zoo/loftq/Llama-2-7b-hf-4bit-16rank"`, where the backbone is stored in `$MODEL_DIR`
and the LoRA adapters are at the sub-folder `$MODEL_DIR/loftq_init`.
### Load and train
Similar to loading from Huggingface Hub, we only need to change the `MODEL_ID` to the `MODEL_DIR`.
```python
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
MODEL_DIR = "model_zoo/loftq/Llama-2-7b-hf-4bit-16rank"
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_DIR,
dtype=torch.bfloat16,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type='nf4',
),
)
peft_model = PeftModel.from_pretrained(
base_model,
MODEL_DIR,
subfolder="loftq_init",
is_trainable=True,
)
# Do training with peft_model ...
```
## LoftQ Fine-tuning
We also provide an example to fine-tune LoftQ on GSM8K.
We load the quantized backbone and LoRA adapters from the [LoftQ Huggingface hub](https://huggingface.co/LoftQ).
```sh
python train_gsm8k_llama.py \
--model_name_or_path LoftQ/Llama-2-13b-hf-4bit-64rank \
--output_dir exp_results/gsm8k/llama-2-13b/bit4-rank64/lr1e-4 \
--learning_rate 1e-4 \
--weight_decay 0.1 \
--lr_scheduler_type cosine \
--num_warmup_steps 100 \
--seed 202 \
--dataset_name gsm8k \
--dataset_config main \
--pad_to_max_length \
--max_source_length 128 \
--max_target_length 256 \
--num_train_epochs 5 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 4 \
--with_tracking \
--report_to tensorboard
```
## Appendix: Off-the-shelf Model List
| Model Name | Bits | Ranks |
| ----------- | ---- | ----- |
| LLAMA-2-7b | 4 | 64 |
| LLAMA-2-13b | 4 | 64 |
| LLAMA-2-70b | 4 | 64 |
| Mistral | 4 | 64 |
| Mistral | 4 | 32 |
| BART-large | 4 | 8 |
| BART-large | 4 | 16 |
| BART-large | 4 | 32 |
| BART-large | 2 | 8 |
## In-place application of LoftQ initialization
PEFT provides a convenience function `replace_lora_weights_loftq` to apply LoftQ initialization in-place to the quantized model. Check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb) for an example.
================================================
FILE: examples/loftq_finetuning/int8_correction.py
================================================
#!/usr/bin/env python
"""
Script to show-case how to offset quantization error with LoRA / LoftQ
when dealing with quantizations that both quantize weights and activations.
This is the case for bnb int8, for example, but also other quantizations
such as BitNet do this.
The math for how this works is explained in MyLinear8bitLt.forward and
can be seen quickly when defining W_q = W + E_W (quantized weight is the
sum of the original weights plus an error term) and the same for
x_q = x + e_x.
To demonstrate the effectiveness, we load a unquantized model, generate
logits for reference inputs and then do the same for a quantized model
and a quantized model with LoftQ and our mitigations applied. The
error between reference model logits and LoftQ logits is significantly
smaller compared to the logits produced by the quantized model without
mitigation.
Note: set llm_int8_threshold=0 in your BitsAndBytesConfig. The thresholding
enables dynamic fp16 quantization (for x values above that threshold).
The quantization error for the affected values is much lower and not
static anymore, LoftQ is not able to deal with this. While technically
possible, this script doesn't filter out these masked values and it
is probably not worth the effort.
Note: Some rudimentary testing showed that the LoftQ mitigation is still
more effective than tuning threshold values but YMMV.
Note: LoftQ is not doing the heavy lifting in this script's case. The error
between LoftQ and zeroed LoRA is only about two percent points (check this
yourself by using the `--no-loftq` flag). This effect is probably dependent
on the quantization strength.
Examples of experiments you can do:
- check the difference between applying no-op LoRA and LoftQ initialized
LoRA by running the following two commands:
* ./int8_correction.py --no-mitigation
* ./int8_correction.py --no-mitigation --no-loftq
- check the effect of the mitigation vs. the static compenstation by LoftQ:
* ./int8_correction.py
* ./int8_correction.py --no-loftq
- check the rank contribution for LoftQ:
* for r in 8 16 32 64 128; do ./int8_correction.py --rank $i --no-mitigation; done
"""
import argparse
from pathlib import Path
from tempfile import TemporaryDirectory
import bitsandbytes as bnb
import torch
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig
import peft
import peft.tuners.lora.bnb
from peft import LoftQConfig, LoraConfig, PeftModel, TaskType, get_peft_model
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
class MyLinear8bitLt(peft.tuners.lora.bnb.Linear8bitLt):
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
self._check_forward_args(x, *args, **kwargs)
adapter_names = kwargs.pop("adapter_names", None)
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif adapter_names is not None:
result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
x = self._cast_input_dtype(x, lora_A.weight.dtype)
# The premise of this is that when we quantize, we introduce an
# error. This means that quantizing x to xq we can state that
# x = x_q + e_x or x_q = x - e_x. The same goes for W: W = W_q + E_W
# or W_q = W - E_W.
#
# LoftQ computes E_W, applies SVD and initializes LoRA's B and A with
# self.r ranks of E_W, giving us ~E_W. For our forward this means:
# y = W_q x + BAx = (W_q + BA) x = (W_q + ~E_W) x = W x
# if ~E_W is close enough to E_W.
#
# This breaks down if x is also quantized (as is the case for bnb int8):
# y = W_q x_q + BA x_q = (W_q + ~E_W) x_q = W x_q = W (x - e_x) = Wx - W e_x
#
# Since e_x is non-zero and W is relatively large, it is a non-negligible
# error term. But we can offset this, since we can compute e_x and we can
# approximate W with W_q - or - in the case of LoftQ with ~E_W. Both work.
# I didn't see a difference empirically but with other quantizations this
# might change. In any case, we can compute ex_mitigation = W_q e_x and
# add it along with the LoRA to remove the W e_x term and be left with
# y = Wx.
#
# This is the term yielding the most error correction gain. There's a
# smaller gain to be had by passing x_q to the LoRA's layers. Let's
# revisit the quantized base layer definition:
# y = W_q x_q = (W - E_W) (x - e_x) = Wx - W e_x - E_W x + E_W e_x
#
# (W e_x) we handled before, (- E_W x) is what we approximate with (~E_W x)
# and is subsequently removed as well but this leaves us (E_W e_x).
# It turns out, if you pass x_q into the LoRA modules, you will end up
# with (~E_W x - ~E_W e_x) - which removes this term as well.
# Compute x_q (int8 quantized x) to pass it into the LoRA's forward.
CB, SCB, _ = bnb.functional.int8_vectorwise_quant(x.half())
CB = CB.reshape(-1, CB.shape[-1])
x_q = bnb.functional.int8_vectorwise_dequant(CB, SCB).to(lora_A.weight.dtype)
x_q = x_q.reshape(*x.shape)
e_x = x - x_q
W_dq = bnb.functional.int8_vectorwise_dequant(self.base_layer.state.CB, self.base_layer.state.SCB).to(
e_x.dtype
)
e_x_mitigation = e_x @ W_dq.T
# e_x_mitigation = e_x @ (W_dq.T + (lora_B.weight @ lora_A.weight * scaling).T)
output = lora_B(lora_A(dropout(x_q))) * scaling
output += e_x_mitigation
if requires_conversion:
output = output.to(expected_dtype)
result = result + output
return result
parser = argparse.ArgumentParser()
parser.add_argument(
"--no-loftq", action="store_true", default=False, help="Disable LoftQ initialization (LoRA no-op init instead)"
)
parser.add_argument(
"--no-mitigation", action="store_true", default=False, help="Disable activation quantization mitigiation"
)
parser.add_argument(
"--model",
choices=["t5-small", "t5-base", "t5-large", "facebook/opt-125m"],
default="t5-base",
help="What model to test.",
)
parser.add_argument("--rank", type=int, default=64)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument(
"--int8-threshold", type=float, default=0.0, help="To demonstrate that int8 threshold > 0 doesn't work"
)
args = parser.parse_args()
device = args.device
qconf = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=args.int8_threshold,
)
input_texts = [
"All I need",
"All I want is",
"Forever yours truly: ",
"Translate French to German: Tu l'as lu?",
"Translate German to French: Last du es?",
(
"Beautiful is better than ugly.\n"
"Explicit is better than implicit.\n"
"Simple is better than complex.\n"
"Complex is better than complicated.\n"
),
]
bits = 8
loftq_iter = 1
rank = args.rank
model_id = args.model
if "t5" in args.model:
target_modules = ["o", "k", "wi", "q", "v"]
task_type = TaskType.SEQ_2_SEQ_LM
else:
target_modules = "all-linear"
task_type = TaskType.CAUSAL_LM
# ----
def get_logits(model, inputs):
torch.manual_seed(0)
if task_type == TaskType.CAUSAL_LM:
return model(**inputs).logits
with torch.inference_mode():
return model(**inputs, labels=inputs["input_ids"]).logits
def mse(a, b, attention_mask=None):
squared_error = torch.pow(a - b, 2)
if attention_mask is not None:
# attention_mask shape: [batch_size, seq_len]
# squared_error shape: [batch_size, seq_len, vocab_size]
# apply the mask (zeros out the squared error for padding tokens)
mask = attention_mask.unsqueeze(-1).expand_as(squared_error)
masked_squared_error = squared_error * mask
return (masked_squared_error.sum() / mask.sum()).item()
return squared_error.mean().item()
def get_model(*args, **kwargs):
if task_type == TaskType.CAUSAL_LM:
return AutoModelForCausalLM.from_pretrained(*args, **kwargs)
return AutoModelForSeq2SeqLM.from_pretrained(*args, **kwargs)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(input_texts, padding=True, return_tensors="pt").to(device)
ref_model = get_model(model_id, dtype=torch.float32, device_map=device)
qref_model = get_model(model_id, quantization_config=qconf, dtype=torch.float32, device_map=device)
loftq_config = LoftQConfig(loftq_bits=bits, loftq_iter=loftq_iter)
lora_config = LoraConfig(
task_type=task_type,
r=rank,
init_lora_weights=True if args.no_loftq else "loftq",
loftq_config=loftq_config,
target_modules=target_modules,
)
base_model = get_model(model_id, dtype=torch.float32, device_map=device)
loftq_model = get_peft_model(base_model, lora_config)
print("APPLYING SAVED ADAPTER TO QUANTIZED MODEL")
with TemporaryDirectory() as tmp_path:
tmp_path = Path(tmp_path)
loftq_model.base_model.peft_config["default"].init_lora_weights = True
loftq_model.save_pretrained(tmp_path / "loftq_model")
lora_config = LoraConfig.from_pretrained(tmp_path / "loftq_model")
model_id = args.model
if not args.no_mitigation:
custom_module_mapping = {bnb.nn.Linear8bitLt: MyLinear8bitLt}
lora_config._register_custom_module(custom_module_mapping)
base_model = get_model(model_id, quantization_config=qconf, dtype=torch.float32, device_map=device)
loftq_model = PeftModel.from_pretrained(
base_model, tmp_path / "loftq_model", is_trainable=True, config=lora_config
)
ref_logits = get_logits(ref_model, inputs)
qref_logits = get_logits(qref_model, inputs)
loftq_logits = get_logits(loftq_model, inputs)
mse_loftq = mse(ref_logits, loftq_logits, attention_mask=inputs["attention_mask"])
mse_qref = mse(ref_logits, qref_logits, attention_mask=inputs["attention_mask"])
print(f"{model_id=}{device=}")
print(f"{mse_qref=}, {mse_loftq=}")
assert mse_loftq < (mse_qref / 1.05), f"{mse_loftq} >= {mse_qref / 1.05}"
print(f"relative reduction of error: {(mse_qref - mse_loftq) / mse_qref * 100:.2f}%")
================================================
FILE: examples/loftq_finetuning/quantize_save_load.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import torch
import torch.nn as nn
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
)
from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
class Shell(nn.Module):
def __init__(self, weight, bias=None):
super().__init__()
self.weight = nn.Parameter(weight, requires_grad=False)
if bias is not None:
self.bias = nn.Parameter(bias, requires_grad=False)
def unwrap_model(model, sub_module_name=".base_layer"):
sub_module_name_list = [k.split(sub_module_name)[0] for k in model.state_dict().keys() if sub_module_name in k]
sub_module_name_set = set(sub_module_name_list)
for name in sub_module_name_set:
# get the parent of the submodule
name_parent = ".".join(name.split(".")[:-1])
name_child = name.split(".")[-1]
sub_module = model.get_submodule(name_parent)
print(sub_module)
# replace with shell
child = getattr(sub_module, name_child)
weight = getattr(child.base_layer, "weight", None)
bias = getattr(child.base_layer, "bias", None)
shell = Shell(weight, bias)
setattr(sub_module, name_child, shell)
print("You have unwrapped the model. Use it on your own risk.")
def print_model(model, name):
print("=" * 10 + name + "=" * 10)
print(model)
for name, param in model.named_parameters():
if torch.is_tensor(param):
if param.dtype in [torch.float32, torch.float16]:
print(
name,
param.shape,
param.device,
param.dtype,
param.requires_grad,
param.mean().item(),
param.max().item(),
)
else:
print(name, param.shape, param.device, param.dtype, param.requires_grad)
def arg_parse():
parser = argparse.ArgumentParser(description="Quantize a model with LoftQ.")
parser.add_argument(
"--model_name_or_path",
type=str,
default=None,
required=True,
help="The name or path of the fp32/16 model.",
)
parser.add_argument(
"--token",
type=str,
default=None,
help="The access token to download model from HuggingFace Hub.",
)
parser.add_argument(
"--bits",
type=int,
default=4,
help="The quantized bits",
)
parser.add_argument(
"--iter",
type=int,
default=1,
help="The alternating steps in LoftQ",
)
parser.add_argument(
"--rank",
type=int,
default=16,
help="The rank of the LoRA adapter",
)
parser.add_argument(
"--save_dir",
type=str,
default="./model_zoo/loftq/",
help="The rank of the LoRA adapter",
)
args = parser.parse_args()
return args
def quantize_and_save():
args = arg_parse()
# Download weights and configure LoRA
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, token=args.token, trust_remote_code=True)
if any(name in args.model_name_or_path.lower() for name in ["llama", "mistral", "falcon"]):
model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path, token=args.token, trust_remote_code=True)
task_type = TaskType.CAUSAL_LM
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "down_proj", "gate_proj"]
elif any(name in args.model_name_or_path.lower() for name in ["bart", "t5"]):
model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name_or_path, token=args.token)
task_type = TaskType.SEQ_2_SEQ_LM
target_modules = ["q_proj", "k_proj", "v_proj", "fc1", "fc2", "out_proj"]
elif any(name in args.model_name_or_path.lower() for name in ["deberta", "roberta", "bert"]):
model = AutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, token=args.token)
task_type = TaskType.SEQ_CLS
target_modules = ["query_proj", "key_proj", "value_proj", "dense"] # embeddings not supported by peft
else:
raise NotImplementedError("Other models not supported yet.")
# Config of LoftQ
loftq_config = LoftQConfig(loftq_bits=args.bits, loftq_iter=args.iter)
lora_config = LoraConfig(
task_type=task_type,
inference_mode=True,
r=args.rank,
lora_alpha=16 if task_type is TaskType.CAUSAL_LM else args.rank,
lora_dropout=0.1,
target_modules=target_modules,
init_lora_weights="loftq",
loftq_config=loftq_config,
)
# Obtain LoftQ model
lora_model = get_peft_model(model, lora_config)
base_model = lora_model.get_base_model()
# Save LoftQ model
model_name = args.model_name_or_path.split("/")[-1] + f"-{args.bits}bit" + f"-{args.rank}rank"
base_model_dir = os.path.join(args.save_dir, model_name)
lora_model_dir = os.path.join(args.save_dir, model_name, "loft_init")
# save lora adapters first
lora_model.base_model.peft_config[
"default"
].base_model_name_or_path = base_model_dir # This can be a local path or Hub model id
lora_model.base_model.peft_config["default"].init_lora_weights = True # Don't apply LoftQ when loading again
lora_model.save_pretrained(lora_model_dir)
print_model(lora_model, "lora_model")
# remove lora adapters and save the backbone
unwrap_model(base_model)
base_model.save_pretrained(base_model_dir)
tokenizer.save_pretrained(base_model_dir)
print_model(base_model, "base_model")
return base_model_dir, lora_model_dir
if __name__ == "__main__":
base_dir, lora_dir = quantize_and_save()
# example command:
# python quantize_save_load.py \
# --model_name_or_path meta-llama/Llama-2-7b-hf \
# --token XXX \
# --bits 4 --iter 5 --rank 16 \
# --save_dir ./model_zoo/loftq/
================================================
FILE: examples/loftq_finetuning/train_gsm8k_llama.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import copy
import logging
import math
import os
import random
import re
from pathlib import Path
import datasets
import torch
import transformers
from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
SchedulerType,
default_data_collator,
get_scheduler,
)
from transformers.utils import send_example_telemetry
from transformers.utils.versions import require_version
from peft import PeftModel
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
# check_min_version("4.32.0.dev0")
logger = get_logger(__name__)
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def parse_args():
parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task")
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
"--train_file", type=str, default=None, help="A csv, txt or a json file containing the training data."
)
parser.add_argument(
"--validation_file", type=str, default=None, help="A csv, txt or a json file containing the validation data."
)
parser.add_argument(
"--validation_split_percentage",
default=5,
help="The percentage of the train set used as validation set in case there's no validation split",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=False,
)
parser.add_argument(
"--config_name",
type=str,
default=None,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--use_slow_tokenizer",
action="store_true",
help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--model_type",
type=str,
default=None,
help="Model type to use if training from scratch.",
choices=MODEL_TYPES,
)
parser.add_argument(
"--ignore_pad_token_for_loss",
type=bool,
default=True,
help="Whether to ignore the tokens corresponding to padded labels in the loss computation or not.",
)
parser.add_argument(
"--max_source_length",
type=int,
default=128,
help=(
"The maximum total input sequence length after "
"tokenization.Sequences longer than this will be truncated, sequences shorter will be padded."
),
)
parser.add_argument(
"--max_target_length",
type=int,
default=128,
help=(
"The maximum total sequence length for target text after "
"tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
"during ``evaluate`` and ``predict``."
),
)
parser.add_argument(
"--pad_to_max_length",
action="store_true",
help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
)
parser.add_argument(
"--preprocessing_num_workers",
type=int,
default=None,
help="The number of processes to use for the preprocessing.",
)
parser.add_argument(
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
)
parser.add_argument(
"--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--trust_remote_code",
type=bool,
default=False,
help=(
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
"should only be set to `True` for repositories you trust and in which you have read the code, as it will"
"execute code present on the Hub on your local machine."
),
)
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument(
"--low_cpu_mem_usage",
action="store_true",
help=(
"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded."
"If passed, LLM loading time and RAM consumption will be benefited."
),
)
##########################
# Generation Config #
##########################
parser.add_argument(
"--temperature",
type=float,
default=0.8,
help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
)
parser.add_argument("--k", type=int, default=40, help="Choose k candidate words")
parser.add_argument("--p", type=float, default=0.95, help="The sum of probability of candidate words is 0.9 ")
##########################
# Exp Args #
##########################
parser.add_argument(
"--adapter_name_or_path",
type=str,
default=None,
help=(
"The LoRA adapter checkpoint. Set None if you want to fine-tune from LoftQ."
"Specify a path if you want to evaluate."
),
)
args = parser.parse_args()
# Sanity checks
if args.dataset_name is None and args.train_file is None and args.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if args.train_file is not None:
extension = args.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file."
if args.validation_file is not None:
extension = args.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
def main():
args = parse_args()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_clm_no_trainer", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator_log_kwargs = {}
if args.with_tracking:
accelerator_log_kwargs["log_with"] = args.report_to
accelerator_log_kwargs["project_dir"] = args.output_dir
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
api = HfApi(token=args.hub_token)
# Create repo (repo_name from args or inferred)
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
args.dataset_name,
args.dataset_config_name,
split=f"train[:{args.validation_split_percentage}%]",
)
raw_datasets["train"] = load_dataset(
args.dataset_name,
args.dataset_config_name,
split=f"train[{args.validation_split_percentage}%:]",
)
else:
data_files = {}
dataset_args = {}
if args.train_file is not None:
data_files["train"] = args.train_file
if args.validation_file is not None:
data_files["validation"] = args.validation_file
extension = args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
extension,
data_files=data_files,
split=f"train[:{args.validation_split_percentage}%]",
**dataset_args,
)
raw_datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{args.validation_split_percentage}%:]",
**dataset_args,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if args.config_name:
config = AutoConfig.from_pretrained(
args.config_name,
trust_remote_code=args.trust_remote_code,
)
elif args.model_name_or_path:
config = AutoConfig.from_pretrained(
args.model_name_or_path,
trust_remote_code=args.trust_remote_code,
)
else:
config = CONFIG_MAPPING[args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
)
elif args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.model_name_or_path,
use_fast=not args.use_slow_tokenizer,
trust_remote_code=args.trust_remote_code,
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
##########################
# Tokenizer #
##########################
tokenizer.pad_token_id = 0 # unk. we want this to be different from the eos token
tokenizer.padding_side = "left" # Allow batched inference
tokenizer.truncation_side = "left"
if args.model_name_or_path:
model = AutoModelForCausalLM.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
low_cpu_mem_usage=True,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=config.dtype,
),
)
else:
logger.info("Training new model from scratch")
model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)
##########################
# Peft Model #
##########################
if args.adapter_name_or_path is None:
model = PeftModel.from_pretrained(model, args.model_name_or_path, subfolder="loftq_init", is_trainable=True)
else:
model = PeftModel.from_pretrained(model, args.adapter_name_or_path, is_trainable=True)
model.print_trainable_parameters()
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
# on a small vocab and want a smaller embedding size, remove this test.
embedding_size = model.get_input_embeddings().weight.shape[0]
if len(tokenizer) > embedding_size:
model.resize_token_embeddings(len(tokenizer))
# Preprocessing the datasets.
# First we tokenize all the texts.
##########################
# GSM8K dataset #
##########################
# Preprocessing the datasets.
# First we tokenize all the texts.
column_names = raw_datasets["train"].column_names
# Get the column names for source/target.
source_column, target_column = "question", "answer"
# Temporarily set max_target_length for training.
padding = "max_length" if args.pad_to_max_length else False
task_prompt = "\nAnswer the above question. First think step by step and then answer the final number.\n"
def prompt_process(sent_1, sent_2, prompt_1="", prompt_2="", prompt_3=""):
sent_2 = sent_2.replace("####", "The final answer is")
return prompt_1 + sent_1 + prompt_2 + sent_2 + prompt_3
def preprocess_function_train(examples):
sources = examples[source_column]
targets = examples[target_column]
inputs = [prompt_process(source, target, prompt_2=task_prompt) for (source, target) in zip(sources, targets)]
model_inputs = tokenizer(
inputs,
max_length=args.max_source_length + args.max_target_length,
padding=padding,
truncation=True,
return_tensors="pt",
)
labels = copy.deepcopy(model_inputs)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and args.ignore_pad_token_for_loss:
# get the length of the target tokens. -1 to kick out the token
target_tokens = tokenizer(targets, padding=False)
target_len = [len(label) - 1 for label in target_tokens["input_ids"]]
# don't calculate the loss from source and padding (left padding)
for i in range(len(labels["input_ids"])):
labels["input_ids"][i, : -target_len[i]] = -100
model_inputs["labels"] = labels["input_ids"]
return model_inputs
def preprocess_function_test(examples):
sources = examples[source_column]
labels = examples[target_column]
inputs = [source + task_prompt for source in sources]
model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True)
labels = tokenizer(labels, max_length=args.max_target_length, padding=padding, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
with accelerator.main_process_first():
train_dataset = raw_datasets["train"].map(
preprocess_function_train,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on training dataset",
)
eval_dataset = raw_datasets["test"].map(
preprocess_function_test,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on test dataset",
)
# Log a few random samples from the set:
for index in random.sample(range(len(train_dataset)), 2):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
for index in random.sample(range(len(eval_dataset)), 2):
logger.info(f"Sample {index} of the validation set: {eval_dataset[index]}.")
# DataLoaders creation:
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(
eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
)
# Optimizer
# Split weights in two groups, one with weight decay and the other not.
no_decay = ["bias", "layer_norm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) and "lora" in n],
"weight_decay": args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
if accelerator.distributed_type == DistributedType.TPU:
model.tie_weights()
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states
checkpointing_steps = args.checkpointing_steps
if checkpointing_steps is not None and checkpointing_steps.isdigit():
checkpointing_steps = int(checkpointing_steps)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("clm_no_trainer", experiment_config)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
starting_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
checkpoint_path = args.resume_from_checkpoint
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
checkpoint_path = path
path = os.path.basename(checkpoint_path)
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
accelerator.load_state(path)
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
completed_steps = starting_epoch * num_update_steps_per_epoch
else:
# need to multiply `gradient_accumulation_steps` to reflect real steps
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
starting_epoch = resume_step // len(train_dataloader)
resume_step -= starting_epoch * len(train_dataloader)
completed_steps = resume_step // args.gradient_accumulation_steps
# update the progress_bar if load from checkpoint
progress_bar.update(completed_steps)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
else:
active_dataloader = train_dataloader
for step, batch in enumerate(active_dataloader):
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(loss)
if completed_steps % 50:
accelerator.print(f"Epoch: {epoch} | Step: {completed_steps} | Loss: {loss}")
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
completed_steps += 1
if isinstance(checkpointing_steps, int):
if completed_steps % checkpointing_steps == 0:
output_dir = f"step_{completed_steps}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if completed_steps >= args.max_train_steps:
break
model.eval()
gen_kwargs = {
"max_new_tokens": args.max_target_length,
"temperature": args.temperature,
"top_k": args.k,
"top_p": args.p,
"do_sample": True,
}
ans_pred_list = []
ans_gold_list = []
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
gen_kwargs["input_ids"] = batch["input_ids"]
gen_kwargs["attention_mask"] = batch["attention_mask"]
generated_tokens = accelerator.unwrap_model(model).generate(**gen_kwargs)
pred_tokens = generated_tokens[:, args.max_source_length :]
pred_tokens = accelerator.pad_across_processes(pred_tokens, dim=1, pad_index=tokenizer.pad_token_id)
gold_tokens = batch["labels"]
if not args.pad_to_max_length:
# If we did not pad to max length, we need to pad the labels too
gold_tokens = accelerator.pad_across_processes(
batch["labels"], dim=1, pad_index=tokenizer.pad_token_id
)
pred_tokens, gold_tokens = accelerator.gather_for_metrics((pred_tokens, gold_tokens))
pred_tokens, gold_tokens = pred_tokens.cpu().numpy(), gold_tokens.cpu().numpy()
if isinstance(pred_tokens, tuple):
pred_tokens = pred_tokens[0]
decoded_pred = tokenizer.batch_decode(pred_tokens, skip_special_tokens=True)
decoded_gold = tokenizer.batch_decode(gold_tokens, skip_special_tokens=True)
# Extract the numbers in sentences
accelerator.print(decoded_pred)
ans_pred_list += [extract_answer_number(sentence_pred) for sentence_pred in decoded_pred]
ans_gold_list += [extract_answer_number(sentence_gold) for sentence_gold in decoded_gold]
accelerator.print(ans_pred_list)
accelerator.print(ans_gold_list)
accuracy = compute_accuracy(ans_gold_list, ans_pred_list)
logger.info(f"epoch {epoch}: accuracy: {accuracy}")
if args.with_tracking:
accelerator.log(
{
"accuracy": accuracy,
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message=f"Training in progress epoch {epoch}",
run_as_future=True,
)
if args.checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.with_tracking:
accelerator.end_training()
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
)
PATTERN_NUMBER = re.compile(r"-?\d+\.?\d*")
def extract_answer_number(sentence: str) -> float:
sentence = sentence.replace(",", "")
pred = PATTERN_NUMBER.findall(sentence)
if not pred:
return float("inf")
segment = sentence.split("The final answer is ")
if len(segment) > 1:
pred_answer = segment[1]
pred_answer = PATTERN_NUMBER.findall(pred_answer)
if len(pred_answer) > 0:
pred_answer = pred_answer[0]
else:
pred_answer = float(pred[-1])
else:
pred_answer = float(pred[-1])
if isinstance(pred_answer, str):
try:
pred_answer = float(pred_answer)
except ValueError:
pred_answer = float("inf")
return pred_answer
def compute_accuracy(pred: list, gold: list):
acc = 0.0
for p, g in zip(pred, gold):
if p == g:
acc += 1
return acc / len(pred)
if __name__ == "__main__":
main()
================================================
FILE: examples/lora_dreambooth/colab_notebook.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kdOhtpergLCQ"
},
"outputs": [],
"source": [
"!git clone https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_LuGk9mihPx7"
},
"outputs": [],
"source": [
"%cd \"peft-lora-sd-dreambooth\"\n",
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BYKO8e5ElJOX"
},
"outputs": [],
"source": [
"!python colab.py"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "premium",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: examples/lora_dreambooth/convert_kohya_ss_sd_lora_to_peft.py
================================================
import argparse
import os
from collections import Counter
from dataclasses import dataclass
from typing import Optional
import safetensors
import torch
from diffusers import UNet2DConditionModel
from transformers import CLIPTextModel
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict
# Default kohya_ss LoRA replacement modules
# https://github.com/kohya-ss/sd-scripts/blob/c924c47f374ac1b6e33e71f82948eb1853e2243f/networks/lora.py#L661
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te"
@dataclass
class LoRAInfo:
kohya_key: str
peft_key: str
alpha: Optional[float] = None
rank: Optional[int] = None
lora_A: Optional[torch.Tensor] = None
lora_B: Optional[torch.Tensor] = None
def peft_state_dict(self) -> dict[str, torch.Tensor]:
if self.lora_A is None or self.lora_B is None:
raise ValueError("At least one of lora_A or lora_B is None, they must both be provided")
return {f"{peft_key}.lora_A.weight": self.lora_A, f"{peft_key}.lora_B.weight": self.lora_A}
def construct_peft_loraconfig(info: dict[str, LoRAInfo]) -> LoraConfig:
"""Constructs LoraConfig from data extracted from kohya checkpoint
Args:
info (Dict[str, LoRAInfo]): Information extracted from kohya checkpoint
Returns:
LoraConfig: config for constructing LoRA
"""
# Unpack all ranks and alphas
ranks = {x[0]: x[1].rank for x in info.items()}
alphas = {x[0]: x[1].alpha or x[1].rank for x in info.items()}
# Determine which modules needs to be transformed
target_modules = list(info.keys())
# Determine most common rank and alpha
r = Counter(ranks.values()).most_common(1)[0]
lora_alpha = Counter(alphas.values()).most_common(1)[0]
# Determine which modules have different rank and alpha
rank_pattern = dict(filter(lambda x: x[1] != r, ranks.items()))
alpha_pattern = dict(filter(lambda x: x[1] != lora_alpha, alphas.items()))
config = LoraConfig(
r=r,
lora_alpha=lora_alpha,
target_modules=target_modules,
lora_dropout=0.0,
bias="none",
init_lora_weights=False,
rank_pattern=rank_pattern,
alpha_pattern=alpha_pattern,
)
return config
def combine_peft_state_dict(info: dict[str, LoRAInfo]) -> dict[str, torch.Tensor]:
result = {}
for key_name, key_info in info.items():
result[f"base_model.model.{key_name}.lora_A.weight"] = key_info.lora_A
result[f"base_model.model.{key_name}.lora_B.weight"] = key_info.lora_B
return result
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--sd_checkpoint", default=None, type=str, required=True, help="SD checkpoint to use")
parser.add_argument(
"--kohya_lora_path", default=None, type=str, required=True, help="Path to kohya_ss trained LoRA"
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
args = parser.parse_args()
# Load all models that we need to add adapter to
text_encoder = CLIPTextModel.from_pretrained(args.sd_checkpoint, subfolder="text_encoder")
unet = UNet2DConditionModel.from_pretrained(args.sd_checkpoint, subfolder="unet")
# Construct possible mapping from kohya keys to peft keys
models_keys = {}
for model, model_key, model_name in [
(text_encoder, LORA_PREFIX_TEXT_ENCODER, "text_encoder"),
(unet, LORA_PREFIX_UNET, "unet"),
]:
models_keys.update(
{
f"{model_key}.{peft_key}".replace(".", "_"): peft_key
for peft_key in (x[0] for x in model.named_modules())
}
)
# Store conversion info (model_type -> peft_key -> LoRAInfo)
lora_info: dict[str, dict[str, LoRAInfo]] = {
"text_encoder": {},
"unet": {},
}
# Open kohya_ss checkpoint
with safetensors.safe_open(args.kohya_lora_path, framework="pt", device="cpu") as f:
# Extract information about LoRA structure
metadata = f.metadata()
# Iterate through available info and unpack all the values
for key in f.keys():
kohya_key, kohya_type = key.split(".")[:2]
# Find which model this key belongs to
if kohya_key.startswith(LORA_PREFIX_TEXT_ENCODER):
model_type = "text_encoder"
elif kohya_key.startswith(LORA_PREFIX_UNET):
model_type = "unet"
else:
raise ValueError(f"Cannot determine model for key: {key}")
# Find corresponding peft key
if kohya_key not in models_keys:
raise ValueError(f"Cannot find corresponding key for diffusers/transformers model: {kohya_key}")
peft_key = models_keys[kohya_key]
if peft_key not in lora_info[model_type]:
lora_info[model_type][peft_key] = LoRAInfo(kohya_key=kohya_key, peft_key=peft_key)
if kohya_type == "alpha":
lora_info[model_type][peft_key].alpha = f.get_tensor(key).item()
elif kohya_type == "lora_down":
tensor = f.get_tensor(key)
lora_info[model_type][peft_key].lora_A = tensor
lora_info[model_type][peft_key].rank = tensor.shape[0]
elif kohya_type == "lora_up":
tensor = f.get_tensor(key)
lora_info[model_type][peft_key].lora_B = f.get_tensor(key)
lora_info[model_type][peft_key].rank = tensor.shape[1]
else:
raise ValueError(f"Unknown weight name in key: {key} - {kohya_type}")
# Process each model
for model, model_name in [(text_encoder, "text_encoder"), (unet, "unet")]:
config = construct_peft_loraconfig(lora_info[model_name])
model = get_peft_model(model, config)
keys_peft = list(get_peft_model_state_dict(model).keys())
keys_new = list(combine_peft_state_dict(lora_info[model_name]).keys())
set_peft_model_state_dict(model, combine_peft_state_dict(lora_info[model_name]))
if args.half:
model.to(torch.float16)
# Save model to disk
model.save_pretrained(os.path.join(args.dump_path, model_name))
================================================
FILE: examples/lora_dreambooth/convert_peft_sd_lora_to_kohya_ss.py
================================================
import argparse
import os
import torch
from diffusers import UNet2DConditionModel
from safetensors.torch import save_file
from transformers import CLIPTextModel
from peft import PeftModel, get_peft_model_state_dict
# Default kohya_ss LoRA replacement modules
# https://github.com/kohya-ss/sd-scripts/blob/c924c47f374ac1b6e33e71f82948eb1853e2243f/networks/lora.py#L664
LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te"
LORA_ADAPTER_NAME = "default"
def get_module_kohya_state_dict(
module: PeftModel, prefix: str, dtype: torch.dtype, adapter_name: str = LORA_ADAPTER_NAME
) -> dict[str, torch.Tensor]:
kohya_ss_state_dict = {}
for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():
kohya_key = peft_key.replace("base_model.model", prefix)
kohya_key = kohya_key.replace("lora_A", "lora_down")
kohya_key = kohya_key.replace("lora_B", "lora_up")
kohya_key = kohya_key.replace(".", "_", kohya_key.count(".") - 2)
kohya_ss_state_dict[kohya_key] = weight.to(dtype)
# Set alpha parameter
if "lora_down" in kohya_key:
alpha_key = f"{kohya_key.split('.')[0]}.alpha"
kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)
return kohya_ss_state_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--sd_checkpoint",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--sd_checkpoint_revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument("--peft_lora_path", default=None, type=str, required=True, help="Path to peft trained LoRA")
parser.add_argument(
"--dump_path",
default=None,
type=str,
required=True,
help="Path to the output safetensors file for use with webui.",
)
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
args = parser.parse_args()
# Store kohya_ss state dict
kohya_ss_state_dict = {}
dtype = torch.float16 if args.half else torch.float32
# Load Text Encoder LoRA model
text_encoder_peft_lora_path = os.path.join(args.peft_lora_path, "text_encoder")
if os.path.exists(text_encoder_peft_lora_path):
text_encoder = CLIPTextModel.from_pretrained(
args.sd_checkpoint, subfolder="text_encoder", revision=args.sd_checkpoint_revision
)
text_encoder = PeftModel.from_pretrained(
text_encoder, text_encoder_peft_lora_path, adapter_name=LORA_ADAPTER_NAME
)
kohya_ss_state_dict.update(
get_module_kohya_state_dict(text_encoder, LORA_PREFIX_TEXT_ENCODER, dtype, LORA_ADAPTER_NAME)
)
# Load UNet LoRA model
unet_peft_lora_path = os.path.join(args.peft_lora_path, "unet")
if os.path.exists(unet_peft_lora_path):
unet = UNet2DConditionModel.from_pretrained(
args.sd_checkpoint, subfolder="unet", revision=args.sd_checkpoint_revision
)
unet = PeftModel.from_pretrained(unet, unet_peft_lora_path, adapter_name=LORA_ADAPTER_NAME)
kohya_ss_state_dict.update(get_module_kohya_state_dict(unet, LORA_PREFIX_UNET, dtype, LORA_ADAPTER_NAME))
# Save state dict
save_file(
kohya_ss_state_dict,
args.dump_path,
)
================================================
FILE: examples/lora_dreambooth/lora_dreambooth_inference.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "acab479f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===================================BUG REPORT===================================\n",
"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
"================================================================================\n"
]
}
],
"source": [
"import argparse\n",
"import gc\n",
"import hashlib\n",
"import itertools\n",
"import logging\n",
"import math\n",
"import os\n",
"import threading\n",
"import warnings\n",
"from pathlib import Path\n",
"from typing import Optional\n",
"import psutil\n",
"import json\n",
"\n",
"import torch\n",
"import torch.nn.functional as F\n",
"import torch.utils.checkpoint\n",
"from torch.utils.data import Dataset\n",
"\n",
"import datasets\n",
"import diffusers\n",
"import transformers\n",
"from accelerate import Accelerator\n",
"from accelerate.logging import get_logger\n",
"from accelerate.utils import set_seed\n",
"from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel\n",
"from diffusers import DDPMScheduler, PNDMScheduler, StableDiffusionPipeline\n",
"from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\n",
"from diffusers.optimization import get_scheduler\n",
"from diffusers.utils import check_min_version\n",
"from diffusers.utils.import_utils import is_xformers_available\n",
"from huggingface_hub import HfFolder, Repository, whoami\n",
"from PIL import Image\n",
"from torchvision import transforms\n",
"from tqdm.auto import tqdm\n",
"from transformers import AutoTokenizer, PretrainedConfig, CLIPFeatureExtractor\n",
"from peft import PeftModel, LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict\n",
"\n",
"# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n",
"check_min_version(\"0.10.0.dev0\")\n",
"\n",
"logger = get_logger(__name__)\n",
"\n",
"\n",
"MODEL_NAME = \"CompVis/stable-diffusion-v1-4\" # \"stabilityai/stable-diffusion-2-1-base\"\n",
"INSTANCE_PROMPT = \"a photo of sks dog\"\n",
"base_path = \"/home/sourab/temp/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06cfd506",
"metadata": {},
"outputs": [],
"source": [
"def get_lora_sd_pipeline(\n",
" ckpt_dir, base_model_name_or_path=None, dtype=torch.float16, device=\"auto\", adapter_name=\"default\"\n",
"):\n",
" if device == \"auto\":\n",
" device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"\n",
" unet_sub_dir = os.path.join(ckpt_dir, \"unet\")\n",
" text_encoder_sub_dir = os.path.join(ckpt_dir, \"text_encoder\")\n",
" if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None:\n",
" config = LoraConfig.from_pretrained(text_encoder_sub_dir)\n",
" base_model_name_or_path = config.base_model_name_or_path\n",
"\n",
" if base_model_name_or_path is None:\n",
" raise ValueError(\"Please specify the base model name or path\")\n",
"\n",
" pipe = StableDiffusionPipeline.from_pretrained(\n",
" base_model_name_or_path, torch_dtype=dtype, requires_safety_checker=False\n",
" ).to(device)\n",
" pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)\n",
"\n",
" if os.path.exists(text_encoder_sub_dir):\n",
" pipe.text_encoder = PeftModel.from_pretrained(\n",
" pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name\n",
" )\n",
"\n",
" if dtype in (torch.float16, torch.bfloat16):\n",
" pipe.unet.half()\n",
" pipe.text_encoder.half()\n",
"\n",
" pipe.to(device)\n",
" return pipe\n",
"\n",
"\n",
"def load_adapter(pipe, ckpt_dir, adapter_name):\n",
" unet_sub_dir = os.path.join(ckpt_dir, \"unet\")\n",
" text_encoder_sub_dir = os.path.join(ckpt_dir, \"text_encoder\")\n",
" pipe.unet.load_adapter(unet_sub_dir, adapter_name=adapter_name)\n",
" if os.path.exists(text_encoder_sub_dir):\n",
" pipe.text_encoder.load_adapter(text_encoder_sub_dir, adapter_name=adapter_name)\n",
"\n",
"\n",
"def set_adapter(pipe, adapter_name):\n",
" pipe.unet.set_adapter(adapter_name)\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.set_adapter(adapter_name)\n",
"\n",
"\n",
"def merging_lora_with_base(pipe, ckpt_dir, adapter_name=\"default\"):\n",
" unet_sub_dir = os.path.join(ckpt_dir, \"unet\")\n",
" text_encoder_sub_dir = os.path.join(ckpt_dir, \"text_encoder\")\n",
" if isinstance(pipe.unet, PeftModel):\n",
" pipe.unet.set_adapter(adapter_name)\n",
" else:\n",
" pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)\n",
" pipe.unet = pipe.unet.merge_and_unload()\n",
"\n",
" if os.path.exists(text_encoder_sub_dir):\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.set_adapter(adapter_name)\n",
" else:\n",
" pipe.text_encoder = PeftModel.from_pretrained(\n",
" pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name\n",
" )\n",
" pipe.text_encoder = pipe.text_encoder.merge_and_unload()\n",
"\n",
" return pipe\n",
"\n",
"\n",
"def create_weighted_lora_adapter(pipe, adapters, weights, adapter_name=\"default\"):\n",
" pipe.unet.add_weighted_adapter(adapters, weights, adapter_name)\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.add_weighted_adapter(adapters, weights, adapter_name)\n",
"\n",
" return pipe"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d4e888d2",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f12b2cca0784cba9dc14ec48de929d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 19 files: 0%| | 0/19 [00:00"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"sks dog playing fetch in the park\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1e1d1f30",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 7.74 ms, sys: 0 ns, total: 7.74 ms\n",
"Wall time: 7.31 ms\n"
]
}
],
"source": [
"%%time\n",
"set_adapter(pipe, adapter_name=\"toy\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0c50c03d",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f978437709b44b391744cf972415027",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/50 [00:00"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"narendra modi rendered in the style of <1>\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e3b9a681",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8871731127904802ba6123128ba22ecf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/50 [00:00"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set_adapter(pipe, adapter_name=\"dog\")\n",
"prompt = \"sks dog in a big red bucket\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a659ca6e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b701336557e4417d8d39d500699c298b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/50 [00:00"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set_adapter(pipe, adapter_name=\"toy\")\n",
"prompt = \"superman rendered in the style of <1>, close up potrait\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "1f0ecb40",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b47385e953b14c768d82bea776129904",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/50 [00:00"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set_adapter(pipe, adapter_name=\"toy_dog\")\n",
"prompt = \"sks dog rendered in the style of <1>, close up potrait, 4K HD\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29720cdb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
},
"vscode": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/lora_dreambooth/requirements.txt
================================================
transformers
accelerate
evaluate
tqdm
datasets
diffusers
Pillow
torchvision
huggingface_hub
safetensors
wandb
================================================
FILE: examples/lora_dreambooth/train_dreambooth.py
================================================
import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from contextlib import nullcontext
from pathlib import Path
import datasets
import diffusers
import numpy as np
import psutil
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from huggingface_hub import HfApi
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
from peft import LoraConfig, get_peft_model
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
logger = get_logger(__name__)
UNET_TARGET_MODULES = ["to_q", "to_v", "query", "value"] # , "ff.net.0.proj"]
TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
# lora args
parser.add_argument("--use_lora", action="store_true", help="Whether to use Lora for parameter efficient tuning")
parser.add_argument("--lora_r", type=int, default=8, help="Lora rank, only used if use_lora is True")
parser.add_argument("--lora_alpha", type=int, default=32, help="Lora alpha, only used if use_lora is True")
parser.add_argument("--lora_dropout", type=float, default=0.0, help="Lora dropout, only used if use_lora is True")
parser.add_argument(
"--lora_bias",
type=str,
default="none",
help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora is True",
)
parser.add_argument(
"--lora_text_encoder_r",
type=int,
default=8,
help="Lora rank for text encoder, only used if `use_lora` and `train_text_encoder` are True",
)
parser.add_argument(
"--lora_text_encoder_alpha",
type=int,
default=32,
help="Lora alpha for text encoder, only used if `use_lora` and `train_text_encoder` are True",
)
parser.add_argument(
"--lora_text_encoder_dropout",
type=float,
default=0.0,
help="Lora dropout for text encoder, only used if `use_lora` and `train_text_encoder` are True",
)
parser.add_argument(
"--lora_text_encoder_bias",
type=str,
default="none",
help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora and `train_text_encoder` are True",
)
parser.add_argument(
"--num_dataloader_workers", type=int, default=1, help="Num of workers for the training dataloader."
)
parser.add_argument(
"--no_tracemalloc",
default=False,
action="store_true",
help="Flag to stop memory allocation tracing during training. This could speed up training on Windows.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of accelerators, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU or Intel XPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU or Intel XPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
gc.collect()
self.device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
self.device_module = getattr(torch, self.device_type, torch.cuda)
self.device_module.empty_cache()
self.device_module.reset_peak_memory_stats() # reset the peak gauge to zero
self.begin = self.device_module.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
"""get resident set size memory for the current process"""
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
# time.sleep(0.001) # 1msec
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
self.device_module.empty_cache()
self.end = self.device_module.memory_allocated()
self.peak = self.device_module.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def collate_fn(examples, with_prior_preservation=False):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple accelerators."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=logging_dir,
)
if args.report_to == "wandb":
import wandb
wandb.login(key=args.wandb_key)
wandb.init(project=args.wandb_project_name)
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
dtype = torch.float16 if accelerator.device.type in ["cuda", "xpu"] else torch.float32
if args.prior_generation_precision == "fp32":
dtype = torch.float32
elif args.prior_generation_precision == "fp16":
dtype = torch.float16
elif args.prior_generation_precision == "bf16":
dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
dtype=dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
api = HfApi(token=args.hub_token)
# Create repo (repo_name from args or inferred)
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDPMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
) # DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
if args.use_lora:
config = LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
target_modules=UNET_TARGET_MODULES,
lora_dropout=args.lora_dropout,
bias=args.lora_bias,
)
unet = get_peft_model(unet, config)
unet.print_trainable_parameters()
print(unet)
vae.requires_grad_(False)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
elif args.train_text_encoder and args.use_lora:
config = LoraConfig(
r=args.lora_text_encoder_r,
lora_alpha=args.lora_text_encoder_alpha,
target_modules=TEXT_ENCODER_TARGET_MODULES,
lora_dropout=args.lora_text_encoder_dropout,
bias=args.lora_text_encoder_bias,
)
text_encoder = get_peft_model(text_encoder, config)
text_encoder.print_trainable_parameters()
print(text_encoder)
if args.enable_xformers_memory_efficient_attention:
if accelerator.device.type == "xpu":
logger.warn("XPU hasn't support xformers yet, ignore it.")
elif is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# below fails when using lora so commenting it out
if args.train_text_encoder and not args.use_lora:
text_encoder.gradient_checkpointing_enable()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB accelerators
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.num_dataloader_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and text_encoder to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1]
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = resume_global_step // num_update_steps_per_epoch
resume_step = resume_global_step % num_update_steps_per_epoch
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
with TorchTracemalloc() if not args.no_tracemalloc else nullcontext() as tracemalloc:
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
global_step += 1
# if global_step % args.checkpointing_steps == 0:
# if accelerator.is_main_process:
# save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
# accelerator.save_state(save_path)
# logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if (
args.validation_prompt is not None
and (step + num_update_steps_per_epoch * epoch) % args.validation_steps == 0
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
safety_checker=None,
revision=args.revision,
)
# set `keep_fp32_wrapper` to True because we do not want to remove
# mixed precision hooks while we are still training
pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
if args.seed is not None:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
else:
generator = None
images = []
for _ in range(args.num_validation_images):
image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
images.append(image)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
import wandb
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
if global_step >= args.max_train_steps:
break
# Printing the accelerator memory usage details such as allocated memory, peak memory, and total memory usage
if not args.no_tracemalloc:
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the train : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the train (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if args.use_lora:
unwarpped_unet = accelerator.unwrap_model(unet)
unwarpped_unet.save_pretrained(
os.path.join(args.output_dir, "unet"), state_dict=accelerator.get_state_dict(unet)
)
if args.train_text_encoder:
unwarpped_text_encoder = accelerator.unwrap_model(text_encoder)
unwarpped_text_encoder.save_pretrained(
os.path.join(args.output_dir, "text_encoder"),
state_dict=accelerator.get_state_dict(text_encoder),
)
else:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
run_as_future=True,
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/lora_finetuning_transformer_engine/Dockerfile
================================================
FROM nvcr.io/nvidia/pytorch:26.01-py3
WORKDIR /workspace/lora_finetuning
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY lora_finetuning_te.py .
================================================
FILE: examples/lora_finetuning_transformer_engine/README.md
================================================
# Transformer Engine ESM2 LoRA Fine-Tuning
This example demonstrates LoRA fine-tuning for Transformer Engine ESM2 token classification.
## Setup
Choose one of the two options below.
### Option A: Docker (recommended)
Build a self-contained image based on the publicly available NVIDIA PyTorch container
(`nvcr.io/nvidia/pytorch:26.01-py3`), which already ships CUDA, cuDNN, and Transformer Engine:
```bash
docker build -t lora-te examples/lora_finetuning_transformer_engine
```
Run the training inside the container:
```bash
docker run --gpus all --rm lora-te \
python lora_finetuning_te.py \
--base_model nvidia/esm2_t6_8M_UR50D \
--output_dir ./esm2_lora_output \
--num_train_samples 256 \
--num_eval_samples 64 \
--num_epochs 1
```
Or start an interactive session to experiment:
```bash
docker run --gpus all --rm -it lora-te bash
```
### Option B: Virtual environment
Create and activate a virtual environment, then install the Python dependencies:
```bash
python -m venv .venv
source .venv/bin/activate
pip install -r examples/lora_finetuning_transformer_engine/requirements.txt
```
**Transformer Engine** must be installed separately and must match the system CUDA toolkit version.
See the [TE installation guide](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/installation.html)
for details.
## What this example does
- Loads a Transformer Engine ESM2 model for token classification
- Applies LoRA adapters via PEFT
- Generates random protein-like sequences
- Assigns randomly generated secondary structure labels (`H`, `E`, `C`)
- Trains/evaluates with `Trainer`
## Run
```bash
python examples/lora_finetuning_transformer_engine/lora_finetuning_te.py \
--base_model nvidia/esm2_t6_8M_UR50D \
--output_dir ./esm2_lora_output \
--num_train_samples 256 \
--num_eval_samples 64 \
--num_epochs 1
```
> **Note:** The default ESM2 models on Hugging Face Hub ship custom modeling code.
> You must pass `--trust_remote_code` to allow loading that code.
## Customize
```bash
python examples/lora_finetuning_transformer_engine/lora_finetuning_te.py \
--base_model nvidia/esm2_t6_8M_UR50D \
--trust_remote_code \
--output_dir ./esm2_lora_output \
--max_length 256 \
--batch_size 4 \
--learning_rate 3e-4 \
--lora_r 16 \
--lora_alpha 32 \
--lora_dropout 0.1
```
## Dataset
By default the script generates a **synthetic dataset** at runtime — random protein-like sequences
with randomly generated secondary structure labels (`H`, `E`, `C`). This is useful for quick sanity checks and testing.
For a more realistic evaluation, you can use the **Porter6** secondary-structure dataset.
A download-and-convert script is available in the BioNeMo repository:
[prepare_porter6_dataset.py](https://github.com/NVIDIA/bionemo-framework/blob/bd72d882bca458d9438e05661c41163949713d1f/bionemo-recipes/recipes/esm2_peft_te/data/prepare_porter6_dataset.py)
Run it to produce train and validation parquet files, then pass them to the training script
with `--train_parquet` and `--val_parquet`:
```bash
python examples/lora_finetuning_transformer_engine/lora_finetuning_te.py \
--base_model nvidia/esm2_t6_8M_UR50D \
--train_parquet porter6_train_dataset_55k.parquet \
--val_parquet porter6_val_dataset_2024_692.parquet \
--output_dir ./esm2_lora_output \
--num_epochs 3
```
## Outputs
After training, the script saves:
- PEFT adapter weights/config in `--output_dir`
- Tokenizer files in `--output_dir`
## More examples
For additional examples of TransformerEngine-accelerated transformers, visit
`https://github.com/NVIDIA/bionemo-framework/bionemo-recipes`.
================================================
FILE: examples/lora_finetuning_transformer_engine/lora_finetuning_te.py
================================================
#!/usr/bin/env python3
"""
Transformer Engine ESM2 LoRA fine-tuning example using Hugging Face Trainer.
This script demonstrates:
1. Loading a Transformer Engine-based ESM2 token classification model
2. Applying LoRA adapters with PEFT
3. Building a synthetic protein token-classification dataset in code
4. Training with the Hugging Face Trainer (no DDP setup required)
"""
import os
# TE-backed models are incompatible with Trainer's DataParallel wrapping.
# Pin to a single GPU before torch is imported so torch.cuda.device_count() == 1.
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")
import argparse
import random
import numpy as np
import pandas as pd
import torch
from datasets import Dataset
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorForTokenClassification,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, TaskType, get_peft_model
SS3_ID2LABEL = {0: "H", 1: "E", 2: "C"}
SS3_LABEL2ID = {label: idx for idx, label in SS3_ID2LABEL.items()}
AMINO_ACIDS = "ACDEFGHIKLMNPQRSTVWY"
HELIX_AA = set("AELMQKRH")
BETA_AA = set("VIFYWT")
def parse_args():
"""Parse command-line arguments."""
parser = argparse.ArgumentParser(description="Transformer Engine ESM2 LoRA fine-tuning with Hugging Face Trainer")
parser.add_argument(
"--base_model",
type=str,
default="nvidia/esm2_t6_8M_UR50D",
help="Transformer Engine ESM2 model name or path",
)
parser.add_argument("--output_dir", type=str, default="./esm2_lora_output", help="Output directory")
parser.add_argument("--max_length", type=int, default=128, help="Maximum sequence length")
parser.add_argument("--num_train_samples", type=int, default=256, help="Number of synthetic training samples")
parser.add_argument("--num_eval_samples", type=int, default=64, help="Number of synthetic evaluation samples")
parser.add_argument("--min_seq_len", type=int, default=32, help="Minimum sequence length")
parser.add_argument("--max_seq_len", type=int, default=96, help="Maximum sequence length")
parser.add_argument("--batch_size", type=int, default=8, help="Per-device batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=5e-4, help="Learning rate")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay")
parser.add_argument("--logging_steps", type=int, default=10, help="Logging frequency (steps)")
parser.add_argument("--eval_steps", type=int, default=25, help="Evaluation frequency (steps)")
parser.add_argument("--save_steps", type=int, default=25, help="Save frequency (steps)")
parser.add_argument("--seed", type=int, default=42, help="Random seed")
parser.add_argument("--lora_r", type=int, default=8, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout")
parser.add_argument(
"--trust_remote_code",
action="store_true",
default=False,
help="Allow loading model code from the Hub. Required for models like nvidia/esm2_*.",
)
parser.add_argument(
"--train_parquet",
type=str,
default=None,
help="Path to a training parquet file with Sequence and Secondary_structure columns. "
"When provided, the synthetic dataset is not generated.",
)
parser.add_argument(
"--val_parquet",
type=str,
default=None,
help="Path to a validation parquet file with the same schema as --train_parquet.",
)
args = parser.parse_args()
if bool(args.train_parquet) != bool(args.val_parquet):
parser.error("--train_parquet and --val_parquet must both be provided or both omitted.")
return args
def set_seed(seed: int):
"""Set random seeds for reproducibility."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def build_synthetic_sequences(num_samples: int, min_len: int, max_len: int):
"""Generate random amino-acid sequences."""
sequences = []
for _ in range(num_samples):
length = random.randint(min_len, max_len)
sequences.append("".join(random.choices(AMINO_ACIDS, k=length)))
return sequences
def ss_char_to_label(char: str) -> int:
"""Map a single secondary structure character to a label id (H/E/C)."""
return SS3_LABEL2ID.get(char, SS3_LABEL2ID["C"])
def tokenize_and_align_labels(sequences, label_strings, tokenizer, max_length: int):
"""Tokenize protein sequences and align per-residue label strings to token positions."""
batch_input_ids = []
batch_attention_mask = []
batch_labels = []
for sequence, label_str in zip(sequences, label_strings):
encoded = tokenizer(
sequence,
truncation=True,
max_length=max_length,
add_special_tokens=True,
)
input_ids = encoded["input_ids"]
attention_mask = encoded["attention_mask"]
labels = [-100] * len(input_ids)
usable_len = min(len(sequence), len(label_str), len(input_ids) - 2)
for idx in range(usable_len):
labels[idx + 1] = ss_char_to_label(label_str[idx])
batch_input_ids.append(input_ids)
batch_attention_mask.append(attention_mask)
batch_labels.append(labels)
return {
"input_ids": batch_input_ids,
"attention_mask": batch_attention_mask,
"labels": batch_labels,
}
def load_parquet_dataset(train_path: str, val_path: str, tokenizer, max_length: int):
"""Load train/val parquet files and return tokenized Datasets."""
train_df = pd.read_parquet(train_path)
val_df = pd.read_parquet(val_path)
train_dataset = Dataset.from_pandas(train_df).map(
lambda ex: tokenize_and_align_labels(ex["Sequence"], ex["Secondary_structure"], tokenizer, max_length),
batched=True,
remove_columns=train_df.columns.tolist(),
)
val_dataset = Dataset.from_pandas(val_df).map(
lambda ex: tokenize_and_align_labels(ex["Sequence"], ex["Secondary_structure"], tokenizer, max_length),
batched=True,
remove_columns=val_df.columns.tolist(),
)
return train_dataset, val_dataset
def compute_metrics(eval_pred):
"""Compute token accuracy while ignoring -100 labels."""
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
mask = labels != -100
correct = (predictions == labels) & mask
total_tokens = mask.sum()
accuracy = float(correct.sum() / total_tokens) if total_tokens > 0 else 0.0
return {"token_accuracy": accuracy}
def residue_to_ss_char(aa: str) -> str:
"""Map an amino acid to a synthetic secondary structure character (H/E/C)."""
if aa in HELIX_AA:
return "H"
if aa in BETA_AA:
return "E"
return "C"
def sequence_to_synthetic_labels(sequence: str) -> str:
"""Derive a synthetic per-residue label string from an amino acid sequence."""
return "".join(residue_to_ss_char(aa) for aa in sequence)
def make_synthetic_dataset(
tokenizer,
num_train_samples: int,
num_eval_samples: int,
min_seq_len: int,
max_seq_len: int,
max_length: int,
):
"""Create a synthetic train/eval dataset for token classification."""
train_sequences = build_synthetic_sequences(num_train_samples, min_seq_len, max_seq_len)
eval_sequences = build_synthetic_sequences(num_eval_samples, min_seq_len, max_seq_len)
train_labels = [sequence_to_synthetic_labels(s) for s in train_sequences]
eval_labels = [sequence_to_synthetic_labels(s) for s in eval_sequences]
train_dataset = Dataset.from_dict({"sequence": train_sequences, "labels_str": train_labels}).map(
lambda ex: tokenize_and_align_labels(ex["sequence"], ex["labels_str"], tokenizer, max_length),
batched=True,
remove_columns=["sequence", "labels_str"],
)
eval_dataset = Dataset.from_dict({"sequence": eval_sequences, "labels_str": eval_labels}).map(
lambda ex: tokenize_and_align_labels(ex["sequence"], ex["labels_str"], tokenizer, max_length),
batched=True,
remove_columns=["sequence", "labels_str"],
)
return train_dataset, eval_dataset
def main():
args = parse_args()
set_seed(args.seed)
if not args.trust_remote_code and "esm2" in args.base_model.lower():
raise ValueError(
f"Model '{args.base_model}' requires remote code execution. "
"Re-run with --trust_remote_code to confirm you trust this model's code."
)
os.makedirs(args.output_dir, exist_ok=True)
use_bf16 = torch.cuda.is_available() and torch.cuda.is_bf16_supported()
model_dtype = torch.bfloat16 if use_bf16 else torch.float32
print(f"Loading tokenizer and model from: {args.base_model}")
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=args.trust_remote_code)
config = AutoConfig.from_pretrained(args.base_model, trust_remote_code=args.trust_remote_code)
config.num_labels = 3
config.id2label = SS3_ID2LABEL
config.label2id = SS3_LABEL2ID
model = AutoModelForTokenClassification.from_pretrained(
args.base_model,
config=config,
trust_remote_code=args.trust_remote_code,
dtype=model_dtype,
)
lora_config = LoraConfig(
task_type=TaskType.TOKEN_CLS,
r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=["layernorm_qkv"],
bias="none",
inference_mode=False,
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
if args.train_parquet and args.val_parquet:
print(f"Loading parquet datasets: train={args.train_parquet}, val={args.val_parquet}")
train_dataset, eval_dataset = load_parquet_dataset(
train_path=args.train_parquet,
val_path=args.val_parquet,
tokenizer=tokenizer,
max_length=args.max_length,
)
else:
print("Building synthetic dataset...")
train_dataset, eval_dataset = make_synthetic_dataset(
tokenizer=tokenizer,
num_train_samples=args.num_train_samples,
num_eval_samples=args.num_eval_samples,
min_seq_len=args.min_seq_len,
max_seq_len=args.max_seq_len,
max_length=args.max_length,
)
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
training_args = TrainingArguments(
output_dir=args.output_dir,
num_train_epochs=args.num_epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
logging_steps=args.logging_steps,
eval_steps=args.eval_steps,
save_steps=args.save_steps,
eval_strategy="steps",
save_strategy="steps",
save_total_limit=2,
load_best_model_at_end=True,
metric_for_best_model="token_accuracy",
greater_is_better=True,
report_to="none",
remove_unused_columns=False,
bf16=use_bf16,
seed=args.seed,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
compute_metrics=compute_metrics,
)
trainer.train()
final_metrics = trainer.evaluate()
print(f"Final evaluation metrics: {final_metrics}")
model.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
print(f"Saved LoRA adapter and tokenizer to: {args.output_dir}")
if __name__ == "__main__":
main()
================================================
FILE: examples/lora_finetuning_transformer_engine/requirements.txt
================================================
torch
transformers
datasets
peft
accelerate
numpy
pandas
pyarrow
safetensors
# transformer-engine must match the system CUDA toolkit version.
# Install it separately or use the system-provided package.
# See: https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/installation.html
================================================
FILE: examples/lora_ga_finetuning/README.md
================================================
# LoRA-GA: Low-Rank Adaptation with Gradient Approximation
## Introduction
[LoRA-GA](https://huggingface.co/papers/2407.05000) improves upon standard LoRA by using gradient information during initialization instead of random initialization. By performing SVD on estimated gradients, LoRA-GA initializes adapter weights in a direction that aligns with full fine-tuning, achieving 2-4x faster convergence while maintaining the same final performance. The method is orthogonal to existing LoRA variants and can be easily integrated with techniques like DoRA and LoRA+.
## Quick start
This example script demonstrates how to fine-tune a language model using LoRA-GA on the WikiText-2 dataset. The script performs gradient estimation on a small number of batches, uses those gradients to initialize LoRA adapters, and then trains the model with the Hugging Face Trainer.
```python
import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from torch.utils.data import DataLoader
from peft import LoraConfig, get_peft_model
from peft.tuners.lora import LoraGAConfig, preprocess_loraga
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
# Prepare dataloader for gradient estimation
train_dataloader = DataLoader(dataset["train"], batch_size=2, shuffle=True)
# Define train_step callback for gradient estimation
def train_step():
"""Run forward and backward passes for gradient estimation."""
data_iter = iter(train_dataloader)
for _ in range(64): # 64 iterations
batch = next(data_iter)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
# Step 1: Create LoRA-GA config
lora_ga_config = LoraGAConfig(
direction="ArB2r",
scale="stable",
stable_gamma=16,
)
lora_config = LoraConfig(
r=8,
lora_alpha=16,
target_modules=["c_attn"],
init_lora_weights="lora_ga",
lora_ga_config=lora_ga_config,
task_type="CAUSAL_LM",
)
# Step 2: Preprocess with LoRA-GA to estimate gradients
preprocess_loraga(model, lora_config, train_step)
# Step 3: Create PEFT model with LoRA-GA initialization
peft_model = get_peft_model(model, lora_config)
# Step 4: Train normally
trainer = Trainer(
model=peft_model,
train_dataset=dataset["train"],
args=TrainingArguments(output_dir="./output", num_train_epochs=3),
)
trainer.train()
# Step 5: Save the trained adapter
peft_model.save_pretrained("./output")
```
## Saving with Modified Base Weights
**Important**: LoRA-GA modifies the base model weights during initialization (unlike standard LoRA). This means you need to handle saving carefully if you want to restore the original base weights.
### Option 1: Save adapter only (default)
The standard `save_pretrained()` saves the adapter with the modified base weights embedded:
```python
# This saves the adapter - base weights remain modified
peft_model.save_pretrained("./output")
```
### Option 2: Restore original base weights
If you need to restore the original base weights (e.g., for model merging or sharing), use `path_initial_model_for_weight_conversion`:
```python
# Save the original model BEFORE LoRA-GA preprocessing
model.save_pretrained("./original_model")
# ... do preprocessing and training ...
# Save adapter and convert back to original base weights
peft_model.save_pretrained(
"./output",
path_initial_model_for_weight_conversion="./original_model"
)
```
This is useful when:
- You want to merge the adapter with the original base weights later
- You're sharing the adapter and want users to apply it to the unmodified base model
- You need the base model weights in their original state for other purposes
## Run the finetuning script
Simply run:
```bash
python examples/lora_ga_finetuning/lora_ga_finetuning.py \
--base_model gpt2 \
--dataset_name wikitext \
--dataset_config wikitext-2-raw-v1 \
--output_dir ./lora_ga_output
```
### Customize LoRA-GA parameters
You can customize the direction and scaling strategies:
```bash
python examples/lora_ga_finetuning/lora_ga_finetuning.py \
--base_model gpt2 \
--direction ArB2r \
--scale stable \
--stable_gamma 16 \
--grad_estimate_iters 64
```
### Full example with all parameters
```bash
python lora_ga_finetuning.py \
--base_model "gpt2" \
--dataset_name "wikitext" \
--dataset_config "wikitext-2-raw-v1" \
--output_dir "./lora_ga_output" \
--r 8 \
--lora_alpha 16 \
--lora_dropout 0.1 \
--direction "ArB2r" \
--scale "stable" \
--stable_gamma 16 \
--grad_estimate_iters 64 \
--grad_estimate_batch_size 2 \
--num_epochs 3 \
--batch_size 8 \
--learning_rate 3e-5
```
## Configuration Options
### Direction Strategies
Controls how SVD components are distributed to lora_A and lora_B:
- `ArBr`: Alternating distribution - A takes odd indices, B takes even indices
- `A2rBr`: A takes second half, B takes first half
- `ArB2r` (default): A takes first half, B takes second half - typically performs best
- `random`: Random selection of singular vectors
### Scaling Strategies
Controls initialization magnitude:
- `stable` (default): Conservative scaling using stable_gamma parameter for stable training
- `weight_svd`: Scales based on SVD of original weights for better alignment
- `gd_scale`: Scales based on gradient descent step size
- `unit`: Unit scaling (no adjustment)
## Use the model on 🤗
You can load and use the model as any other 🤗 models:
```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(model, "path/to/lora_ga_output")
```
## LoRA-GA vs. LoRA
Key differences and advantages:
1. **Faster Convergence**: LoRA-GA achieves 2-4x faster convergence compared to standard LoRA due to gradient-aligned initialization.
2. **Same Final Performance**: LoRA-GA maintains the same or better final performance as standard LoRA.
3. **Initialization Overhead**: LoRA-GA requires a gradient estimation phase (typically 1-2 minutes for 64 iterations), but this is quickly amortized during training.
4. **Orthogonal to Other Methods**: LoRA-GA can be combined with DoRA, LoRA+, quantization, and other LoRA enhancements.
## API Design
LoRA-GA follows the same pattern as PiSSA, OLoRA, and CorDA:
1. **Preprocessing**: Use `preprocess_loraga(model, lora_config, train_step)` to estimate gradients and attach them to model layers
2. **Configuration**: Use `LoraGAConfig` as a sub-config within `LoraConfig` with `init_lora_weights="lora_ga"`
3. **Initialization**: Call `get_peft_model()` after preprocessing to create the PEFT model with LoRA-GA initialization
4. **Training**: Train normally using Hugging Face Trainer or your own training loop
5. **Saving**: Use standard `save_pretrained()` to save the trained adapter
## Using LoRA-GA with Quantized Models
LoRA-GA requires full-precision gradients during preprocessing. For quantized models (e.g., BitsAndBytes 4-bit/8-bit), use a two-stage workflow:
### Step 1: Estimate gradients with full-precision model
```python
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
from peft.tuners.lora import LoraGAConfig, preprocess_loraga
# Load model in full precision for gradient estimation
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Configure LoRA-GA
lora_config = LoraConfig(
r=8,
target_modules=["q_proj", "v_proj"],
init_lora_weights="lora_ga",
lora_ga_config=LoraGAConfig(direction="ArB2r", scale="stable"),
)
# Define your train_step (same as before)
def train_step():
for _ in range(64):
# Your training logic here
outputs = model(**batch)
loss = outputs.loss
loss.backward()
# Estimate and cache gradients
preprocess_loraga(model, lora_config, train_step, cache_file="loraga_gradients.pt")
# Clean up full-precision model
del model
torch.cuda.empty_cache()
```
### Step 2: Load quantized model and apply LoRA-GA
```python
# Load model with quantization
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=quantization_config,
device_map="auto"
)
# Apply LoRA-GA - gradients will be loaded from cache automatically
peft_model = get_peft_model(model, lora_config)
# Train normally
trainer.train()
```
**Key points:**
- Gradient estimation must use a non-quantized model (full precision or bfloat16/float16)
- Cache gradients with `cache_file` parameter to avoid re-computation
- Cached gradients are automatically loaded when applying LoRA to the quantized model
- This workflow allows memory-efficient training with quantized models while benefiting from LoRA-GA's faster convergence
## Tips
- **Gradient Estimation**: 64-128 iterations is typically sufficient. More iterations provide more accurate estimation but increase initialization time.
- **Batch Size**: Use smaller batch sizes (2-4) for gradient estimation to maximize gradient diversity.
- **Direction and Scale**: The default `direction="ArB2r"` and `scale="stable"` work well in most cases.
- **User-Defined Callback**: The `train_step` callback gives you full control over the gradient estimation process. You can customize batching, loss functions, and more.
- **Gradient Accumulation**: Do NOT call `model.zero_grad()` or `optimizer.zero_grad()` inside your `train_step` callback. LoRA-GA relies on PyTorch's natural gradient accumulation across iterations.
## Citation
```bibtex
@article{wang2024loraga,
title={LoRA-GA: Low-Rank Adaptation with Gradient Approximation},
author={Wang, Shaowen and Zhu, Linxi and Ding, Hengyuan and Liu, Jiaqi and Chen, Jiaming and Zhu, Kaikai and Pang, Wei and Zhu, Jun and You, Yang},
journal={arXiv preprint arXiv:2407.05000},
year={2024}
}
```
================================================
FILE: examples/lora_ga_finetuning/lora_ga_finetuning.py
================================================
#!/usr/bin/env python3
"""
Example script demonstrating LoRA-GA (Low-Rank Adaptation with Gradient Approximation) fine-tuning.
LoRA-GA improves upon standard LoRA by using gradient information during initialization,
achieving 2-4x faster convergence while maintaining the same final performance.
This example shows:
1. How to define a train_step callback for gradient estimation
2. How to use preprocess_loraga for LoRA-GA initialization
3. Training with standard Hugging Face Trainer
4. Saving the trained adapter
"""
import argparse
import os
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
default_data_collator,
)
from peft import LoraConfig, get_peft_model
from peft.tuners.lora import LoraGAConfig, preprocess_loraga
def parse_args():
parser = argparse.ArgumentParser(description="LoRA-GA fine-tuning example")
# Model arguments
parser.add_argument("--base_model", type=str, default="gpt2", help="Base model name or path")
parser.add_argument("--output_dir", type=str, default="./lora_ga_output", help="Output directory")
# Dataset arguments
parser.add_argument("--dataset_name", type=str, default="wikitext", help="Dataset name")
parser.add_argument("--dataset_config", type=str, default="wikitext-2-raw-v1", help="Dataset configuration")
parser.add_argument("--max_length", type=int, default=512, help="Maximum sequence length")
# LoRA-GA configuration
parser.add_argument("--r", type=int, default=8, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.1, help="LoRA dropout")
parser.add_argument(
"--target_modules",
type=str,
nargs="+",
default=["c_attn"],
help="Target modules for LoRA (e.g., c_attn for GPT-2)",
)
parser.add_argument(
"--direction",
type=str,
default="ArB2r",
choices=["ArBr", "A2rBr", "ArB2r", "random"],
help="Direction strategy for LoRA-GA initialization",
)
parser.add_argument(
"--scale",
type=str,
default="stable",
choices=["stable", "weight_svd", "gd_scale", "unit"],
help="Scaling strategy for LoRA-GA initialization",
)
parser.add_argument("--stable_gamma", type=int, default=16, help="Gamma for stable scaling")
# Gradient estimation arguments
parser.add_argument(
"--grad_estimate_iters", type=int, default=64, help="Number of iterations for gradient estimation"
)
parser.add_argument("--grad_estimate_batch_size", type=int, default=2, help="Batch size for gradient estimation")
# Training arguments
parser.add_argument("--num_epochs", type=int, default=3, help="Number of training epochs")
parser.add_argument("--batch_size", type=int, default=8, help="Training batch size")
parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate")
parser.add_argument("--warmup_steps", type=int, default=100, help="Warmup steps")
parser.add_argument("--logging_steps", type=int, default=10, help="Logging steps")
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint steps")
parser.add_argument("--eval_steps", type=int, default=500, help="Evaluation steps")
# Other arguments
parser.add_argument("--seed", type=int, default=42, help="Random seed")
return parser.parse_args()
def prepare_dataset(dataset_name, dataset_config, tokenizer, max_length):
"""Load and prepare the dataset."""
print(f"\nLoading dataset: {dataset_name}/{dataset_config}")
dataset = load_dataset(dataset_name, dataset_config)
def tokenize_function(examples):
# For causal language modeling, we tokenize and set labels = input_ids
result = tokenizer(
examples["text"], padding="max_length", truncation=True, max_length=max_length, return_tensors="pt"
)
result["labels"] = result["input_ids"].clone()
return result
# Tokenize the dataset
print("Tokenizing dataset...")
tokenized_datasets = dataset.map(
tokenize_function, batched=True, remove_columns=dataset["train"].column_names, desc="Tokenizing"
)
return tokenized_datasets
def main():
args = parse_args()
# Set random seed
torch.manual_seed(args.seed)
# Create output directory
os.makedirs(args.output_dir, exist_ok=True)
# Load tokenizer and model
print(f"\nLoading model: {args.base_model}")
tokenizer = AutoTokenizer.from_pretrained(args.base_model)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(args.base_model)
# Prepare dataset
tokenized_datasets = prepare_dataset(args.dataset_name, args.dataset_config, tokenizer, args.max_length)
# Create LoRA-GA configuration
print("\nCreating LoRA-GA configuration...")
lora_ga_config = LoraGAConfig(
direction=args.direction,
scale=args.scale,
stable_gamma=args.stable_gamma,
)
lora_config = LoraConfig(
r=args.r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=args.target_modules,
bias="none",
task_type="CAUSAL_LM",
init_lora_weights="lora_ga",
lora_ga_config=lora_ga_config,
)
print(f" Direction: {args.direction}")
print(f" Scale: {args.scale}")
print(f" Rank: {args.r}, Alpha: {args.lora_alpha}")
# ===== GRADIENT ESTIMATION PHASE =====
print("\n" + "=" * 70)
print("GRADIENT ESTIMATION PHASE")
print("=" * 70)
print(f"Estimating gradients over {args.grad_estimate_iters} iterations...")
print("This allows LoRA-GA to initialize adapters aligned with full fine-tuning.")
# Prepare gradient estimation dataloader
train_dataset = tokenized_datasets["train"]
# Create a simple DataLoader for gradient estimation
grad_dataloader = DataLoader(
train_dataset,
batch_size=args.grad_estimate_batch_size,
shuffle=True,
collate_fn=default_data_collator,
)
# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.train()
# Define train_step callback
def train_step():
"""Run forward and backward passes for gradient estimation."""
grad_iter = iter(grad_dataloader)
for _ in range(args.grad_estimate_iters):
batch = next(grad_iter)
# Move batch to device
batch = {k: v.to(device) for k, v in batch.items()}
# Forward pass
outputs = model(**batch)
loss = outputs.loss
# Backward pass
loss.backward()
# Preprocess with LoRA-GA
print("Running gradient estimation...")
preprocess_loraga(model, lora_config, train_step)
print("✓ Gradient estimation complete!")
# ===== MODEL INITIALIZATION PHASE =====
print("\n" + "=" * 70)
print("LORA-GA INITIALIZATION PHASE")
print("=" * 70)
print("Initializing LoRA adapters with gradient information...")
# Create PEFT model with LoRA-GA initialization
peft_model = get_peft_model(model, lora_config)
# Print trainable parameters
peft_model.print_trainable_parameters()
# ===== TRAINING PHASE =====
print("\n" + "=" * 70)
print("TRAINING PHASE")
print("=" * 70)
print("Starting training with LoRA-GA initialized adapters...")
print("LoRA-GA achieves 2-4x faster convergence compared to random initialization!\n")
# Setup training arguments
training_args = TrainingArguments(
output_dir=args.output_dir,
num_train_epochs=args.num_epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.learning_rate,
warmup_steps=args.warmup_steps,
logging_steps=args.logging_steps,
save_steps=args.save_steps,
eval_steps=args.eval_steps,
eval_strategy="steps",
save_total_limit=2,
load_best_model_at_end=True,
report_to="none",
seed=args.seed,
)
# Data collator
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Create Trainer
trainer = Trainer(
model=peft_model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
data_collator=data_collator,
)
# Train the model
trainer.train()
# ===== SAVING PHASE =====
print("\n" + "=" * 70)
print("SAVING PHASE")
print("=" * 70)
print("Saving trained adapter...")
# Save the trained adapter
peft_model.save_pretrained(args.output_dir)
print(f"\n✓ Training complete! Model saved to: {args.output_dir}")
print("\nSaved files:")
print(" - adapter_model.safetensors: Trained adapter weights")
print(" - adapter_config.json: Configuration file")
print("\n" + "=" * 70)
print("DONE!")
print("=" * 70)
print("\nYou can now use the trained adapter with:")
print(" from peft import PeftModel")
print(" from transformers import AutoModelForCausalLM")
print(f" model = AutoModelForCausalLM.from_pretrained('{args.base_model}')")
print(f" model = PeftModel.from_pretrained(model, '{args.output_dir}')")
if __name__ == "__main__":
main()
================================================
FILE: examples/lorafa_finetune/README.md
================================================
# LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning
## Introduction
[LoRA-FA](https://huggingface.co/papers/2308.03303) is a noval Parameter-efficient Fine-tuning method, which freezes the projection down layer (matrix A) during LoRA training process and thus lead to less accelerator memory consumption by eliminating the need for storing the activations of input tensors (X). Furthermore, LoRA-FA narrows the gap between the update amount of pre-trained weights when using the low-rank fine-tuning method and the full fine-tuning method. In conclusion, LoRA-FA reduces the memory consumption and leads to superior performance compared to vanilla LoRA.
## Quick start
```python
import torch
from peft import LoraConfig, get_peft_model
from peft.optimizers import create_lorafa_optimizer
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
lora_rank = 16
lora_alpha = 32
lora_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
bias="none",
)
peft_model = get_peft_model(model, lora_config)
optimizer = create_lorafa_optimizer(
model=peft_model,
r=lora_rank,
lora_alpha=lora_alpha,
lr=7e-5,
)
# you can also use scheduler, we recommend get_cosine_schedule_with_warmup from transformers
# for better model performance
scheduler = None
trainer = transformers.Trainer(
model=peft_model,
train_dataset=dataset,
dataset_text_field="text",
max_length=2048,
processing_class=tokenizer,
optimizers=(optimizer, None),
)
trainer.train()
peft_model.save_pretrained("lorafa-llama-3-8b-inst")
```
The only change in your code is to pass the LoRA-FA optimizer to the trainer (if training with trainer). Do not forget `from peft.optimizers import create_lorafa_optimizer`!
## Example
In this dir, we also provide you a simple example for fine-tuning with LoRA-FA optimizer.
### Run on CPU, single-accelerator or multi-accelerator
This 👇 by default will load the model in peft set up with LoRA config, and train the model with LoRA-FA optimizer.
0. CPU
You can simply run LoRA-FA as below:
```bash
python lorafa_finetuning.py --base_model_name_or_path meta-llama/Meta-Llama-3-8B --dataset_name_or_path meta-math/MetaMathQA-40K --output_dir path/to/output --lorafa
```
1. Single-accelerator
Run the finetuning script on 1 accelerator:
```bash
export CUDA_VISIBLE_DEVICES=0 # force to use CUDA GPU 0
export ZE_AFFINITY_MASK=0 # force to use Intel XPU 0
python lorafa_finetuning.py --base_model_name_or_path meta-llama/Meta-Llama-3-8B --dataset_name_or_path meta-math/MetaMathQA-40K --output_dir path/to/output --lorafa
```
2. Multi-accelerator
LoRA-FA can also be run on multi-accelerator, with 🤗 Accelerate:
```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3 # force to use CUDA GPU 0,1,2,3
export ZE_AFFINITY_MASK=0,1,2,3 # force to use Intel XPU 0,1,2,3
accelerate launch lorafa_finetuning.py --base_model_name_or_path meta-llama/Meta-Llama-3-8B --dataset_name_or_path meta-math/MetaMathQA-40K --output_dir path/to/output --lorafa
```
The `accelerate launch` will automatically configure multi-accelerator for you. You can also utilize `accelerate launch` in single-accelerator scenario.
### Use the model from 🤗
You can load and use the model as any other 🤗 models.
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
```
## Best practice in fine-tuning Llama using LoRA-FA: the hyper-params
Sometimes, achieving optimal LoRA fine-tuning can be challenging due to the larger number of hyperparameters to consider compared to full fine-tuning. For instance, not only do we need to adjust the commonly used learning rate, but the ideal LoRA rank may also vary depending on the specific model and task. Additionally, there are other factors to consider, such as LoRA alpha and sequence length. To assist with this, we have created a repository of reproducible best practices in the [LoRA-FA examples](https://github.com/AaronZLT/lorafa) for reference. This resource showcases the optimal LoRA-FA fine-tuning hyperparameters for different models across various datasets. By doing so, we significantly reduce the time and effort spent on hyperparameter tuning, and it may also provide insights for tuning other training hyperparameters. We encourage you to experiment and fine-tune on your own downstream tasks as well.
## LoRA-FA's advantages and limitations
By eliminating the activation of adapter A, LoRA-FA uses less memory for fine-tuning compared to LoRA. For instance, when fine-tuning Llama-2-7b-chat-hf with a batch size of 8 and a sequence length of 1024, LoRA-FA requires 36GB of memory to store activations. This allows it to run successfully on an 80GB accelerator. In contrast, LoRA requires at least 60GB of memory for activations, leading to an Out of Memory (OOM) error. Additionally, the memory consumption of LoRA-FA is not sensitive to the rank, allowing for performance improvements by increasing the LoRA rank without additional memory usage. LoRA-FA further narrows the performance gap with Full-FT by minimizing the discrepancy between the low-rank gradient and the full gradient, enabling it to achieve performance that is on par with or even superior to vanilla LoRA.
Despite its advantages, LoRA-FA is inherently limited by its low-rank approximation nature and potential issues with catastrophic forgetting. The gradient approximation can impact training throughput. Addressing these limitations, especially in terms of approximation accuracy and forgetting phenomena, presents a promising direction for future research.
## Citation
```
@misc{zhang2023lorafamemoryefficientlowrankadaptation,
title={LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning},
author={Longteng Zhang and Lin Zhang and Shaohuai Shi and Xiaowen Chu and Bo Li},
year={2023},
eprint={2308.03303},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://huggingface.co/papers/2308.03303},
}
```
================================================
FILE: examples/lorafa_finetune/lorafa_finetuning.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Optional
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from peft.optimizers import create_lorafa_optimizer
def train_model(
base_model_name_or_path: str,
dataset_name_or_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
lr: float,
cutoff_len: int,
quantize: bool,
eval_step: int,
save_step: int,
lora_rank: int,
lora_alpha: int,
lora_dropout: float,
lora_target_modules: Optional[str],
lorafa: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
is_bf16_supported = False
device_map = "cpu"
if torch.cuda.is_available():
is_bf16_supported = torch.cuda.is_bf16_supported()
device_map = "cuda"
elif torch.xpu.is_available():
is_bf16_supported = torch.xpu.is_bf16_supported()
device_map = "xpu"
compute_dtype = torch.bfloat16 if is_bf16_supported else torch.float16
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
# load model
if quantize:
model = AutoModelForCausalLM.from_pretrained(
base_model_name_or_path,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type="nf4",
),
dtype=compute_dtype,
device_map=device_map,
)
# setup for quantized training
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(
base_model_name_or_path, dtype=compute_dtype, device_map=device_map
)
# LoRA config for the PEFT model
if lora_target_modules is not None:
if lora_target_modules == "all-linear":
target_modules = "all-linear"
else:
target_modules = lora_target_modules.split(",")
else:
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
lora_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
target_modules=target_modules,
lora_dropout=lora_dropout,
bias="none",
)
# get the peft model with LoRA config
model = get_peft_model(model, lora_config)
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(dataset_name_or_path)
def tokenize_function(examples):
inputs = tokenizer(examples["query"], padding="max_length", truncation=True, max_length=cutoff_len)
outputs = tokenizer(examples["response"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = outputs["input_ids"].copy()
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
dataset = tokenized_datasets["train"].train_test_split(test_size=0.1, shuffle=True, seed=42)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
gradient_accumulation_steps=1,
bf16=True if compute_dtype == torch.bfloat16 else False,
fp16=True if compute_dtype == torch.float16 else False,
learning_rate=lr,
)
# Here we initialize the LoRA-FA Optimizer
# After this, all adapter A will be fixed, only adapter B will be trainable
if lorafa:
optimizer = create_lorafa_optimizer(
model=model, r=lora_rank, lora_alpha=lora_alpha, lr=lr, weight_decay=training_args.weight_decay
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
optimizers=(optimizer, None),
)
else:
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune Meta-Llama-3-8B-Instruct with LoRA-FA and PEFT")
parser.add_argument(
"--base_model_name_or_path",
type=str,
default="meta-llama/Meta-Llama-3-8B-Instruct",
help="Base model name or path",
)
parser.add_argument(
"--dataset_name_or_path", type=str, default="meta-math/MetaMathQA-40K", help="Dataset name or path"
)
parser.add_argument("--output_dir", type=str, help="Output directory for the fine-tuned model")
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=3, help="Number of training epochs")
parser.add_argument("--lr", type=float, default=7e-5, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=1024, help="Cutoff length for tokenization")
parser.add_argument("--quantize", action="store_true", help="Use quantization")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--lora_rank", type=int, default=16, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=32, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate")
parser.add_argument(
"--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA"
)
parser.add_argument("--lorafa", action="store_true", help="Use LoRA-FA Optimizer")
args = parser.parse_args()
train_model(
base_model_name_or_path=args.base_model_name_or_path,
dataset_name_or_path=args.dataset_name_or_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
lr=args.lr,
cutoff_len=args.cutoff_len,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
lorafa=args.lorafa,
)
================================================
FILE: examples/miss_finetuning/README.md
================================================
# MiSS: Balancing LoRA Performance and Efficiency with Simple Shard Sharing
## Introduction ([Paper](https://huggingface.co/papers/2409.15371), [code](https://github.com/JL-er/MiSS))
MiSS (Matrix Shard Sharing) is a novel PEFT method that adopts a low-rank structure, requires only a single trainable matrix, and introduces a new update mechanism distinct from LoRA, achieving an excellent balance between performance and efficiency.
## Quick Start
```python
import torch
from peft import MissConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer.pad_token_id = tokenizer.eos_token_id
miss_config = MissConfig(
r = 64
)
#bat: In this mode, you can enable nonlinear updates across different shards.
# miss_config = MissConfig(
# r = 64,
# init_weights="bat"
# )
# mini: In this mode, you can set a smaller rank to use fewer trainable parameters, but it is recommended to keep `out_features % mini_r == 0`.
# miss_config = MissConfig(
# r = 64,
# init_weights="mini",
# mini_r = 8
# )
peft_model = get_peft_model(model, miss_config)
peft_model.print_trainable_parameters()
dataset = load_dataset("imdb", split="train[:1%]")
training_args = SFTConfig(dataset_text_field="text", max_length=128)
trainer = SFTTrainer(
model=peft_model,
args=training_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("miss-llama-2-7b")
```
To utilize the fine-tuned MiSS modules, simply run the following command:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto"
)
peft_model = PeftModel.from_pretrained(model, "miss-llama-2-7b")
```
## Advanced Usage
### Fine-tune
```shell
#Bat performs better than MiSS, but it uses more memory and is twice as slow. If you want to use the Bat method, you only need to add the parameter init_weights="bat".
python miss_finetuning.py \
--base_model_name_or_path meta-llama/Llama-2-7b-hf \
--output_dir output/miss-llama-2-7b-metamath-10k \
--miss_r 64 \
--init_weights True \
--bits bf16 \
--data_path meta-math/MetaMathQA \
--dataset_split train[:100000] \
--dataset_field query response \
--bf16 True \
--num_train_epochs 1 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--save_strategy "steps" \
--save_steps 1000 \
--save_total_limit 1 \
--logging_steps 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_steps 0.03 \
--tf32 True \
--report_to none
```
# Citation
```bib
@misc{kang2025balancingloraperformanceefficiency,
title={Balancing LoRA Performance and Efficiency with Simple Shard Sharing},
author={Jiale Kang and Qingyu Yin},
year={2025},
eprint={2409.15371},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.15371},
}
================================================
FILE: examples/miss_finetuning/miss_finetuning.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dataclasses import dataclass, field
from typing import Literal, Optional
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
from trl import SFTConfig, SFTTrainer
from peft import MissConfig, get_peft_model
@dataclass
class ScriptArguments(SFTConfig):
# model configs
base_model_name_or_path: Optional[str] = field(
default=None, metadata={"help": "The name or path of the fp32/16 base model."}
)
bits: str = field(default="bf16", metadata={"help": "(`['bf16', 'fp16', fp32]`)"})
init_weights: Literal[True, "bat"] = field(
default=True,
metadata={
"help": (
"True -> MiSS efficience and balance; `bat` -> Bat, `mini` -> smaller MiSS efficience and balance"
),
},
)
miss_r: int = field(default=16)
merge_and_save: bool = field(default=False)
# dataset configs
data_path: str = field(default="imdb", metadata={"help": "Path to the training data."})
dataset_split: str = field(default="train[:1%]", metadata={"help": "(`['train', 'test', 'eval']`):"})
dataset_field: list[str] = field(default=None, metadata={"help": "Fields of dataset input and output."})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
print(script_args)
print(f"Load pre-processed residual model in {script_args.bits} bits.")
if script_args.bits in ["nf4", "fp4", "int8"]:
print("MiSS currently does not support quantization.")
elif script_args.base_model_name_or_path is not None:
print(f"No available pre-processed model, manually initialize a MiSS using {script_args.base_model_name_or_path}.")
model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name_or_path,
dtype=(
torch.float16
if script_args.bits == "fp16"
else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
),
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name_or_path)
tokenizer.pad_token_id = tokenizer.eos_token_id
miss_config = MissConfig(
r=script_args.miss_r,
target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
bias="none",
task_type="CAUSAL_LM",
init_weights=script_args.init_weights,
)
peft_model = get_peft_model(model, miss_config)
print(peft_model)
peft_model.print_trainable_parameters()
print(f"Training MiSS with trl on the {script_args.data_path}[{script_args.dataset_split}] dataset.")
dataset = load_dataset(script_args.data_path, split=script_args.dataset_split)
dataset = dataset.map(
lambda example: {
"text": f"### USER: {example[script_args.dataset_field[0]]}\n### ASSISTANT: {example[script_args.dataset_field[1]]}"
}
)
trainer = SFTTrainer(
model=peft_model,
args=script_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
trainer.save_state()
peft_model.save_pretrained(
os.path.join(script_args.output_dir, "miss_ft"),
)
if script_args.merge_and_save:
model = peft_model.merge_and_unload()
model.save_pretrained(os.path.join(script_args.output_dir, "miss_merged"))
tokenizer.save_pretrained(os.path.join(script_args.output_dir, "miss_merged"))
================================================
FILE: examples/multi_adapter_examples/Lora_Merging.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "db4208b9-5da4-46df-b77a-0f1836c9e4ec",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\" # force using CUDA device 1\n",
"os.environ[\"ZE_AFFINITY_MASK\"] = \"1\" # force using Intel XPU device 1\n",
"from peft import PeftConfig, PeftModel\n",
"from peft import PeftModel, PeftConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
"from datasets import load_dataset\n",
"import torch\n",
"import random\n",
"\n",
"peft_model_id = \"smangrul/tinyllama_lora_norobots\"\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model_kwargs = {\"device_map\": \"auto\"}\n",
"model_kwargs[\"quantization_config\"] = BitsAndBytesConfig(load_in_4bit=True)\n",
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, **model_kwargs)\n",
"tokenizer = AutoTokenizer.from_pretrained(peft_model_id)\n",
"model.resize_token_embeddings(len(tokenizer))\n",
"model = PeftModel.from_pretrained(model, peft_model_id, adapter_name=\"norobots\")\n",
"_ = model.load_adapter(\"smangrul/tinyllama_lora_sql\", adapter_name=\"sql\")\n",
"_ = model.load_adapter(\"smangrul/tinyllama_lora_adcopy\", adapter_name=\"adcopy\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "541dab43-9675-42a2-8d90-7437df9f0fa0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 17.1 s, sys: 458 ms, total: 17.5 s\n",
"Wall time: 1.94 s\n"
]
}
],
"source": [
"%%time\n",
"# [0.8, 0.1, 0.1] linear #[1.0, 0.2] 0.7 density dare_linear #[1.5, 0.3] 0.5 density ties #[0.8, 0.5] cat\n",
"adapters = [\"norobots\", \"adcopy\", \"sql\"]\n",
"weights = [2.0, 0.3, 0.7]\n",
"adapter_name = \"merge\"\n",
"density = 0.2\n",
"combination_type = \"ties\"\n",
"if adapter_name in model.peft_config:\n",
" model.delete_adapter(adapter_name)\n",
"model.add_weighted_adapter(adapters, weights, adapter_name, combination_type=combination_type, density=density)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "76596671-3677-47f0-9d66-81f40bc4d726",
"metadata": {},
"outputs": [],
"source": [
"model.eval()\n",
"model.set_adapter(\"merge\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d59f9f3-6313-43d8-be36-4ca2bbb105b2",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Write an essay about Generative AI.\"},\n",
"]\n",
"text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)\n",
"inputs = tokenizer(text, return_tensors=\"pt\") # , add_special_tokens=False)\n",
"inputs = {k: v.to(device) for k, v in inputs.items()}\n",
"outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens=256,\n",
" do_sample=True,\n",
" top_p=0.95,\n",
" temperature=0.2,\n",
" repetition_penalty=1.2,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
")\n",
"print(tokenizer.decode(outputs[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5c1daeb-59c8-41d7-bebb-7abd052ab917",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<|im_start|>system \n",
"Create a text ad given the following product and description.<|im_end|> \n",
"<|im_start|>user \n",
"Product: Sony PS5 PlayStation Console\n",
"Description: The PS5™ console unleashes new gaming possibilities that you never anticipated.<|im_end|> \n",
"<|im_start|>assistant \n",
"Ad Text: Experience the next-gen power of the all-new Sony PS5 with its stunning visuals, innovative gameplay features, and more! Get ready to play in style as you experience the future of gaming on your own terms.<|im_end|>\n"
]
}
],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": \"Create a text ad given the following product and description.\"},\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Product: Sony PS5 PlayStation Console\\nDescription: The PS5™ console unleashes new gaming possibilities that you never anticipated.\",\n",
" },\n",
"]\n",
"text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)\n",
"inputs = tokenizer(text, return_tensors=\"pt\") # , add_special_tokens=False)\n",
"inputs = {k: v.to(device) for k, v in inputs.items()}\n",
"outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens=128,\n",
" do_sample=True,\n",
" top_p=0.95,\n",
" temperature=0.2,\n",
" repetition_penalty=1.2,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
")\n",
"print(tokenizer.decode(outputs[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5bb08b46-90ae-48a8-8783-ca74b3e26e42",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Table: 2-11365528-2\n",
"Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location']\n",
"Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic?\n",
"SQL Query: SELECT Head Coach FROM 2-11365528-2 WHERE President = Mario Volarevic\n"
]
}
],
"source": [
"text = \"\"\"Table: 2-11365528-2\n",
"Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location']\n",
"Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic?\n",
"SQL Query:\"\"\"\n",
"\n",
"inputs = tokenizer(text, return_tensors=\"pt\") # , add_special_tokens=False)\n",
"inputs = {k: v.to(device) for k, v in inputs.items()}\n",
"outputs = model.generate(\n",
" **inputs, max_new_tokens=64, repetition_penalty=1.1, eos_token_id=tokenizer(\"\").input_ids[-1]\n",
")\n",
"print(tokenizer.decode(outputs[0]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "jONLwzXgLg-I",
"metadata": {
"id": "jONLwzXgLg-I"
},
"outputs": [],
"source": [
"!pip install -q git+https://github.com/huggingface/transformers.git\n",
"!pip install -q git+https://github.com/huggingface/peft.git\n",
"!pip install -q git+https://github.com/huggingface/accelerate.git@main\n",
"!pip install huggingface_hub\n",
"!pip install bitsandbytes\n",
"!pip install SentencePiece"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36460935",
"metadata": {
"id": "36460935"
},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" # force using CUDA device 0\n",
"os.environ[\"ZE_AFFINITY_MASK\"] = \"0\" # force using Intel XPU device 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1351e04c",
"metadata": {
"id": "1351e04c"
},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d85af699",
"metadata": {
"id": "d85af699"
},
"outputs": [],
"source": [
"from peft import PeftModel\n",
"from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig, BitsAndBytesConfig\n",
"\n",
"model_name = \"meta-llama/Llama-2-7b-hf\"\n",
"tokenizer = LlamaTokenizer.from_pretrained(model_name)\n",
"model = LlamaForCausalLM.from_pretrained(model_name, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map=\"auto\", use_auth_token=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0f515ed",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f0f515ed",
"outputId": "312488a5-f4f8-48a4-8c63-7b4a59e80418"
},
"outputs": [],
"source": [
"%%time\n",
"model = PeftModel.from_pretrained(model, \"tloen/alpaca-lora-7b\", adapter_name=\"eng_alpaca\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "67a0c121",
"metadata": {
"id": "67a0c121"
},
"outputs": [],
"source": [
"%%time\n",
"model.load_adapter(\"22h/cabrita-lora-v0-1\", adapter_name=\"portuguese_alpaca\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b655fca",
"metadata": {
"id": "4b655fca"
},
"outputs": [],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9ebd572",
"metadata": {
"id": "e9ebd572"
},
"outputs": [],
"source": [
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"\n",
"model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "138805b3",
"metadata": {
"id": "138805b3"
},
"outputs": [],
"source": [
"def generate_prompt(instruction, input=None):\n",
" if input:\n",
" return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
"### Instruction:\n",
"{instruction}\n",
"### Input:\n",
"{input}\n",
"### Response:\"\"\"\n",
" else:\n",
" return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
"### Instruction:\n",
"{instruction}\n",
"### Response:\"\"\"\n",
"\n",
"\n",
"def evaluate(\n",
" instruction,\n",
" input=None,\n",
" temperature=0.1,\n",
" top_p=0.75,\n",
" top_k=40,\n",
" num_beams=4,\n",
" max_new_tokens=256,\n",
" **kwargs,\n",
"):\n",
" prompt = generate_prompt(instruction, input)\n",
" inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
" input_ids = inputs[\"input_ids\"].to(device)\n",
" generation_config = GenerationConfig(\n",
" temperature=temperature,\n",
" top_p=top_p,\n",
" top_k=top_k,\n",
" num_beams=num_beams,\n",
" no_repeat_ngram_size=3,\n",
" **kwargs,\n",
" )\n",
"\n",
" with torch.no_grad():\n",
" generation_output = model.generate(\n",
" input_ids=input_ids,\n",
" generation_config=generation_config,\n",
" return_dict_in_generate=True,\n",
" output_scores=True,\n",
" max_new_tokens=max_new_tokens,\n",
" )\n",
" s = generation_output.sequences[0]\n",
" output = tokenizer.decode(s)\n",
" return output.split(\"### Response:\")[1].strip()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fd5e6b3b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fd5e6b3b",
"outputId": "ec72241b-c427-4258-b02f-2101df0d171a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.16 ms, sys: 443 μs, total: 5.6 ms\n",
"Wall time: 5.58 ms\n"
]
}
],
"source": [
"%%time\n",
"model.set_adapter(\"eng_alpaca\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "33650851",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "33650851",
"outputId": "aae24052-0f09-4812-88c3-6fb53dec656c"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n",
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The alpaca (Vicugna pacos) is a domesticated species of South American camelid. It resembles a small llama in appearance. It is kept in herds that graze on the level heights of the Andes of southern Peru, southern Bolivia, Ecuador, and northern Chile, at an altitude of about 3,800 m (12,500 ft) to 5,000 meters (16,404 ft). It is bred for its fiber, which is similar to sheep's wool but finer, silkier, and more durable. Alpaca fiber is used for making knitted and woven items, such as sweaters, hats, gloves, scarves, a variety of textiles, rugs, and blankets. The wool can be dyed, and is used to make ponchos, blankets, and sweaters in Peru and other Andean countries. The animals are also raised for meat and as a source of dairy products, including milk, butter, and cheese.\n",
"Alpaca fleece comes in 22 natural colors, the most\n"
]
}
],
"source": [
"instruction = \"Tell me about alpacas.\"\n",
"\n",
"print(evaluate(instruction))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "fdc7196e",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fdc7196e",
"outputId": "44cb6742-066b-470e-f507-cbf21e5ae030"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6 ms, sys: 0 ns, total: 6 ms\n",
"Wall time: 5.86 ms\n"
]
}
],
"source": [
"%%time\n",
"model.set_adapter(\"portuguese_alpaca\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "31997da3",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "31997da3",
"outputId": "8071de75-dc9d-4e89-e85f-674f1de22658"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n",
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm sorry, but I can't make it to the party. I'm not feeling well. I have a headache and I don't think it's a good idea for me to go out tonight. I hope you understand and that you have a great time at the party!\n"
]
}
],
"source": [
"instruction = \"Invente uma desculpa criativa pra dizer que não preciso ir à festa.\"\n",
"\n",
"print(evaluate(instruction))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8b8e4e9a",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8b8e4e9a",
"outputId": "84226223-e018-4feb-e189-969c344fd940"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n",
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Eu não posso ir porque tenho que fazer uma tarefa para o meu professor.\n",
"\n"
]
}
],
"source": [
"with model.disable_adapter():\n",
" instruction = \"Invente uma desculpa criativa pra dizer que não preciso ir à festa.\"\n",
"\n",
" print(evaluate(instruction))"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/multi_adapter_examples/multi_adapter_weighted_inference_diffusers.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook shows how to use the adapter merging methods from `peft` and apply them image generation models using `diffusers`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QaEZ3dPgGtza"
},
"source": [
"## Turn `diffusers` LoRA checkpoints into `PeftModel`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KwvBjN-e62ts",
"outputId": "19c58dc7-95db-49c6-beb8-3ade1a8fe284"
},
"outputs": [],
"source": [
"!pip install diffusers accelerate transformers -U -q\n",
"!pip install git+https://github.com/huggingface/peft -q"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5S64sUQhJqB3"
},
"outputs": [],
"source": [
"from google.colab import userdata\n",
"TOKEN = userdata.get(\"HF_TOKEN\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 150,
"referenced_widgets": [
"c9f764b5036042af9f1505e3729cbc32",
"b999b3e3af3744a79c0c90657ef37d4e",
"f3baef4fbf4b4ec08480522be921f841",
"bd2740e191a74558a77a965b4f2d7f28",
"6f6a6cfd50404f1ea09f83e95b04550a",
"713dec1904ce46f6b2d5a9b7e3e0373a",
"a6bb8206de044c74a03d1a64c801e742",
"f2c67c29e1224df3b2def5a87eb8d368",
"486282a4ead148868005c592d74a4ed4",
"48f51c96b7574946bf9542633eb39135",
"35c810f4bfe741f091f172cede413950",
"67567eea233b423c8acd62773a4adb30",
"5161248cd0384d5887ed231ecd48c82e",
"6f72ea2c284e4e40899375c7b07c517f",
"d99a364420454ba5bfd510d1226b94af",
"d33c90e8ea0945d397659f6a90cf51b6",
"097fa44254ea4ecda7c8db995f370afc",
"9fb982789fbc4582bceb356e351db438",
"0204153b17ca4bb0b21fc033393ce9bd",
"f02041b1d5e1485bb2ba02b00fc2c242",
"4d327c9e91b34c7b84cedd8f9660e9fd",
"b44f7154c55146a3bf5f4bd9e438086f",
"b27ac2aaff694dd5999ab2cba91195da",
"556730e12d5d4e0ea51a0dd1b5aac331",
"584dd148b2344bdc92f1d0850399aed7",
"ccc5bdc185a84901994577ff7f1bc962",
"2c75632d8fdc4458814055172d1d72c3",
"2f016774e7854ef589442734d0bb2f08",
"a15c3a32bfd347a98c2a50d27cd5b9f9",
"67ef71b6521b47dd90e3dc0fd03016f2",
"989c54778eb7469fb91e4337d6f49b0b",
"439cf6c6f9e845cea4008e3219454ff4",
"460c3c96d1724713b78bddcfc1f3eb97"
]
},
"id": "1YH9xWDcyhaa",
"outputId": "8b7b32f4-4b77-411e-f499-7b2cf7650613"
},
"outputs": [],
"source": [
"from diffusers import UNet2DConditionModel\n",
"import torch\n",
"\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"\n",
"model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n",
"unet = UNet2DConditionModel.from_pretrained(\n",
" model_id, subfolder=\"unet\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n",
").to(device)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "-kfTBaLR1Mp-"
},
"outputs": [],
"source": [
"# So that we can populate it later.\n",
"import copy\n",
"\n",
"sdxl_unet = copy.deepcopy(unet)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 593,
"referenced_widgets": [
"0b7ffee735044ece90f010c6771d2d69",
"27671f87d0f3400b89e34bc428daa53c",
"33a018bf2ed547caa69f8ca91dcbb112",
"a91df5ceea8647d98f4d49701bb00969",
"c8555cb30ade40b9bd9a3435d6deeb67",
"b28a2b59aab9478e9ac3ab24a1de6f9a",
"c08fca70486040069d4f8f1df46a1074",
"bab2458ba3f54b229828ad9c8706aba4",
"2a0f746d6eab4680b6c44ec5cdbd8fd9",
"5269d405725946b3a657d3a8d7b25885",
"b4a697d8335c435ab21219716a1da022",
"85f22e78d47641d19efb6e6d62f6a014",
"d8bbb7402f3e44b2899fc98f02cee87e",
"8b41763280a048d485f06682ddc12ca2",
"05791b70a24a42138755141602399c47",
"753f64d9069640d985f399a058fc9b5b",
"19679186751b42e3ad2c44ea46c82a9c",
"d4baf7891f854c9c9898314633f97356",
"9c6ab017f5fb46eda8e3c22e9dd2b838",
"10bc970e474a46249e2a5e9e43fec7be",
"5c838962c7854541b61877c6d42481c4",
"4d54c07dd8e94557b82521772c1a2825",
"efd33121bdfc4ce195a07c9ef523a477",
"d2cd2572973249baa8e130b5777f4147",
"4e77d8b6cdb94fc68974d27b24cfb3fd",
"c292a598350d4dd4bb9b70aab1320c29",
"c68a5ad3bb664e8785e724085d208e96",
"b27c74f0c7bf493fa8bcb4c5b9c9c100",
"09638c8da74f4dddaf1d5d94dd8ad885",
"6a8a634cd3844fa081a4622d224ec940",
"2460b9e05b58481e898b139b94532c14",
"cdd8f9b1592842b48e1ffa80f8ec8246",
"ba9b002888b448738ba4b127e1046f5d",
"54ab158c643540abb8b3a96c1ae3ecab",
"55130e37444844f88286a87c8d153eba",
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]
},
"id": "EMTVH9cLEZYi",
"outputId": "7a24b4b0-71f6-4c65-a242-fb3d502da6a8"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 10.25it/s]\n"
]
}
],
"source": [
"# Load the pipeline too.\n",
"from diffusers import DiffusionPipeline\n",
"\n",
"pipe = DiffusionPipeline.from_pretrained(\n",
" model_id, variant=\"fp16\", torch_dtype=torch.float16, unet=unet\n",
").to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"5e677518051c43768dbf06243701c817",
"d727913663634e368ade4a7dc64fe74b",
"eb73095c804a4272856fe348fa3cb1e9",
"7e9b46b10fa24dfea489dfbc150d2a2e",
"c8156b0cc68e4b3693dcabc530a4ea9a",
"d987907f09084d44b452f939aadff65e",
"e976b994189343f5ba7d762ef92c79e2",
"9810873713024e79ae6d338dfeae5876",
"2e18ce21c01a4a3ca992622957e7d297",
"2db944a049a04426bba181fddb2801b1",
"683863a313034025ab99fab5810f39c7"
]
},
"id": "D5hL5156zPis",
"outputId": "2510b8e7-c030-40f8-dcd2-ef76fc8529c6"
},
"outputs": [],
"source": [
"# Only UNet\n",
"pipe.load_lora_weights(\"CiroN2022/toy-face\", weight_name=\"toy_face_sdxl.safetensors\", adapter_name=\"toy\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "p-7YWoOs02La"
},
"outputs": [],
"source": [
"from peft import get_peft_model, LoraConfig\n",
"\n",
"toy_peft_model = get_peft_model(\n",
" sdxl_unet,\n",
" pipe.unet.peft_config[\"toy\"],\n",
" adapter_name=\"toy\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101,
"referenced_widgets": [
"c6bdeef396174d51af9eee277752bec7",
"685797f8907c47ffab4fe7a81ca22e63",
"414f8301f76043758f69bbfb6960072d",
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"550b3ad10fcb422eb66f71ad95988616",
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"6a2b5bbd4afe4e0cabe39e95ccf528be",
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"5405d8a1aa1d411b895b0523fb8e4ce7",
"04846af1def142ffad328d434ba228fe"
]
},
"id": "a_2n4Odz2a0c",
"outputId": "4b3b801b-649f-4c69-b75c-f800ac75c17f"
},
"outputs": [],
"source": [
"original_state_dict = {f\"base_model.model.{k}\": v for k, v in pipe.unet.state_dict().items()}\n",
"\n",
"toy_peft_model.load_state_dict(original_state_dict, strict=True)\n",
"toy_peft_model.push_to_hub(\"toy_peft_model-new\", token=TOKEN)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "z1DWL0X12rxD"
},
"outputs": [],
"source": [
"pipe.delete_adapters(\"toy\")\n",
"sdxl_unet.delete_adapters(\"toy\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"d18c97fe685e4be080125ae770526255",
"e44c018bc76c48cd8738fee5966767ce",
"57b491647f1e49cea7ce34774a963936",
"484f694f734b4a92a14ecc4d048db0af",
"bcb3ec98d25b4c138c5e8f84c1e937c6",
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"aef81ec1a7e844f883beba8c5754a8af",
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"157c0c1e85ef40fb99e6cfe0e176be38",
"99717def9b6b4afe8a411a3bb83320c9"
]
},
"id": "9PW-SfwH5L7e",
"outputId": "90721ffa-faa5-4628-994a-7b719a4ef02c"
},
"outputs": [],
"source": [
"pipe.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\", adapter_name=\"pixel\")\n",
"pipe.set_adapters(adapter_names=\"pixel\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101,
"referenced_widgets": [
"adb2daf9d62f49f8ab1f0144b717d41e",
"bba38c266ceb4f30bb4bc1eaf5e3aa96",
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"d872c3900b8b4275b2224c9ec5e7d78f",
"4fbaa1bd51bf4b1e90337a435604d2bd"
]
},
"id": "jHSb-iIf7IEb",
"outputId": "29124d4c-b58f-4f0e-c59b-d79b44cb162f"
},
"outputs": [],
"source": [
"pixel_peft_model = get_peft_model(\n",
" sdxl_unet,\n",
" pipe.unet.peft_config[\"pixel\"],\n",
" adapter_name=\"pixel\"\n",
")\n",
"\n",
"original_state_dict = {f\"base_model.model.{k}\": v for k, v in pipe.unet.state_dict().items()}\n",
"pixel_peft_model.load_state_dict(original_state_dict, strict=True)\n",
"pixel_peft_model.push_to_hub(\"pixel_peft_model-new\", token=TOKEN)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "yoPzMtyqG2ZO"
},
"outputs": [],
"source": [
"del pipe, sdxl_unet, toy_peft_model, pixel_peft_model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zis3zKZpGy8w"
},
"source": [
"## Weighted adapter inference"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"d74b1c667d42472b865ee1cbefc33a60",
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"a6f2ce8830734be4b8efe5ca0e14e990",
"07da49088e8d480fad03a2e828357872",
"5dd09616ba6544c499271054e8d8d2c5",
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"fbf3d268f30344b7864ce691d5bcb1f3",
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"c8e40d44aeac47a78f6502771f1471b7",
"270ddad8d7704f929a28c9fbbdabfa26",
"e57d317b3dda43bba13ecd4514f776d3",
"22b55b5ee1af4ea7b1acfe511b194cd8",
"a48d3ad24e9744f7898d1f2c5a696ea2",
"f29673e57d174839a0bde70bfa165715",
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"5a7d87338ddc45df84080e0096a31631",
"cb9a536bc56f4d0ebf285e7f73d4730e",
"49bb475a11104e9496f2623e3d5caebd",
"887c5eae5b154eccb4a1caa3deef6e94",
"e87dffe17f1948e9ba794eddb605a908",
"2c352a90375443da835eef55b3e63303",
"db8fd6b2687c449fa0600d3e87c96999",
"17691a346ca5407a99a5e385450c97eb",
"0bca833a6aa74ebaa8f69feb738806bf",
"6bdb9b0b68c24b84a748c18ed927a8d3",
"355efc45ddaf42498d72d6134a28c87b",
"8b6464ce614c4aa29ac66ecce29b6cbf",
"bc07cdaad5b64fb3b0e1f8c214bba813",
"9e2e87c131a140a2a37dfdf483d27ced",
"47468a75637d436f849283c295a74ab6",
"af5003cf40ae4dfaa0660f247598856e"
]
},
"id": "gEqT1vFtG0_e",
"outputId": "282ce865-c653-4912-e497-ff825c896ae7"
},
"outputs": [],
"source": [
"from peft import PeftModel\n",
"\n",
"base_unet = UNet2DConditionModel.from_pretrained(\n",
" model_id, subfolder=\"unet\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n",
").to(device)\n",
"\n",
"toy_id = \"sayakpaul/toy_peft_model-new\"\n",
"model = PeftModel.from_pretrained(base_unet, toy_id, use_safetensors=True, subfolder=\"toy\", adapter_name=\"toy\")\n",
"model.load_adapter(\"sayakpaul/pixel_peft_model-new\", use_safetensors=True, subfolder=\"pixel\", adapter_name=\"pixel\")\n",
"\n",
"# https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter\n",
"model.add_weighted_adapter(\n",
" adapters=[\"toy\", \"pixel\"],\n",
" weights=[0.7, 0.3],\n",
" combination_type=\"linear\",\n",
" adapter_name=\"toy-pixel\"\n",
")\n",
"model.set_adapters(\"toy-pixel\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 186
},
"id": "QStyurhKsP_g",
"outputId": "5e3a2627-27a2-4771-e62f-1b81ded2b87e"
},
"outputs": [
{
"data": {
"text/plain": [
"diffusers.models.unets.unet_2d_condition.UNet2DConditionModel"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(model.base_model.model)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"28a5059a2cc445d783e94ad5d83a0748",
"faddc146c69545cdaeb81edc8a0cda70",
"cd08b99de03c483a965248ff4df752ba",
"d37c6ac25fd34a65bf307896443a5063",
"66da596ae59d474f8a82a600174adff6",
"b600178b161d4a87a0b832a169d7caf2",
"b3eaa188cc2e48d081488eea4ed2971f",
"c05842ed12c848c68c4a69de9aa742a8",
"18e3ba4a61784f04b3238cf273c90c3e",
"42a623abc5d84c0eb7de4e5323bb6546",
"a65484354d254516936fcb425917a4b7",
"64029a1e70e040b49e38d38bd36823fd",
"3e1ec3a51e9b4fbbab489d34640cda90",
"ea0910fc31e44597968b2129272cc94d",
"3a0e9adc345f409cbcd79d1bd19219e6",
"3fe7e7f00ca746cc8cc762da6f365fde",
"6466eff2786241eeb142f17758894bb2",
"c181408b9b2b437ca11d584e3d1e94e7",
"0371aa5607604c06a868deb2a413cb31",
"c212598c5a8747f783a6efc18816e868",
"89ed71090d5c4366b21d25ad102e7da7",
"4ab21d7b956e46348ad6f6542fd92c2d"
]
},
"id": "iHwVV8f6s1EC",
"outputId": "47cb80da-266e-40c2-cfc1-3f3e5421b50b"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 21.14it/s]\n",
"Expected types for unet: (,), got .\n",
"100%|██████████| 30/30 [00:09<00:00, 3.19it/s]\n"
]
},
{
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",
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = model.to(torch_dtype=torch.float16, device=device)\n",
"\n",
"pipe = DiffusionPipeline.from_pretrained(\n",
" model_id, unet=model, variant=\"fp16\", torch_dtype=torch.float16,\n",
").to(device)\n",
"\n",
"prompt = \"toy_face of a hacker with a hoodie, pixel art\"\n",
"image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "adLnc7sMRZlq"
},
"outputs": [],
"source": [
"del pipe"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "nIwIQK5zRX25"
},
"outputs": [],
"source": [
"base_unet = UNet2DConditionModel.from_pretrained(\n",
" model_id, subfolder=\"unet\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n",
").to(device)\n",
"\n",
"toy_id = \"sayakpaul/toy_peft_model-new\"\n",
"model = PeftModel.from_pretrained(base_unet, toy_id, use_safetensors=True, subfolder=\"toy\", adapter_name=\"toy\")\n",
"model.load_adapter(\"sayakpaul/pixel_peft_model-new\", use_safetensors=True, subfolder=\"pixel\", adapter_name=\"pixel\")\n",
"\n",
"# https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter\n",
"model.add_weighted_adapter(\n",
" adapters=[\"toy\", \"pixel\"],\n",
" weights=[0.5, 0.5],\n",
" combination_type=\"cat\",\n",
" adapter_name=\"toy-pixel\"\n",
")\n",
"model.set_adapters(\"toy-pixel\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"65bf3df199b44763aa223fee96889e17",
"a32666723b0e4b9883a78f56295c4356",
"c08373a044204308ac882dd8cf9cdd3e",
"fa7876ade8e240fc89a35a1f8c7c7d3c",
"de583920d3b54774a486aef4c052e50d",
"979c68bd2e224a40b939c27f32c25dac",
"0027f3aa006d4276983691ad985ce91b",
"a0f8a1a3512443ac84799b95da70ca26",
"e08d739f59874064994212363a307f6e",
"52c7e22284b0468c8bc0c3b1cad047fb",
"17c29bbcbc0c437c9c8bc83e0b085f1a",
"65b15618abfd4e6f9fafd64813e86ace",
"1e8747251a1f4cca970857911d1c4a98",
"caa46820018f47abab4a962afe51cc34",
"22a2ea45880f4b0da47f1b213882dcb0",
"0cf822f588244e54b5264176f9611164",
"dd9666d76af04b72b08f59023eb04ee3",
"2702528a2ca049fc800ad44c492690ae",
"aed53b6480de4dd4bc7463af04840952",
"447d29db05384c55a57c5fb1bd121af4",
"2d7e7e816a63428b8f30471a12b57bc4",
"e15aab8dd01f4d5582db80e6ad9931fc"
]
},
"id": "29iGITdnRhFG",
"outputId": "dc6a1e54-3f76-457e-da1b-e8677be5c31f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 19.85it/s]\n",
"Expected types for unet: (,), got .\n",
"100%|██████████| 30/30 [00:05<00:00, 5.35it/s]\n"
]
},
{
"data": {
"image/jpeg": 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",
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = model.to(torch_dtype=torch.float16, device=device)\n",
"\n",
"pipe = DiffusionPipeline.from_pretrained(\n",
" model_id, unet=model, variant=\"fp16\", torch_dtype=torch.float16,\n",
").to(device)\n",
"\n",
"prompt = \"toy_face of a hacker with a hoodie, pixel art\"\n",
"image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"8b88a1a63cf242de8a68962f50498c72",
"777c5583820248838f0c39e82362d9e3",
"491da86a94c44b7c87366b4bb72a3bd1",
"31c3a375a2964a06a2473925fe9b197b",
"0ba0a0fca31c482bb628a6739d341601",
"734e9a834ff74b64b17453013208a116",
"9784d9210d6e4214b78ab5f8c33e8044",
"490be826ebc14d5ab4e9f0e10ec79d5f",
"64ea3c5cc12841f59d1acba12deb6a88",
"fc1391aaeaad4eecad967e800a669ec1",
"272fceb4e9484b389067080872b1abf9",
"5ab5097f19cf4474945f96741c444d71",
"6d8fdd0303774305ae20dd39e2a1706c",
"031a326124f1496abe1f3bf8de720029",
"b9feee6f48bd49209e72e5c5e3136f67",
"a2d497a2ddb04d8fac8a4c0f8aa7f5dc",
"7bb3f4cda33947138c61aac74d952289",
"d3b73a841e68425994632d0f05cf4f16",
"71ad1e3dbe44437d8df985cfae207dcd",
"598fa824f517445394d08c37393f9f3d",
"daad0d12aff8470d990fbbbbe19d5891",
"16e18e872ab64b3f8bfb32f580c3371b"
]
},
"id": "sQOnSrteuS-S",
"outputId": "44c5e61a-370b-44bf-a5e5-80b7787088e5"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 14.10it/s]\n",
"100%|██████████| 30/30 [00:03<00:00, 9.26it/s]\n"
]
},
{
"data": {
"image/jpeg": 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",
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",
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del pipe\n",
"\n",
"pipe = DiffusionPipeline.from_pretrained(\n",
" model_id, variant=\"fp16\", dtype=torch.float16,\n",
").to(device)\n",
"\n",
"prompt = \"toy_face of a hacker with a hoodie, pixel art\"\n",
"image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\n",
"image"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "A100",
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"0027f3aa006d4276983691ad985ce91b": {
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================================================
FILE: examples/multilayer_perceptron/README.md
================================================
# Fine-tuning a multilayer perceptron using LoRA and 🤗 PEFT
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb)
PEFT supports fine-tuning any type of model as long as the layers being used are supported. The model does not have to be a transformers model, for instance. To demonstrate this, the accompanying notebook `multilayer_perceptron_lora.ipynb` shows how to apply LoRA to a simple multilayer perceptron and use it to train a model to perform a classification task.
================================================
FILE: examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "8e8743c8",
"metadata": {},
"source": [
"# Using PEFT with custom models"
]
},
{
"cell_type": "markdown",
"id": "c42c67e1",
"metadata": {},
"source": [
"`peft` allows us to fine-tune models efficiently with LoRA. In this short notebook, we will demonstrate how to train a simple multilayer perceptron (MLP) using `peft`."
]
},
{
"cell_type": "markdown",
"id": "ce314af5",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "markdown",
"id": "b28b214d",
"metadata": {},
"source": [
"Make sure that you have the latest version of `peft` installed. To ensure that, run this in your Python environment:\n",
" \n",
" python -m pip install --upgrade peft"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4d9da3d9",
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"import os\n",
"\n",
"# ignore bnb warnings\n",
"os.environ[\"BITSANDBYTES_NOWELCOME\"] = \"1\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "44075f54",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.11/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import peft\n",
"import torch\n",
"from torch import nn\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f72acdfb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.manual_seed(0)"
]
},
{
"cell_type": "markdown",
"id": "2b127a78",
"metadata": {},
"source": [
"## Data"
]
},
{
"cell_type": "markdown",
"id": "f265da76",
"metadata": {},
"source": [
"We will create a toy dataset consisting of random data for a classification task. There is a little bit of signal in the data, so we should expect that the loss of the model can improve during training."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b355567e",
"metadata": {},
"outputs": [],
"source": [
"X = torch.rand((1000, 20))\n",
"y = (X.sum(1) > 10).long()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a60a869d",
"metadata": {},
"outputs": [],
"source": [
"n_train = 800\n",
"batch_size = 64"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8859572e",
"metadata": {},
"outputs": [],
"source": [
"train_dataloader = torch.utils.data.DataLoader(\n",
" torch.utils.data.TensorDataset(X[:n_train], y[:n_train]),\n",
" batch_size=batch_size,\n",
" shuffle=True,\n",
")\n",
"eval_dataloader = torch.utils.data.DataLoader(\n",
" torch.utils.data.TensorDataset(X[n_train:], y[n_train:]),\n",
" batch_size=batch_size,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "97bddd2c",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "markdown",
"id": "db694a58",
"metadata": {},
"source": [
"As a model, we use a simple multilayer perceptron (MLP). For demonstration purposes, we use a very large number of hidden units. This is totally overkill for this task but it helps to demonstrate the advantages of `peft`. In more realistic settings, models will also be quite large on average, so this is not far-fetched."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1b43cd8f",
"metadata": {},
"outputs": [],
"source": [
"class MLP(nn.Module):\n",
" def __init__(self, num_units_hidden=2000):\n",
" super().__init__()\n",
" self.seq = nn.Sequential(\n",
" nn.Linear(20, num_units_hidden),\n",
" nn.ReLU(),\n",
" nn.Linear(num_units_hidden, num_units_hidden),\n",
" nn.ReLU(),\n",
" nn.Linear(num_units_hidden, 2),\n",
" nn.LogSoftmax(dim=-1),\n",
" )\n",
"\n",
" def forward(self, X):\n",
" return self.seq(X)"
]
},
{
"cell_type": "markdown",
"id": "1277bf00",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "markdown",
"id": "02caf26a",
"metadata": {},
"source": [
"Here are just a few training hyper-parameters and a simple function that performs the training and evaluation loop."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5d14c0c4",
"metadata": {},
"outputs": [],
"source": [
"lr = 0.002\n",
"batch_size = 64\n",
"max_epochs = 30\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "657d6b3e",
"metadata": {},
"outputs": [],
"source": [
"def train(model, optimizer, criterion, train_dataloader, eval_dataloader, epochs):\n",
" for epoch in range(epochs):\n",
" model.train()\n",
" train_loss = 0\n",
" for xb, yb in train_dataloader:\n",
" xb = xb.to(device)\n",
" yb = yb.to(device)\n",
" outputs = model(xb)\n",
" loss = criterion(outputs, yb)\n",
" train_loss += loss.detach().float()\n",
" loss.backward()\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
"\n",
" model.eval()\n",
" eval_loss = 0\n",
" for xb, yb in eval_dataloader:\n",
" xb = xb.to(device)\n",
" yb = yb.to(device)\n",
" with torch.no_grad():\n",
" outputs = model(xb)\n",
" loss = criterion(outputs, yb)\n",
" eval_loss += loss.detach().float()\n",
"\n",
" eval_loss_total = (eval_loss / len(eval_dataloader)).item()\n",
" train_loss_total = (train_loss / len(train_dataloader)).item()\n",
" print(f\"{epoch=:<2} {train_loss_total=:.4f} {eval_loss_total=:.4f}\")"
]
},
{
"cell_type": "markdown",
"id": "b382dcbe",
"metadata": {},
"source": [
"### Training without peft"
]
},
{
"cell_type": "markdown",
"id": "b40d4873",
"metadata": {},
"source": [
"Let's start without using `peft` to see what we can expect from the model training."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f059ced4",
"metadata": {},
"outputs": [],
"source": [
"module = MLP().to(device)\n",
"optimizer = torch.optim.Adam(module.parameters(), lr=lr)\n",
"criterion = nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "17698863",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=0 train_loss_total=0.7970 eval_loss_total=0.6472\n",
"epoch=1 train_loss_total=0.5597 eval_loss_total=0.4898\n",
"epoch=2 train_loss_total=0.3696 eval_loss_total=0.3323\n",
"epoch=3 train_loss_total=0.2364 eval_loss_total=0.5454\n",
"epoch=4 train_loss_total=0.2428 eval_loss_total=0.2843\n",
"epoch=5 train_loss_total=0.1251 eval_loss_total=0.2514\n",
"epoch=6 train_loss_total=0.0952 eval_loss_total=0.2068\n",
"epoch=7 train_loss_total=0.0831 eval_loss_total=0.2395\n",
"epoch=8 train_loss_total=0.0655 eval_loss_total=0.2524\n",
"epoch=9 train_loss_total=0.0380 eval_loss_total=0.3650\n",
"epoch=10 train_loss_total=0.0363 eval_loss_total=0.3495\n",
"epoch=11 train_loss_total=0.0231 eval_loss_total=0.2360\n",
"epoch=12 train_loss_total=0.0162 eval_loss_total=0.2276\n",
"epoch=13 train_loss_total=0.0094 eval_loss_total=0.2716\n",
"epoch=14 train_loss_total=0.0065 eval_loss_total=0.2237\n",
"epoch=15 train_loss_total=0.0054 eval_loss_total=0.2366\n",
"epoch=16 train_loss_total=0.0035 eval_loss_total=0.2673\n",
"epoch=17 train_loss_total=0.0028 eval_loss_total=0.2630\n",
"epoch=18 train_loss_total=0.0023 eval_loss_total=0.2835\n",
"epoch=19 train_loss_total=0.0021 eval_loss_total=0.2727\n",
"epoch=20 train_loss_total=0.0018 eval_loss_total=0.2597\n",
"epoch=21 train_loss_total=0.0016 eval_loss_total=0.2553\n",
"epoch=22 train_loss_total=0.0014 eval_loss_total=0.2712\n",
"epoch=23 train_loss_total=0.0013 eval_loss_total=0.2637\n",
"epoch=24 train_loss_total=0.0012 eval_loss_total=0.2733\n",
"epoch=25 train_loss_total=0.0011 eval_loss_total=0.2738\n",
"epoch=26 train_loss_total=0.0010 eval_loss_total=0.2477\n",
"epoch=27 train_loss_total=0.0010 eval_loss_total=0.2584\n",
"epoch=28 train_loss_total=0.0009 eval_loss_total=0.2844\n",
"epoch=29 train_loss_total=0.0008 eval_loss_total=0.2633\n",
"CPU times: user 1.31 s, sys: 236 ms, total: 1.54 s\n",
"Wall time: 1.56 s\n"
]
}
],
"source": [
"%time train(module, optimizer, criterion, train_dataloader, eval_dataloader, epochs=max_epochs)"
]
},
{
"cell_type": "markdown",
"id": "4cef0029",
"metadata": {},
"source": [
"Okay, so we got an eval loss of ~0.26, which is much better than random."
]
},
{
"cell_type": "markdown",
"id": "4f106078",
"metadata": {},
"source": [
"### Training with peft"
]
},
{
"cell_type": "markdown",
"id": "8dd47aa4",
"metadata": {},
"source": [
"Now let's train with `peft`. First we check the names of the modules, so that we can configure `peft` to fine-tune the right modules."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "922db29b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[('', __main__.MLP),\n",
" ('seq', torch.nn.modules.container.Sequential),\n",
" ('seq.0', torch.nn.modules.linear.Linear),\n",
" ('seq.1', torch.nn.modules.activation.ReLU),\n",
" ('seq.2', torch.nn.modules.linear.Linear),\n",
" ('seq.3', torch.nn.modules.activation.ReLU),\n",
" ('seq.4', torch.nn.modules.linear.Linear),\n",
" ('seq.5', torch.nn.modules.activation.LogSoftmax)]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[(n, type(m)) for n, m in MLP().named_modules()]"
]
},
{
"cell_type": "markdown",
"id": "5efb275d",
"metadata": {},
"source": [
"Next we can define the LoRA config. There is nothing special going on here. We set the LoRA rank to 8 and select the layers `seq.0` and `seq.2` to be used for LoRA fine-tuning. As for `seq.4`, which is the output layer, we set it as `module_to_save`, which means it is also trained but no LoRA is applied."
]
},
{
"cell_type": "markdown",
"id": "cf2c608d",
"metadata": {},
"source": [
"*Note: Not all layers types can be fine-tuned with LoRA. At the moment, linear layers, embeddings, `Conv2D` and `transformers.pytorch_utils.Conv1D` are supported."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b342438f",
"metadata": {},
"outputs": [],
"source": [
"config = peft.LoraConfig(\n",
" r=8,\n",
" target_modules=[\"seq.0\", \"seq.2\"],\n",
" modules_to_save=[\"seq.4\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "829b4e2d",
"metadata": {},
"source": [
"Now let's create the `peft` model by passing our initial MLP, as well as the config we just defined, to `get_peft_model`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "602b6658",
"metadata": {},
"outputs": [],
"source": [
"module = MLP().to(device)\n",
"module_copy = copy.deepcopy(module) # we keep a copy of the original model for later\n",
"peft_model = peft.get_peft_model(module, config)\n",
"optimizer = torch.optim.Adam(peft_model.parameters(), lr=lr)\n",
"criterion = nn.CrossEntropyLoss()\n",
"peft_model.print_trainable_parameters()"
]
},
{
"cell_type": "markdown",
"id": "2103737d",
"metadata": {},
"source": [
"Checking the numbers, we see that only ~1% of parameters are actually trained, which is what we like to see.\n",
"\n",
"Now let's start the training:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9200cbc6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=0 train_loss_total=0.6695 eval_loss_total=0.6388\n",
"epoch=1 train_loss_total=0.5614 eval_loss_total=0.5456\n",
"epoch=2 train_loss_total=0.3897 eval_loss_total=0.3035\n",
"epoch=3 train_loss_total=0.2529 eval_loss_total=0.2510\n",
"epoch=4 train_loss_total=0.1914 eval_loss_total=0.2191\n",
"epoch=5 train_loss_total=0.1236 eval_loss_total=0.2586\n",
"epoch=6 train_loss_total=0.1076 eval_loss_total=0.3205\n",
"epoch=7 train_loss_total=0.1834 eval_loss_total=0.3951\n",
"epoch=8 train_loss_total=0.1037 eval_loss_total=0.1646\n",
"epoch=9 train_loss_total=0.0724 eval_loss_total=0.1409\n",
"epoch=10 train_loss_total=0.0691 eval_loss_total=0.1725\n",
"epoch=11 train_loss_total=0.0641 eval_loss_total=0.1423\n",
"epoch=12 train_loss_total=0.0382 eval_loss_total=0.1490\n",
"epoch=13 train_loss_total=0.0214 eval_loss_total=0.1517\n",
"epoch=14 train_loss_total=0.0119 eval_loss_total=0.1717\n",
"epoch=15 train_loss_total=0.0060 eval_loss_total=0.2366\n",
"epoch=16 train_loss_total=0.0029 eval_loss_total=0.2069\n",
"epoch=17 train_loss_total=0.0021 eval_loss_total=0.2082\n",
"epoch=18 train_loss_total=0.0016 eval_loss_total=0.2119\n",
"epoch=19 train_loss_total=0.0011 eval_loss_total=0.1984\n",
"epoch=20 train_loss_total=0.0010 eval_loss_total=0.1821\n",
"epoch=21 train_loss_total=0.0009 eval_loss_total=0.1892\n",
"epoch=22 train_loss_total=0.0007 eval_loss_total=0.2062\n",
"epoch=23 train_loss_total=0.0006 eval_loss_total=0.2408\n",
"epoch=24 train_loss_total=0.0006 eval_loss_total=0.2038\n",
"epoch=25 train_loss_total=0.0005 eval_loss_total=0.2374\n",
"epoch=26 train_loss_total=0.0004 eval_loss_total=0.2139\n",
"epoch=27 train_loss_total=0.0004 eval_loss_total=0.2085\n",
"epoch=28 train_loss_total=0.0004 eval_loss_total=0.2395\n",
"epoch=29 train_loss_total=0.0003 eval_loss_total=0.2100\n",
"CPU times: user 1.41 s, sys: 48.9 ms, total: 1.46 s\n",
"Wall time: 1.46 s\n"
]
}
],
"source": [
"%time train(peft_model, optimizer, criterion, train_dataloader, eval_dataloader, epochs=max_epochs)"
]
},
{
"cell_type": "markdown",
"id": "20f6f452",
"metadata": {},
"source": [
"In the end, we see that the eval loss is very similar to the one we saw earlier when we trained without `peft`. This is quite nice to see, given that we are training a much smaller number of parameters."
]
},
{
"cell_type": "markdown",
"id": "fa55d1d4",
"metadata": {},
"source": [
"#### Check which parameters were updated"
]
},
{
"cell_type": "markdown",
"id": "a6e2146b",
"metadata": {},
"source": [
"Finally, just to check that LoRA was applied as expected, we check what original weights were updated what weights stayed the same."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c7dcde21",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New parameter model.seq.0.lora_A.default.weight | 160 parameters | updated\n",
"New parameter model.seq.0.lora_B.default.weight | 16000 parameters | updated\n",
"New parameter model.seq.2.lora_A.default.weight | 16000 parameters | updated\n",
"New parameter model.seq.2.lora_B.default.weight | 16000 parameters | updated\n"
]
}
],
"source": [
"for name, param in peft_model.base_model.named_parameters():\n",
" if \"lora\" not in name:\n",
" continue\n",
"\n",
" print(f\"New parameter {name:<13} | {param.numel():>5} parameters | updated\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "022e6c41",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter seq.0.weight | 40000 parameters | not updated\n",
"Parameter seq.0.bias | 2000 parameters | not updated\n",
"Parameter seq.2.weight | 4000000 parameters | not updated\n",
"Parameter seq.2.bias | 2000 parameters | not updated\n",
"Parameter seq.4.weight | 4000 parameters | not updated\n",
"Parameter seq.4.bias | 2 parameters | not updated\n",
"Parameter seq.4.weight | 4000 parameters | updated\n",
"Parameter seq.4.bias | 2 parameters | updated\n"
]
}
],
"source": [
"params_before = dict(module_copy.named_parameters())\n",
"for name, param in peft_model.base_model.named_parameters():\n",
" if \"lora\" in name:\n",
" continue\n",
"\n",
" name_before = (\n",
" name.partition(\".\")[-1].replace(\"base_layer.\", \"\").replace(\"original_\", \"\").replace(\"module.\", \"\").replace(\"modules_to_save.default.\", \"\")\n",
" )\n",
" param_before = params_before[name_before]\n",
" if torch.allclose(param, param_before):\n",
" print(f\"Parameter {name_before:<13} | {param.numel():>7} parameters | not updated\")\n",
" else:\n",
" print(f\"Parameter {name_before:<13} | {param.numel():>7} parameters | updated\")"
]
},
{
"cell_type": "markdown",
"id": "4c09b43d",
"metadata": {},
"source": [
"So we can see that apart from the new LoRA weights that were added, only the last layer was updated. Since the LoRA weights and the last layer have comparitively few parameters, this gives us a big boost in efficiency."
]
},
{
"cell_type": "markdown",
"id": "b46c6198",
"metadata": {},
"source": [
"## Sharing the model through Hugging Face Hub"
]
},
{
"cell_type": "markdown",
"id": "6289e647",
"metadata": {},
"source": [
"### Pushing the model to HF Hub"
]
},
{
"cell_type": "markdown",
"id": "06dcdfa0",
"metadata": {},
"source": [
"With the `peft` model, it is also very easy to push a model the Hugging Face Hub. Below, we demonstrate how it works. It is assumed that you have a valid Hugging Face account and are logged in:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "1b91a0af",
"metadata": {},
"outputs": [],
"source": [
"user = \"BenjaminB\" # put your user name here\n",
"model_name = \"peft-lora-with-custom-model\"\n",
"model_id = f\"{user}/{model_name}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1430fffd",
"metadata": {},
"outputs": [],
"source": [
"peft_model.push_to_hub(model_id);"
]
},
{
"cell_type": "markdown",
"id": "632bd799",
"metadata": {},
"source": [
"As we can see, the adapter size is only 211 kB."
]
},
{
"cell_type": "markdown",
"id": "4ff78c0c",
"metadata": {},
"source": [
"### Loading the model from HF Hub"
]
},
{
"cell_type": "markdown",
"id": "e5c7e87f",
"metadata": {},
"source": [
"Now, it only takes one step to load the model from HF Hub. To do this, we can use `PeftModel.from_pretrained`, passing our base model and the model ID:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce0fcced",
"metadata": {},
"outputs": [],
"source": [
"loaded = peft.PeftModel.from_pretrained(module_copy, model_id)\n",
"type(loaded)"
]
},
{
"cell_type": "markdown",
"id": "cd4b4eac",
"metadata": {},
"source": [
"Let's check that the two models produce the same output:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2cf6ac4",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"y_peft = peft_model(X.to(device))\n",
"y_loaded = loaded(X.to(device))\n",
"torch.allclose(y_peft, y_loaded)"
]
},
{
"cell_type": "markdown",
"id": "eeeb653f",
"metadata": {},
"source": [
"### Clean up"
]
},
{
"cell_type": "markdown",
"id": "61c60355",
"metadata": {},
"source": [
"Finally, as a clean up step, you may want to delete the repo."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b747038f",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import delete_repo"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "7e5ab237",
"metadata": {},
"outputs": [],
"source": [
"delete_repo(model_id)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/oft_dreambooth/oft_dreambooth_inference.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "acd7b15e",
"metadata": {},
"source": [
"# Dreambooth with OFT\n",
"This Notebook assumes that you already ran the train_dreambooth.py script to create your own adapter."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acab479f",
"metadata": {},
"outputs": [],
"source": [
"from diffusers import DiffusionPipeline\n",
"from diffusers.utils import check_min_version, get_logger\n",
"from peft import PeftModel\n",
"\n",
"# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n",
"check_min_version(\"0.10.0.dev0\")\n",
"\n",
"logger = get_logger(__name__)\n",
"\n",
"BASE_MODEL_NAME = \"stabilityai/stable-diffusion-2-1-base\"\n",
"ADAPTER_MODEL_PATH = \"INSERT MODEL PATH HERE\""
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 24.13it/s]\n"
]
}
],
"source": [
"import torch\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"pipe = DiffusionPipeline.from_pretrained(\n",
" BASE_MODEL_NAME,\n",
")\n",
"pipe.to(device)\n",
"pipe.unet = PeftModel.from_pretrained(pipe.unet, ADAPTER_MODEL_PATH + \"/unet\", adapter_name=\"default\")\n",
"pipe.text_encoder = PeftModel.from_pretrained(\n",
" pipe.text_encoder, ADAPTER_MODEL_PATH + \"/text_encoder\", adapter_name=\"default\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 50/50 [00:11<00:00, 4.46it/s]\n"
]
},
{
"data": {
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",
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",
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"A photo of a sks dog\"\n",
"image = pipe(\n",
" prompt,\n",
" num_inference_steps=50,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: examples/oft_dreambooth/train_dreambooth.py
================================================
import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from contextlib import nullcontext
from pathlib import Path
import datasets
import diffusers
import numpy as np
import psutil
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from huggingface_hub import HfApi
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
from peft import get_peft_model
from peft.tuners.oft.config import OFTConfig
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
logger = get_logger(__name__)
UNET_TARGET_MODULES = ["to_q", "to_v", "query", "value"] # , "ff.net.0.proj"]
TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
# oft args
parser.add_argument("--use_oft", action="store_true", help="Whether to use OFT for parameter efficient tuning")
parser.add_argument("--oft_r", type=int, default=0, help="OFT rank, only used if use_oft is True")
parser.add_argument("--oft_block_size", type=int, default=32, help="OFT block size, only used if use_oft is True")
parser.add_argument("--oft_dropout", type=float, default=0.0, help="OFT dropout, only used if use_oft is True")
parser.add_argument(
"--oft_use_coft", action="store_true", help="Using constrained OFT, only used if use_oft is True"
)
parser.add_argument(
"--oft_eps",
type=float,
default=0.0,
help="The control strength of COFT. Only has an effect if `oft_use_coft` is set to True.",
)
parser.add_argument(
"--oft_text_encoder_r",
type=int,
default=0,
help="OFT rank for text encoder, only used if `use_oft` and `train_text_encoder` are True",
)
parser.add_argument(
"--oft_text_encoder_block_size",
type=int,
default=32,
help="OFT block size for text encoder, only used if `use_oft` and `train_text_encoder` are True",
)
parser.add_argument(
"--oft_text_encoder_dropout",
type=float,
default=0.0,
help="OFT dropout for text encoder, only used if `use_oft` and `train_text_encoder` are True",
)
parser.add_argument(
"--oft_text_encoder_use_coft",
action="store_true",
help="Using constrained OFT on the text encoder, only used if use_oft is True",
)
parser.add_argument(
"--oft_text_encoder_eps",
type=float,
default=0.0,
help="The control strength of COFT on the text encoder. Only has an effect if `oft_text_encoder_use_coft` is set to True.",
)
parser.add_argument(
"--num_dataloader_workers", type=int, default=1, help="Num of workers for the training dataloader."
)
parser.add_argument(
"--no_tracemalloc",
default=False,
action="store_true",
help="Flag to stop memory allocation tracing during training. This could speed up training on Windows.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of accelerators, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU or Intel XPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU or Intel XPU. Default to fp16 if a GPU/XPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
self.device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
self.device_module = getattr(torch, self.device_type, torch.cuda)
gc.collect()
self.device_module.empty_cache()
self.device_module.reset_peak_memory_stats() # reset the peak gauge to zero
self.begin = self.device_module.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
"""get resident set size memory for the current process"""
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
# time.sleep(0.001) # 1msec
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
self.device_module.empty_cache()
self.end = self.device_module.memory_allocated()
self.peak = self.device_module.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def collate_fn(examples, with_prior_preservation=False):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple accelerators."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=logging_dir,
)
if args.report_to == "wandb":
import wandb
wandb.login(key=args.wandb_key)
wandb.init(project=args.wandb_project_name)
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
dtype = torch.float16 if accelerator.device.type in ["cuda", "xpu"] else torch.float32
if args.prior_generation_precision == "fp32":
dtype = torch.float32
elif args.prior_generation_precision == "fp16":
dtype = torch.float16
elif args.prior_generation_precision == "bf16":
dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
dtype=dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
api = HfApi(token=args.hub_token)
# Create repo (repo_name from args or inferred)
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDPMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
) # DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
if args.use_oft:
config = OFTConfig(
r=args.oft_r,
oft_block_size=args.oft_block_size,
target_modules=UNET_TARGET_MODULES,
module_dropout=args.oft_dropout,
init_weights=True,
coft=args.oft_use_coft,
eps=args.oft_eps,
)
unet = get_peft_model(unet, config)
unet.print_trainable_parameters()
print(unet)
vae.requires_grad_(False)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
elif args.train_text_encoder and args.use_oft:
config = OFTConfig(
r=args.oft_text_encoder_r,
oft_block_size=args.oft_text_encoder_block_size,
target_modules=TEXT_ENCODER_TARGET_MODULES,
module_dropout=args.oft_text_encoder_dropout,
init_weights=True,
coft=args.oft_text_encoder_use_coft,
eps=args.oft_text_encoder_eps,
)
text_encoder = get_peft_model(text_encoder, config)
text_encoder.print_trainable_parameters()
print(text_encoder)
if args.enable_xformers_memory_efficient_attention:
if accelerator.device.type == "xpu":
logger.warn("XPU hasn't support xformers yet, ignore it.")
elif is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# below fails when using oft so commenting it out
if args.train_text_encoder and not args.use_oft:
text_encoder.gradient_checkpointing_enable()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB accelerators
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.num_dataloader_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and text_encoder to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1]
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = resume_global_step // num_update_steps_per_epoch
resume_step = resume_global_step % num_update_steps_per_epoch
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
with TorchTracemalloc() if not args.no_tracemalloc else nullcontext() as tracemalloc:
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
global_step += 1
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if (
args.validation_prompt is not None
and (step + num_update_steps_per_epoch * epoch) % args.validation_steps == 0
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
safety_checker=None,
revision=args.revision,
)
# set `keep_fp32_wrapper` to True because we do not want to remove
# mixed precision hooks while we are still training
pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
if args.seed is not None:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
else:
generator = None
images = []
for _ in range(args.num_validation_images):
image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
images.append(image)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
import wandb
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
if global_step >= args.max_train_steps:
break
# Printing the accelerator memory usage details such as allocated memory, peak memory, and total memory usage
if not args.no_tracemalloc:
accelerator.print(
f"{accelerator.device.type.upper()} Memory before entering the train : {b2mb(tracemalloc.begin)}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Memory consumed at the end of the train (end-begin): {tracemalloc.used}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}"
)
accelerator.print(
f"{accelerator.device.type.upper()} Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if args.use_oft:
unwarpped_unet = accelerator.unwrap_model(unet)
unwarpped_unet.save_pretrained(
os.path.join(args.output_dir, "unet"), state_dict=accelerator.get_state_dict(unet)
)
if args.train_text_encoder:
unwarpped_text_encoder = accelerator.unwrap_model(text_encoder)
unwarpped_text_encoder.save_pretrained(
os.path.join(args.output_dir, "text_encoder"),
state_dict=accelerator.get_state_dict(text_encoder),
)
else:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
run_as_future=True,
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
================================================
FILE: examples/olora_finetuning/README.md
================================================
# OLoRA: Orthonormal Low Rank Adaptation of Large Language Models
## Introduction
[OLoRA](https://huggingface.co/papers/2406.01775) is a novel approach that leverages orthonormal low rank adaptation through QR decomposition. Unlike the default LoRA implementation, OLoRA decomposes original weights into their $\mathbf{Q}$ and $\mathbf{R}$ parts, and then uses the first `rank` rows of $\mathbf{R}$ and the first `rank` columns of $\mathbf{Q}$ to initialize $\mathbf{A}$ and $\mathbf{B}$, respectively. This results in significantly faster convergence, more stable training, and superior performance.
## Quick start
```python
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
dataset = load_dataset("imdb", split="train[:1%]")
lora_config = LoraConfig(
init_lora_weights="olora"
)
peft_model = get_peft_model(model, lora_config)
training_args = SFTConfig(dataset_text_field="text", max_length=128)
trainer = SFTTrainer(
model=peft_model,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("olora-opt-350m")
```
There is no additional change needed to your standard LoRA procedure, except for specifying `init_lora_weights = "olora"` option in your lora configuration.
Additionally you can refer to olora finetuning script.
Run the script simply by running:
```bash
python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m
```
OLoRA also supports quantization. To use 4-bit quantization try:
```bash
python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m --quantize
```
or you can just pass a quantized model without the quantize flag.
If you want to run DDP by [accelerate](https://huggingface.co/docs/accelerate/en/index), please run `accelerate config` to set your ddp config, and run:
```bash
accelerate launch examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m
```
please add `--device_map cpu` if you want to run finetune on CPU.
If you want to train a quantized model like AWQ and GPTQ which do not support olora init method, please pass `--init_lora_weights gaussian`. For example:
```bash
python3 examples/olora_finetuning/olora_finetuning.py --base_model hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 --init_lora_weights gaussian
```
## Use the model
You can load and use the model as any other 🤗 PEFT model
```python
from peft import PeftModel
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
olora_model = PeftModel.from_pretrained(model, "olora-opt-350m")
```
## OLoRA and LoRA
OLoRA differs from LoRA in that it mutates the original weights. To utilize multiple adapters simultaneously, you can leverage the `path_initial_model_for_weight_conversion` option. Below is a simple template illustrating how to convert OLoRA to conventional LoRA:
```python
base_model = AutoModel.from_pretrained("facebook/opt-350m")
olora_config = LoraConfig(
...
init_lora_weights = "olora" # Initialize the model with OLoRA
)
olora_model = get_peft_model(base_model, olora_config)
init_path =
olora_model.save_pretrained(init_path) # Save the model *before* performing any training
# Train the model
train(olora_model) # Your training loop
#Save the model after training
olora_model.save_pretrained(output_dir, path_initial_model_for_weight_conversion=init_path)
```
After completing training, you can save and convert your OLoRA model to a conventional LoRA model by setting `path_initial_model_for_weight_conversion` to `init_path`, that is the path of your untrained OLoRA model. This conversion enables you to use multiple adapters with your LoRA model. Note that this conversion is not supported if `rslora` is used in combination with `rank_pattern` or `alpha_pattern`.
## Citation
```
@misc{büyükakyüz2024olora,
title={OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models},
author={Kerim Büyükakyüz},
year={2024},
eprint={2406.01775},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
================================================
FILE: examples/olora_finetuning/olora_finetuning.py
================================================
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Optional
import torch
import transformers
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed
from peft import (
LoraConfig,
get_peft_model,
)
def train(
base_model: str = "path/to/model",
data_path: str = "yahma/alpaca-cleaned",
output_dir: str = "olora",
batch_size: int = 16,
num_epochs: int = 1,
learning_rate: float = 3e-4,
cutoff_len: int = 256,
val_set_size: int = 16,
quantize: bool = False,
eval_step: int = 100,
save_step: int = 100,
device_map: str = "auto",
lora_r: int = 32,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
lora_target_modules: list[str] = None,
dtype: str = "float16",
init_lora_weights="olora",
seed: Optional[int] = None,
):
# Set device_map to the right place when enabling DDP.
world_size = int(os.environ.get("WORLD_SIZE", 0)) or int(os.environ.get("PMI_SIZE", 0))
if world_size > 1 and device_map != "cpu":
from accelerate import Accelerator
device_map = {"": Accelerator().process_index}
# Set seed
if seed is not None:
set_seed(seed)
model_kwargs = {"dtype": getattr(torch, dtype), "device_map": device_map}
if quantize:
model_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
model = AutoModelForCausalLM.from_pretrained(base_model, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
# For some tokenizer with no pad token like llama
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
def tokenize(prompt, add_eos_token=True):
result = tokenizer(
prompt,
truncation=True,
max_length=cutoff_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < cutoff_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
result["labels"] = result["input_ids"].copy()
return result
def generate_and_tokenize_prompt(example):
full_prompt = generate_prompt(example)
tokenized_full_prompt = tokenize(full_prompt)
return tokenized_full_prompt
config = LoraConfig(
r=lora_r,
lora_alpha=lora_alpha,
target_modules=lora_target_modules,
lora_dropout=lora_dropout,
bias="none",
task_type="CAUSAL_LM",
init_lora_weights=init_lora_weights,
)
model = get_peft_model(model, config)
data = load_dataset(data_path)
train_val = data["train"].train_test_split(test_size=val_set_size, shuffle=True, seed=42)
train_data = train_val["train"].shuffle().map(generate_and_tokenize_prompt)
val_data = train_val["test"].shuffle().map(generate_and_tokenize_prompt)
trainer = transformers.Trainer(
model=model,
train_dataset=train_data,
eval_dataset=val_data,
args=transformers.TrainingArguments(
per_device_train_batch_size=batch_size,
warmup_steps=100,
num_train_epochs=num_epochs,
learning_rate=learning_rate,
logging_steps=100,
optim="adamw_torch",
eval_strategy="steps",
save_strategy="steps",
eval_steps=eval_step,
save_steps=save_step,
output_dir=output_dir,
save_total_limit=3,
load_best_model_at_end=True,
ddp_find_unused_parameters=False if world_size > 1 else None,
),
data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
)
trainer.train()
model.save_pretrained(output_dir)
def generate_prompt(example):
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{example["instruction"]}
### Response:
{example["output"]}"""
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--base_model", type=str, default="path/to/model")
parser.add_argument("--data_path", type=str, default="yahma/alpaca-cleaned")
parser.add_argument("--output_dir", type=str, default="olora")
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--num_epochs", type=int, default=1)
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--cutoff_len", type=int, default=256)
parser.add_argument("--val_set_size", type=int, default=16)
parser.add_argument("--quantize", action="store_true")
parser.add_argument("--eval_step", type=int, default=100)
parser.add_argument("--save_step", type=int, default=100)
parser.add_argument("--device_map", type=str, default="auto")
parser.add_argument("--lora_r", type=int, default=32)
parser.add_argument("--lora_alpha", type=int, default=16)
parser.add_argument("--lora_dropout", type=float, default=0.05)
parser.add_argument("--lora_target_modules", type=str, default=None)
parser.add_argument("--dtype", type=str, default="float16")
parser.add_argument("--init_lora_weights", type=str, default="olora")
parser.add_argument("--seed", type=int, default=None)
args = parser.parse_args()
train(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
device_map=args.device_map,
lora_r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
dtype=args.dtype,
init_lora_weights=args.init_lora_weights,
seed=args.seed,
)
================================================
FILE: examples/orthogonal_subspace_learning/README.md
================================================
# Orthogonal Subspace Fine-tuning (OSF) - Continual Learning Example
This example demonstrates OSF's ability to learn multiple tasks sequentially while preventing catastrophic forgetting, a key challenge in continual learning.
## Introduction
**Orthogonal Subspace Fine-tuning (OSF)** is a parameter-efficient fine-tuning method designed specifically for continual learning scenarios. Unlike traditional fine-tuning which suffers from catastrophic forgetting when learning new tasks, OSF constrains parameter updates to be orthogonal to previously important directions, effectively preserving knowledge from earlier tasks.
### Key Features
- **Prevents Catastrophic Forgetting**: Maintains performance on previous tasks while learning new ones
- **Full Model Capacity**: Unlike LoRA-based methods, OSF allows full-rank updates within the trainable subspace
- **Progressive Budget Allocation**: Gradually allocates more capacity to preserve previous knowledge
- **No Additional Parameters**: Modifies weights in-place without adding extra parameters per task
## Quick Start
### Installation
```bash
pip install -e ".[dev]"
```
### Basic Usage
Run the continual learning example with OSF:
```bash
python osf_continual_learning.py \
--model_name meta-llama/Llama-3.1-8B-Instruct \
--num_train 1000 \
--num_eval 200 \
--num_epochs 2 \
--output_dir ./outputs
```
To compare with full fine-tuning baseline:
```bash
python osf_continual_learning.py \
--model_name meta-llama/Llama-3.1-8B-Instruct \
--run_baseline \
--output_dir ./outputs
```
## Continual Learning Scenario
This example trains a model on three different tasks sequentially:
1. **ScienceQA** - Science question answering across natural, language, and social sciences
2. **NumGLUE** - Mathematical reasoning and numerical understanding
3. **FOMC** - Financial sentiment classification (Dovish/Hawkish/Neutral)
### Progressive Capacity Allocation
OSF uses a progressive budget allocation strategy where each task gets decreasing trainable capacity while preserving more knowledge from previous tasks:
| Task | Effective Rank | Preserved | Trainable | Description |
|------|----------------|-----------|-----------|-------------|
| Task 1 (ScienceQA) | 0.3 | 30% | 70% | Maximum capacity for first task |
| Task 2 (NumGLUE) | 0.5 | 50% | 50% | Balanced capacity allocation |
| Task 3 (FOMC) | 0.7 | 70% | 30% | Minimal capacity, maximum preservation |
This allocation ensures:
- Early tasks get sufficient capacity to learn effectively
- Later tasks can still learn new patterns
- Previous knowledge is progressively protected from interference
## How OSF Works
OSF decomposes each weight matrix using SVD into high-rank (preserved) and low-rank (trainable) components:
```
W = U_high @ S_high @ V_high^T + U_low @ S_low @ V_low^T
└─────────┬─────────┘ └──────┬──────┘
frozen trainable
(previous tasks) (current task)
```
During training:
1. **Initialization**: Perform SVD on each weight matrix
2. **Partitioning**: Split singular values based on `effective_rank`
3. **Freezing**: Freeze top-k singular directions (high-rank subspace)
4. **Training**: Update remaining directions (low-rank subspace)
5. **Gradient Projection**: Ensure updates are orthogonal to frozen subspace
Between tasks:
1. **Unload**: Merge OSF components back into base model
2. **Re-initialize**: Perform fresh SVD with increased `effective_rank`
3. **Continue**: Train on next task with larger frozen subspace
## Command Line Arguments
```
--model_name Model to use (default: meta-llama/Llama-3.1-8B-Instruct)
--num_train Number of training samples per task (default: 1000)
--num_eval Number of evaluation samples per task (default: 200)
--output_dir Directory for outputs (default: ./osf_continual_learning_outputs)
--num_epochs Training epochs per task (default: 2)
--learning_rate Learning rate (default: 5e-6)
--batch_size Batch size per device (default: 32)
--gradient_accumulation_steps Gradient accumulation (default: 1)
--max_length Maximum sequence length (default: 512)
--seed Random seed (default: 42)
--run_baseline Also run full fine-tuning baseline for comparison
```
## Expected Results
### OSF Performance
When using OSF (with 2 epochs per task), you should observe:
- **Reduced catastrophic forgetting**: Performance on earlier tasks degrades less compared to full fine-tuning
- **Continued learning**: Model successfully learns each new task
- **Better retention**: OSF maintains higher average accuracy across all tasks
### Full Fine-tuning Baseline
Standard full fine-tuning typically shows:
- **Catastrophic forgetting**: Significant performance degradation on earlier tasks
- **Last task bias**: Model performs well only on the most recent task
- **Task interference**: New task learning overwrites previous knowledge
## Understanding the Results
### Forgetting Analysis
The script prints a forgetting analysis showing how much earlier task performance changes.
**Example results from training with 2 epochs per task:**
```
SUMMARY METRICS
================================================================================
1. Average Accuracy Across All 3 Tasks (After Final Task):
OSF: 53.42%
Full FT: 46.26%
Difference: +7.17% (OSF better)
2. Average Forgetting (Task 1 & 2):
Forgetting = Final Accuracy - Initial Accuracy (negative is worse)
ScienceQA:
OSF: +30.50% (initial: 55.00% → final: 85.50%)
Full FT: -13.00% (initial: 84.50% → final: 71.50%)
Difference: +43.50% (OSF better)
NumGLUE:
OSF: +30.00% (initial: 16.00% → final: 46.00%)
Full FT: +1.00% (initial: 37.50% → final: 38.50%)
Difference: +29.00% (OSF better)
Average Forgetting:
OSF: +30.25%
Full FT: -6.00%
Difference: +36.25% (OSF better)
```
**Interpreting Forgetting Metrics:**
- **Negative values** = Forgetting occurred (performance decreased)
- **Positive values** = Backward transfer occurred (performance improved)
- **Values closer to 0** = Better retention
In this example, OSF shows significant positive backward transfer (+30.25% average), while Full FT shows slight forgetting (-6.00% average). This demonstrates OSF's ability to not only prevent catastrophic forgetting but also enable beneficial knowledge transfer across tasks.
## Advanced Usage
### Custom Task Configuration
You can modify the tasks and capacity allocation in the script:
```python
tasks = [
{
"name": "Task1",
"train": task1_train,
"eval": task1_eval,
"effective_rank": 0.2, # Freeze 20%, train 80%
},
{
"name": "Task2",
"train": task2_train,
"eval": task2_eval,
"effective_rank": 0.6, # Freeze 60%, train 40%
},
]
```
### Using Different Models
OSF works with any transformer-based model:
```bash
# Smaller model for faster experimentation
python osf_continual_learning.py --model_name gpt2
# Different LLaMA variant
python osf_continual_learning.py --model_name meta-llama/Llama-2-7b-hf
```
### Adjusting Target Modules
In the script, you can modify which modules to apply OSF to:
```python
config = OSFConfig(
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], # Attention only
effective_rank=task["effective_rank"],
)
```
Common configurations:
- **Attention only**: `["q_proj", "k_proj", "v_proj", "o_proj"]`
- **Attention + MLP**: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`
- **All linear**: `target_modules="all-linear"`
## Customization
### Adding Your Own Tasks
To add custom tasks, create data loading and formatting functions in `utils.py`:
```python
def load_my_task(num_train=1000, num_eval=200, seed=42):
"""Load your custom dataset."""
dataset = load_dataset("your/dataset")
# ... split and return
return train_dataset, eval_dataset
def format_my_task_for_llama(examples, tokenizer, max_length=512):
"""Format your task for instruction following."""
prompts = []
labels_text = []
for i in range(len(examples)):
prompt = f"Your instruction template: {examples['input'][i]}"
label = examples['output'][i]
prompts.append(prompt)
labels_text.append(label)
# ... tokenization logic
return formatted_examples
```
Then add to the tasks list in `osf_continual_learning.py`.
## Performance Tips
### Memory Optimization
For large models, consider:
- Reducing `batch_size` and increasing `gradient_accumulation_steps`
- Using smaller `max_length`
- Enabling gradient checkpointing (add to model before OSF):
```python
model.gradient_checkpointing_enable()
```
### Training Speed
To speed up training:
- Reduce `num_train` and `num_eval` for initial testing
- Use smaller models (e.g., `gpt2` or `Llama-2-7b`)
- Reduce `max_length` for shorter sequences
### Better Results
For improved continual learning performance:
- Play around with `num_epochs` per task (try 2-3 epochs)
- Adjust `learning_rate`
- Experiment with different capacity allocation strategies
## Citation
If you use OSF in your research, please cite:
```bibtex
@misc{nayak2025sculptingsubspacesconstrainedfinetuning,
title={Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning},
author={Nikhil Shivakumar Nayak and Krishnateja Killamsetty and Ligong Han and Abhishek Bhandwaldar and Prateek Chanda and Kai Xu and Hao Wang and Aldo Pareja and Oleg Silkin and Mustafa Eyceoz and Akash Srivastava},
year={2025},
eprint={2504.07097},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.07097},
}
```
## Additional Resources
- [OSF Documentation](../../docs/source/package_reference/osf.md)
- [PEFT Documentation](https://huggingface.co/docs/peft)
- [Original Paper](https://huggingface.co/papers/2504.07097)
## License
This example is licensed under Apache 2.0. See the PEFT repository for full license details.
================================================
FILE: examples/orthogonal_subspace_learning/osf_continual_learning.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
OSF Continual Learning Example
This script demonstrates OSF's ability to learn multiple tasks sequentially while preventing
catastrophic forgetting, compared to standard full fine-tuning.
Tasks:
1. ScienceQA - Science question answering
2. NumGLUE - Mathematical reasoning
3. FOMC - Financial sentiment classification
OSF Configuration:
- Task 1: effective_rank=0.3 (train 70%, freeze 30%)
- Task 2: effective_rank=0.5 (train 50%, freeze 50%)
- Task 3: effective_rank=0.7 (train 30%, freeze 70%)
"""
import argparse
import os
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from utils import (
DataCollatorForCompletionOnly,
format_fomc_for_llama,
format_numglue_for_llama,
format_scienceqa_for_llama,
load_fomc,
load_numglue,
load_scienceqa,
)
from peft import OSFConfig, get_peft_model
def compute_accuracy_scienceqa(model, eval_dataset, tokenizer, data_collator):
"""Compute accuracy for ScienceQA (extract predicted letter)."""
model.eval()
correct = 0
total = 0
# Create a simple dataloader
from torch.utils.data import DataLoader
dataloader = DataLoader(eval_dataset, batch_size=8, collate_fn=data_collator)
with torch.no_grad():
for batch in dataloader:
input_ids = batch["input_ids"].to(model.device)
attention_mask = batch["attention_mask"].to(model.device)
labels = batch["labels"]
# Generate predictions
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=5,
pad_token_id=tokenizer.pad_token_id,
do_sample=False,
)
# Extract predictions and ground truth
for i in range(len(outputs)):
# Decode the generated text
generated_text = tokenizer.decode(outputs[i], skip_special_tokens=True)
# Extract the answer (last letter in the generated text)
# Look for single capital letters A, B, C, D
matches = re.findall(r"\b([A-D])\b", generated_text)
pred = matches[-1] if matches else "X"
# Get ground truth (find the label that's not -100)
label_ids = labels[i][labels[i] != -100]
if len(label_ids) > 0:
gt = tokenizer.decode(label_ids, skip_special_tokens=True).strip()
if pred == gt:
correct += 1
total += 1
accuracy = correct / total if total > 0 else 0.0
return accuracy
def compute_accuracy_numglue(model, eval_dataset, tokenizer, data_collator):
"""Compute accuracy for NumGLUE (extract predicted number)."""
model.eval()
correct = 0
total = 0
from torch.utils.data import DataLoader
dataloader = DataLoader(eval_dataset, batch_size=8, collate_fn=data_collator)
with torch.no_grad():
for batch in dataloader:
input_ids = batch["input_ids"].to(model.device)
attention_mask = batch["attention_mask"].to(model.device)
labels = batch["labels"]
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=20,
pad_token_id=tokenizer.pad_token_id,
do_sample=False,
)
for i in range(len(outputs)):
generated_text = tokenizer.decode(outputs[i], skip_special_tokens=True)
# Extract number from generated text
numbers = re.findall(r"-?\d+\.?\d*", generated_text)
pred = numbers[-1] if numbers else "-999"
# Get ground truth
label_ids = labels[i][labels[i] != -100]
if len(label_ids) > 0:
gt = tokenizer.decode(label_ids, skip_special_tokens=True).strip()
if pred == gt:
correct += 1
total += 1
accuracy = correct / total if total > 0 else 0.0
return accuracy
def compute_accuracy_fomc(model, eval_dataset, tokenizer, data_collator):
"""Compute accuracy for FOMC (extract predicted sentiment)."""
model.eval()
correct = 0
total = 0
from torch.utils.data import DataLoader
dataloader = DataLoader(eval_dataset, batch_size=8, collate_fn=data_collator)
valid_labels = ["Dovish", "Hawkish", "Neutral"]
with torch.no_grad():
for batch in dataloader:
input_ids = batch["input_ids"].to(model.device)
attention_mask = batch["attention_mask"].to(model.device)
labels = batch["labels"]
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=10,
pad_token_id=tokenizer.pad_token_id,
do_sample=False,
)
for i in range(len(outputs)):
generated_text = tokenizer.decode(outputs[i], skip_special_tokens=True)
# Extract sentiment label
pred = None
for label in valid_labels:
if label in generated_text:
pred = label
break
# Get ground truth
label_ids = labels[i][labels[i] != -100]
if len(label_ids) > 0:
gt = tokenizer.decode(label_ids, skip_special_tokens=True).strip()
if pred == gt:
correct += 1
total += 1
accuracy = correct / total if total > 0 else 0.0
return accuracy
def evaluate_model(model, eval_dataset, data_collator, tokenizer, task_name, task_type):
"""Evaluate model on a dataset and return loss and accuracy."""
# Compute loss
trainer = Trainer(
model=model,
data_collator=data_collator,
eval_dataset=eval_dataset,
args=TrainingArguments(
label_names=["labels"],
),
)
results = trainer.evaluate()
loss = results["eval_loss"]
# Compute accuracy based on task type
if task_type == "scienceqa":
accuracy = compute_accuracy_scienceqa(model, eval_dataset, tokenizer, data_collator)
elif task_type == "numglue":
accuracy = compute_accuracy_numglue(model, eval_dataset, tokenizer, data_collator)
elif task_type == "fomc":
accuracy = compute_accuracy_fomc(model, eval_dataset, tokenizer, data_collator)
else:
accuracy = 0.0
print(f" {task_name}: Loss = {loss:.4f}, Accuracy = {accuracy * 100:.2f}%")
return loss, accuracy
def train_with_osf(
model_name,
num_train,
num_eval,
output_dir,
num_epochs,
learning_rate,
batch_size,
gradient_accumulation_steps,
max_length,
seed,
):
"""Train using OSF with progressive rank allocation."""
print("\n" + "=" * 80)
print("TRAINING WITH OSF (Orthogonal Subspace Fine-tuning)")
print("=" * 80)
# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
base_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
# Load all datasets with task-specific sizes
# FOMC only has 496 samples total, so we use 350 train + 146 eval for it
print("\nLoading datasets...")
scienceqa_train, scienceqa_eval = load_scienceqa(1000, 200, seed)
numglue_train, numglue_eval = load_numglue(1000, 200, seed)
fomc_train, fomc_eval = load_fomc(350, 146, seed)
# Store original eval datasets for later
scienceqa_eval_original = scienceqa_eval
numglue_eval_original = numglue_eval
fomc_eval_original = fomc_eval
# Format datasets
scienceqa_train = scienceqa_train.map(
lambda x: format_scienceqa_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=scienceqa_train.column_names,
)
scienceqa_eval = scienceqa_eval.map(
lambda x: format_scienceqa_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=scienceqa_eval.column_names,
)
numglue_train = numglue_train.map(
lambda x: format_numglue_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=numglue_train.column_names,
)
numglue_eval = numglue_eval.map(
lambda x: format_numglue_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=numglue_eval.column_names,
)
fomc_train = fomc_train.map(
lambda x: format_fomc_for_llama(x, tokenizer, max_length), batched=True, remove_columns=fomc_train.column_names
)
fomc_eval = fomc_eval.map(
lambda x: format_fomc_for_llama(x, tokenizer, max_length), batched=True, remove_columns=fomc_eval.column_names
)
data_collator = DataCollatorForCompletionOnly(tokenizer, max_length)
# Task configurations
tasks = [
{
"name": "ScienceQA",
"train": scienceqa_train,
"eval": scienceqa_eval,
"eval_original": scienceqa_eval_original,
"effective_rank": 0.3, # Freeze 30%, train 70%
"type": "scienceqa",
},
{
"name": "NumGLUE",
"train": numglue_train,
"eval": numglue_eval,
"eval_original": numglue_eval_original,
"effective_rank": 0.5, # Freeze 50%, train 50%
"type": "numglue",
},
{
"name": "FOMC",
"train": fomc_train,
"eval": fomc_eval,
"eval_original": fomc_eval_original,
"effective_rank": 0.7, # Freeze 70%, train 30%
"type": "fomc",
},
]
# Store evaluation history: {task_name: [(loss, accuracy), ...]}
eval_history = {
"ScienceQA": [],
"NumGLUE": [],
"FOMC": [],
}
# Sequential task training
model = base_model
for task_idx, task in enumerate(tasks):
print(f"\n{'=' * 80}")
print(f"TASK {task_idx + 1}: {task['name']}")
print(f"Effective Rank: {task['effective_rank']} (preserving {task['effective_rank'] * 100:.0f}%)")
print(f"{'=' * 80}")
# Configure OSF for this task
config = OSFConfig(
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
effective_rank=task["effective_rank"],
)
# Apply OSF to the model
model = get_peft_model(model, config)
# Training arguments
training_args = TrainingArguments(
output_dir=f"{output_dir}/osf_{task['name'].lower()}",
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
learning_rate=learning_rate,
logging_steps=10,
save_strategy="no",
bf16=True,
remove_unused_columns=False,
)
# Train on current task
trainer = Trainer(
model=model,
args=training_args,
train_dataset=task["train"],
data_collator=data_collator,
)
print(f"\nTraining on {task['name']}...")
trainer.train()
# Evaluate on all tasks seen so far
print(f"\nEvaluating on all tasks after training on {task['name']}:")
for eval_task_idx in range(task_idx + 1):
eval_task = tasks[eval_task_idx]
loss, accuracy = evaluate_model(
model, eval_task["eval"], data_collator, tokenizer, eval_task["name"], eval_task["type"]
)
eval_history[eval_task["name"]].append((loss, accuracy))
# Unload OSF to get the updated base model for next task (if not last task)
if task_idx < len(tasks) - 1:
print("\nUnloading OSF adapter to prepare for next task...")
model = model.unload()
# Save final model
final_model_path = f"{output_dir}/osf_final"
model.save_pretrained(final_model_path)
print(f"\nFinal OSF model saved to {final_model_path}")
return eval_history
def train_full_finetuning(
model_name,
num_train,
num_eval,
output_dir,
num_epochs,
learning_rate,
batch_size,
gradient_accumulation_steps,
max_length,
seed,
):
"""Train using standard full fine-tuning (baseline for comparison)."""
print("\n" + "=" * 80)
print("TRAINING WITH FULL FINE-TUNING (Baseline)")
print("=" * 80)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
# Load all datasets with task-specific sizes
# FOMC only has 496 samples total, so we use 350 train + 146 eval for it
print("\nLoading datasets...")
scienceqa_train, scienceqa_eval = load_scienceqa(1000, 200, seed)
numglue_train, numglue_eval = load_numglue(1000, 200, seed)
fomc_train, fomc_eval = load_fomc(350, 146, seed)
# Store original eval datasets
scienceqa_eval_original = scienceqa_eval
numglue_eval_original = numglue_eval
fomc_eval_original = fomc_eval
# Format datasets
scienceqa_train = scienceqa_train.map(
lambda x: format_scienceqa_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=scienceqa_train.column_names,
)
scienceqa_eval = scienceqa_eval.map(
lambda x: format_scienceqa_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=scienceqa_eval.column_names,
)
numglue_train = numglue_train.map(
lambda x: format_numglue_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=numglue_train.column_names,
)
numglue_eval = numglue_eval.map(
lambda x: format_numglue_for_llama(x, tokenizer, max_length),
batched=True,
remove_columns=numglue_eval.column_names,
)
fomc_train = fomc_train.map(
lambda x: format_fomc_for_llama(x, tokenizer, max_length), batched=True, remove_columns=fomc_train.column_names
)
fomc_eval = fomc_eval.map(
lambda x: format_fomc_for_llama(x, tokenizer, max_length), batched=True, remove_columns=fomc_eval.column_names
)
data_collator = DataCollatorForCompletionOnly(tokenizer, max_length)
tasks = [
{"name": "ScienceQA", "train": scienceqa_train, "eval": scienceqa_eval, "type": "scienceqa"},
{"name": "NumGLUE", "train": numglue_train, "eval": numglue_eval, "type": "numglue"},
{"name": "FOMC", "train": fomc_train, "eval": fomc_eval, "type": "fomc"},
]
# Store evaluation history
eval_history = {
"ScienceQA": [],
"NumGLUE": [],
"FOMC": [],
}
# Load base model once
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
# Sequential task training
for task_idx, task in enumerate(tasks):
print(f"\n{'=' * 80}")
print(f"TASK {task_idx + 1}: {task['name']}")
print(f"{'=' * 80}")
# Training arguments
training_args = TrainingArguments(
output_dir=f"{output_dir}/full_{task['name'].lower()}",
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
learning_rate=learning_rate,
logging_steps=10,
save_strategy="no",
bf16=True,
remove_unused_columns=False,
)
# Train on current task
trainer = Trainer(
model=model,
args=training_args,
train_dataset=task["train"],
data_collator=data_collator,
)
print(f"\nTraining on {task['name']}...")
trainer.train()
# Evaluate on all tasks seen so far
print(f"\nEvaluating on all tasks after training on {task['name']}:")
for eval_task_idx in range(task_idx + 1):
eval_task = tasks[eval_task_idx]
loss, accuracy = evaluate_model(
model, eval_task["eval"], data_collator, tokenizer, eval_task["name"], eval_task["type"]
)
eval_history[eval_task["name"]].append((loss, accuracy))
# Save final model
final_model_path = f"{output_dir}/full_final"
model.save_pretrained(final_model_path)
print(f"\nFinal full fine-tuning model saved to {final_model_path}")
return eval_history
def print_results_comparison(osf_history, full_history):
"""Print comparison table of OSF vs Full Fine-tuning."""
print("\n" + "=" * 80)
print("RESULTS COMPARISON: OSF vs Full Fine-tuning")
print("=" * 80)
tasks = ["ScienceQA", "NumGLUE", "FOMC"]
# Print detailed results
print("\n" + "-" * 80)
print("DETAILED RESULTS (Accuracy %)")
print("-" * 80)
print(f"{'Task':<15} {'After Task':<15} {'OSF Acc %':<15} {'Full FT Acc %':<15} {'Difference':<15}")
print("-" * 80)
for task_idx, task in enumerate(tasks):
for eval_after_idx in range(task_idx, len(tasks)):
eval_after = tasks[eval_after_idx]
osf_acc = osf_history[task][eval_after_idx - task_idx][1] * 100
full_acc = full_history[task][eval_after_idx - task_idx][1] * 100
diff = osf_acc - full_acc
print(
f"{task:<15} {eval_after:<15} {osf_acc:<15.2f} {full_acc:<15.2f} {diff:+15.2f}{' (OSF better)' if diff > 0 else ''}"
)
# Summary statistics
print("\n" + "=" * 80)
print("SUMMARY METRICS")
print("=" * 80)
# Final average accuracy across all 3 tasks
osf_final_accs = [osf_history[task][-1][1] * 100 for task in tasks]
full_final_accs = [full_history[task][-1][1] * 100 for task in tasks]
osf_avg_final = sum(osf_final_accs) / len(osf_final_accs)
full_avg_final = sum(full_final_accs) / len(full_final_accs)
print("\n1. Average Accuracy Across All 3 Tasks (After Final Task):")
print(f" OSF: {osf_avg_final:.2f}%")
print(f" Full FT: {full_avg_final:.2f}%")
print(
f" Difference: {osf_avg_final - full_avg_final:+.2f}% {'(OSF better)' if osf_avg_final > full_avg_final else '(Full FT better)'}"
)
# Average forgetting (for tasks 1 and 2 only, since task 3 is the final task)
print("\n2. Average Forgetting (Task 1 & 2):")
print(" Forgetting = Final Accuracy - Initial Accuracy (negative is worse)\n")
osf_forgetting_vals = []
full_forgetting_vals = []
for task_idx, task in enumerate(tasks[:-1]): # Exclude last task
osf_initial_acc = osf_history[task][0][1] * 100 # Right after learning task
osf_final_acc = osf_history[task][-1][1] * 100 # After learning all tasks
osf_forgetting = osf_final_acc - osf_initial_acc
full_initial_acc = full_history[task][0][1] * 100
full_final_acc = full_history[task][-1][1] * 100
full_forgetting = full_final_acc - full_initial_acc
osf_forgetting_vals.append(osf_forgetting)
full_forgetting_vals.append(full_forgetting)
print(f" {task}:")
print(f" OSF: {osf_forgetting:+.2f}% (initial: {osf_initial_acc:.2f}% → final: {osf_final_acc:.2f}%)")
print(
f" Full FT: {full_forgetting:+.2f}% (initial: {full_initial_acc:.2f}% → final: {full_final_acc:.2f}%)"
)
print(
f" Difference: {osf_forgetting - full_forgetting:+.2f}% {'(OSF better)' if osf_forgetting > full_forgetting else '(Full FT better)'}\n"
)
osf_avg_forgetting = sum(osf_forgetting_vals) / len(osf_forgetting_vals)
full_avg_forgetting = sum(full_forgetting_vals) / len(full_forgetting_vals)
print(" Average Forgetting:")
print(f" OSF: {osf_avg_forgetting:+.2f}%")
print(f" Full FT: {full_avg_forgetting:+.2f}%")
print(
f" Difference: {osf_avg_forgetting - full_avg_forgetting:+.2f}% {'(OSF better)' if osf_avg_forgetting > full_avg_forgetting else '(Full FT better)'}"
)
print("\n" + "=" * 80)
def main():
parser = argparse.ArgumentParser(description="OSF Continual Learning Example")
parser.add_argument(
"--model_name",
type=str,
default="meta-llama/Llama-3.1-8B-Instruct",
help="Model name or path",
)
parser.add_argument("--num_train", type=int, default=1000, help="Number of training samples per task")
parser.add_argument("--num_eval", type=int, default=200, help="Number of evaluation samples per task")
parser.add_argument("--output_dir", type=str, default="./osf_continual_learning_outputs", help="Output directory")
parser.add_argument("--num_epochs", type=int, default=2, help="Number of training epochs per task")
parser.add_argument("--learning_rate", type=float, default=5e-6, help="Learning rate")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size per device")
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
parser.add_argument("--max_length", type=int, default=512, help="Maximum sequence length")
parser.add_argument("--seed", type=int, default=42, help="Random seed")
parser.add_argument(
"--run_baseline",
action="store_true",
help="Also run full fine-tuning baseline for comparison",
)
args = parser.parse_args()
# Create output directory
os.makedirs(args.output_dir, exist_ok=True)
# Train with OSF
osf_history = train_with_osf(
args.model_name,
args.num_train,
args.num_eval,
args.output_dir,
args.num_epochs,
args.learning_rate,
args.batch_size,
args.gradient_accumulation_steps,
args.max_length,
args.seed,
)
# Optionally train with full fine-tuning baseline
if args.run_baseline:
full_history = train_full_finetuning(
args.model_name,
args.num_train,
args.num_eval,
args.output_dir,
args.num_epochs,
args.learning_rate,
args.batch_size,
args.gradient_accumulation_steps,
args.max_length,
args.seed,
)
# Print comparison
print_results_comparison(osf_history, full_history)
else:
print("\n" + "=" * 80)
print("OSF TRAINING COMPLETE")
print("=" * 80)
print("\nTo compare with full fine-tuning baseline, run with --run_baseline flag")
# Print OSF-only summary
tasks = ["ScienceQA", "NumGLUE", "FOMC"]
print("\n" + "=" * 80)
print("OSF SUMMARY METRICS")
print("=" * 80)
# Final average accuracy
osf_final_accs = [osf_history[task][-1][1] * 100 for task in tasks]
osf_avg_final = sum(osf_final_accs) / len(osf_final_accs)
print(f"\n1. Average Accuracy Across All 3 Tasks (After Final Task): {osf_avg_final:.2f}%")
for task, acc in zip(tasks, osf_final_accs):
print(f" {task}: {acc:.2f}%")
# Average forgetting
print("\n2. Average Forgetting (Task 1 & 2):")
osf_forgetting_vals = []
for task_idx, task in enumerate(tasks[:-1]):
osf_initial_acc = osf_history[task][0][1] * 100
osf_final_acc = osf_history[task][-1][1] * 100
osf_forgetting = osf_initial_acc - osf_final_acc
osf_forgetting_vals.append(osf_forgetting)
print(f" {task}: {osf_forgetting:+.2f}% (initial: {osf_initial_acc:.2f}% → final: {osf_final_acc:.2f}%)")
osf_avg_forgetting = sum(osf_forgetting_vals) / len(osf_forgetting_vals)
print(f" Average: {osf_avg_forgetting:+.2f}%")
print("\n" + "=" * 80)
if __name__ == "__main__":
main()
================================================
FILE: examples/orthogonal_subspace_learning/utils.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from datasets import load_dataset
def load_scienceqa(num_train=1000, num_eval=200, seed=42):
"""
Load ScienceQA dataset for science question answering.
Args:
num_train: Number of training samples
num_eval: Number of evaluation samples
seed: Random seed for reproducibility
Returns:
train_dataset, eval_dataset
"""
dataset = load_dataset("derek-thomas/ScienceQA", split="train")
# Shuffle and split
dataset = dataset.shuffle(seed=seed)
train_dataset = dataset.select(range(num_train))
eval_dataset = dataset.select(range(num_train, num_train + num_eval))
return train_dataset, eval_dataset
def load_numglue(num_train=1000, num_eval=200, seed=42):
"""
Load NumGLUE dataset for mathematical reasoning.
Args:
num_train: Number of training samples
num_eval: Number of evaluation samples
seed: Random seed for reproducibility
Returns:
train_dataset, eval_dataset
"""
import json
from datasets import Dataset
from huggingface_hub import hf_hub_download
# Download the NumGLUE JSON file manually
json_path = hf_hub_download(repo_id="metaeval/num-glue", filename="NumGLUE_train.json", repo_type="dataset")
# Read and process the JSON file line by line
data = []
with open(json_path) as f:
for line in f:
if line.strip(): # Skip empty lines
item = json.loads(line)
# Extract the number from the answer JSON structure
answer = item.get("answer", "")
if isinstance(answer, dict):
# NumGLUE answers are JSON with 'number' and 'date' fields
# Extract just the number field
answer_str = answer.get("number", "")
else:
answer_str = str(answer)
data.append({"question": item.get("question", ""), "answer": answer_str})
# Create dataset from processed data
dataset = Dataset.from_list(data)
# Shuffle and split
dataset = dataset.shuffle(seed=seed)
train_dataset = dataset.select(range(min(num_train, len(dataset))))
# If not enough samples, use what's available
eval_start = min(num_train, len(dataset))
eval_end = min(num_train + num_eval, len(dataset))
eval_dataset = dataset.select(range(eval_start, eval_end))
return train_dataset, eval_dataset
def load_fomc(num_train=1000, num_eval=200, seed=42):
"""
Load FOMC dataset for financial sentiment classification.
Args:
num_train: Number of training samples
num_eval: Number of evaluation samples
seed: Random seed for reproducibility
Returns:
train_dataset, eval_dataset
"""
dataset = load_dataset("TheFinAI/finben-fomc", split="test")
# Shuffle and split
dataset = dataset.shuffle(seed=seed)
train_dataset = dataset.select(range(min(num_train, len(dataset))))
eval_start = min(num_train, len(dataset))
eval_end = min(num_train + num_eval, len(dataset))
eval_dataset = dataset.select(range(eval_start, eval_end))
return train_dataset, eval_dataset
def format_scienceqa_for_llama(examples, tokenizer, max_length=512):
"""Format ScienceQA examples for Llama instruction following."""
prompts = []
labels_text = []
for i in range(len(examples["question"])):
# Build the question with choices
question = examples["question"][i]
choices = examples["choices"][i]
# Format choices
choices_text = "\n".join([f"{chr(65 + j)}. {choice}" for j, choice in enumerate(choices)])
prompt = f"""Answer the following science question by selecting the correct option.
Question: {question}
Choices:
{choices_text}
Answer (just the letter):"""
# Get the answer (convert index to letter)
answer_idx = examples["answer"][i]
answer = chr(65 + answer_idx)
prompts.append(prompt)
labels_text.append(answer)
# Tokenize
model_inputs = tokenizer(prompts, max_length=max_length, truncation=True, padding=False)
# Tokenize labels
labels = tokenizer(labels_text, max_length=10, truncation=True, padding=False)
# Combine input and label for training
combined_input_ids = []
combined_attention_mask = []
combined_labels = []
for i in range(len(model_inputs["input_ids"])):
input_ids = model_inputs["input_ids"][i]
label_ids = labels["input_ids"][i]
# Combine input and label
combined = input_ids + label_ids + [tokenizer.eos_token_id]
combined_input_ids.append(combined)
# Attention mask
combined_attention_mask.append([1] * len(combined))
# Labels (mask the prompt part, only train on answer)
label_masked = [-100] * len(input_ids) + label_ids + [tokenizer.eos_token_id]
combined_labels.append(label_masked)
return {
"input_ids": combined_input_ids,
"attention_mask": combined_attention_mask,
"labels": combined_labels,
}
def format_numglue_for_llama(examples, tokenizer, max_length=512):
"""Format NumGLUE examples for Llama instruction following."""
prompts = []
labels_text = []
for i in range(len(examples["question"])):
question = examples["question"][i]
answer = str(examples["answer"][i])
prompt = f"""Solve the following math problem and provide just the numerical answer.
Question: {question}
Answer:"""
prompts.append(prompt)
labels_text.append(answer)
# Tokenize
model_inputs = tokenizer(prompts, max_length=max_length, truncation=True, padding=False)
labels = tokenizer(labels_text, max_length=20, truncation=True, padding=False)
combined_input_ids = []
combined_attention_mask = []
combined_labels = []
for i in range(len(model_inputs["input_ids"])):
input_ids = model_inputs["input_ids"][i]
label_ids = labels["input_ids"][i]
combined = input_ids + label_ids + [tokenizer.eos_token_id]
combined_input_ids.append(combined)
combined_attention_mask.append([1] * len(combined))
label_masked = [-100] * len(input_ids) + label_ids + [tokenizer.eos_token_id]
combined_labels.append(label_masked)
return {
"input_ids": combined_input_ids,
"attention_mask": combined_attention_mask,
"labels": combined_labels,
}
def format_fomc_for_llama(examples, tokenizer, max_length=512):
"""Format FOMC examples for Llama instruction following."""
prompts = []
labels_text = []
for i in range(len(examples["text"])):
text = examples["text"][i]
# FOMC dataset has 'answer' column with values like 'dovish', 'hawkish', 'neutral'
label = examples["answer"][i].capitalize() # Capitalize first letter
prompt = f"""Classify the sentiment of the following Federal Reserve statement as Dovish, Hawkish, or Neutral.
Statement: {text}
Sentiment:"""
prompts.append(prompt)
labels_text.append(label)
# Tokenize
model_inputs = tokenizer(prompts, max_length=max_length, truncation=True, padding=False)
labels = tokenizer(labels_text, max_length=10, truncation=True, padding=False)
combined_input_ids = []
combined_attention_mask = []
combined_labels = []
for i in range(len(model_inputs["input_ids"])):
input_ids = model_inputs["input_ids"][i]
label_ids = labels["input_ids"][i]
combined = input_ids + label_ids + [tokenizer.eos_token_id]
combined_input_ids.append(combined)
combined_attention_mask.append([1] * len(combined))
label_masked = [-100] * len(input_ids) + label_ids + [tokenizer.eos_token_id]
combined_labels.append(label_masked)
return {
"input_ids": combined_input_ids,
"attention_mask": combined_attention_mask,
"labels": combined_labels,
}
class DataCollatorForCompletionOnly:
"""Data collator that pads sequences for training."""
def __init__(self, tokenizer, max_length=512):
self.tokenizer = tokenizer
self.max_length = max_length
def __call__(self, features):
# Pad sequences
max_len = min(max(len(f["input_ids"]) for f in features), self.max_length)
input_ids = []
attention_mask = []
labels = []
for f in features:
# Truncate if needed
curr_input_ids = f["input_ids"][:max_len]
curr_attention_mask = f["attention_mask"][:max_len]
curr_labels = f["labels"][:max_len]
# Pad
padding_length = max_len - len(curr_input_ids)
curr_input_ids = curr_input_ids + [self.tokenizer.pad_token_id] * padding_length
curr_attention_mask = curr_attention_mask + [0] * padding_length
curr_labels = curr_labels + [-100] * padding_length
input_ids.append(curr_input_ids)
attention_mask.append(curr_attention_mask)
labels.append(curr_labels)
return {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"attention_mask": torch.tensor(attention_mask, dtype=torch.long),
"labels": torch.tensor(labels, dtype=torch.long),
}
================================================
FILE: examples/peanut_finetuning/README.md
================================================
# PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers
## Introduction
[**PEANuT**](https://arxiv.org/abs/2410.01870) is a PEFT method that introduces a **weight-aware neural tweaker** to generate adapter updates from the base weight itself. Instead of directly learning a low-rank decomposition `Delta W = A @ B` as in LoRA, PEANuT transforms the target layer weight through a small neural network (the neural tweaker) to produce `Delta W`.
PEANuT is built on three key ideas:
- **Weight-aware adaptation**: `Delta W` is produced by transforming the base weight using `A`, `B`, and optional intermediate layers. Because PEANuT applies `A` on the output dimension of the base weight, `A` has shape `(out_features, r)` instead of LoRA's typical `(in_features, r)`. When `in_features > out_features`, PEANuT can use fewer parameters than LoRA at the same rank.
- **Non-linearity inside the tweaker**: PEANuT inserts activation functions in the neural tweaker (default: `relu`) to increase expressiveness.
- **Depth capacity increase**: Besides mandatory `A` and `B`, PEANuT can insert intermediate `r x r` layers in residual encoder/decoder pairs. Here, `depth` counts the number of residual pairs, so `depth=0` means only `A` and `B`.
## Quick start
With respect to your standard PEFT training procedure with LoRA, simply swap your `LoraConfig` for a `PeanutConfig`.
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTConfig, SFTTrainer
from peft import PeanutConfig
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
peanut_config = PeanutConfig()
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
processing_class=tokenizer,
peft_config=peanut_config,
args=SFTConfig(
max_length=2048,
dataset_text_field="text",
per_device_train_batch_size=2,
),
)
trainer.train()
trainer.model.save_pretrained("peanut-llama-3.2-3b")
```
Run the finetuning script simply by running:
```sh
python examples/peanut_finetuning/peanut_finetuning.py --base_model meta-llama/Llama-3.2-3B --data_path timdettmers/openassistant-guanaco
```
## Use the model on Hugging Face
You can load and use the model as any other Hugging Face model.
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.2-3B", dtype=torch.bfloat16, device_map="auto"
)
peft_model = PeftModel.from_pretrained(model, "peanut-llama-3.2-3b")
```
## Additional Notes
- `r` controls the hidden rank of the neural tweaker. Larger `r` increases capacity and trainable parameters.
- `depth` controls the number of intermediate encoder/decoder residual pairs. It must be a non-negative integer.
- `depth=0` means only `A` and `B`.
- `depth=1` adds one encoder/decoder residual pair between `A` and `B`.
- Larger depths add more `r x r` residual pairs.
- `act_fn` controls the non-linearity inside PEANuT and defaults to `relu`.
- `scaling` is a direct scalar multiplier on the adapter output before it is added to the frozen base layer output.
- PEANuT can perform better than LoRA across a range of tasks. We also find it strong in very low-parameter regimes (for example around `0.2M` trainable parameters).
- Compared with LoRA, PEANuT typically uses more GPU memory and runs slower because it explicitly constructs `Delta W` during forward passes. Adding intermediate layers (higher `depth`) increases this overhead further.
## Citation
```bibtex
@misc{zhong2025peanutparameterefficientadaptationweightaware,
title={PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers},
author={Yibo Zhong and Haoxiang Jiang and Lincan Li and Ryumei Nakada and Tianci Liu and Linjun Zhang and Huaxiu Yao and Haoyu Wang},
year={2025},
eprint={2410.01870},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.01870},
}
```
================================================
FILE: examples/peanut_finetuning/peanut_finetuning.py
================================================
# This script is based on examples/lily_finetuning/lily_finetuning.py
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import PeanutConfig, get_peft_model
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
eval_step: int,
save_step: int,
device: str,
peanut_r: int,
peanut_depth: int,
peanut_scaling: float,
peanut_act_fn: str,
peanut_target_modules: str,
peanut_init_weights: bool,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
# PEANuT config for the PEFT model
peanut_config = PeanutConfig(
r=peanut_r,
depth=peanut_depth,
scaling=peanut_scaling,
act_fn=peanut_act_fn,
init_weights=peanut_init_weights,
target_modules=(
peanut_target_modules.split(",")
if peanut_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
),
)
# get the peft model with PEANuT config
model = get_peft_model(model, peanut_config)
model.print_trainable_parameters()
model.to(device)
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
# Clear device cache to free memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
trainer.push_to_hub(commit_message="Fine-tuned model with PEANuT")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with PEANuT and PEFT")
parser.add_argument("--base_model", type=str, default="meta-llama/Llama-3.2-3B", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--peanut_r", type=int, default=32, help="PEANuT rank")
parser.add_argument(
"--peanut_depth",
type=int,
default=0,
help="Total number of PEANuT transforms including A and B (must be even and >= 2)",
)
parser.add_argument(
"--peanut_scaling", type=float, default=1.0, help="PEANuT scaling factor applied to adapter output"
)
parser.add_argument(
"--peanut_act_fn",
type=str,
default="relu",
help="Activation used inside PEANuT neural tweakers (must be a valid transformers ACT2FN key)",
)
parser.add_argument(
"--peanut_target_modules", type=str, default=None, help="Comma-separated list of target modules for PEANuT"
)
parser.add_argument(
"--peanut_init_weights",
action=argparse.BooleanOptionalAction,
default=True,
help="Use PEANuT default init: zero-init B and Kaiming-init the other layers",
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
peanut_r=args.peanut_r,
peanut_depth=args.peanut_depth,
peanut_scaling=args.peanut_scaling,
peanut_act_fn=args.peanut_act_fn,
peanut_target_modules=args.peanut_target_modules,
peanut_init_weights=args.peanut_init_weights,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
================================================
FILE: examples/pissa_finetuning/README.md
================================================
# PiSSA: Principal Singular values and Singular vectors Adaptation
## Introduction ([Paper](https://huggingface.co/papers/2404.02948), [code](https://github.com/GraphPKU/PiSSA))
PiSSA represents a matrix $W\in\mathbb{R}^{m\times n}$ within the model by the product of two trainable matrices $A \in \mathbb{R}^{m\times r}$ and $B \in \mathbb{R}^{r\times n}$, where $r \ll \min(m, n)$, plus a residual matrix $W^{res}\in\mathbb{R}^{m\times n}$ for error correction. Singular value decomposition (SVD) is employed to factorize $W$, and the principal singular values and vectors of $W$ are utilized to initialize $A$ and $B$. The residual singular values and vectors initialize the residual matrix $W^{res}$, which keeps frozen during fine-tuning. This straightforward modification allows PiSSA to converge more rapidly than LoRA and ultimately attain superior performance. Moreover, PiSSA reduces the quantization error compared to QLoRA, leading to further enhancements.
## Quick Start
```python
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer.pad_token_id = tokenizer.eos_token_id
lora_config = LoraConfig(
# init_lora_weights="pissa", # Configure the initialization method to "pissa", which may take several minutes to execute SVD on the pre-trained model.
init_lora_weights="pissa_niter_4", # Initialize the PiSSA with fast SVD, which completes in just a few seconds.
)
peft_model = get_peft_model(model, lora_config)
peft_model.print_trainable_parameters()
dataset = load_dataset("imdb", split="train[:1%]")
training_args = SFTConfig(dataset_text_field="text", max_length=128)
trainer = SFTTrainer(
model=peft_model,
args=training_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("pissa-llama-2-7b")
```
When utilizing fast SVD, reducing the rank and the number of iterations decreases the time required. However, this approach leads to higher errors in the computed matrices $A$ and $B$. To preserve the model's initial capabilities, we calculate the residual matrix by $W^{res} = W - BA$. Even with potential errors in $A$ and $B$, the sum of $W^{res}$ and $BA$ accurately equals $W$.
To utilize the fine-tuned PiSSA modules, simply run the following command:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto"
)
# Performs SVD again to initialize the residual model and loads the state_dict of the fine-tuned PiSSA modules.
peft_model = PeftModel.from_pretrained(model, "pissa-llama-2-7b")
```
## Advanced Usage
### Access the preprocessed models
We recommend downloading decomposed models directly from the [Hugging Face Collections](https://huggingface.co/collections/fxmeng/pissa-661ce700721235e542a5d7a8) instead of performing SVD every time.
If the existing models do not meet your needs, apply PiSSA initialization to a pre-trained model and store the decomposed model locally:
```bash
python preprocess.py \
--base_model_name_or_path meta-llama/Llama-2-7b-hf \
--init_lora_weights pissa \
--output_dir pissa-llama-2-7b-r32-alpha-32 \
--lora_r 32 \
--lora_alpha 32 \
--lora_dropout 0 \
--bits bf16
```
### Convert PiSSA to LoRA
The main advantage of PiSSA is concentrated during the training phase. For a trained PiSSA adapter, we recommend converting it equivalently to the LoRA adapter for using and sharing.
```python
# The fine-tuned matrices $A$ and $B$ in PiSSA adapter is saved and should be combined with the residual model.
peft_model.save_pretrained(output_dir)
# Given the matrices $A_0$ and $B_0$, initialized by PiSSA and untrained, and the trained matrices $A$ and $B$,
# we can convert these to LoRA by setting $\Delta W = A \times B - A_0 \times B_0 = [A \mid A_0] \times [B \mid -B_0]^T = A'B'$.
peft_model.save_pretrained(output_dir, path_initial_model_for_weight_conversion="pissa_init")
```
This conversion enables the loading of LoRA on top of a standard base model:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", dtype=torch.bfloat16, device_map="auto"
)
# No SVD is performed during this step, and the base model remains unaltered.
peft_model = PeftModel.from_pretrained(model, "pissa-llama-2-7b-lora")
```
Utilizing the converted LoRA does not require modifying the parameters of the base model. When multiple converted LoRAs are needed simultaneously, each adapter operates independently without interference, allowing for the adapters to be freely deleted or added.
Note that this conversion is not supported if `rslora` is used in combination with `rank_pattern` or `alpha_pattern`.
### Fine-tune in 4-bit or 8-bit
If quantization fine-tuning is desired, it is necessary to first decompose the original model at full precision and then reload the residual model in either 4-bit or 8-bit configurations.
```shell
python pissa_finetuning.py \
--residual_model_name_or_path fxmeng/pissa-llama-2-7b-r16-alpha-16 \
--output_dir output/pissa-llama-2-7b-r16-alpha-16-metamath-10k \
--bits nf4 \
--data_path meta-math/MetaMathQA \
--dataset_split train[:100000] \
--dataset_field query response \
--bf16 True \
--num_train_epochs 1 \
--per_device_train_batch_size 32 \
--gradient_accumulation_steps 4 \
--save_strategy "steps" \
--save_steps 1000 \
--save_total_limit 1 \
--logging_steps 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_steps 0.03 \
--tf32 True \
--report_to none \
--convert_pissa_to_lora
```
This approach ensures the preservation of high-frequency, out-of-distribution parameters in the low-rank PiSSA modules, resulting in reduced quantization errors during the quantization of the residual model.
## Citation
```
@article{meng2024pissa,
title={PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models},
author={Meng, Fanxu and Wang, Zhaohui and Zhang, Muhan},
journal={arXiv preprint arXiv:2404.02948},
year={2024}
}
```
================================================
FILE: examples/pissa_finetuning/pissa_finetuning.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser
from trl import SFTConfig, SFTTrainer
from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training
@dataclass
class ScriptArguments(SFTConfig):
# model configs
base_model_name_or_path: Optional[str] = field(
default=None, metadata={"help": "The name or path of the fp32/16 base model."}
)
residual_model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": "The name or path of the fp32/16 residual model. (`['fxmeng/pissa-llama-2-7b-r16-alpha-16']`)"
},
)
bits: str = field(default="fp32", metadata={"help": "(`['fp4', 'nf4', 'int8', 'bf16', 'fp16', fp32]`)"})
init_lora_weights: str = field(default="pissa", metadata={"help": "(`['gaussian', 'pissa', 'pissa_niter_4']`)"})
lora_r: int = field(default=16)
lora_alpha: int = field(default=16)
lora_dropout: float = field(default=0)
convert_pissa_to_lora: bool = field(default=False)
merge_and_save: bool = field(default=False)
# dataset configs
data_path: str = field(default="imdb", metadata={"help": "Path to the training data."})
dataset_split: str = field(default="train[:1%]", metadata={"help": "(`['train', 'test', 'eval']`):"})
dataset_field: list[str] = field(default=None, metadata={"help": "Fields of dataset input and output."})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
print(script_args)
print(f"Load pre-processed residual model in {script_args.bits} bits.")
if script_args.bits in ["nf4", "fp4", "int8"]:
quantization_config = BitsAndBytesConfig(
load_in_4bit=(script_args.bits == "nf4" or script_args.bits == "fp4"),
load_in_8bit=script_args.bits == "int8",
bnb_4bit_quant_type=script_args.bits,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
res_model = AutoModelForCausalLM.from_pretrained(
script_args.residual_model_name_or_path, quantization_config=quantization_config, low_cpu_mem_usage=True
)
res_model = prepare_model_for_kbit_training(res_model)
print("Wrapping the residual model with PiSSA.")
peft_model = PeftModel.from_pretrained(
res_model, script_args.residual_model_name_or_path, subfolder="pissa_init", is_trainable=True
)
tokenizer = AutoTokenizer.from_pretrained(script_args.residual_model_name_or_path)
elif script_args.residual_model_name_or_path is not None:
res_model = AutoModelForCausalLM.from_pretrained(
script_args.residual_model_name_or_path,
dtype=(
torch.float16
if script_args.bits == "fp16"
else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
),
device_map="auto",
)
print("Wrapping the residual model with PiSSA.")
peft_model = PeftModel.from_pretrained(
res_model, script_args.residual_model_name_or_path, subfolder="pissa_init", is_trainable=True
)
tokenizer = AutoTokenizer.from_pretrained(script_args.residual_model_name_or_path)
elif script_args.base_model_name_or_path is not None:
print(
f"No available pre-processed model, manually initialize a PiSSA using {script_args.base_model_name_or_path}."
)
model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name_or_path,
dtype=(
torch.float16
if script_args.bits == "fp16"
else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
),
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name_or_path)
tokenizer.pad_token_id = tokenizer.eos_token_id
lora_config = LoraConfig(
r=script_args.lora_r,
lora_alpha=script_args.lora_alpha,
init_lora_weights=script_args.init_lora_weights,
lora_dropout=script_args.lora_dropout,
target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
bias="none",
task_type="CAUSAL_LM",
)
peft_model = get_peft_model(model, lora_config)
print(peft_model)
peft_model.print_trainable_parameters()
print(f"Training PiSSA with trl on the {script_args.data_path}[{script_args.dataset_split}] dataset.")
dataset = load_dataset(script_args.data_path, split=script_args.dataset_split)
dataset = dataset.map(
lambda example: {
"text": f"### USER: {example[script_args.dataset_field[0]]}\n### ASSISTANT: {example[script_args.dataset_field[1]]}"
}
)
trainer = SFTTrainer(
model=peft_model,
args=script_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
trainer.save_state()
############################## Upon training completion, convert and save PiSSA in LoRA format ##############################
if script_args.convert_pissa_to_lora:
peft_model.save_pretrained(
os.path.join(script_args.output_dir, "pissa_lora"),
path_initial_model_for_weight_conversion=os.path.join(script_args.residual_model_name_or_path, "pissa_init"),
)
else:
peft_model.save_pretrained(
os.path.join(script_args.output_dir, "pissa_ft"),
)
if script_args.merge_and_save:
model = peft_model.merge_and_unload()
model.save_pretrained(os.path.join(script_args.output_dir, "pissa_merged"))
tokenizer.save_pretrained(os.path.join(script_args.output_dir, "pissa_merged"))
================================================
FILE: examples/pissa_finetuning/preprocess.py
================================================
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model
parser = argparse.ArgumentParser(description="Merge Adapter to Base Model")
parser.add_argument(
"--base_model_name_or_path",
help="The name or path of the fp32/16 base model.",
)
parser.add_argument("--output_dir", type=str, help="The directory to save the PiSSA model.")
parser.add_argument("--bits", type=str, default="bf16", choices=["bf16", "fp16", "fp32"])
parser.add_argument(
"--init_lora_weights", type=str, default="pissa", help="(`['pissa', 'pissa_niter_[number of iters]']`)"
)
parser.add_argument("--lora_r", type=int, default=128)
parser.add_argument("--lora_alpha", type=int, default=128)
parser.add_argument("--lora_dropout", type=int, default=0)
script_args = parser.parse_args()
print(script_args)
model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name_or_path,
dtype=(
torch.float16
if script_args.bits == "fp16"
else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
),
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name_or_path)
tokenizer.pad_token_id = tokenizer.eos_token_id
lora_config = LoraConfig(
r=script_args.lora_r,
lora_alpha=script_args.lora_alpha,
init_lora_weights=script_args.init_lora_weights,
lora_dropout=script_args.lora_dropout,
target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
bias="none",
task_type="CAUSAL_LM",
)
peft_model = get_peft_model(model, lora_config)
# Save PiSSA modules:
peft_model.peft_config["default"].init_lora_weights = True
peft_model.save_pretrained(os.path.join(script_args.output_dir, "pissa_init"))
# Save residual model:
peft_model = peft_model.unload()
peft_model.save_pretrained(script_args.output_dir)
# Save the tokenizer:
tokenizer.save_pretrained(script_args.output_dir)
================================================
FILE: examples/poly/peft_poly_seq2seq_with_generate.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2edec24d8563b583",
"metadata": {
"collapsed": false,
"execution": {
"shell.execute_reply.end": "2023-12-22T03:34:15.998083Z",
"shell.execute_reply.started": "2023-12-22T03:34:15.994854Z",
"to_execute": "2023-12-22T03:34:15.875Z"
},
"libroFormatter": "formatter-string"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: CUDA_VISIBLE_DEVICES=0 # force using CUDA GPU device 0\n",
"env: ZE_AFFINITY_MASK=0 # force using Intel XPU device 0\n",
"env: TOKENIZERS_PARALLELISM=false\n"
]
}
],
"source": [
"%env CUDA_VISIBLE_DEVICES=0 # force using CUDA GPU device 0\n",
"%env ZE_AFFINITY_MASK=0 # force using Intel XPU device 0\n",
"%env TOKENIZERS_PARALLELISM=false"
]
},
{
"cell_type": "markdown",
"id": "95b4cfd741795038",
"metadata": {
"id": "95b4cfd741795038",
"libroFormatter": "formatter-string"
},
"source": [
"## Initialize PolyModel"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a5c7a99-5208-4d22-ac15-bacebe1b52f9",
"metadata": {
"execution": {
"shell.execute_reply.end": "2023-12-22T03:34:29.137789Z",
"shell.execute_reply.started": "2023-12-22T03:34:18.146604Z",
"to_execute": "2023-12-22T03:34:18.025Z"
},
"id": "1a5c7a99-5208-4d22-ac15-bacebe1b52f9",
"libroFormatter": "formatter-string"
},
"outputs": [],
"source": [
"import torch\n",
"from transformers import (\n",
" AutoModelForSeq2SeqLM,\n",
" AutoTokenizer,\n",
" default_data_collator,\n",
" Seq2SeqTrainingArguments,\n",
" Seq2SeqTrainer,\n",
")\n",
"from datasets import load_dataset, concatenate_datasets\n",
"from peft import PolyConfig, get_peft_model, TaskType, PeftModel, PeftConfig\n",
"\n",
"model_name_or_path = \"google/flan-t5-xl\"\n",
"\n",
"r = 8 # rank of lora in poly\n",
"n_tasks = 4 # number of tasks\n",
"n_skills = 2 # number of skills (loras)\n",
"n_splits = 4 # number of heads\n",
"\n",
"batch_size = 8\n",
"lr = 5e-5\n",
"num_epochs = 8"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "89a1d2c6-0d35-4254-b9fb-035a426d86ae",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 241,
"referenced_widgets": [
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]
},
"execution": {
"shell.execute_reply.end": "2023-12-22T03:35:33.229420Z",
"shell.execute_reply.started": "2023-12-22T03:34:37.266443Z",
"to_execute": "2023-12-22T03:34:37.242Z"
},
"id": "89a1d2c6-0d35-4254-b9fb-035a426d86ae",
"libroFormatter": "formatter-string",
"outputId": "fc90c2cc-9cab-40ed-bf4a-d76bec85b72f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 22.43it/s]\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)\n",
"base_model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path, trust_remote_code=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "29d701a4-7a4f-4eae-84bd-9e3a02b7ffca",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"shell.execute_reply.end": "2023-12-22T03:35:33.396336Z",
"shell.execute_reply.started": "2023-12-22T03:35:33.250286Z",
"to_execute": "2023-12-22T03:35:33.272Z"
},
"id": "29d701a4-7a4f-4eae-84bd-9e3a02b7ffca",
"libroFormatter": "formatter-string",
"outputId": "63898f68-926e-40c4-ca13-ffd1df32fcce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 9,441,792 || all params: 2,859,198,976 || trainable%: 0.3302\n"
]
}
],
"source": [
"peft_config = PolyConfig(\n",
" task_type=TaskType.SEQ_2_SEQ_LM,\n",
" poly_type=\"poly\",\n",
" r=r,\n",
" n_tasks=n_tasks,\n",
" n_skills=n_skills,\n",
" n_splits=n_splits,\n",
")\n",
"\n",
"model = get_peft_model(base_model, peft_config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "markdown",
"id": "aa695c2d-cf9c-432c-ab74-7e89f816ba13",
"metadata": {
"id": "aa695c2d-cf9c-432c-ab74-7e89f816ba13",
"libroFormatter": "formatter-string"
},
"source": [
"## Prepare datasets\n",
"\n",
"For this example, we selected four `SuperGLUE` benchmark datasets: `boolq`, `multirc`, `rte`, and `wic`, each with a training set of 1,000 examples and an evaluation set of 100 examples."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d0b36e7eff50657c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"boolq example: \n",
"{'input': 'Persian language -- Persian (/ˈpɜːrʒən, -ʃən/), also known by its endonym Farsi (فارسی fārsi (fɒːɾˈsiː) ( listen)), is one of the Western Iranian languages within the Indo-Iranian branch of the Indo-European language family. It is primarily spoken in Iran, Afghanistan (officially known as Dari since 1958), and Tajikistan (officially known as Tajiki since the Soviet era), and some other regions which historically were Persianate societies and considered part of Greater Iran. It is written in the Persian alphabet, a modified variant of the Arabic script, which itself evolved from the Aramaic alphabet.\\nQuestion: do iran and afghanistan speak the same language\\nA. Yes\\nB. No\\nAnswer:', 'output': 'A', 'task_name': 'boolq'}\n",
"multirc example: \n",
"{'input': 'While this process moved along, diplomacy continued its rounds. Direct pressure on the Taliban had proved unsuccessful. As one NSC staff note put it, \"Under the Taliban, Afghanistan is not so much a state sponsor of terrorism as it is a state sponsored by terrorists.\" In early 2000, the United States began a high-level effort to persuade Pakistan to use its influence over the Taliban. In January 2000, Assistant Secretary of State Karl Inderfurth and the State Department\\'s counterterrorism coordinator, Michael Sheehan, met with General Musharraf in Islamabad, dangling before him the possibility of a presidential visit in March as a reward for Pakistani cooperation. Such a visit was coveted by Musharraf, partly as a sign of his government\\'s legitimacy. He told the two envoys that he would meet with Mullah Omar and press him on Bin Laden. They left, however, reporting to Washington that Pakistan was unlikely in fact to do anything,\" given what it sees as the benefits of Taliban control of Afghanistan.\" President Clinton was scheduled to travel to India. The State Department felt that he should not visit India without also visiting Pakistan. The Secret Service and the CIA, however, warned in the strongest terms that visiting Pakistan would risk the President\\'s life. Counterterrorism officials also argued that Pakistan had not done enough to merit a presidential visit. But President Clinton insisted on including Pakistan in the itinerary for his trip to South Asia. His one-day stopover on March 25, 2000, was the first time a U.S. president had been there since 1969. At his meeting with Musharraf and others, President Clinton concentrated on tensions between Pakistan and India and the dangers of nuclear proliferation, but also discussed Bin Laden. President Clinton told us that when he pulled Musharraf aside for a brief, one-on-one meeting, he pleaded with the general for help regarding Bin Laden.\" I offered him the moon when I went to see him, in terms of better relations with the United States, if he\\'d help us get Bin Laden and deal with another issue or two.\" The U.S. effort continued. \\nQuestion: What did the high-level effort to persuade Pakistan include?\\nAnswer: Children, Gerd, or Dorian Popa\\nIs it true?\\nA. Yes\\nB. No\\nAnswer:', 'output': 'B', 'task_name': 'multirc'}\n",
"rte example: \n",
"{'input': 'No Weapons of Mass Destruction Found in Iraq Yet.\\nWeapons of Mass Destruction Found in Iraq.\\nIs the sentence below entailed by the sentence above?\\nA. Yes\\nB. No\\nAnswer:', 'output': 'B', 'task_name': 'rte'}\n",
"wic example: \n",
"{'input': \"Sentence 1: Do you want to come over to my place later?\\nSentence 2: A political system with no place for the less prominent groups.\\nAre 'place' in the above two sentences the same?\\nA. Yes\\nB. No\\nAnswer:\", 'output': 'B', 'task_name': 'wic'}\n"
]
}
],
"source": [
"# boolq\n",
"boolq_dataset = (\n",
" load_dataset(\"super_glue\", \"boolq\")\n",
" .map(\n",
" lambda x: {\n",
" \"input\": f\"{x['passage']}\\nQuestion: {x['question']}\\nA. Yes\\nB. No\\nAnswer:\",\n",
" # 0 - False\n",
" # 1 - True\n",
" \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
" \"task_name\": \"boolq\",\n",
" }\n",
" )\n",
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
")\n",
"print(\"boolq example: \")\n",
"print(boolq_dataset[\"train\"][0])\n",
"\n",
"# multirc\n",
"multirc_dataset = (\n",
" load_dataset(\"super_glue\", \"multirc\")\n",
" .map(\n",
" lambda x: {\n",
" \"input\": (\n",
" f\"{x['paragraph']}\\nQuestion: {x['question']}\\nAnswer: {x['answer']}\\nIs it\"\n",
" \" true?\\nA. Yes\\nB. No\\nAnswer:\"\n",
" ),\n",
" # 0 - False\n",
" # 1 - True\n",
" \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
" \"task_name\": \"multirc\",\n",
" }\n",
" )\n",
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
")\n",
"print(\"multirc example: \")\n",
"print(multirc_dataset[\"train\"][0])\n",
"\n",
"# rte\n",
"rte_dataset = (\n",
" load_dataset(\"super_glue\", \"rte\")\n",
" .map(\n",
" lambda x: {\n",
" \"input\": (\n",
" f\"{x['premise']}\\n{x['hypothesis']}\\nIs the sentence below entailed by the\"\n",
" \" sentence above?\\nA. Yes\\nB. No\\nAnswer:\"\n",
" ),\n",
" # 0 - entailment\n",
" # 1 - not_entailment\n",
" \"output\": [\"A\", \"B\"][int(x[\"label\"])],\n",
" \"task_name\": \"rte\",\n",
" }\n",
" )\n",
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
")\n",
"print(\"rte example: \")\n",
"print(rte_dataset[\"train\"][0])\n",
"\n",
"# wic\n",
"wic_dataset = (\n",
" load_dataset(\"super_glue\", \"wic\")\n",
" .map(\n",
" lambda x: {\n",
" \"input\": (\n",
" f\"Sentence 1: {x['sentence1']}\\nSentence 2: {x['sentence2']}\\nAre '{x['word']}'\"\n",
" \" in the above two sentences the same?\\nA. Yes\\nB. No\\nAnswer:\"\n",
" ),\n",
" # 0 - False\n",
" # 1 - True\n",
" \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
" \"task_name\": \"wic\",\n",
" }\n",
" )\n",
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
")\n",
"print(\"wic example: \")\n",
"print(wic_dataset[\"train\"][0])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9fca2225-aaee-47aa-957a-5f8ed3177cdb",
"metadata": {
"execution": {
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},
"id": "9fca2225-aaee-47aa-957a-5f8ed3177cdb",
"libroFormatter": "formatter-string"
},
"outputs": [],
"source": [
"# define a task2id map\n",
"TASK2ID = {\n",
" \"boolq\": 0,\n",
" \"multirc\": 1,\n",
" \"rte\": 2,\n",
" \"wic\": 3,\n",
"}\n",
"\n",
"\n",
"def tokenize(examples):\n",
" inputs, targets = examples[\"input\"], examples[\"output\"]\n",
" features = tokenizer(inputs, max_length=512, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = labels[\"input_ids\"]\n",
" labels[labels == tokenizer.pad_token_id] = -100\n",
" features[\"labels\"] = labels\n",
" features[\"task_ids\"] = torch.tensor([[TASK2ID[t]] for t in examples[\"task_name\"]]).long()\n",
" return features"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0bf6c31c-73cd-4eed-931b-0cad5d7290fb",
"metadata": {
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"libroFormatter": "formatter-string",
"tags": []
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"outputs": [],
"source": [
"def get_superglue_dataset(\n",
" split=\"train\",\n",
" n_samples=500,\n",
"):\n",
" ds = concatenate_datasets(\n",
" [\n",
" boolq_dataset[split].shuffle().select(range(n_samples)),\n",
" multirc_dataset[split].shuffle().select(range(n_samples)),\n",
" rte_dataset[split].shuffle().select(range(n_samples)),\n",
" wic_dataset[split].shuffle().select(range(n_samples)),\n",
" ]\n",
" )\n",
" ds = ds.map(\n",
" tokenize,\n",
" batched=True,\n",
" remove_columns=[\"input\", \"output\", \"task_name\"],\n",
" load_from_cache_file=False,\n",
" )\n",
" return ds"
]
},
{
"cell_type": "markdown",
"id": "oNvh2WGlLo4z",
"metadata": {
"id": "oNvh2WGlLo4z",
"libroFormatter": "formatter-string"
},
"source": [
"As a toy example, we only select 1,000 from each subdataset for training and 100 each for eval."
]
},
{
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"execution_count": 8,
"id": "1bf88dd1a6aaa6a5",
"metadata": {
"collapsed": false,
"execution": {
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"libroFormatter": "formatter-string"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Map: 0%| | 0/4000 [00:00\\n### ASSISTANT: '. "
"If None, must already have a 'text' column."
)
},
)
def _dtype_from_bits(bits: str) -> torch.dtype:
bits = bits.lower()
if bits == "bf16":
return torch.bfloat16
if bits == "fp16":
return torch.float16
if bits == "fp32":
return torch.float32
raise ValueError(f"Unknown bits={bits}. Use one of: bf16, fp16, fp32.")
def main():
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
print(script_args)
if script_args.base_model_name_or_path is None:
raise ValueError("--base_model_name_or_path is required.")
# PSOFT does NOT support quantized layers (nf4/int8/etc.).
# We only allow fp16/bf16/fp32 here to avoid accidental quantized loading.
if script_args.bits.lower() not in {"bf16", "fp16", "fp32"}:
raise ValueError("PSOFT example only supports bits in ['bf16','fp16','fp32'] (no quantization).")
torch_dtype = _dtype_from_bits(script_args.bits)
# Load base model
model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name_or_path,
dtype=torch_dtype,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name_or_path)
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
# Build PSOFT config
psoft_kwargs = {
"r": script_args.r,
"psoft_alpha": script_args.psoft_alpha,
"target_modules": script_args.target_modules,
"ab_svd_init": script_args.ab_svd_init,
"psoft_svd": script_args.psoft_svd,
"psoft_orth": script_args.psoft_orth,
"psoft_mag_a": script_args.psoft_mag_a,
"psoft_mag_b": script_args.psoft_mag_b,
"use_cayley_neumann": script_args.use_cayley_neumann,
"num_cayley_neumann_terms": script_args.num_cayley_neumann_terms,
"cayley_neumann_eps": script_args.cayley_neumann_eps,
"task_type": "CAUSAL_LM",
}
# Only pass lowrank_niter when user sets it (and typically when psoft_svd='lowrank')
if script_args.psoft_svd_lowrank_niter is not None:
psoft_kwargs["psoft_svd_lowrank_niter"] = script_args.psoft_svd_lowrank_niter
peft_config = PsoftConfig(**psoft_kwargs)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Load dataset
dataset = load_dataset(script_args.data_path, split=script_args.dataset_split)
# Ensure a "text" field for SFTTrainer
if script_args.dataset_field is not None:
if len(script_args.dataset_field) != 2:
raise ValueError("dataset_field must be a list of exactly 2 field names: [input_field, output_field].")
in_f, out_f = script_args.dataset_field[0], script_args.dataset_field[1]
def to_sft_text(example):
return {"text": f"### USER: {example[in_f]}\n### ASSISTANT: {example[out_f]}"}
dataset = dataset.map(to_sft_text)
else:
if "text" not in dataset.column_names:
raise ValueError("dataset_field is None but dataset has no 'text' column. Provide dataset_field.")
# Train
trainer = SFTTrainer(
model=model,
args=script_args,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
trainer.save_state()
# Save adapter (PSOFT)
os.makedirs(script_args.output_dir, exist_ok=True)
model.save_pretrained(os.path.join(script_args.output_dir, "psoft_ft"))
tokenizer.save_pretrained(os.path.join(script_args.output_dir, "psoft_ft"))
if __name__ == "__main__":
main()
================================================
FILE: examples/pvera/README.md
================================================
# Generating confidence intervals with PVeRA
In normal mode, PVeRA samples from the learned distribution during training, and does a deterministic sample during inference at the learned latent distribution mean. Setting `sample_at_inference=True` enables to generate Monte Carlo confidence interval estimations by running multiple passes through each sample. The accompanying `examples/pvera/confidence_interval_generation.py` script shows an example of training a model on a simple dataset, saving the adapters, loading them with ```sample_at_inference=True```, and running a Monte Carlo confidence interval estimation on a sample.
================================================
FILE: examples/pvera/confidence_interval_generation.py
================================================
import tempfile
import numpy as np
import scipy
import torch
from datasets import load_dataset
from torch.nn import LazyLinear, Sequential, Softmax
from torchvision.transforms import Compose, Normalize, Resize
from tqdm import tqdm
from transformers import AutoModel
from peft import PeftModel, PveraConfig, get_peft_model
# load the dataset
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
dataset = load_dataset("beans", split="train").with_format("torch")
transform = Compose((Resize((224, 224)), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))))
num_classes = dataset.features["labels"].num_classes
# load the model with adapters and create the linear probe
base_model = AutoModel.from_pretrained("facebook/dinov2-base")
config = PveraConfig(r=128, sample_at_inference=False)
model = get_peft_model(base_model, config).to(device)
linear_probe = Sequential(LazyLinear(num_classes), Softmax(-1)).to(device)
# train the model
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(list(model.parameters()) + list(linear_probe.parameters()), lr=1e-4)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
for batch in tqdm(dataloader):
imgs, lbls = transform(batch["image"].float()), batch["labels"]
pred = linear_probe(model(imgs.to(device)).pooler_output)
loss = criterion(pred, lbls.to(device))
loss.backward()
optimizer.step()
# save the model and load it with sample_at_inference=True
model.eval()
linear_probe.eval()
with tempfile.TemporaryDirectory() as tmpdir:
# save the model and the linear probe
model.save_pretrained(tmpdir)
torch.save(linear_probe.state_dict(), tmpdir + "/linear_probe.bin")
# load the model with sample_at_inference=True
base_model = AutoModel.from_pretrained("facebook/dinov2-base")
config = PveraConfig.from_pretrained(tmpdir)
config.sample_at_inference = True
loaded_model = PeftModel.from_pretrained(base_model, tmpdir, config=config).to(device)
loaded_model.eval()
# load the linear probe
loaded_linear_probe = Sequential(LazyLinear(num_classes), Softmax(-1)).to(device)
loaded_linear_probe.load_state_dict(torch.load(tmpdir + "/linear_probe.bin"))
loaded_linear_probe.eval()
# make multiple predictions on an image
img = dataset[0]["image"].unsqueeze(0).to(device)
with torch.no_grad():
all_preds = [loaded_linear_probe(loaded_model(img).pooler_output) for _ in range(16)]
all_preds = torch.vstack(all_preds)
top_pred = all_preds.argmax(-1).mode(0).values
softmax_top_pred = all_preds[:, top_pred]
def mean_confidence_interval(data, confidence=0.95):
a = 1.0 * np.array(data)
n = len(a)
m, se = np.mean(a), scipy.stats.sem(a)
h = se * scipy.stats.t.ppf((1 + confidence) / 2.0, n - 1)
return max(0, m - h), min(1, m + h)
print(mean_confidence_interval(softmax_top_pred.cpu()))
================================================
FILE: examples/qalora_finetuning/README.md
================================================
# QALoRA: Quantization-Aware Low-Rank Adaptation
## Introduction
[QALoRA](https://huggingface.co/papers/2309.14717) is a quantization-aware version of Low-Rank Adaptation that enables efficient fine-tuning of quantized large language models.
QALoRA uses input feature pooling and a specialized grouping technique to work with quantized weights, significantly reducing memory requirements while preserving performance.
QALoRA enables fine-tuning of models that would otherwise be too large for consumer GPUs. In PEFT it only works for GPTQ.
## Quick start
```python
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
from datasets import load_dataset
# Load a quantized model (example with GPTQ quantization)
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Llama-2-7b-GPTQ",
revision="gptq-4bit-32g-actorder_True",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-7b-GPTQ")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
# Configure QALoRA parameters
lora_config = LoraConfig(
use_qalora=True,
qalora_group_size=8,
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
lora_dropout=0.05,
)
# Create the PEFT model
peft_model = get_peft_model(model, lora_config)
# Set up trainer and train
trainer = Trainer(
model=peft_model,
train_dataset=dataset,
args=TrainingArguments(
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
num_train_epochs=3,
learning_rate=3e-4,
output_dir="qalora-llama-2-7b"
),
data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
trainer.train()
peft_model.save_pretrained("qalora-llama-2-7b")
```
To use QALoRA, simply set `use_qalora = True` and specify a `qalora_group_size` in your LoRA configuration. The group size controls the memory/performance tradeoff - smaller values use less memory but may affect performance.
## Command Line Examples
Run the finetuning script with a GPTQ quantized model:
You can customize the pooling group size (default is 16):
```bash
python examples/qalora_finetuning/qalora_gptq_finetuning.py \
--base_model TheBloke/Llama-2-7b-GPTQ \
--use_qalora \
--qalora_group_size 32
```
### Full example of the script
```bash
python qalora_gptq_finetuning.py \
--base_model "TheBloke/Llama-2-13b-GPTQ" \
--output_dir "PATH_TO_OUTPUT_DIR" \
--batch_size 1 \
--num_epochs 3 \
--learning_rate 3e-4 \
--cutoff_len 512 \
--use_qalora \
--qalora_group_size 32 \
--eval_step 10 \
--save_step 100 \
--device "auto" \
--lora_r 16 \
--lora_alpha 32 \
--lora_dropout 0.05 \
--lora_target_modules "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj" \
--push_to_hub
```
## Use the model on 🤗
You can load and use the finetuned model like any other PEFT model:
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the base quantized model
base_model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Llama-2-7b-GPTQ",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-7b-GPTQ")
# Load the PEFT adapter
peft_model_id = "YOUR_HF_REPO"
model = PeftModel.from_pretrained(base_model, peft_model_id)
# Generate text
input_text = "Hello, I'm a language model"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## QALoRA vs. LoRA
QALoRA offers several advantages over standard LoRA:
1. **Memory efficiency**: QALoRA works directly with quantized models, reducing memory usage by up to 60-70% compared to standard LoRA.
2. **Hardware accessibility**: Enables fine-tuning of larger models (13B, 70B) on consumer GPUs that would be impossible with standard LoRA.
3. **Performance preservation**: Despite quantization, QALoRA can achieve comparable performance to full-precision LoRA in many tasks.
## Implementation Details: Merging with Quantized Models
> **Note:** The current implementation differs from the original QA-LoRA paper's approach.
While the QA-LoRA paper describes a direct weight modification technique using "beta shift" to modify quantized weights without full dequantization, this implementation uses a different approach:
1. The quantized model is first dequantized to full precision
2. The QALoRA adapter weights are then merged with the dequantized model
3. The merged model must be re-quantized if quantization is still desired
### Memory Considerations
This process requires significant memory (enough to hold the full dequantized model) and additional computation for the re-quantization step. For large models, this may not be possible on consumer hardware.
For most use cases, we recommend keeping the base quantized model and the QALoRA adapter separate, loading them with `PeftModel.from_pretrained()` as shown in the usage example above. This approach maintains the memory efficiency benefits of quantization throughout the deployment pipeline.
## Citation
```
@article{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
@article{xu2023qalora,
title={QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models},
author={Xu, Yuhui and Liu, Lingxi and Rao, Longhui and Zhao, Teng and Xiong, Zhiwei and Gao, Mingkui},
journal={arXiv preprint arXiv:2309.14717},
year={2023}
}
```
================================================
FILE: examples/qalora_finetuning/qalora_gptq_finetuning.py
================================================
#!/usr/bin/env python3
"""
Training script for fine-tuning language models with QALoRA using GPTQ quantization.
This script supports cached quantization to avoid repeating expensive quantization processes.
"""
import argparse
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
GPTQConfig,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model
def load_or_quantize_model(
base_model: str, tokenizer, bits: int = 4, cache_dir: str = "./quantized_models"
) -> AutoModelForCausalLM:
"""
Load a pre-quantized model from cache or quantize and cache a new one.
Automatically detects if the model is already GPTQ-quantized.
Args:
base_model: Model identifier or path
tokenizer: Tokenizer for the model
bits: Bit-width for quantization (default: 4)
cache_dir: Directory to store quantized models
Returns:
The loaded (quantized) model
"""
# First, check if the model is already GPTQ-quantized by trying to load it
print(f"Checking if {base_model} is already GPTQ-quantized...")
try:
# Try to load the model and check if it has GPTQ quantization
test_model = AutoModelForCausalLM.from_pretrained(
base_model,
device_map="auto",
dtype=torch.float16,
trust_remote_code=True, # Some GPTQ models might need this
)
# Check if the model has GPTQ quantization attributes
has_gptq = False
for module in test_model.modules():
if hasattr(module, "qweight") or hasattr(module, "qzeros") or "gptq" in str(type(module)).lower():
has_gptq = True
break
if has_gptq:
print(f"✅ Model {base_model} is already GPTQ-quantized. Using directly.")
return test_model
else:
print(f"Model {base_model} is not GPTQ-quantized. Will quantize it.")
# Clean up the test model to free memory
del test_model
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
except Exception as e:
print(f"Could not load model {base_model} directly: {e}")
print("Will attempt to quantize it...")
# If we get here, the model needs to be quantized
os.makedirs(cache_dir, exist_ok=True)
model_id = base_model.replace("/", "_").replace("\\", "_") # Handle Windows paths too
quantized_model_path = os.path.join(cache_dir, f"{model_id}_gptq_{bits}bit")
# Check if we already have a cached quantized version
if os.path.exists(quantized_model_path) and os.path.exists(os.path.join(quantized_model_path, "config.json")):
print(f"Loading pre-quantized model from cache: {quantized_model_path}")
return AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto")
print(f"Quantizing model and saving to cache: {quantized_model_path}")
# Configure GPTQ for first-time quantization
gptq_config = GPTQConfig(
bits=bits,
dataset="c4",
tokenizer=tokenizer,
group_size=128,
desc_act=False,
sym=False,
)
# Load and quantize the model
model = AutoModelForCausalLM.from_pretrained(
base_model, device_map="auto", quantization_config=gptq_config, dtype=torch.float16
)
# Save the quantized model to cache
print(f"Saving quantized model to {quantized_model_path}")
model.save_pretrained(quantized_model_path)
tokenizer.save_pretrained(quantized_model_path)
return model
def tokenize_and_preprocess(examples, tokenizer, max_length: int = 128):
"""
Tokenize text data and prepare it for language modeling.
Args:
examples: Dataset examples with 'text' field
tokenizer: Tokenizer to use
max_length: Maximum sequence length
Returns:
Processed examples with input_ids and labels
"""
# Tokenize the text with truncation and padding
tokenized_output = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=max_length)
# Preprocess labels (set pad tokens to -100 for loss masking)
labels = tokenized_output["input_ids"].copy()
labels = [[-100 if token == tokenizer.pad_token_id else token for token in seq] for seq in labels]
tokenized_output["labels"] = labels
return tokenized_output
def train_model(
base_model: str,
data_path: str,
data_split: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
use_qalora: bool,
eval_step: int,
save_step: int,
device: str,
lora_r: int,
lora_alpha: int,
lora_dropout: float,
lora_target_modules: str,
push_to_hub: bool,
qalora_group_size: int,
bits: int,
) -> None:
"""
Train a model with QALoRA and GPTQ quantization.
Args:
base_model: Base model to fine-tune
data_path: Dataset path
output_dir: Directory to save model outputs
batch_size: Training batch size
num_epochs: Number of training epochs
learning_rate: Learning rate
cutoff_len: Maximum sequence length
val_set_size: Validation set size
use_dora: Whether to use DoRA
use_qalora: Whether to use QALoRA
quantize: Whether to use quantization
eval_step: Steps between evaluations
save_step: Steps between saving checkpoints
device: Device to use (cuda:0, xpu:0, etc.)
lora_r: LoRA rank
lora_alpha: LoRA alpha
lora_dropout: LoRA dropout rate
lora_target_modules: Target modules for LoRA
push_to_hub: Whether to push to Hugging Face Hub
"""
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
device = torch.device(device)
print(f"Using device: {device}")
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load or quantize model
model = load_or_quantize_model(base_model, tokenizer, bits=bits)
# Configure LoRA
target_modules = (
lora_target_modules.split(",")
if lora_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
)
print("use_qalora", use_qalora)
lora_config = LoraConfig(
task_type="CAUSAL_LM",
use_qalora=use_qalora,
qalora_group_size=qalora_group_size,
r=lora_r,
lora_alpha=lora_alpha,
target_modules=target_modules,
lora_dropout=lora_dropout,
bias="none",
)
# Get PEFT model with adapters
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Move model to device if not already there
if not hasattr(model, "device") or model.device.type != device.type:
model = model.to(device)
# Load and prepare dataset
dataset = load_dataset(data_path, data_split)
tokenized_datasets = {
"train": dataset["train"].map(
lambda x: tokenize_and_preprocess(x, tokenizer, max_length=cutoff_len),
batched=True,
remove_columns=["text"],
load_from_cache_file=True,
),
"test": dataset["test"].map(
lambda x: tokenize_and_preprocess(x, tokenizer, max_length=cutoff_len),
batched=True,
remove_columns=["text"],
load_from_cache_file=True,
),
}
# Data collator for language modeling
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Configure training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
label_names=["labels"],
)
# Clear accelerator cache to free memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Initialize trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start training
print("\nStarting training...")
trainer.train()
# Save the final model
if push_to_hub:
trainer.push_to_hub(commit_message="Fine-tuned model with QALoRA")
# Always save locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print(f"\nTraining complete. Model saved to {output_dir}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Fine-tune LLMs with QALoRA and GPTQ quantization")
# Model and dataset parameters
parser.add_argument("--base_model", type=str, default="TheBloke/Llama-2-7b-GPTQ", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument("--data_split", type=str, default="", help="Dataset path or name")
parser.add_argument(
"--output_dir", type=str, default="./qalora_output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--bits", type=int, default=4, help="Init quantization bits")
# Training parameters
parser.add_argument("--batch_size", type=int, default=4, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=3e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=128, help="Max sequence length")
# Adapter configuration
parser.add_argument("--use_qalora", action="store_true", help="Apply QALoRA")
parser.add_argument("--qalora_group_size", type=int, default=32, help="LoRA rank")
parser.add_argument("--lora_r", type=int, default=8, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate")
parser.add_argument(
"--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA"
)
# Training process options
parser.add_argument("--eval_step", type=int, default=100, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=500, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
# Hugging Face Hub options
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
device = args.device
if args.device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
# If use_qalora isn't explicitly set in args but passed to train_model
if not args.use_qalora:
args.use_qalora = True # Default to True as in the original code
train_model(
base_model=args.base_model,
data_path=args.data_path,
data_split=args.data_split,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
use_qalora=args.use_qalora,
eval_step=args.eval_step,
save_step=args.save_step,
device=device,
lora_r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
push_to_hub=args.push_to_hub,
qalora_group_size=args.qalora_group_size,
bits=args.bits,
)
================================================
FILE: examples/randlora_finetuning/README.md
================================================
# RandLora: Full-rank parameter-efficient fine-tuning of large models
## Introduction
[RandLora](https://huggingface.co/papers/2502.00987) is a parameter-efficient fine-tuning technique that is similar to LoRA and VeRA but performs full rank updates to improve performance. RandLora can be particulary usefull when adapting large model to hard tasks that require complex updates while preserving the parameter efficiency of LoRA. The full rank update of RandLora is acheived by linearly scaling random bases. The random bases are a collection of multiple low rank matrices such that the summation of their ranks if greater or equal to the full rank of the parameter matrices. The trainable parameters of RandLora are two diagonal matrices (vectors) that get multiplied with the right hand low rank random bases, in a similar way to VeRA's update. To maintain low memory usage, RandLora uses a custom function that prevents storing unnecessary bases in memory for backpropagation.
## Quick start
```python
import torch
from peft import RandLoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
randlora_config = RandLoraConfig()
peft_model = get_peft_model(model, lora_config)
trainer = transformers.Trainer(
model=peft_model,
train_dataset=dataset,
dataset_text_field="text",
max_length=2048,
processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("randlora-llama-7b")
```
There is no additional change needed to your standard PEFT training procedure, simply swap your `LoraConfig` for a `RandLoraConfig`. Note however that RandLora's trainable parameter count is **inversely proportional** to the rank parameter `r`. Lower `r` to increase and increase it to reduce trainable parameters of RandLora.
Run the finetuning script simply by running:
```bash
python examples/randlora_finetuning/randlora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco
```
This 👆🏻 by default will load the model in peft set up with RandLora config. Now if you wanna quickly compare it with Lora, all you need to do is to input ` --use_lora` in the command line and reduce `--randlora_alpha` to 2x the rank. So same above example would be 👇🏻;
```bash
python examples/randlora_finetuning/randlora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco --use_lora --rank 32 --randlora_alpha 64
```
RandLora can be made to use sparse or very sparse random bases. These sparse matrices can help reduce overfitting. Add `--very_sparse` to run with very sparse matrices or `--sparse` for sparse matrices:
```bash
python examples/randlora_finetuning/randlora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --sparse
```
RandLora also supports quantization. To use 4-bit quantization try:
```bash
python examples/randlora_finetuning/randlora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --quantize
```
By default the RandLora layers are the key and value layers of LLama model. Adding adapters on more layers will increase memory usage. If you wish to choose a different set of layers for RandLora to be applied on, you can simply define it using:
```bash
python examples/randlora_finetuning/randlora_finetuning.py --randlora_target_modules "q_proj,k_proj,v_proj"
```
### Full example of the script
```bash
python randlora_finetuning.py \
--base_model "PATH_TO_MODEL" \
--data_path "PATH_TO_DATASET" \
--output_dir "PATH_TO_OUTPUT_DIR" \
--batch_size 1 \
--num_epochs 3 \
--learning_rate 3e-4 \
--cutoff_len 512 \
--val_set_size 500 \
--quantize \
--eval_step 10 \
--save_step 100 \
--device "auto" \
--rank 32 \
--randlora_alpha 640 \
--randlora_dropout 0.05 \
--randlora_target_modules "k_proj,v_proj" \
--hub_model_id "YOUR_HF_REPO" \
--push_to_hub
```
## RandLora vs. LoRA
RandLora differs from LoRA and other related low rank approximation algorithms by chanllenging the low rank paradigm. RandLora adapters learn **full-rank** updates as the [paper](https://huggingface.co/papers/2502.00987) shows that the low rank constraint of LoRA can constrain performance gains as trainable parameters increase (with higher ranks). As a result, using RandLora is specifically recommended for difficult tasks that are underfit by LoRA. RandLoRA however also often improves performance for common tasks. If increasing LoRA's rank improves performance for your task, RandLora will most likely outperform.
RandLora is expected to increase performance over LoRA for equivalent amounts of trainable parameters, mostly for larger equivalent amounts (> LoRA rank 4).
RandLora's performance increase comes with two limitations:
1. Performance is dependent on using a large `randlora_alpha` scaling parameter (usually 20x the basis rank). This large parameter can sometimes make training the update unstable, reduce the learning rate or the scaling parameter if this is the case.
2. Increase training time over LoRA when using very low RandLora basis ranks.
## RandLora vs. VeRA
RandLora shares similarities with VeRA in that both algorithms use random basis combinations to address some of LoRA's limitations. The limitations addressed by each algorithm is however different.
VeRA aims to reduce trainable parameters beyond rank 1 LoRAs while RandLoRA reduces the performance limitation due to the low rank of the update as the trainable parameter count increases.
RandLora is expected to:
1. Improve performance over VeRA when more trainable parameters are required (hard tasks)
2. Reduce memory usage over VeRA thanks to RandLora's random base sharing strategy
## Citation
```
@inproceedings{2025_ICLR_RandLoRA,
title="{RandLoRA: Full rank parameter-efficient fine-tuning of large models}",
author="Albert, Paul and Zhang, Frederic Z. and Saratchandran, Hemanth and Rodriguez-Opazo, Cristian and van den Hengel, Anton and Abbasnejad, Ehsan",
booktitle="{International Conference on Learning Representations (ICLR)}",
year="2025"
}
```
================================================
FILE: examples/randlora_finetuning/qrandlora_finetuning.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "CV_gQs58bsvM"
},
"source": [
"# Fine-tuning [Llama3-8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) Dataset using QRandLora (quantized RandLora) on T4 Free Colab GPU."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FuXIFTFapAMI",
"outputId": "b95d8260-65bd-405f-f1e2-8d353aa46814"
},
"outputs": [],
"source": [
"# Install the libraries\n",
"!pip install -q -U bitsandbytes\n",
"!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
"!pip install -q -U git+https://github.com/huggingface/peft.git\n",
"!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
"!pip install -q datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"8cc86330c2af436c9af314e8c04c8c2b",
"e25f9ca445b14e3f8397779df071dfb4",
"8365680c634a44aa880317e36fa5e46e",
"0c4ac7c3db0b431397cc812f7c9e785c",
"c84f542c863043dea8a3675fa153e78d",
"b3b3f4ddd4ed4d938c923887939a0440",
"35186465f87341f683affb9399661540",
"791df472db174df69b8c9f0e200af254",
"6bb9c7182d2a464ea21809e59043562a",
"31c574113731403b88edc5bb0798bc6d",
"3b8bc5b9392e45758813a1db9db824a9",
"90661b333d6f496ca606b3046622660e",
"5f551f9b217e44cf8b5433f314b3844b",
"d2d81cc8296c4b10bf80b86c0a3302d3",
"7e3a386e672f4748882211227b7721a9",
"57f251691b4c453896b2508c431dfc2f",
"4bdb196cd1494f809829651ec5b6cbf8",
"7cd50bcc8fcc4b83abcda6d3604bd4cc",
"7a00aa4a97a34da39cc052c6926dbe13",
"14c73d88df9e46e3bbb6690fdb48ad07",
"0e2beab611114239b6ee48a3cbb09c49",
"006b78b5191b4fb888d98bdf6c20ec1e",
"5f6ffa1d929443a5bd9c7c550f0690f0",
"668a7f88506148a9ba2b48920afc028f",
"57b0096985ab44aea342e52795c4f999",
"a4c404e420cc4ce781ce569f9ab3f987",
"ee4e4af964ec4dd597cb04a90f0697f9",
"974e3687f18a4e1a975969b880d086aa",
"93a50117ece543d4857ba02505dc4514",
"71a3a56edbdb45669d382fef4b097e1b",
"53f287d4927541d08e2ae7d4d0b3c396",
"afa442ab223b46cb82569438c0047823"
]
},
"id": "wAAPv5CRmg7e",
"outputId": "687f979a-04c1-4160-d71c-4de8ecdb07d9"
},
"outputs": [],
"source": [
"# Required when training models/data that are gated on HuggingFace, and required for pushing models to HuggingFace\n",
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MJ-5idQwzvg-"
},
"source": [
"### Loading the model and its tokenizer in quantized setup!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 546,
"referenced_widgets": [
"5924b266e95a42039634a334ff561a82",
"eadeec171e7b4c0f9e26964f031cfb71",
"feae525923d5407bb69a922954c474f7",
"00371a48e64c45cd97020a78b710e64c",
"156f95b0012449e8a0c604e6e03bf35f",
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},
"id": "E0Nl5mWL0k2T",
"outputId": "a942d9b0-1f38-4a9b-ea20-e55bd7593920"
},
"outputs": [],
"source": [
"# setting up the config for 4-bit quantization of QRandLora\n",
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
"\n",
"model_id = \"meta-llama/Meta-Llama-3-8B\"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=torch.bfloat16\n",
")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\": 0})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Xpx2Fq-icX56"
},
"outputs": [],
"source": [
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mp2gMi1ZzGET"
},
"source": [
"#### Prepare model for PEFT fine-tuning"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "a9EUEDAl0ss3"
},
"outputs": [],
"source": [
"from peft import prepare_model_for_kbit_training\n",
"\n",
"model.gradient_checkpointing_enable()\n",
"model = prepare_model_for_kbit_training(model)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "gkIcwsSU01EB"
},
"outputs": [],
"source": [
"def print_trainable_parameters(model):\n",
" \"\"\"\n",
" Prints the number of trainable parameters in the model.\n",
" \"\"\"\n",
" trainable_params = 0\n",
" all_param = 0\n",
" for _, param in model.named_parameters():\n",
" all_param += param.numel()\n",
" if param.requires_grad:\n",
" trainable_params += param.numel()\n",
" print(\n",
" f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HVTAJuKyM0gX"
},
"source": [
"### Setup `RandLoraConfig`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ybeyl20n3dYH",
"outputId": "ea35ec70-13e4-4f23-9481-1b33b1b06dec"
},
"outputs": [],
"source": [
"from peft import RandLoraConfig, get_peft_model\n",
"\n",
"config = RandLoraConfig(\n",
" r=32,\n",
" randlora_alpha=640,\n",
" target_modules=[\n",
" \"q_proj\",\n",
" \"k_proj\",\n",
" \"v_proj\",\n",
" \"o_proj\",\n",
" \"gate_proj\",\n",
" \"up_proj\",\n",
" \"down_proj\",\n",
" ], # parameters specific to llama\n",
" randlora_dropout=0.05,\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
")\n",
"\n",
"model = get_peft_model(model, config)\n",
"print_trainable_parameters(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ybeyl20n3dYH",
"outputId": "ea35ec70-13e4-4f23-9481-1b33b1b06dec"
},
"outputs": [],
"source": [
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FCc64bfnmd3j"
},
"source": [
"## Step 2) Fine-tuning process 💥\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 277,
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},
"id": "s6f4z8EYmcJ6",
"outputId": "8ece7a1a-cf27-4602-c70b-ce4f3d7e11bf"
},
"outputs": [],
"source": [
"# Load the dataset from HF\n",
"from datasets import load_dataset\n",
"\n",
"data = load_dataset(\"timdettmers/openassistant-guanaco\")\n",
"data = data.map(lambda samples: tokenizer(samples[\"text\"]), batched=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_0MOtwf3zdZp"
},
"source": [
"## Training\n",
"\n",
"For the sake of the demo, we just ran it for 10 steps just to showcase how to use this integration with existing tools on the HF ecosystem."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 498
},
"id": "jq0nX33BmfaC",
"outputId": "94e17005-065b-48ab-9192-e3ab55c0c292"
},
"outputs": [],
"source": [
"import transformers\n",
"\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"\n",
"trainer = transformers.Trainer(\n",
" model=model,\n",
" train_dataset=data[\"train\"],\n",
" args=transformers.TrainingArguments(\n",
" per_device_train_batch_size=1,\n",
" gradient_accumulation_steps=4,\n",
" warmup_steps=2,\n",
" max_steps=10,\n",
" learning_rate=2e-4,\n",
" fp16=True,\n",
" logging_steps=1,\n",
" output_dir=\"path/to/your/HF/repo\", # change it to your desired repo!\n",
" optim=\"paged_adamw_8bit\",\n",
" label_names=[\"labels\"],\n",
" ),\n",
" data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
")\n",
"model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mr3rLrHwqhf6"
},
"source": [
"## Usage Example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9mrOJ9l8SMHv"
},
"outputs": [],
"source": [
"model.config.use_cache = True\n",
"model.eval();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 122
},
"id": "AM6FNOFzqKfI",
"outputId": "fdbe28b1-e440-45d3-bd6d-c15e744ad23d"
},
"outputs": [],
"source": [
"from transformers import GenerationConfig\n",
"\n",
"max_new_tokens = 120\n",
"top_p = 0.9\n",
"temperature = 0.7\n",
"user_question = \"What is the purpose of quantization in LLMs?\"\n",
"\n",
"\n",
"prompt = (\n",
" \"A chat between a curious human and an artificial intelligence assistant. \"\n",
" \"The assistant gives helpful, detailed, and polite answers to the user's questions. \"\n",
" \"### Human: {user_question}\"\n",
" \"### Assistant: \"\n",
")\n",
"\n",
"\n",
"def generate(model, user_question, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature):\n",
" device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
" inputs = tokenizer(prompt.format(user_question=user_question), return_tensors=\"pt\").to(device)\n",
"\n",
" outputs = model.generate(\n",
" **inputs,\n",
" generation_config=GenerationConfig(\n",
" do_sample=True,\n",
" max_new_tokens=max_new_tokens,\n",
" top_p=top_p,\n",
" temperature=temperature,\n",
" ),\n",
" )\n",
"\n",
" text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
" # print(text)\n",
" return text\n",
"\n",
"\n",
"generate(model, user_question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T5t_gl2_f5OO"
},
"outputs": [],
"source": [
"# trainer.push_to_hub()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"00371a48e64c45cd97020a78b710e64c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
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================================================
FILE: examples/randlora_finetuning/randlora_finetuning.py
================================================
# This script is based on examples/dora_finetuning/dora_finetuning.py
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, RandLoraConfig, get_peft_model, prepare_model_for_kbit_training
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
use_lora: bool,
quantize: bool,
eval_step: int,
save_step: int,
device: str,
rank: int,
randlora_alpha: int,
randlora_dropout: float,
randlora_target_modules: str,
hub_model_id: str,
push_to_hub: bool,
sparse: bool,
very_sparse: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
# Compute type
device_type = device.type
device_module = getattr(torch, device_type, torch.cuda)
bf16_suppotrted = device_module.is_available() and device_module.is_bf16_supported()
dtype = torch.bfloat16 if bf16_suppotrted else torch.float16
# QRandLora (quantized randlora): IF YOU WANNA QUANTIZE THE MODEL
if quantize:
model = AutoModelForCausalLM.from_pretrained(
base_model,
token=hf_token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16 if bf16_suppotrted else torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
dtype=dtype,
)
# setup for quantized training
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(
base_model,
dtype=dtype,
token=hf_token,
)
# LoRa config for the PEFT model
if use_lora:
peft_config = LoraConfig(
r=rank, # Rank of matrix
lora_alpha=randlora_alpha,
target_modules=(randlora_target_modules.split(",") if randlora_target_modules else ["k_proj", "v_proj"]),
lora_dropout=randlora_dropout,
bias="none",
)
else:
peft_config = RandLoraConfig(
r=rank, # Rank of random bases
randlora_alpha=randlora_alpha,
target_modules=(randlora_target_modules.split(",") if randlora_target_modules else ["k_proj", "v_proj"]),
randlora_dropout=randlora_dropout,
bias="none",
sparse=sparse,
very_sparse=very_sparse,
)
# get the peft model with RandLora config
model = get_peft_model(model, peft_config)
model.to(device) # MODEL TO ACCELERATOR
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Compute the total amount of training step for warmup
max_steps = int((len(dataset) // batch_size) * num_epochs)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=int(max_steps * 0.1), # 10% of total trainig steps
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16
// batch_size, # Maintaining a minimum batch size of 16 post accumulation is recommended to ensure good performance
learning_rate=learning_rate,
hub_token=hf_token,
label_names=["labels"],
)
# Clear accelerator cache to free memory
device_module.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
# Push the main model to the hub
trainer.push_to_hub(commit_message="Fine-tuned model")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DoRA and PEFT")
parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=3e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--use_lora", action="store_true", help="Apply Lora instead of RandLora")
parser.add_argument("--quantize", action="store_true", help="Use quantization")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--rank", type=int, default=32, help="RandLora basis rank")
parser.add_argument("--randlora_alpha", type=int, default=640, help="RandLora alpha")
parser.add_argument("--randlora_dropout", type=float, default=0.05, help="RandLora dropout rate")
parser.add_argument(
"--randlora_target_modules", type=str, default=None, help="Comma-separated list of target modules for RandLora"
)
parser.add_argument("--sparse", action="store_true", help="Use sparse matrix multiplication")
parser.add_argument("--very_sparse", action="store_true", help="Use very sparse matrix multiplication")
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
if args.device == "auto":
args.device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
use_lora=args.use_lora,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
rank=args.rank,
randlora_alpha=args.randlora_alpha,
randlora_dropout=args.randlora_dropout,
randlora_target_modules=args.randlora_target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
sparse=args.sparse,
very_sparse=args.very_sparse,
)
================================================
FILE: examples/road_finetuning/README.md
================================================
# RoAd: 3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
## Introduction
[RoAd](https://huggingface.co/papers/2409.00119) is a novel method that adapts LLMs using simple 2D rotations. It is highly parameter-efficient,
achieving strong performance with less than 0.1% trainable parameters.
RoAd also supports efficient serving of mixed-adapter requests within a batch, incurring only element-wise computation overhead rather than costly batch matrix multiplications.
Additionally, it improves model interpretability through structured and composable transformations.
## Quick start
```python
import torch
from peft import RoadConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
from datasets import load_dataset
model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
road_config = RoadConfig(
variant="1",
)
peft_model = get_peft_model(model, road_config)
trainer = transformers.Trainer(
model=peft_model,
train_dataset=dataset,
dataset_text_field="text",
max_length=2048,
tokenizer=tokenizer,
)
trainer.train()
peft_model.save_pretrained("road-llama-3-8b")
```
RoAd requires a higher learning rate compared to LoRa and similar approaches, set it to around 1e-3.
Run the finetuning script simply by running:
```bash
python examples/road_finetuning/road_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco
```
RoAd also supports quantization. To use 4-bit quantization try:
```bash
python examples/road_finetuning/road_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --quantize
```
### Full example of the script
```bash
python road_finetuning.py \
--base_model "PATH_TO_MODEL" \
--data_path "PATH_TO_DATASET" \
--output_dir "PATH_TO_OUTPUT_DIR" \
--batch_size 1 \
--num_epochs 3 \
--learning_rate 1e-3 \
--cutoff_len 512 \
--val_set_size 500 \
--quantize \
--eval_step 10 \
--save_step 100 \
--device "cuda:0" \
--variant 1 \
--road_target_modules "q_proj,k_proj,v_proj,o_proj" \
--hub_model_id "YOUR_HF_REPO" \
--push_to_hub
```
## Use the model on 🤗
You can load and use the model as any other 🤗 models.
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("ppetrushkov/llama-2-7b-sql-road-test")
```
## Citation
```
@inproceedings{
liao2024in,
title={3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability},
author={Baohao Liao and Christof Monz},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=rYjYwuM6yH}
}
```
================================================
FILE: examples/road_finetuning/road_finetuning.py
================================================
# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import RoadConfig, get_peft_model, prepare_model_for_kbit_training
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
quantize: bool,
eval_step: int,
save_step: int,
device: str,
variant: str,
road_target_modules: str,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
# IF YOU WANNA QUANTIZE THE MODEL
if quantize:
model = AutoModelForCausalLM.from_pretrained(
base_model,
token=hf_token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=(
torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16
),
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
)
# setup for quantized training
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token, device_map="auto")
# RoAd config for the PEFT model
road_config = RoadConfig(
variant=variant, # Rank of matrix
target_modules=(
road_target_modules.split(",")
if road_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
),
)
# get the peft model with RoAd config
model = get_peft_model(model, road_config)
model.to(device) # MODEL TO GPU/CUDA
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
# Clear CUDA cache to free memory
torch.cuda.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
# Push the main model to the hub
trainer.push_to_hub(commit_message="Fine-tuned model")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DoRA and PEFT")
parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=3e-3, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--quantize", action="store_true", help="Use quantization")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="cuda:0", help="Device to use for training")
parser.add_argument(
"--variant", type=str, default="road_1", choices=["road_1", "road_2", "road_4"], help="RoAD variant"
)
parser.add_argument(
"--road_target_modules", type=str, default=None, help="Comma-separated list of target modules for RoAd"
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
variant=args.variant,
road_target_modules=args.road_target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
================================================
FILE: examples/semantic_segmentation/README.md
================================================
# Fine-tuning for semantic segmentation using LoRA and 🤗 PEFT
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb)
We provide a notebook (`semantic_segmentation_peft_lora.ipynb`) where we learn how to use [LoRA](https://huggingface.co/papers/2106.09685) from 🤗 PEFT to fine-tune an semantic segmentation by ONLY using **14%%** of the original trainable parameters of the model.
LoRA adds low-rank "update matrices" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://huggingface.co/papers/2106.09685).
================================================
FILE: examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "JAeWcsvLF2_6"
},
"source": [
"## Introduction\n",
"\n",
"In this notebook, we will learn how to use [LoRA](https://huggingface.co/papers/2106.09685) from 🤗 PEFT to fine-tune a SegFormer model variant for semantic segmentation by ONLY using **14%** of the original trainable parameters of the model. \n",
"\n",
"LoRA adds low-rank \"update matrices\" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://huggingface.co/papers/2106.09685). \n",
"\n",
"Let's get started by installing the dependencies. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lveGHtBcGNyc"
},
"source": [
"## Install dependencies\n",
"\n",
"Here we're installing `peft` from source to ensure we have access to all the bleeding edge features of `peft`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lbYTKXv4ZTwg",
"outputId": "5a033ebd-6bbd-4bf4-802c-a1bac6a48a07"
},
"outputs": [],
"source": [
"!pip install transformers accelerate evaluate datasets==3.6.0 git+https://github.com/huggingface/peft -q"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B0fmCvTsGPah"
},
"source": [
"## Authentication\n",
"\n",
"We will share our fine-tuned model at the end of training. So, to do that we just authenticate using our 🤗 token. This token is available from [here](https://huggingface.co/settings/tokens). If you don't have a 🤗 account already, we highly encourage you to do so; it's free!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331,
"referenced_widgets": [
"f2a722f371904cce80dc1c087b153ad6",
"6c88a55a635b4c9f946a1aa838d69f20",
"6c6d19cd893e4d82bae9972fa10c6d74",
"fc48ee28c2e44f1daa03149c8004c314",
"cb4053f102fc4207a1c9513f81ad6415",
"0dcc5a2866a349e0843673bef499dc66",
"b7431f99d93b4e9b8c8177ac4a7b4070",
"14ac809ba0bc4cd5bcd51f83105947b0",
"e7393e78f41b496495982490b72ef2a3",
"42a7b8268d8945b5bbe9d3f20bc8840c",
"554b55f29a5e4d5a81608d912d3635e8",
"3f69c7b15e5e48039777e4b6b1a51f53",
"5bcfe3da0ffb41ccbb9404ac35ae8945",
"21bf954f54db41e6ba78f76195721614",
"f83dd354396e4aa3acd214a6fd98efb2",
"24fde588dc1a49a397e379cda320ed71",
"9746875e74b845daab06619393b4d46b"
]
},
"id": "OYhwMOj5ZTwm",
"outputId": "ff2d4cc4-4363-4093-8bdc-763761cbe3ef"
},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B9Cu7j_QGVbH"
},
"source": [
"## Load a dataset\n",
"\n",
"We're only loading the first 150 instances from the training set of the [SceneParse150 dataset](https://huggingface.co/datasets/scene_parse_150) to keep this example runtime short. "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sGJWDwtHZTwn",
"outputId": "260c874e-1844-42ba-9dc2-0f727e2930cc"
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"ds = load_dataset(\"scene_parse_150\", split=\"train[:150]\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RpSPx8EHGeLM"
},
"source": [
"## Prepare train and test splits"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "ydWKIqCUZTwo"
},
"outputs": [],
"source": [
"ds = ds.train_test_split(test_size=0.1)\n",
"train_ds = ds[\"train\"]\n",
"test_ds = ds[\"test\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yHtqAQ2WGhlR"
},
"source": [
"## Prepare label mappers\n",
"\n",
"We create two dictionaries:\n",
"\n",
"* `label2id`: maps the semantic classes of the dataset to integer ids.\n",
"* `id2label`: `label2id` reversed. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hu8Y4dEIZTwq",
"outputId": "eba72235-c1a7-4c95-8c89-5ef5588d6581"
},
"outputs": [],
"source": [
"import json\n",
"from huggingface_hub import hf_hub_download\n",
"\n",
"repo_id = \"huggingface/label-files\"\n",
"filename = \"ade20k-id2label.json\"\n",
"id2label = json.load(open(hf_hub_download(repo_id=repo_id, filename=filename, repo_type=\"dataset\"), \"r\"))\n",
"id2label = {int(k): v for k, v in id2label.items()}\n",
"label2id = {v: k for k, v in id2label.items()}\n",
"num_labels = len(id2label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5V8nhdt0HBsk"
},
"source": [
"## Prepare datasets for training and evaluation"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fNi_TKYpZTwq",
"outputId": "a28647b4-0deb-49cc-a1b8-c4a2ab99bb4d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.11/dist-packages/transformers/image_processing_base.py:412: UserWarning: The following named arguments are not valid for `SegformerImageProcessor.__init__` and were ignored: 'reduce_labels'\n",
" image_processor = cls(**image_processor_dict)\n"
]
}
],
"source": [
"from transformers import AutoImageProcessor\n",
"\n",
"checkpoint = \"nvidia/mit-b0\"\n",
"image_processor = AutoImageProcessor.from_pretrained(checkpoint, do_reduce_labels=True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "JAjiYzklZTwr"
},
"outputs": [],
"source": [
"from torchvision.transforms import ColorJitter\n",
"\n",
"jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "_HaS12U0ZTwr"
},
"outputs": [],
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"\n",
"def handle_grayscale_image(image):\n",
" np_image = np.array(image)\n",
" if np_image.ndim == 2:\n",
" tiled_image = np.tile(np.expand_dims(np_image, -1), 3)\n",
" return Image.fromarray(tiled_image)\n",
" else:\n",
" return Image.fromarray(np_image)\n",
"\n",
"\n",
"def train_transforms(example_batch):\n",
" images = [jitter(handle_grayscale_image(x)) for x in example_batch[\"image\"]]\n",
" labels = [x for x in example_batch[\"annotation\"]]\n",
" inputs = image_processor(images, labels)\n",
" return inputs\n",
"\n",
"\n",
"def val_transforms(example_batch):\n",
" images = [handle_grayscale_image(x) for x in example_batch[\"image\"]]\n",
" labels = [x for x in example_batch[\"annotation\"]]\n",
" inputs = image_processor(images, labels)\n",
" return inputs"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "Qyjsvup2ZTws"
},
"outputs": [],
"source": [
"train_ds.set_transform(train_transforms)\n",
"test_ds.set_transform(val_transforms)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lu8RjicxHJiO"
},
"source": [
"## Evaluation function\n",
"\n",
"Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load a evaluation method with the [🤗 Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [mean Intersection over Union (IoU)](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "TMSnlebfZTwt"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading builder script: 12.9kB [00:00, 34.2MB/s]\n"
]
}
],
"source": [
"import torch\n",
"from torch import nn\n",
"import evaluate\n",
"\n",
"metric = evaluate.load(\"mean_iou\")\n",
"\n",
"\n",
"def compute_metrics(eval_pred):\n",
" with torch.no_grad():\n",
" logits, labels = eval_pred\n",
" logits_tensor = torch.from_numpy(logits)\n",
" # scale the logits to the size of the label\n",
" logits_tensor = nn.functional.interpolate(\n",
" logits_tensor,\n",
" size=labels.shape[-2:],\n",
" mode=\"bilinear\",\n",
" align_corners=False,\n",
" ).argmax(dim=1)\n",
"\n",
" pred_labels = logits_tensor.detach().cpu().numpy()\n",
" # currently using _compute instead of compute\n",
" # see this issue for more info: https://github.com/huggingface/evaluate/pull/328#issuecomment-1286866576\n",
" metrics = metric._compute(\n",
" predictions=pred_labels,\n",
" references=labels,\n",
" num_labels=len(id2label),\n",
" ignore_index=0,\n",
" reduce_labels=image_processor.do_reduce_labels,\n",
" )\n",
"\n",
" # add per category metrics as individual key-value pairs\n",
" per_category_accuracy = metrics.pop(\"per_category_accuracy\").tolist()\n",
" per_category_iou = metrics.pop(\"per_category_iou\").tolist()\n",
"\n",
" metrics.update({f\"accuracy_{id2label[i]}\": v for i, v in enumerate(per_category_accuracy)})\n",
" metrics.update({f\"iou_{id2label[i]}\": v for i, v in enumerate(per_category_iou)})\n",
"\n",
" return metrics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r304cnpxHxp5"
},
"source": [
"## Load a base model\n",
"\n",
"For this example, we use the [SegFormer B0 variant](https://huggingface.co/nvidia/mit-b0). "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "Krvppe44a_7y"
},
"outputs": [],
"source": [
"def print_trainable_parameters(model):\n",
" \"\"\"\n",
" Prints the number of trainable parameters in the model.\n",
" \"\"\"\n",
" trainable_params = 0\n",
" all_param = 0\n",
" for _, param in model.named_parameters():\n",
" all_param += param.numel()\n",
" if param.requires_grad:\n",
" trainable_params += param.numel()\n",
" print(\n",
" f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q_Wwl_ewID9I"
},
"source": [
"We pass the `label2id` and `id2label` dictionaries to let the `AutoModelForSemanticSegmentation` class know that we're interested in a custom base model where the decoder head should be randomly initialized w.r.t our custom dataset. Note, however, that the rest of the model parameters are pre-trained and will be fine-tuned in a regular transfer learning setup.\n",
"\n",
"We also notice that the 100% parameters in the `model` are trainable. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kcdLdvIlZTwt",
"outputId": "a6b71dce-905e-4389-dcf6-46b43e769fcc"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.batch_norm.bias', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.batch_norm.running_mean', 'decode_head.batch_norm.running_var', 'decode_head.batch_norm.weight', 'decode_head.classifier.bias', 'decode_head.classifier.weight', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.linear_c.2.proj.bias', 'decode_head.linear_c.2.proj.weight', 'decode_head.linear_c.3.proj.bias', 'decode_head.linear_c.3.proj.weight', 'decode_head.linear_fuse.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 3752694 || all params: 3752694 || trainable%: 100.00\n"
]
}
],
"source": [
"from transformers import AutoModelForSemanticSegmentation, TrainingArguments, Trainer\n",
"\n",
"model = AutoModelForSemanticSegmentation.from_pretrained(\n",
" checkpoint, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True\n",
")\n",
"print_trainable_parameters(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4yhyYVTCInF0"
},
"source": [
"## Wrap `model` as a `PeftModel` for LoRA training\n",
"\n",
"This involves two steps:\n",
"\n",
"* Defining a config with `LoraConfig`\n",
"* Wrapping the original `model` with `get_peft_model()` with the config defined in the step above. "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YPg4W5eFB__n",
"outputId": "9995eb44-1c30-43e7-cc4e-691ecb1b1878"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 566422 || all params: 4317068 || trainable%: 13.12\n"
]
}
],
"source": [
"from peft import LoraConfig, get_peft_model\n",
"\n",
"config = LoraConfig(\n",
" r=32,\n",
" lora_alpha=32,\n",
" target_modules=[\"query\", \"value\"],\n",
" lora_dropout=0.1,\n",
" bias=\"lora_only\",\n",
" modules_to_save=[\"decode_head\"],\n",
")\n",
"lora_model = get_peft_model(model, config)\n",
"print_trainable_parameters(lora_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4M3wYekOI95X"
},
"source": [
" Let's unpack what's going on here. \n",
"\n",
"In order for LoRA to take effect, we need to specify the target modules to `LoraConfig` so that `PeftModel` knows which modules inside our model needs to be amended with LoRA matrices. In this case, we're only interested in targetting the query and value matrices of the attention blocks of the base model. Since the parameters corresponding to these matrices are \"named\" with `query` and `value` respectively, we specify them accordingly in the `target_modules` argument of `LoraConfig`. \n",
"\n",
"We also specify `modules_to_save`. After we wrap our base model `model` with `PeftModel` along with the `config`, we get a new model where only the LoRA parameters are trainable (so-called \"update matrices\") while the pre-trained parameters are kept frozen. These include the parameters of the randomly initialized classifier parameters too. This is NOT we want when fine-tuning the base model on our custom dataset. To ensure that the classifier parameters are also trained, we specify `modules_to_save`. This also ensures that these modules are serialized alongside the LoRA trainable parameters when using utilities like `save_pretrained()` and `push_to_hub()`. \n",
"\n",
"Regarding the other parameters:\n",
"\n",
"* `r`: The dimension used by the LoRA update matrices.\n",
"* `alpha`: Scaling factor.\n",
"* `bias`: Specifying if the `bias` parameters should be trained. `lora_only` denotes only the LoRA `bias` parameters will be trained. \n",
"\n",
"`r` and `alpha` together control the total number of final trainable parameters when using LoRA giving us the flexbility to balance a trade-off between end performance and compute efficiency.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XTF68xfjJEci"
},
"source": [
"We can also how many parameters we're actually training. Since we're interested in performing **parameter-efficient fine-tuning**, we should expect to notice a less number of trainable parameters from the `lora_model` in comparison to the original `model` which is indeed the case here. \n",
"\n",
"For sanity, let's also manually verify the modules that are actually trainable in `lora_model`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PUe1Gzvd1PEP",
"outputId": "7b8ba17f-01fd-4ab4-d703-9a0c01cff31b"
},
"outputs": [],
"source": [
"for name, param in lora_model.named_parameters():\n",
" if param.requires_grad:\n",
" print(name, param.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can confirm that only the LoRA parameters appended to the attention blocks and the `decode_head` parameters are trainable."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rX75AyI7JYVC"
},
"source": [
"## Train!\n",
"\n",
"This is a two-step process: \n",
"\n",
"1. Define your training hyperparameters in [TrainingArguments](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/trainer#transformers.TrainingArguments). It is important you don’t remove unused columns because this’ll drop the image column. Without the image column, you can’t create `pixel_values`. Set `remove_unused_columns=False` to prevent this behavior! The only other required parameter is output_dir which specifies where to save your model. At the end of each epoch, the `Trainer` will evaluate the IoU metric and save the training checkpoint.\n",
"2. Pass the training arguments to [Trainer](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/trainer#transformers.Trainer) along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.\n",
"3. Call `train()` to finetune your model.\n",
"\n",
"\n",
"**Note** that This example is meant to walk you through the workflow when using PEFT for semantic segmentation. We didn't perform extensive hyperparameter tuning to achieve optimal results. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "K6HVcNkDZTwu",
"outputId": "1b28a072-0e16-4b1a-ec32-d78e93630ef3"
},
"outputs": [],
"source": [
"model_name = checkpoint.split(\"/\")[-1]\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=f\"{model_name}-scene-parse-150-lora\",\n",
" learning_rate=5e-4,\n",
" num_train_epochs=50,\n",
" per_device_train_batch_size=4,\n",
" per_device_eval_batch_size=2,\n",
" save_total_limit=3,\n",
" eval_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" logging_steps=5,\n",
" remove_unused_columns=False,\n",
" push_to_hub=True,\n",
" label_names=[\"labels\"],\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=lora_model,\n",
" args=training_args,\n",
" train_dataset=train_ds,\n",
" eval_dataset=test_ds,\n",
" compute_metrics=compute_metrics,\n",
")\n",
"\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dacaBLE6KLdu"
},
"source": [
"## Saving the model and inference \n",
"\n",
"Here we use the `save_pretrained()` method of the `lora_model` to save the *LoRA-only parameters* locally. However, you can also use thr `push_to_hub()` method to upload these parameters directly to the Hugging Face Hub (as shown [here](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipynb)). "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "pvkLkrQo-6l6"
},
"outputs": [],
"source": [
"model_id = \"segformer-scene-parse-150-lora\"\n",
"lora_model.save_pretrained(model_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ur8n41kBK4uj"
},
"source": [
"We can see that the LoRA-only parameters are just **2.2 MB in size**! This greatly improves the portability when using very large models. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "grzLeOT-__ht",
"outputId": "1ce26a27-2f38-43f3-9454-8ba11f2cfc59"
},
"outputs": [],
"source": [
"!ls -lh {model_id}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KFYC6Z3FLB5F"
},
"source": [
"Let's now prepare our `inference_model` and run an inference. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T7zeMQTaACur",
"outputId": "762b7fbc-07d4-4572-f107-c836e3e7928a"
},
"outputs": [],
"source": [
"from peft import PeftConfig, PeftModel\n",
"\n",
"config = PeftConfig.from_pretrained(model_id)\n",
"model = AutoModelForSemanticSegmentation.from_pretrained(\n",
" checkpoint, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True\n",
")\n",
"# Load the Lora model\n",
"inference_model = PeftModel.from_pretrained(model, model_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2L1R0LDWLImd"
},
"source": [
"Fetch an image."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"id": "lwjRvZOmA7Hh",
"outputId": "44ab267d-e2b9-4bda-a52b-91968eabce29"
},
"outputs": [
{
"data": {
"image/jpeg": 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",
"image/png": 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sBRNds0/eQUoZ4JhilhLA/G1hhMBYYjhduSwgY4b9OPTJyRmhpOcO2I0xhhkYenfA7iRQ7txU88QY7+3tkQRMICMc8RzO493/3mvgXgM3a4BWQ/OBzhtRwKkBsRPczPc9UoRIAbzHxCuSCjECcKLwBnA9ntCgDOD6KEEWwO8Hvefi3Tyr7ZenZTZFrSMAvUu0i2udvHjW7fXYi+0MutyDPZ0v50sumqbVYR0zY27c/ZbKdZGXUimVaCE0OUTBKGIssa+afNvBZrzHx00sMSez2PTFS9I09Rhuw5aEcDGp5TUkjwsZSOy07xaTI4hN8rUseOMS7TXWIHiWMO5N4SuJQe5Kn+KTwldLmaK+x99rYFMDu9ZPb0HEiogO0NZgrAZlDafU0DbTlC+il4JS+BJZeFP0KbxHjNBtIDiXKN8pPiVGFV4tvgq/FjAV+p3E1+1bAGZ5JH8ADoU3ANZkd3Mpg7pbz1qs8AEbYJJdK1KUC1St/+r//F8eHh4+efzhoyePDw6P+3v73cF+r9s7PbtYNdu1Bi/8tJgTc18Hr9xiklPvAReZbqZenbEwrghNXLzEwtDu7++zFs2c2G0wxtjttDd4aDwhnu4gBvBQnji8kIEPvYQ8SuuO3mMOnt9RwLW0Lfy20rZpboNJ8U/Fvat0U/x3xafk/6bk3FWeFP2ueqBBeRQy7nn3J02VJDwVBxz20F1TeXf0LhX8S0B431HSKf6B3wZcki181n+W8KHnEj6y84eGj4y/ZyAKwoHwlsTw4ig+IcDbevHVV/z94he/aNRbvcHw+PGTjz7+5PjhBx997/uNVq3V0xu3dSB9Jaox51NF1fa0lNzNXho2nJm8AmAs3R7TqrG7PJm/spULHi+Oee2zZ8/IG45QHICnERNfOJAfvGwAcxLLLTGYoFRkGeB7d6+Bew3cVgPeZXjb8XbnMC2XIBztjrbmDu9t+b5fuhAsgPebfjm1ECMApwhvANfjg2+J/g8WHxl/zwCN4voUo4AciGf9v/g//XNWmtn5XTDFXdU5e8Vm6YIt2m5vuHf46PEHTz7+5NGTD4+PHwyG+xi2N7UHpMSEmIbHrZHsCreYKDdb0xkM2nZRZYN7unhfCT+U3dmlJ+bt1qXcliYwvICcGUkE0dEvBuDLem055zUkWvqCKfFEFnuqBWqa/Vmt7XEZQCBep9ftdHoArKozYiAmImaJ2m97lb1P7Mh4wi3gIpDSbAhcJAZu7NgFpfiU2IZXOXqXLpXfVJop+hQ+xSeFp9NPBVXiU/pMyZMqrhQ9JVyZbpq+kry8OVIgql4iS+WrEHEDTMqzo43s9ho+PmZoy7qUj3fB0MR44kgIY0zatEwGyo16d0OO3LOr/Hm8285cU/zvCh/6DIYBuKjhdSC8kZESWQofER2IKUMJH9EdH6HN/OM6JXxJ/qBP8Ql8CWDE5Zh3xH+5qPjsbFGGVLpFGuDtDOZiV/fzpehrb34Z8xqzCYViHWhyhqpwRjhgWorHK9G36k2spU1r6426THEWu1mrn528efnq1d/94ufd3mDv4Ojw8Gg4HH70j/4V09O9vQNOG/c77cVyNV/OFtNpr9WZL8azeV2zZCyw1q7Zu20Ohj1rp3osocaZDfFGizQSCMJc6Ml0nvWJZoCXtSU2GEuMbcXU1pYdO+G80LK1ucMmh8WyJWjem2o2eIdZV3NYN7bU942jx0z1tZsKvffda+BeA6EBzjM6zGAX++ozXUarwN54edLcaOy0O553tULmidK0S0AI9o0AKXlS+F2FTPG5EV9KKOi/Jr4UPbzvmn8k9C0HZLzMFYGACUFReENdHlQkaDV53xcD1tK3f7nmyg0wc9hGk2gN2ttsMZ9cXsym4/M3r7mh49/+j/8zBvjg+Ojhw8cP2Td+/MHR8YP+cL+x6vQb7b1+p9HkRd7aZDadjEcY3edvlgyWu31mpF1e0yUtM8XcbDWT5BhIsrBYzyoyWRl3+EeZskPU2FOuAMHCMt5uN+odTHLdJpudVocoGHU9caYQZXmej9RMQfFYJkY0RaUEsQTMu4Ai8lp8ifAGb4p/Ktp3fRSR0nMyvwn9p+i/K/h0ud9NCSf576hPDLAvQvjRSPaDNJK2dwqwuDg3yQCc3mCAzhC+sgiS8lRSF5AR0YHwFkgEpvAlsrf2Bv9twHmW8OEtpXgjPghyoDwEcTztKCco5/2u8CXJw/uu+UdC32Yg+rESwNQzxHZF+dPJSsRQtkAxI5XpVcvBx6kysRiPLjF1g167wdRT7yPpqwnzq/EPnjzktsjxm6e/efa7X69WnW5/ONxn8/jh4yeDwd7R8cOjB48ODo5kcTutere+7D2mxV5NJ+dnb9jqJYVWq2PTVL4DQYpKy6pYJvd8piVit5JMeIHNkHICDNimtaAgyKplrTEZyZuPRCxQNdJ7DbKW8fUAcSshsoASh5y8XLlvgQ+SWwFRm29FnenqlrT3ZDdrIK9HN1PmFLvHyGPe7vcd89/RAF9eXFFFcRo326dTaIl4GVUz6p3PsgMcq45gFr84m1WZTaJU4m+DjLgAAZcipvAlsvCm6FP4iAgQNAF4aHgBAi5GLMa9Bh9xDVjrbROfMSgh45BpCR/J7YqPiA5sRw9MiuB6fIn/t82LQawUqWBw4piRgBiAmloU1/Xj9sVjGZyxbdlOrU0fLR1Wnu0Dhat+r0M7W85ni9nUTl5xEKvWbtZHrz9noWnQ7Ax7WO7GajmujWfjq5Nff/Up6WNce/3B4eHx8aOHTJIZEbee/EmX95xwww7mlmXomQ5LsZc7ZT7L+jDvGTFtZRlasuLL79gsZ1unLjW9JTI7zVHPGpMZ+cHc8sSxN+00rrdsgaAwKl9m93lss682zGW6e/+9Bv6QNMBSMyNvHM1OS1ndrlrZagWGhWgAlMHeMO2bJ0F3qBtnDsMA7pD5W7AKMQJwJuEN4C2YEyWiB3Aj/6AM4PooQRbA9fQeuv2M6AFczyfIAtjm+X4wdqhoh6QS9lfnm6K2r29X1LpsZqQ8lPw64BNCT9gx/mxhZUvisPbMxFFnoZYcYlK4WfbMyDUWs1aTpqg7LmZcIM1xKIz0fLl3cMjS1OTq8uz85cnz333xG9qjrtcYDz85Pj7+8KNPPvjgg/2jI7aTG61Ovdnq9tvYfgywXgbm5JfNbhG3YZNfkymziEubs2K5MdWIurInCkAAzd1bbUNKUTLLeoMZkIn8Ol8xKoGSDJXym3kThj/Zo2zpzfkkuFenCXbXGrkr/2TC9wGmgd31uXuM3VT9jvkn6m1KRsbQo5H2kjC3GF2f/gJ790G3glXmhQUcNpj2zluBlax2refOJGJtA6VUgqCET3lT9Cl8Sp4UPsXnenyEBkAPQRLhDaCYr0ACxMzkmlhF+hSfIr4I+6qk7I9LFYCjwhuARw5vAEWmG/B6mXMDvfZ4BYtqZhzXoQEFQWDeBvB6vh3T8REaQOjWowQ+BbAEXTElbNaWl2enzHRpXZysoJlhJjUbXi4HXewi5lKfRmgu6/1Ofa/fqzfbZ2cXkO33+GggZ6U4I837CePlmC3k1lcnTz/71f9vvlp2O/3jhw+ffPgxK9Wf/PCP6g2O7LWarU6z3eHoFFaZ4cN8qkswpVR2oPXDoHqhqTZpY5tNWsPb4WgZXZOf+zWsZ7ElZytCEQmw8lkXxrJWfcquOEJx3eXP6plxSdE58c6/u/JZ52TnpL4VEaIi3lKaXfWzK/9binHnZOl8VZfwrvlK8t/RALOzSyeA8y+muAFGGA6CuPXVQNs+hQKSRFPppvA3KjYiOhDeUsQUfle9ldiGN/hvA05Twoc3OJTIUviImANZfci9mTGOfJXwsU+3hbeOMG3Lt+hLApa9Kfpd8WW+3xF/6L8E+EVPjiwGBUz+iqH2xYWNJu9Gq36wv89CsXZe2dsxpTDn5ZMNjIO1XGxzUeJxXxY0fBJhyIzWZp98zYHXmYjRbPBX764uMbLNbpP93/Hs/OTp2cmzzzDs/+GvB7wz1B9yvPoYe7x/eMxYm5a8t/fEUtPstmG3nOjVplWDrw7aijdGlRVxmjoElPVqlglP62clWnlr1dt4NEjX1Nllj50pvI6xRAqPqDcFnMA7XVQr8b733mvg26+BOhfl8Q6CbQHr0hvsLkNzWr0bZofx8lYgf5UD+rfOZLTKAN6a1Z1EDDECcLbhDeDtkovoAdySf4k+Ur8rfDAsAe+afym5b8CbXIOW4TFTugGwQgDSV41zAjciQQba16WF0SEsM5davzWXWRyuxwDDlnCtpa3acCtOHbOki3nEvvHSgUVmZsp2kM82WarGOjueWIPlmDfHsJZc59Fu1ZctKGmts/HJ2dWq9qZW/4LbtthD6g66vNbU6Xz4yZ8x6GbVerh/wIay3jJudthbnvHGoVJR5rC0WHjsu96cqrG1nNlUagMT4YluC9EtHyTD+KGu16KkKa8rvb7jNWN2jM99HY5s3gikZsy78tmVXlsCuzirIrtESNDeFR8VyS7urtJN6dm1GakEsKucqfqwS16vo03Jn4qTos/mQVvRUvlVe6OtNRqa53Zoa5lz/gxzcTlOv2k+1Smn5EzxKaZ1G/it+UdEB1LeG2UoRY8KVsKX+BTINtpLAZ/1AyU+0TuU8MHf8XgD8KAUfUTMyPxQbI6N+Qldb47TbwG/lsgIwlskL8B+/raAKIHOOSpTKd0CcXVCnCQq0KzB0EZo2AEsigPFJ9GKp53FJbiafEEM4HD0D+71J/H0Bv1OblG3KExKTeH6OpIbcL+TE8UwPeUpIy3GndWV1o0ZGIgA7TJnZum60e7qtBf3TrO5pMXt+eXlqDmq1z/7zVdY4r3hwf7hwdHRg2O97PSYDWYujGaNmo//AsAWK85cnPch2JXCi/pw5EqvHusbDC02rsgzMKe4AXw2T6seXV5ADwZif/oOMdHBbzubRm+j6Wmq6X3JuyrCPe7bqwGvPMgXwLdX1nvJ3r0GojcIwNMseW8UJEWfwt/IsERQwccGTMJnNirvpswb+ADW+SrRl1IKb84vEL9PQNjFIlCEyWx4r884ZNuU2xjNgJ1R9qPFZd9j5XDy0qbFtiVr5QrlSvNbGz3pN5sKC7L3n2R0jZwCNsacnWYpWFNUVq91SYhNn1kq5qsPvO/EW8Odpswwxpi1a2IN+QrTbHTx6vLN888/b3BWq7e3f8ClXB99/L0mrxMP9/aG+8P9PSCOXRJ5al8tZEaOccXJKs/G8Bn0dFwTp81oTl7Laerds+NjHgQZGJ44jLFyseW4hGsLJ0SKftcZaiXza5DFQxbXkN0HlTRAiZcw7qXoAQgtAZXE1yA9+jUEtw66mx7uruSJpawt+av1SZewRSlESp4UvpLJWyBT/FP4SCIIrgGCuBLYjuhkKXwlk2uQKT6V+G2kY6Lmk1DQXJPodz0o1Q9EvoLAAT/VTGgJH/QlIIuV9zZ4HZPiwK5u3pDyX9lKszrs9frktpiGdmbzRikg3ju2DWBPV+icG986UtrGAtPMdR+cUMbc1hfzLIgpra0oMznGTe2qM27mWDRW+j7i5fhscnH+uvX5r37GUa82p6jZNsYdP+DBSZDDD/6YvShedGJ9jMt4Fq3VfKaN6fnsknEEhtneodLMuNttYe45uO3ZIa2wvsDFPBbhjfX3QgBxC741uK2xddhdQPcG+C60uOZB0VNZvQIEsA6ugJJVpYL2bVB3w/+aKl0p1K70lUxA7sonRZ/C75puik8KX+QfNAEUM1hEFmOV4CALwAnCG0ApYomsMhRkRHcgak8JX4xeGVSJLMZyOMhKQSl8iey9edcWaDNJVmw3EZmvgA3rJcDeKnKanMSMhee3mIrDef+vuBhBs4MZTFnByMgCSC5BMx1MGiVjASvRZKIZLau6uaRr/LKu14Rwkoy7pu29JiBuliYP2h3WAWvmjdlMdHb1psWls1xrx9tOnSY0ttY7b7Ygu1pcjk7PX58/bzxts3HM1VrNw8c/593EPdarDx/wf7h/2OsNuq12l+iabzfYAp4uZktNhJm+N2abLwJLS7YWnTKofJvR5S89iVXCuDcvgMrAu0AWasRt2KXylZrB34bn7xnNdt+RKtzfs4zfZ6dSA1EfAgiywAQQQZVAkAXgZOENoDL6jciIHsA1/IMmgOAfmAAiaAuo7g/DFmzRB8KNQmYaArsFpPhvEWaIGxmmIpbx0eoDgCLgSs2AdIIgiyhg3BWTAePeANZL0EHnC9C8BOyYkkWJF5mxjM5MMriltwiSSYBmFeAXjfXBDUwpi9qYBKZxjRp3gACyaCxC06LYHA/7+DHNTGLn3B3NvFgR6lyvBTvS5HUmrSlzKGs8ZmX52cUZmeGsFse4BhzfOjjCBnf7g+PHj5kts3bNWetOQ1de84oxy8yNlS6LtyTioQ8gx+jBMlF4pPZ688FHgVTgqlltmEtkb+1NDosSHMlkZUgKX0n8e4wMPVCLHA4gketqfSaI3wJ9N/wjX7eUYFf6FNtd+aToU/hd003xSeGdf4QGUEw3kAEUQ7fhIAvgNqmUaLbZBibYAtDFVuJL3CJKvjqp8EAGEKwCSAWl8GFpgsP7AVLpXt87R6wAtqX1nPqzSJbBbvzwmCM6v/F0bo5xJAbYG3xmbjGquoySwshORSt23iUI36jpDmeSdy4GGK0lLLGMg8sH5Sw3wGCgM2MK6/rVNN7HbWK03EFfm1xCJJPMzTsYTNaOtWvbvDh9yaSNm6uZ9SqJ+rLZYFN52WzpigC+jcTc+OLkqxdcV93uNrido9nh28a84HR4/IAzXHzqeO/wiCXrq6m2b7kxQHxYarePIfJ0rwTYdARtIjJfKLEUqtHFLi7FJ8UjYU9T5OtGVaKIAirhf1+9KT27HgjdBnZSxd3pc7f6kxLyruRJ8Unpc1d5UvxTfHbFp/in8ME/CByI/JbwQZ8CUvQpfIpPCp/iU8KXvHBzzFvnKyXPtxwf+U3JGQQ5gJ6yJulAeCs5eKw8rqxvwE4fXgfsSLNCMDOZDS7wdeO8ts0EYUN5YrtCDgOyrVIZX4m7ZB7oBKtGD3rBHGfC6SYqPKt6S2xdCEIVZIvQHSbJ8OAgmM1JjY1mw0d7zIz51BJu0WBGzKeMSUNfd5hzLJoPECIW81h2l2fsHU/11vFkfPnm5QteFOb4NOvSbB0Ph3sP/+gvWLJmqry3t8fdPdhdZBDXhKFFyErnuasKspFIVcA97lurAUozqqID31pR7wV7DxqI1h1AKdEUvkQW3hR9Ch8Rbwmk+JTw4Q2gxD+FL5H9vnqj7QcQCnEgnkGQUgUE19BEEDPgbI/WGWlWaBbErmx0y5+/52c+DCMkWn32Ka9F0+Kw7K6fn1aAL0MD9OcyyW5c2YQVkbZm9T4SIOvJHpE3lTDehE1qA/DbbuoTUVKXiGu3WsjvTADYY+/Wa92mLDrTb/By/E5fTc9qXN/18tOfcZ8I69VMi48ePOSDTkePHw+GByvdw8Udme0Vx6rrjbn2jHV+url86Pyx+VoesPfImMpPZmO9C0Z66IN9We1ta2wynLBIT9Y8oyoDxhM8YWXieTYAGarwWI6a+/JsOlgUZ+TF+7Gb9hEpI1dBSM+2YqF7T2AZ3qwsIDnjf5R3ABw6Nyblh4SqcrzRVYVeJ1MKlWruwq0Fvh039hIqCVMDrMLWQ5717LeaT/ZxkMo0qpF3o4dq3sKua1SRJiU9tbRI9tZwik2KfwqfEmBX+lT5pvjkm3FZ1xFkfkhHzchzGPnM1eaKDfUmjojk0Wl6lsMC/Ub1CnzowdMNeWJHq4QP+jKH6Hk9nbXYIlwT5xAbes7KEdYJB+8KILt5cCskBC6F3Ph93xI9y6QlzPXe1FsqqYysWnnON/nSTzvCM0Jv7t5m/n3iXD+G5ihy1s/QtwtjT0XhGo7Mb2vL6r4cI6y5IDBfzICz0Lf7wZa4vCwu+85wtrB9LTuyavQ+aRZphbg5h1QBe7h309AAOCUGzAGe4SCeLyaref3sanR++ubZ0y85P83suNXuf/+HP+augW5vb7C3z9cV+8PBsNtvDHpnb67gyevEsrIyeEsGDeS239YlYjL4XJXJGvhs6dfT7/f2bfncCgOTnR3iwutG2ssxz1U2msj32wNNQnnBK1HH+1thfnWoqinrABZAHUSQjDFveGUtCohg757RALArx3XlLO+f9xr4Q9aAt4ttDaTw25R3i0mlm8KnUt+VPsXnDw3/1nojYqlf3cZUKvNuDDCsw+LeOIBxc+I224QWAmvh5qSUjUqhN5D5+KKUYT/l6wrlGQC3VXPomtntdDG3terxdNRc1BuvXzzFzLJ/3OkPOKG9t8c3Fgfc0PXx936CLeeyLraNG802No2ZIOMj3mnmaDU8WPTWpnK73e8NoZmcXWyIZx4yJXmqBneN5XpKK1q7CCbbf88YZfYXH2aUt7MydG5cxdZVqPEBgRslkA+NMusbcXcFXIEVsdbibAQm6Teo3t6TqiepdHelf3vJyjETCiqTvbX/3fJP6TMl7q70KT4p/B3yD1YBeKLhDSAlzN3iI7kA3k6eiB7A3cr5HedWvWIkC5bPegO4TU4h9r4lAGJVwOvO36dK/ky+hmST0ar0/buBEZKx0Swt7wi0wRvha0Az45xkjTXIM+AzttTSZWqJCUGdG0yKinOejiwGLa4uEI+0+kTkVSXsmZaIa+PpmEXi+fh8cl6/qDVfNFlz5RBY41e//Fu2igf7e1zLdcRJrv1jLgPptbrt7oArvWaLGjaYi0RWfARmzpcRuRdES7WkqERZ38zFm07mVWpptAsfh4hBjHJUdSUb+tuoPqZPfUpK9Bt2VxhzoZNincgDd/gNPuU4iTJN0pfj37E/Ic56SfCO07uZXUqim2PejuLd8t+1HHelT+Uxxeeu8JFuMHQg+rESPuhLQN5pl9DJ+hZsyxFyfxDk8mQBJXxOnvy9JX2QJRltBqToU/hSj7XJrMKX95cVQZWoKlMjwhSfVGsJ+beBynSdzLt34AA46GSpa+tAsnmQ7od2NqxUCooV8p1nwGFFNqyFM4/JVyGX2F1811hfW4XOLJYkywTNOd70W5zpOm3ktRg1kNxFDZwJaDvFWieurfY7Xc1r7TUnFIq9X870KfKn519giVmcZoKL6R0OeMdpr93qf/DhJx0u6do/GgwP9zpd3qXCxPLdigU3e7l+acrzOrNt2+fWLNm3N0wVYSxXXBwScppUmYWNu0ZzWTOqWWEUk02Gs22ojGeuv4zPcqU9/sg+sFedSLQEuD5LSLypAVDDl8S3IiTp72hveCvBe8S9BnbWQLSFAJxFeAPYmfVbRYjkAng7eSJ6AElxqgb6SWIFFDr3DboUfoPoG/eUutOSPKjL+88ASgQlr8jMYgWQE/gmsk745guf0ed7Chk+aYCLXXbOVL9hIPOOXkh4h/pLOczsgKjkgsy9PEkIk2Vb1foCUhWJaJPyKDBz0KAI9wQQER0z7Ok9YJy+2GDvO2mfFvN5dQ4la8ncYEmo9nExVbXVRw8GbPHOZuPZxWh0+uIlXz/ke0u19u8Ojtudwd6QF5weMjk+2OdrTvutbqfRGUoMS4KnVoz1nQp42taxTnEpzHIqIA5ByFNwIX+Wnzwo3nKCo232SsHwFL0+EBUazuN5BOWJoKy65Mwqf/OIpUDjUMLh1RLCLi7ytUukHWhT0rzrdNMipiRKx9gt5N3y31Vvu9Kn8pric1f4YrrBE2DdgKjb+MwFUIyVBUWD2wwr8imGpPAZjbdT7zBMgNwiKDzECKDIOYfXPW6QBZDTrH9vkGdNmEEpVin8FoMbENFd30CXBye6paS9SOW3KH/AAeSprX8JclGLAMF2CMoui8wtpTPJfWGPEVwmOWmA10klIIYKJVtb9K67Za9SkmyjqkpuMJv6w7jxwbPKBFMFU2QQuriGw2gyzVmtayr0/Z4uDEEAHHzUEO1vOh3j5xPH7VaNzyXy6aYGnzvk7ePZxWR8cfHq6Zd8nqLTw/ru7x/ynvFHf/KPWIVud7kcc8A3nnqtTqPbXtWbV+OpGV2UpOs4XftIoJQ2nYduobPF5+LbUlK45188lJ3imUAVgWUl2HvWwlsJbMtTSbZGbgu6DquAduZfwUOovBDLwbvy35W+nN7N/urT5jfHezcUKb2lUttVPyn6XdO9K3lSfAIfAgfgQeENIKK8UyCSC+Dt5InoAbxTsb8zzJMz/qwflrqsU82AdP8mQp+t5UBWUkyvhOePqV1+OlZh3lk7VTYhfgsDnHEpmtuydfUk7Em2spwVkAEGE70Nkg8SIvT2ABlGX7k6lKDDFRza2c1cPkQwMpkupsM8dY00fGKaWeONJs3PfZ7H+8irmV5pmqPKldlh3mZuYUTnk/Ozy9PPifu3P/sb9oz1lePjY+4A4V2nwWAPg3x4fMRryrzgpIlqtiEvSzwtL+E2JEcmj5tq868fWuL2ya6AFeezXcG87kRUlWvcVhaqd+XwVNSvoWeleO/uNfB7pwFvGtvZSuG3Ke8Wk0o3hU+lvit9is/b46OLvyWLopG6TZRsA26bdFdGGxxuqTfISt1pZlboZuGnUPpbdcuGp/uFXF7NlMwKJA1wSgLbKOW1GR3rFTN/w9dOAhMExl+sxFYhAF7ugOSwcAYbPVFEVtgLFFnu5oVDSUWVRFYhtKzJlkhItz4F0iJBAZ2DjXwJeqXPNFk2tWTMbZZYtQZ7svBsalHaC7Dly+LsCturPmaJ9f6PRdT3yTXMMa/zbU1aq9nFxcuzi5efs3vc4sJq7qVutR88fKy7QI6wyke86dTlHSftCtfmzYOQDG4sIsvay8jPdApMX69gbLHegmWgQChmlhtE/MAXK+TLxQyrz7BBO6/4bKmZUKkipsymN1OOiskkz1Mu/po+XYegAyDDRaqAuYws4CKwjljEsmyeVwZHp8gikssZZAEop1XOiy3IAkjRV/EQLqmfRIRIKBFeRqfod5UzxSeFT+UrhS/Lnft3pc/jlX9TfFJ6SNGn8OX0cn+K/413v0dCDug++2tdTlbdfCJqsA2MA35wJJBRrEn6+F6v9V/W6BU7RR8TtFvLWd3uQsJvCZDKL119MadBFoBr2K2Y6a06v1EQpfzChyBz2GD6acqdkljyYSE/v2ymT6bTXDNpgEt8w9s0y+teEsMBa55thpYXfJwxiVLFmTG6kcDkOyUAhkNRzAFA79z0Y9K713w31NqgvD2Qfc9YilGCJE4TQk8mhl6FMpNuJ7JtHuwXc0KqrzpoNIf9Ix4eCoaRBK8jySLmz9Vep0/GuccDtnzsuDGb1RZjrtX44jcvMdS4Tq+PHX748OHxwwd8WrH++Cdcr9lqdfz0NNdfz+bj2XLVabctOcRBj5IOg43r1DvYMDdjyMLASpP2RX060leePBc2CuLKMo2+vIMAj0D+vI2ulCPj5rFuE2UnmmAbwPXRgyyAu6W/ntvXC921Du9KnzWfryfkfexbaYDq53QBXB/tlmTXM3kPod8VOb++KjynN+YXAutL6QQT7TGanQF0w05PzwyACaCvpus2JB10nd1JE14rlDBnqkUg5nJnA+zfEBQDc5pN21QYEyRboPnZklkvdherDIm8udqIJRDzb78ucYQ6FXI7GU8IsiiZaXEDI0IPEoe8SXj0G58YzIym3mLGCMwVGzZlxPhKO4S70ZICufCyxilifhGGULKDfkXU9KGNBIQl+WHoobFwm9sw68Ti6JUpCBOp7yPW9to97tGc8LHi08urs5evvvwNLw0zh10c/6TT7x0eHj548ODo6Hif+zL7w2anzZ6xitB0OJvOeWuZ6BKjsW9atfLWFyZ0JQt3W0tyjSoyzTA/lGDkrmm69lBTqchywNAbD4LcX1T+BsWmJ+g30Tf4gnkA10cIsgBS9C59kAWQon/3+LX+b5PW7vrcjX9Kht3Trea0K58U/V3hq6XcHRvybAMpZrenTHF4CzwVvjJWCLMdGkEBbNN8ezGJPd1tPXjuvG8nO5HZADyP9H8e14FtPiVVED2nF9C1LU7H8CQUh02ZcZbInHp1S956a1mKaucstsO0gWkL2prjsT7CJRUNti1X09kUi8tnFfhCEVc4Nu0yRa6EbgDbNioWBHEymXwyWEjDmOlBik7jTxcAfNA64JROvC3kNZhidCyXVm1leEFnYxPFlQ22FWoBSC0Mi74CtEWro1pmd8Gy+A7M5BiUOCyndsiLFYE6n47g1mrFEbf5hO8/dAcdviiBdzqbTSaT+WT+ZvrrRr31pt1+1ucCLjm+5sTpreOjh8yVB4MB33Tq9+DTdrtbm82x6TKtYmtG2b55zBxauvMxhSRBY4Wz1qZYhedAVg/EZ8MFAYDraiN4yxP0WyFJhFRlYgSQJLWAIAvgGnqV1Y78r+F2F0Eqltu73fW5G/+UJLunW81pVz4p+rvCV0tZaAhlgryBlPEFf8hmwA363yQucNkEg2wTLUHLGPOn6BP2d93wt7hZX1JQSIqzR6RtbXG4HpHxv54oQhPZjfBtoFoeOoEgLeZofb4nT8npnCaLFV0fNGYug1UARXpgjwjgcDxzeibGmg2bKWR6mqEhSxrgPGb51xc/iYk9gKNbX25hvJpOsBRcJTWf1aaTyZT0lnrDtV9npqdPDxGFWytsbZeJpM/N18zNEmrWZlZMeMAI9uzxdAC8A0WaIL4eaPgeiZlYp8yZSCuwNa+2AByP/TXra8JIJKyvrKoujtaE2I9AITlUqmrk2Y6TZZvcMKV0wKMwXkhiLqv1eu2MN/b6vPHU67e0pDxlBfn87PXp09cqI05ZN7mHizeP9w+P9o8O9/cOuRqTXd52uz3sP1LeXZ/LGsMdbgKRjBJXC9JacRB7Ls3memuGXhsNIM8sOfXcl58sYoBy9VrGydm6IMrUDDi2UYbJOGyFIljIEIEpYid41/QhxtcBaA6J6NXauz7LCVbfYXRKP9uF+63NZIgagImaKncFBmUA6dxV1xPG2ekolSEpeVJ4MQnxAqhk/Z1DenYiUwLydlrZACFwfADXZznIAhiPJ3Rx1kPrCI5PdznJMxzwMSFNWpdLJl/MRtls1KeFkga4Uj5Jgx3huC3dPFPhtjr6yWR8enGOCersceB30F71W+MxZ4iurq4ux1e1Bl/jZY+z5T0v80IJYVYwhIYVomTOe/2tfFfKAzJhR7bi54iWXsvN6rqbRuVJNlMr9cBwzAiM9bKpYYRZNtlYnGIzXc5SFivQwbPetkMZ2hwWUs4MWK8/xLYxUpkvpihfaHON5piCGjYaw7ZaGmMUldFydfHi1WW98epLjV2aXAPS7w/6e8yMjx5/n685DfrD3lCT47ZmyX3uyCRVlhXmyxrXcmW3f9gll+26Tn1Hcp5oJSaCAiDWjQre5hzRK4Ggh7PDAVTSBzLIAoigIpCp1cYQO/EvMnkLOPJVjptfzV3GJ+r5FtlNCKu0NxHdHJ6U/+aoGxQpPrviN5gWPO+ajzXvQnqbYKQeAG1rk6TCF8QBbBMlg/KOohTlzugLfINnAIXA9wTunnS1/p1PPAOg0ynlhP4EDATbgCh9tbEUZ4M+m644Y+ZI8MHY2dKwdlv14ktteXV1jtGdziZYRSwjz+l0yuwpaYC3UswQsAbydQjvCkeT8euTN5iI/XqtP+SF2D0OZI3GV8+fP796Nj15feIxMQtkktg858sFYmqcYA4Mzg2wnyl0XfAMAMIiDD1ens7h9k/NgN3E2jqtEmY+CTPLkzxmjw1tY08zwOzrWpZt8VdiY4CbHJq25ocYlhGfvLd0fxZi2XOhWJbNs/OLRrPeZokAK2qzQBHwhvHiXMvV0Og8YgPzqeull/UHjw8x09PZnKWF2Wh0ecVHIWSMP/3sCwwwE2Juqx7uHQwPDgcHh93eEGNcazF1ZrjD6S14aYcfRryGHMohOdehpXyz6m6jYVgF/9sATh+cA7g+bpAFkKJ3aYIsgBT9XeHTethNP7vLczf80/JXS4RiKwNSfHbFVzIHeVd8UvxT+Ei3BCTUsGZTol8HvBsoktuVfUQMYFcO3yp6zwXPcIgH7K9oqluwkVMAWkLMD1IF4FFS+YKbt4IAoMQAe4pmX7VAybSLU7mvXr1gysutEpPJ1XQ25tJEQqDc2QAzkyVZjCVpe/LsZV5eXjY6ShjbcPzo4d7BPulgb0/Pzp6efeHTX3IseqwDNmY+58sG657DRM5nwMqv8VYL9ySEuiPHhwrhRNK8AeQzW/NraVzTYE3u2WNlmg6VQvw1GwTP5r+MIIRmHNFABaxI1/XtY5lqt0TL1dgBPZko85FDm+v0uMiDF4RYMGaNXtF0Tg2nT0PYWgQfdcDxNhTvLdU79fM3L1hC5jWmYae1bDftUBUUy1lzDouTN5evX71gyltrtbr9QbMz+OCjj7r9/SFL1ge85qQ3j9sdXXXNzZtoVxnLn0rlpm5jJ2LJvaNz/kQK4HoGQRbA3dJfz+0+9F4DrgGqXwm4pWYi4i3p35rsayb0NaO/tdh3HtEzwjMcSQg2K8tvZR+4jVeMKufRt+lHl2MsLtZ3PNY092p8icVl6ov1tSVopn+8E9PoYg9affp/dglTCWTJugQhB8YCI899UJM51mTG54Gb7VZ3OOAlmOHDh53jw8be8KM/+Qnnip+OLqdffnHePlwuu4P9Ryevn2O8sH//y7/6y3/zb/7N6Vev/9Vf/a9Xy9bp2fhf/MV/Vm90x1fzF69ev/7tvx/jri45pdRs1DstnfCViVtx5YSOeTGxYySxWs515Isz2asjJs+5eIwLZB61zi1K5VnZYN/WZt6QNRZDKJh8sx1NzjFh/GnxlrmnVg5a9pYUVlZ3RELfyi7GyLShH1lbzKZ9xUgmOAvyGzVIyv0mksweiYDRrw6Mt5DavHYHNJlpHsLByBQvA5a1RleHqrj1gzXpNb5W25uNit7VdFWbvgHz6unPeJKBTqfDMgTHqg8ODli4nn3yE566JrPT49aOmV5zYpmaXWLuvmbCzfEulIHU0iW16s34XOMCc1kl09JFnR3olUpcDi+jCtKCoL6SPBo+ymlYpmvIgVCf8CoO6hwPrcTUalet7CIUeRgJ+Ca6yyDVolzpV0nbikJjNXavp6AEfaXBo+cjiaCv2ckDeU3tAYh8F2cRKyLEss1WWFbuJTyH40sY96b4q6ZWuRS9n7qriOEFUhFQjUrlK2tB25FS/PM9tnKMrIZkNTwqvOreLs5PWlTEyPmXg9YvYWyEJPXpPcYGrXmMf5RlADpyUeUig9RlwjW8N0d7qyK3/qEyoLb5tbSchmF7Dm782t7XBsY9BXkqQkFVZ2Ob1to1aGcYbOkwtmnBaANvF9dafx2u1BAyrxecnuo06tOV9FOsFdYDyyLgvNeXrszLnG/R0uUKiq4uwt7S4WFHho0EYyKRIXDXrTeymeFyDkYmjDmVHbxVB8iS6HLOFJQ1ytFoBDA9faEZlTkJoLVo+6qPvtcuBxPbaVz4GamdZ8Cw8NxYFpV10sAkY4kP9vf/+Ec/Hhzsn52cfvX82VdfPp1cjZnpdjudH3zyvX/+z/7s5M2rX//DL7ki6k//9E9/+Yt/ePz4MfanP5jjbXeGnE9itfXNj4/JyfnZ2enpm8vz08vL8/EV10eOMA5c4UCGNQEnR622bAS91ZSbmZdoFKlkmzXflwKgo5KgapFBp1VfLcky/6am6C1eDmsTQ69O4V21210i6Gu/ur9ZE1z7crNbDdPcWz1QN8WwHbWED28AHiW8RWCbGxgZSOWQa6tnp6enFxcXz549I9evf/5bliX2WKxmanz06Oj44eHxo8PB4Xgy507rXpN3sfjm9IqFbj6RTLU5+vAR+jM7O5VqVGNkUimKZl0b+VIn13GqGvEnm2s0WGSI7aidvS4FIWEw19qBmUzvMErjPbPQVE3twqtFWeUSc7Uui6GCpeykQ1eCUs5cptoMn2P5JcAkXAOFwHvwG9NAVmDfWPrrhL89kqxl+g5C3vTuVPCS3c14l1q0vN7MvXPN+wTwGCb1PEbANIr4CEnHRVcuaUUpEnopdRHMANS9qeMCL5Oh/kWAdzmYHDu/hP3pqn9d2rHX+YLZLXdrjMcj5rnMF/UxeDM9QwyMuSxRJSzn2TDmAgPY3QDL+LNPqWS8C6ZD7nW6JxdnZ29O2GXGqD3/8ulvfvubkxevZiO+8nf1fHT5y1/87M///D/ps0DNBY2HhwhNX49hGF3NR+MxhpDeGnbsHz/+Z3+uqyhwk9HV5cXl6GJ0cc7WNfacs8LM6Mejy8n0CrazyWw+nxx2PAsojj9mcV1uhyR7zPplY2VDWDDXry+7H+XvabHv6m8AM8MmaS8zhFfBYTfYx9YApzG1ElN4ySXwheoTpjcAZxFeAV4S24CTbuO9phHL9G9VymqK0yMzeCyorYGwGL6q73NK7vKK9Yca4yR2itk7PmJK/OFH32t1OcF1MBzsd7i3etBZDfWq+KurU8WyW8LtRSpVHpLroTeMomvKRzka6WgqTShEZnG1UJGNO2WYGYyWB/y6z2TtVIXqmnPARAsOhGhKBAuGSjYXtF10gpQKrC2q6IPHGm/HnZwmkAEE/dcEXILbM8ll3iHG7UmhZHSzE/2uxLvLn0whWAUg+Qu1NxlzI6A6v0k+1eTJdKllG6nlniT/nOAd/X5T6dJwqnNkeKSKluWUKTmTKzTV3DUXKofEilfWP9A/Q0MPoPW2IEYAHFaWHtwsqIqYvhACzaKgNCvIvhy9FhMM/quvl9WQgwxYnb66fXVoGeeJSLWLi7GZXDHHxdxykBaYNVq6Wc5VwRByzUyaTciMlXeJxg6G676LVDKZHdCqY5bSrX/UU5oueDJlYY6LZT09r/39L34Jj0cfPHn55vWLFy9evTl99vTp62fn7Pmen7y6OHnFsuiDh0esZP7yZz9nk/Lo6KhWH119/oLvDO01eyyNNpqdl+cjpGa9VMd7Hzx5uGSiqkntn86nkxEG+fzy/Gx0AXB2fn6OIZ+enDL6QCmYbFSEABhgpry8QauJLgMF7ICZkGZ7oCHM1UTFokyb9NK7hkU8VXisRZiKGqsFG6hUhtQKGUqtVBjJGWv9inNUmgQeNlKjcQvAaKvxnm5QBkDBq+pgvvwGSuvaCJ2uLL8a8bE1cTG5eH324kteWPrs1z/n1WFM8HBfQyKurd7fO2Aa3RwcwMG27RkV2pb1+JKbuUCiNcsN+dI1mJ02Yxg+MsESOSMZ8qDLX8xmSzMsVysX0Xg8SzQGW+HJfDL0pihpAJxbVi31GIEaT25gGEsxR45lLlezwk15Tq9I7ucJHPpXwB25SPiW/FySWxIb2W4p5PrZJYVdaHeVP0Uf+C1gt/x6hdnOQbAtB+3IPsUnhS8nl/tT9LvjrR3lbOM3xWdn/QTHTSBvg5tY81UmXYmEfNf66V3zZqqVRai+gkS9qwAQzIRXv9r1oS8imiO1Z0ZXXLM7qiZs64khVxz58q11m3ahkXjBAIPL6ypmpJnJXIzMvjAhlInB6NoenKbCkBMXi05fyHqf8akvRhPSwuF1jOcFTGQqYICdZ8DWo5IhsxnGlDV1Dvw8evDw2Yvnf/P//fd8u37MeibHrkeXz5+/ai26jx4+7PaaTNZfP39+cfLw7z/6EPN89OAR8tHR804Uf61Omwlao9maLLRGz96kRiALUmPWxcGt2vDgoD08Hj5k8Vgqxt6wzc344+rkxXQ8YsWVdVdWrbHNTJ3RERi9jzOnnNj05jiTWyZOJiE8OO3w8kMqeLX0OVexyXgrc/TdMlgqw2ZxzzJ0mARczWjWizkAjxDeAMADl9hF3G28R/QokEVcNOnEIN1l3rEGNFqv53hem3xzkwe1a7UcTa6Wy/MVm8cMd/SaE47d/Ic/+kfsIjN84VOLDII6baa+mD1d6K3vPqhFoRreYJvyIKFu69iSo9Iy86Ya8uUn1Kko9mdL1F5pLJeN+VUhUzRP50ljULCaiX5kaLOmawvRnk3NkmGr9qN1aaP3WIqkGDgb0AgouNBSAfeWINXiLWPeLlqKfyoLiS3j2yX23qmoKp5mACkRUnpQ0Ve5lH6qaK/F+cZHBUkmeUVINSpFvyu+mvu7xiYLyEowQreBkmBbfVspvOxV53yt81VlI1FN0NfOcUiFKHI2PRQCe8zd/ta9attS516pJIuZ+kO6Q3d0OF5zZrMpVU7TXJZZc4d3ZWjbkmOmIRPe1hStSXfoRhdDBZKo2DESpafFiyOVXDSJhRekYwLAm5wBE8epy09bgtaOHZ0tG9TW62EzuUQRi3h6fn7x+mTCfRPzeXu5eswn+vY+GA77B4d7XCPx+Re/4+zuX/+7f3f65jW3TLBJeXE1ZyGZ7LGyzkTqajbnG7veC6sFsqWr+averHp+ckEe2P/G0rSaXd5ubTX7jf7y0aMjJERazIpW5C8uWbXmsPeXn3/GcsEVM+bLy8nYtse5HWShF2W1SmG5gi2fNGoxdtHeo0aamsrpXBDZovBkYIyw4lFUYjEYC+KxtL1g0aUrc+ENQOi8woF0ngFErBIer3NwAn+iHJDhIuiw30WT4DGZPME32UBY1fb29lidV52T4RzzntrZlUKfvnxO1eRjipzbYl7MvvHewUG3w0T5gEULvrLIa1QIwKk+rb7M5py01sE+jnLBB1ArO1RqxKHyw1BNwFoDT2WVs3Q8q5wrwmnWi0uZGbYIyO+qMB8tzau4suXr1U7lzEXp+s8Bx9/6mRfMZgTX4SbuTn0mcxXHlDzV+CoOjqs2YOuKuBlz1/ym6Iv4gA1Qu9t2vni4jd8Z48cAt6IVKtJGWMi2gbUqXMJc792Vz13RI2mlYCn+lcQgk/T5ADQIAqhkVS1NJentkMXkgG0t06Wl01bna2ywlXRLrMfRpdMZyYJ4iQ/b2WuZTMFYR2ZGi6nCAJ2dncmI2hkaNnfp3Bzf0tofrm4Lfrqfnz4Tyh4fmbWLNTisS/equRz9DFaRLWUztDyLogbSc+le4J1nwMS01UCdmlUCWhjUfin2/8H+IcvRmL16fQ/pEYslzX7v6OLijAlUq9Z5dHR43m69ePZVr9PBjH766W/GM07t8nGCfY5SM+tFaxwmo+/OpLcOlyuT2/UGt0L57IhufsyGt96Dov9tPD87hRWTtm6Hy0aGg8NHR/Mplvun//ifaSyDNT7TiaSLcyzxBbK9+PxXxGVVAQklJCeqUcN80W33bAWaAkWhKjbWWbU2nfXyrrfbPGU8XPUBeLTwBgAemCf0JcCjbOPJfgQVAVMIPNYjLw+dT88JwjFcQlHtJmsA+uLT6PJMKVJfNB7UiEPnEDj1zUHpxXhyenr55tnzz1mSaLM4wak3vubEzjGfkTh68ODw4BhN93kbud06G3FfCMe5OKFF+eg9Zhs4kTGqljD854AeasxahhYdyg412zDFfk0nWTtSd+IdtMw2AmuES+FkDhsceLcrpKZ0jHIDyKN83V9U9HVZXBt/Z/47i7NbhJ3luTZ3BAZDB8JbipfCl8hu9ibKK8mfGlTlUvRqRO/SpdJ9l2mKdzJd02eEBpCSh3loKqgav/Fm7DputGgdKynIsNkfquxs1rtUv6Z+AFMCOaeM3HCyYarTUjhNz660p8svdpcf4vqwj7RwzHTpKpsyrLGvZx0p57SxPtgFm20zd4OAuSGWF8vFlmkxX2KU22PHuxfYgZ0NMMeIixyZ+3ifx940a5iDdre5rLF+qcXhqwmfo+cscZu3VVe1i/NTBh2PHhyx1/rw4ePPv3rxs7/9u3m9+6M//jMmVtIb83RmtR2bwmsEowUB5qyL6aJmB7kxitKlAjSjx5pgd1s9KQVDiknlxD7TVn0JotHCRNeb/eHBYP/4kSLOdTwYhpPJv0TXjHdYsD45OTk/P+WVJ8ZClxfn9RX3LE8pP+hZZpBGVXpFfd4Me/u1iBUxb8SXCMIbQEkCr4uOhAa1OCwtmeNVJH7xioMdLNabyItpVyM41SBb05NVpK5qjrqYsA6vl7r1Rhc1l3P+08Ws8eXvTjDXjHTa2F1GTPv7g/0DCu7D7/1U4x/YtbqY8xYrDNzmpRPmMxRJXeHJSomPYxjdsEJdygJe05QPRCDMejQEJsg9SGoaUO4sE1k2tSiUnWD0Cp3pnKx59AC2E73HfIMaKNbbb0SMlADv2J7untfEgGB3RrvFSOkHGwWjCA3Am9tuaVxHvTa9ovKznNYXqNPKBSB11t4UTtcXexOM5Xm9VW9hMPxXJ6gejyVmu3nq4vQrOkNZYMyBnS7yLPR6HeNGJJKm96DbMktENwiHpU7S0LsyF8bq8MR407fZdVfZtBh+0NA/OkMYuYs+GW9oKQD7/G1OWvnrpBEB6bCgOFvWBM3oQKuN/W6PLVcm5rx0hMR8Ta/LKy5oa7nwazC7LTRSn8+mbC2yrj4Y9F6+fPnbz561e4fQE4Y1HV3N+GwEGkVoulgO7GB/mbi1W3Wdr/ZVVt1KxdhEy8a62Iu0tfZA4Wj1XbrXZVK8v4vd5/0a3nWVKeW1JaZyCNQ7PqIA+K88IN5iBufF7Gp0eT4dXbx6/sXZ6auvPv/0/M2rvV6fNW0EIY+hRETFedzQSQAoUGZN1UI04HHOAdsf6nW8eyELvAOEApTw4fVQCALwWE4QZCSKI4jPKKFIFIpAGDosFtvrrDGrgqOtbHwKpSPYBfcZZ2Opyz8Yf8iC8mCXgdgsPHOLy8n4/PULt4y1/t//kpfQuCYTo3x8/ODw+OHe/nG/12ecyXbBstai8qNgTC/byRTxeCypXAPknDTQDEIiLdWaERihpmNC5Dorf/2OQZGkRR4tda/mECtfOK4a8cNZpvCavV8NOtQagGhNt0UAuNIVYxUJXKoixuHgXApCqBLmevpmqsNNCERxVvJPIW0fvSJwqxpmNDfKH1XOgZQeIskSvfenERpAXlCByABtVe3itLlU5VJy0tlWka9rTik0pTfqcInSvcniTTGq5FJAlvTprZ7wEj7yW8IHpxI+vEHggL03WMLJG/SeUDzp/yuorYevxJf1bx0ULZ7OzLsFdffWkNXBaRN0pp+8i6a4XfNXozNMLGYSp1dmOPdiJeL3CljSS71o6q/nkgqv+eLLp5dkSDVH67sYD1Ik0BqyttiwK9M20xR37HHYnI3VQkdE3vE6nFGalkqYnWfAXv31VKY1TzJQh7BtbUC2h65Xb6xovko2EN/nkZq1K6e2Ddjlsw0dm/XyDmunz9XU7NSyJM5+JIw12ECttWWbe0N4eVo3cvJ6rjhiXtG3ltyxx9g5jvxgg636oi5+2Y0kDYRTSUlALSNLIMv/eIrgLea3tiWvuTmCtJb7w6PHs8nlk+99D6H+w//0P/yb//b/efryDRZhT68HW15dJPhyoMuc1zllMm88oJliK5BBidF4XJ5cQMnTiXmiJIc5Q+f4CA0vQDiYRSqBvA1Q+P4xGUEjKMubBDqCQV6NrHbJn++ZkR5lh8r18vSKvQoe5ImsKopOrJl7/ew3yMb4RuvbXY7Da5O42ep9/P0fDDHFR497wz0ti2gcOmEQtVg9IiOsbIgRRpdhUbfdb/bPL7nCU8sephy9QGb6YxeH8rPKkOuTMRUpw1BS1G3hvGYHp/114TxDmXz5D2xDh0UgD7//fRsNRJ0M4HouTnZL4utZ3YeigdBkAJVIQqPOlwhS+JR6iwkVaTwJZ+48i5yLlA4zC9pGgqFf9iR4mtNMib4Ho0LnYxcQ0MLVqVtvVuNrNpqm8ekBjv5yDwZXSdhMl2E9x4J44lhzhgn9FyyYiWTpZnMP94EURwicrQFu6zJy/WxEKeC3QIu+hc34b+B3NsAbsfEwZLAeGVslS2fzdLMumrmT57kmzBpJUAUYYfBaJxdbob+94bA7bA37Lzut9t5geDVZ8iZRf9C9uHyDGjSb5VVcqV1WW8MurhSSqSKMPtoYypaguPU5MowzpSqjQdW0FCHV1A/za3dKEHG0WmAqWs0OOrKirWlJgdlVbcHcfdhr8jmJx189HRw+OKu9IR1eN44sEwWnkYG5Il62RDPwOoYoQuFPPcOhCo8VceGCstwgOx8Jkzs4hDfgAHKqW/3qlWY5HTOmEsqMoiHTE0hVxqxWZYbLxn2oEHIKjmECFjBr5yvdHkb9VcuBWtxqtaM99hw4lc6xfd1ZPb3Q4Al7/dVn/8A5rqPDBwd85Hj/kFVrjn1xJqDTO2LGzKo1xcKizZiTc5cjOPAqskqOXWRy3qCSSM84TlsjJ6XnA4NcLbr7hdQx5dhgA8iXtdG1FkGvHRFxZGqNKvRfRaTDJcogSLCP8DJQKNVyUKVfVXsXtyu9WsYujlpQSY4yHb8NVNKniCP6NbG+TtCu/HelT8mW4pOrrRwvSX9TeUVEB2xgmjF3zPX4iE6cIn1ZvvAnMlDis+6sEh8jCQIietzsydB+w85Zm11x/SLNXJEQky1HbKv3qycjnXHByurYFKdC7TUhKUF9m3VvWAKtABIXRz8f9d+7AtEYZ7zAlpylImrcRn7XvUdwIa7Icocni1gAPLASv7ZeOYebfk2hyKa5r7OkY1fXZwuByK81To1iwPDE9mnZU2IimxB06y12Avu9J0ePv3z6RlNRzWPthLNNB1EfcWGCKHCW7bXhEpE1ATaNGKwHaSAGRloeS8AQUorryKboGvbIZjBpWiyYdtHTs3eAASAtLiejdFlb5arLxXzM3VvT5eqTH/74yfRD3m+qX6mAiUUZU7zuwLCaaglKBzIUKltB2lWVacJDMZJdfnWHJhyUf21x4qRkSUuZ5y9ug/GALFgk0pfjA7CoFna7h838qdLkPrdPGgtRm1GJVi14GicAc3W99EXCeGRhpWFJRkVBk1oLMjwFbLYctapEWK8me07LmEnxuIX68uT52Zunn5JMm2MB+/sHPFeDT7DEhw8ecl8mc2VO7fd7XSbP4ynrHxrBaK3D9q+x8wsuD7ELyRBGZ+LkspaExXd5fUlKBK5w3WtT4VyBPEWWa7WC7iaU9LKLs6R2iLCrQd2VnpLZQRpptZrcdehhAQdQHUe1yepVQSk3RimxKkQthSS8O0agLicYJRSRoE7xSWk/VY6pJfpisqFDA2I8KpIIcvrwBnA93kOLz2u0EDyjiRGReVgx+hqGyMUjWr4cCJg1b0Jx9JuyAnRcNa6B0AKZtm8xtTheydWlVKOLE2OQpQKl3pRhuVPXOIiZZNAsTo6U1DevHbEyrzoPh9U9BhKgSL+OGTRrlEGB3wacsoT/GjNg+m6bnUrAvFNTHll8pjbZn6TH0JGjLBukLpNM993utD968sGHT55zdoxrq1eNLnxYpOQ7u0S23p2TW7QGRjoslrLYyPxIc2k2AbV8qbS1OsoXcX2uBI5yVMIkkjs/8uP2UKWs2d+S93CIzSSQz/NSPZijc6UEJa1Xmbgmos5UTldpY0G6g+7+oe6HQiIr+6zwMca8KhaGmUNKbEdLau5q1lJ3JgJzYptt6wZHoiMUQTivMTaU0HtjjnR8PB1JFGcbQBDfEsiWlGUdEUslIQ0lexnMsskjMlsjUmkqXwhgRpfWoAxYVlTpJ1cXJomyzUvUNs5RdWBBgP0VBlZomG9TTs5ej09fETrvfM4VHsx3OcDVH+wdHh5xvnrv8KjJFWa8z85pLr5FYTvBVpCr84lNuNGgDWLXufbqJ1kgUPtyOfld0xSgyIUHS5+F0AowL6xSUGrLNa/hJXLqffamYimAylPCuDf7XnVlWCVy427tSopNpIZcO7hcqxVRIigAiFL5KsZ3en/qTcbdnPelt49TzV99RZVLFPut8lXF7x3iQu0BkFjoP5CZnvOMlfAhXwofBA4kxrcbVBusEnvAIWcxJq1SH62xXDDr0ekp3eePW5ycviEKE1x/cYijuXgJYPmSJm8zvpUN0emaZLZp3l61sBnOz/iw71W41yHrjyUC6iF1QbgcUNQcZwHxyEx7+APgGj/gYBWAE4Q3gN1nwHlSYkHnrGVmOkBZWXIo0Xli8cAqQK8/s2trXST6YqbaxGrSlbMaycSTL+oxEzId6VN9aJUDzwyI4AMEI4hxHKbiOggMMAdwsJAWSnJYizqLn5rI2VFYRUT/3P8sY+GO5pcrCxuB/lfsJXPLFW+s8jFkicKmr1ZZ+XT9dNLvNff3elx++e/+x/9+Nh198MHj/Z7emNILsL2ensPB8GCfvDP+QjBGYmw84OySFI3ITAHa49TrYbMplO58gVqqMkOip+0TazJuFQRxvUq53NADEET0AOTl/y5OmTZTaoAVCdFz68MgJmeWM67rGBkSaetARWkrCOq1EURGDovu8ihifTnkGw9yYQJ14BkDfTW60vUnDcqNi9JU6UEScVQfwXt+cTo5Xb5Z1r5i97jDYfn+4ycftrvD7BuLw31OWve6fTQ/7UrbloRS15vc9kaBL0GDN/14JxsyOPnG02X2p6s02G7Q3eSxMrmJaCM8NLyJTTBC4xt0N3k8RzdRFcIT6RYoNkBq5YZ/0xOpbwObhGtfmXJneXbTD5VmnXYBCjEKuOvAXelTvBLiqG5XRknhnThCC4BC3BvI4LwrPiIGUC2lpVhMLmBf4YvoAUTrA3DnQZyrpRe1JWXds2Rf7qM31Yf87HCtFsfoD+g/2/zH3GnfKubZkk69hV7B0OxNHbvx1UYK1om+I+9MhC7ADiKJkVtn51C5x83Gf0GJBBmh/RQPOQZNACXm4HeeAdN1m8nIpcslVv5yWQWBtyoFwLavrRNiOLWgjGGhv0a3Xz39gneBHj75AVdJTxe8aUQHTWvXndcwQF2a5MpEMFjljw/zMoPLDrl6YtoZnM0hxbBLII1qmYHL4lKEbs9IXmbZlzc5DNXkVNecdXReNIKtlmU50EU1YRa7mCl3iwbvDj97+iV3Xo5H5xz8xQyw4KyXrAYDhgu6Mard5h5NomI8uEVqz2ynJ7e8OAWI9WoAHKaV7YnQPjUHnphYgMbWqU7Y4iDm6fU4AOew01PTfZSSN28/gUU2C26jBs3QpNI1ffJrdBpfYTbNwSrTNYEKNp9SYG2CCCxPyEI/efBwzswXN+EMtO+j20iocUam+y2+W9zSysZiwhBmfHX6s6efttp9vTfe39eF1Xtar+aqzP0f/7npX8M1ALO7UgtKpgB9eOCCadDA+CrPaY7MfqF3rQZeAucaDmQACBlwEUiwL5KU4A1dR1hKTtdpkN0IpPgkI+6agZvoQwAHwpsUIK+NGeVN/K/hc5uglDwpfIrnrvRpPtUhKf4pfHAJAgdQZwkDJRjqcyW+2ASKBKXGEslhAAMuAsQtRvdE9SxePVuI4PIQhYYZTl4uxOB78daFsrHLvh/2mF6L5khj5alRvfZ0ceohR5Nznnj8SQo+qZA377KE9C6vyUUF3l+5KBiBLDthxxTP+zVDrQ32RjdpFDbryI1eIW8GIoCjAih5Hb+zAYaLrFQxF5Gel7FmmeoiSYAnquGZdUIWiwVZAl+/evXZ588+f/rmxz/+Myixoyw2aHJjKoFQNzuQTcWWPbaXj2QIHKUVT4JJYsHLvzZhs0lwlknMaP6iEbpnlYvSgiW8OHBFWclwy1Koy8ZczDho3aj32dZdzs7PLimAjz7+4PK8ywBqYh935ANNr169Qk6sJjaAp1tl7DG2IqwyeL4CSErKsqZrGsrxxHEhF08ZJHaebTsZMhjOpSdfRLf3cFSxVJmI7gQOAL+d06EqJZQ3S3HdrEqEFmwEywxKSKqX2nVZJ+NGhpxEssrqJb+WygwbAqvENOCkiMSBS8lIGWyXFQQb5xGFdsXnjtHDfDbiqLx9qqrLFTMcSj8cDmgb2OrRxZuL09cvOdTPUnW7/fIXz7DEXP/x8OHDBw8e8P4xSxEYYktRCoRtqMtssMSvdC5zEAdQSXyPdA2sC3pTI2gPRIQGsElV9gVZAGWKe/8uGgg1loCSF5aOuT0+JQVNuDLI8cVUHFZHUuWYgKkfsMNTrCDi9a6yO9dbJNYHynbQA/BeBe2aGbDQ9DDWoVlXqoE+3z2H3qKoQjIHkyS6l95uF4LCrobWrFndoJ1PqZJHXZUTWKgoi2TlLpMw6yfXNOGl21w7+Kw9BSjwySVoV18hSga2uASy6HL+yraZR/XalLfTYFmnmrbSg/PekKai2pXVezzHnUF7tuh9MPjBxx9fjM5n3eEZpnDQrl1d+DlidoSRgeuV7BVWbjxiscEHcerXOSWrCXWtPmsNmPfOF3oZSeWjHlmvlmr/wM436dItkLpmTPOw1/PasM+eb202GYFkO5ItexYs6q0uGxCsePClw/Fourwcf7w/7LArvXeoMjTntlPMZ/PR6dlotTqhHLyWdDlDLYPROThkpRqb4Yd+2e1kqYQj153DI5NN9YPKZOvW7Gew26yKiINnc66ax1vLOMwddh6GrMwT0QmIu2rrXD5eO9mmKkUUvXSu2shHFdvwxAPAmUD4dFYdBNZCDfdHzmfGsT2xr1ZQxVCMDWqs1torQM0Wh6Hk4KZnXg8ZthgarWvQphIwp1QdysO9Kqs+5HVy4i8+QYCcc+2ps76eRdfolipLpsbaQNbl0rqfLeM6r308erY4Xc2/qD9vtl7bxyO6wwPuxPzej37MXHmwd8C91dwPwtt6xCaPl3PWIzgYoJG6homqNTLweumKF/V0+qzOfaTSpNHvNc+UBXNEl/MM6biZdOty6no0G0Bsb+l6vrkNzCnzCFk8spRDeaOQVqVf8FoRMhcj2q0FkSz2WpIMkePn+l7ytiMf20gwOkpX5chdFZqpR6rDrab3fG2zKvEPIax73CZPYjjtVxlW4l+gKReIB6Xo+bx2IW4BTOgnpedCzA2Qz7V50jzDQQHMU62GMrKBvtp1ozHjLcx8HA8NY1nHcy5Um2KdPkhOrNCfABDEEUqYUP2dD08QzlzVTX53ebHa2wRqDla91YVrHcs+vhKVEvFycn2txhz9rf3ylMGbL+z1S9oMPYZY6U48+Oy31LdYFnRGmb6Ing4En9JRRKQzPnTcOgzDvVOcusyNJaGwoKHCx8fwIYXezlWTN8VBIAmVOcW1TGrSID/8rQsznpDR8Qu/5dSjWmeuztfe6AWD67ElKpfpEP4omSdl4jxyaUO0TMc5PvNGgiV81mtE8NcE4I6wMCkC8sgJzX9TV40P8DSa8+Z4oZVG7u5gH5dO2CKGDMSBmz8DuQ04a8cDmzeLRdnzBlBEkce6VKk0d4R6LJ7SfV0Lzji+DaGlc04FeykbvTFUYTAbo3ioUjgrKb4QKTc6OXU+cKCFDAasWGvJ+tHjB2JuDtvMTA4mcF5wS8WCDy1qL5lZI08OevFKG0vfEkw2ss4QwiTq2MViIOptbhmRVNoRITrH0uDNZ6H1zi6Dnjk2/oqc8o7tdMQpJsyoaiHZg2B6NaIZ8PYtHLSBYjVXZliMc4NAmDkwLmeOuJvfYBtAJAdAioHnYxB6iViH8ZZ8FhrtLC/OyMk//OY37V6/v7e/x3Xj+QWZLEXMuRWTXWfOZrR0TJ3PTkz5+gTLJJyO5CgBCx5qkerGbBOgOb7MRtwqHRvAmQDa49eOkVbU1a/hCGebZL4sGjxVLSlW/z0H1qmg2MynvDiNEKbdrFvLliai3eYR1lEDI0AqqHIa1VY5slOFvsd9wxqggYcEVPKAKS+8jqGsaaH0CQCXi0v6DD+DwvdqqE5q78slvQrfST89Odd7BFwSOxxSY9V76JVXdVDuYKjxJ+1a9adUVeRlCyhksORlsmSmpnztR+LRKYqJwuSlA+EJc8644NRAbHgNQDPA1pvRpNvKZOB7PCLW/Mc6K/uSKQhP1HiqgeQu2k2OuPlXrAp8QqUlILzVHOmryQ5qR41oPufJRUbrczx0AtJCodSqee2CvTMD7GKh6zJg2jFk1qsKdlPXqjE5JVcITOZVQja9gkA0BRdeC1FQEeOEEeShsEUYFErFxv45PanQ7ypFnaTW0MwF9lGT9MvHj1p1qjuu02HexPBTSo8UoQf2p8tMgSG5hDduXGcJzGtq+irT+fmZbVvSup49/YKGZCYZqzzQ6JUDv8xx9RKOnv3lUBd0aZgohleX+gyWWtRkwsclVhMZVJy+ymtbyG7LhcIstDT31X1SfJu51+NEmO4DaTYn3E2GpW02dR/WatHtdfnwFMa512mjGcwGdlklZurUj3Z6s/ZArj2bAbieb/l0pVURS5mW6gYQlBHkGJYz6BCUcW5DozgYc3A7GssDLe7Yujh5PXr1+isWyFmoohviK5ZHT36MBvb3Djkrx0Yym/S6GaTN4vaUUTOzcgpK2x0TTmZLgIO9ISl62bFCYImqxLkBW3gGL9RM62YYAWjVQYtgcjK6ua5sWySQArIOBrJmNmMzu2toUzAsNUYvMFE0ITZXmBx7zdMq5HY4OdpGgkmXSyU5JZXCVwegtMoIaXwleRKZYJ81yWS0rYCkPHmZlmKk6Etk4U3TV9sY2iNF5o4yomeggfPkGKPDDJHpEDTE52378Ywv2QAwA2A0//HHH3/wwQfUf2r+5FIXGVGPIMaQMOWkLsY6jg8BQ0jrd7MqQSxktriqsUxEeILRIDY36GDqi8K+qabUVs1oDIwEsV8Mdmla2rvNsjO7OI3k4JWNQXkVxQYcHmSJZlQOBwaAWP4MPiWgSFwKur2XLhdil8pTlO5RHG/ZFBxBlNHt2d5ImVyCTsdUwVQ6l5ugAIDJgw7n2MwdvLwMf1gO1bd0MLrKj9y6y8p4E8X58CwmB77oBc71I3TAHh21YkGDD0Cm4qxuiTOUWr82BwwB002sI5/vay6WXZsBi7XVViIYoUYMtCSaCVd6eShPgvaNFaF2+TStQI5DfC+fn5B3ChRLSdPyJ/7+0b4bZtqPJcqmRh8xugMtLiE90dEVlpgn3tVkytI8qJVduYW0fpbLU29yx0m7w4tZg+E+uaCV1rp9PlaJAZZIao+MD5bDbh99WwvTkJU+Jx+CoFxVRJLm6Tp0ABjgLly08416EpwjUcdwGbXpjVrE5Sm69INz40jHChd1F3vMCFxjt+nkfHJCdXvx6iXrBWiSqz9w+4dHXAPS7XF2/VDXWPcHzI6ZDdOs6CLI5tV8zBPlYGZJkWvO2HWiRBj5KMtaEGERTQXhfUo/78xEbbPVtSXWcv6Wy04NSslul21tzAmjJQeATtbVaYtXFWKzdQSFl2B43xpIsM9qyDbbVLpp/Drv29y2MbvKs83BMUl53rEB9iMWnrpOvORtjQoITE9E7+AVnic1kItoJqMJtpYPynCDPSu3fHGVPoHaqGZQq3Ghvb7uenB4fKQTEo8fPaJvwSmuOdKiIjt/r4WuAXvS3anrA+bpzkPpfYtIgjCxOHaInIwGY+y1UcgkGKObLQhq6i5YnRVvu1h7UVNiX1Z9CPNpGPsrHkpCQeYyQDg594p3wetw8VmOnkcs0hThoC8igVEprd4diZK1kgDQuDDb+BKrnbx3NgMm1e28IbGQ/JcTif6rSLRyRg1jBkxdUbYB6VgxZrY96XkgTsbBMp+xcEb29FDxlsvTyWGYQIALbRqZ5ty+Q0CQJxSUzsSsGg/tH/b5SpKROR9YlRhGXA/iVDP8NY+u6zuAtBOGhNRFTgVbJ24zXGzq6OpqodWiVxenpgfpIFOF7YEzS8aPIWFUy2TuoHnkCdWvZIZzq3xFXec/CTHrhZjkTk6Zdbd+8IMfHD948NvPP+fqKWwHi9GcMmM6N8MeNPRCFyMN7dfo4LK2KW0Ii74oGRlgAjzXAYC8KwfPEivSQgkgPVEPdTI+rQFS80WtfrGGqyJjQ5cvXlKBbJe81ehy8lEXj3OMj49A8DWn8cX88uTLl/6OE8e82mzBP+C9Y5sZc95rTxsfPW0NLNp8/pJOgTtQOamOYqdjvutJb0FKOk+vtWo2i1krIDH+eBfRxPN5TG48dH+A5LKgLHfWg+mEvSFzSuSXkuW1EMMXFv6vOTVqfG77IDuVpNvKryRbI9dNZI0TVGg7GwEJvCtog9I8CfJtwm8pJqVnamOlxFqyLbggQz80WwbNNARaMRaXY5s06suZRt5ugFkV0+KWTS7ZlqL2ingyP786v7rgNvsrDDAE1O3hnvb1YE4rpzrStnjJQ8mWWx4tSjVXYmR/spp0B4zTqSp0A/xHNqYV8gHad3Nlb3Ez3YYBiBJoOJhh4tJUSYfOxW7spWF6fvUkjMQyhDUWONKyaA4Ccqd0cq8DRMwDt36t7RiZaAJY0zmrnCGteh1UgDgMRFysEv072cF5IJ83AiAIh7ZxDgRBgcfbgHc5A/b0TcvSZgaQc5PeNGBa5kFmuHifXTmZY2nEsqwTbHic3LkBl7QPxoOCIKf3KiIfiRMKEMydxtVXjBisICAhOzggeaDU1B3HYRxPkaeutIIqcx5XHlUqbIhxyMtXeWH7tt3kO4lOiTleLnu0HyyovdOmqFeWc5ATvp14fiGfCU9rRHqfi2ODWaPGMSg4aGp+xsIq2z612jERfThMc2UniKPaLFJNFvNWb/jwg487g1/xEhUfYiQuE+yrKzUYor85Pe93ukzFKH7dRGbZ9JYBqAyZ9gJwr+fiaz+z4oM5acHNAYedeciAV++M41CKpFLroU9BbD49aQMaVpN1nl2UGhvV+0PeM1t2F9xRSZ+j6cPVVe2q1jh7+SUFxIKepsd8AdMch+UOPvzHrD1osbrXHbQ5ldBksYA2x9tl8J+yjrEYw0vVoanFwBUX8sipWfoMWGvLJoBhsroHTIXgyTTB8B7ob82bevOJhwXYFr1Da1breECW/Q1M7nF5ct9d/6a4p+RJ4VNy7UpvjaOC2a58UvQ2PtqBf5JPohypv3D32s7TATAscWE7NZ6mldqhKu8o+IwASVC3MYWMxlmb891VpsKMN3kZdm+fr9v0Br1s/YxPxfF5melUMyuYE9ctorKUDV8F5oNFDLOmsI7ycSEmkX2XDh/6VuWlMbDApDsPcJCNzy7E0ladDKelNZIQDjtqxt6yJVurv/URHIsXSWWHOpUyVF6s0ob1CZk8+U9oKUesf4tBAQNInioXNKVAekXvT4hIW/fBDcRchwylRM8d+aVoUnxKbG/03tkM2AVCyBJghQDO0ap8qoG2PGI9lDBkSWuJrMJjlnORiQC33Jf9Gh9FiVDn65gi7BhU6ZQkAYCWqW5B7PRSrG2sgo96QJB2hPlB/3mmFFFzRSXPBeAeXUSWiqHrfIhA2bEtYaysDJw5briEAGm67CzopJ/uryBkrFquEsVpST6LuBpNxmx2ji4nfMZRAjIbs1SOuodsT2Iz3GGhzR7rO7+d3oBN385gqPbX6x8/fvLgyQeHvd7f/vxnl5PTwXzAEhZt9bDXV+td1dqcOWKfCZmY22kkqobkUrnmEc8BkLs64iaiCA9bJwigSB9IUVoDhpmPh1wJEDPilq5smYu9MvAaHrE4f3VGKPNW5hLsFKNyG5ajveYcq8zO72h8cXE6Omm87mjp5fxnv94b7nOkjhedDvaP+Mbi3v5BvzeYzs9ZAelis1X8un1Ew2N20gtV0o6f+GuHpIysqry21+t596Zb0IOFqRKxecZNMKYgq0/apjEfi9qFBBxlT0ug4F+Du9EX9bzmYVWx6A24mruVYNAUgbScRao1vCt9Qj2qUWumt4BS9HdlgGnUlVLQs4MndRwV2AGeGGBML/ci0DYBiE6VZsCHB3pKDadLFqnqVO5a/eHxA7oiKnC3rQ0XKCEj1v7hAZTEpVNx5iCpqFDmqtsQTMswUh1tH6cnNhPRSIPuS52SdsBspmuXTy3H2aEka4m22pcf0nL77cZYvHQV0aLdyqYfVu15ZKlLKHNIiwSexwJGWcYLHwBJdZNzeqgCuCnGRjha8r4XTaIrUvTxECc3wVBqINXJmEgQu8I3WLyV584MsKceygoAPDAOyXmWVAnG61mTzVkdwWolTolflzlxz0vRk4sStVqiRHHAuJDTY1lEFTNBRpUlZKGannc42G9ZIBTAarMI+YYEXotitUe9qswzSzIANBV7T0erQOYWh3v7Rq8mF4UNaMeZOWHUqdmpXcaQIuDVAhZCbU1VbUANQY6IJ2enXlHgRrXADPN9Xp8oswDFIUimdqwtUGV4EerBo0f/6i/+fDSbvH79GhoU/OTRQ45svHj27LNPf8cWN8v+bGLDinQxxCwdAePIPqk7/C6ewT8AT8W9PPG6AI2mviJlVkq9A4MUWgrCcYZDd13SSTGDt6Jjco9jLqDpMu1bvZteu2fhAnZs+LKY3Gs2uOoSFlpn4Gg0V68s56PJ2eXrL35HdhstVhH4egT7xx9+/Alr1IO9o/5gyKeOeQ0O9aP5eXZIKh8oeq3IuhWQgc+AbACRdX56+8ruB+Aki896UbjmDTUSkGOkZb9bD0lc7arxrsPqGN8qrK/U34FIu1bXBH0CfQcCGgvsE0WpiiTblrVsMKwbc+zk4cNjbvphwRkzzKbv1dXlYHBIqLUFvVvImUqth7H43KzzBVhtVGEYbCznlmMq3lohJkq2boy5VseVvTajOlZwNCP8JGBC0bpoKFjuBYN19UM+CTZbbl+6qQ162SFBE4mouvyeuLCkynHimjapjpD6TQUnyMb0nmDUyQByQeh71Ai+vntrPlKXZiJyKBz9c4DmzZs3S65H7Pe5dglH1xpjHYrv60sLB83PKhmZfitDboXcjm4JydxaDdCJdaoRfSDZgyMDQI7IXPK0qapbSpfNTSM0VCyeeHEuBIBeLbGyh9id1wzsDSM3XAy1vB4zM4KA6gax8wFomzWFuMtFWVbdJdtizI0P+tps7ogXSQCAtrQUHEB8PCCMGdUbGLY2tJTkmIK2XXcMrApvnThPW2clDXXJvNnq3oW9h0ardbIxh3E1Y9akGSON3kaXY/Y+qTesbbPGCtnx0cOHj578T//+Pz796vkXnz89O734oz/6ox/96EefffbZ9773vf/FP/mz3/32t//Df/ff/+xv/45jWdwcspzMyAB1ixOWzW5mg0MqZS/tnCwdnmkmyAKIKK5JfwYSsgxjlkllzFRYO1KYXjatVXxWAqYWzNaS4yGNbquL7bXmz35UFJB07nthvgOHUdZEU+8E1/ZaU8oDVVOzmOEur8YnV2/e1Bqf//rvOGDHosKejrc8PH74gK8d6xBXX6emqYocpbYKqUZbb7Y5tGVDGM1QkIzXtH1Bo9lW9aauZdWPVHndyfb5wOOsk4IlVVJ56XR9j9kD9fR8rv2bkDRzFy7agjOLRJnGgAlvAIi7U7J+4Gg7Cl30NvItMCFYKi669SAHUvRqp+acLGIFfWAc0MzSmj/ecGCgxws3Kok/PfTsRANoYDoojZ7ZD+nZiHC+oM/BpnI774R/g+FZ/wxLfDVdQsNlrhBjeonVauv0hpWXbdhJgWoO9AozzvabweP7NmoFJpme3FdU2NM1dCYsL2oAEcrtyupR5NjNhSHqUpdFK9NqMbNtay/Z3fLiWSg4bWOZenmowihIkfhzMfDLZUVApdf3EqoGm9kikanU4/AkdzjkFIu8HB2P6UcVOKdxAmjQe46RBNa2QOsKZKa2KJMn5cIYiAPk7NyhfOjZ2YPm888//+Uvf8kQBM3v9dosSNDNQonXJ8eUAsSwxTkQXkfeHn/HM+Bi8uQkvNvAtsRBs50lMHDzZ5BdAxQpiejO2V4fq0gD7I4ocHAYAC9wik85QNXUcKqgWq4WByGy+tRhmIrRsIkvQb5JSAPl/g03wJlV1scjCOdUhU1YqTsarApFhefJIQwORWgDaTJrty5+99tPP//8SwZxk7PTX/ziZy9eajRHteM+KYZyzznYNRwyfOY4NQ2Oldy27QTrkFG2u5nlj5zCPJXZ2+N35bOm58uE5vRBDtOcyYMGpTK1YelT3YTtaIC2kSljnExsjUg0D7aWTWeCFwsOS89X1y5e0DCe7X2VhRLhgOB0sphNGrPL+ujN01df0OLU9zFuOvrgTxkIo8P9w0N6RGLx3VE+xcF5GLpuZjS8eazKYgtZtOrTiwueNF3WsAhQ9zbRoTzeO0MGHQnVDN4qhFUqroDx/MbT5UxVuV3LJsWH6kSKERqAZSarAyCvFyZk/lYBLjMiFYGAbylq0JcAt6bOnCAP5YlJMF9WPkXVOaz2a1uJeIEhposv8vEWyqogIzz6fRyDSNiawMum+hMx57UFS0hoVeLcyIF0SuG5v0cNRW/wcQrUHqSm1aQmo3ejZLJrfOhNMF363Lrsulh4xciSo4oY23go1FwJH+icwDZocqz/EuTZ2UQXfCjHfQ5komZZU4FqlxbR7aJEMhgEKI2IBKFa4tpsSz++vM9ci4KjFTPE4Rj5V199BT0riHifP3/+6aefYp6ZqzBpuTx9TXvXPMcOoqsUeKtzqlczCmK+PXj3BphMuhYc0DOvEyEmSJTDM1dvhKy7AEc5hSlabCMCQKQCbE4xACwigXLOhHoGHq/TiSYgA9yLTk2tGrE6K7x2sEDhzk1xVa6ZJJFEAJ6intkIMWswVlFcHmgzwQC8AKg4zhYMxgCkUsytMkvPHsWeWgYwenHWBU9qKA3MKbb32fTZmEs2Z/Mvv/xyNBpTaZYXJ5fnl1+tnvL2wuPHj7/38Uecn/j1r36FPeZFZewtR8Lo/zkILCNUcMUse+qFwB3AEh9X4DXxS/RByR6vVyRty9vQ3/dNZVszCDGxolmVpqDcrGq4sy6vLH0oBekUi/TZkonmxVz0ito1Ie732trC0kiaj12xD3YxsxnAp5+/YBzDC072qeNDfWNxuG8fdjrgWGpr2MXaT+jhaOH0dNPR8fCB5Wi6mvLSFP1mnSteeFt7MtYWAz0uT+Uxuqd23mFFzg2wmrmJMt9GmVWEl1FZcmV05s+0s25Hwme6KkS5nkmBcA2momx3Dus4u0Ap/kUeTlN8FkMdptkFMniWohTxbjuJAtLx8XQgCNAt7uj4QKbPjJ/eSB/bCHixoFIpvlVLungufeHgJYXeaQ+9u/IksNqYFJ4cIbCezqVl3J6JzTt0MBGJqq66MojBYEIA5MkOgSoIfGOan2XR8hJ2SmdJ9cNKEk5D2zwJ6/r8jIOCMkcvtF1jq4xT1h8Wqro4EHeDGBFyzhu/5AU/AuM8wClRmHtBh5UliGwq63nPCQal8kSljIaxr7y7Bc8vvviC6S9kPB89esQg++XLl6w/01V+//vfxwCfvhowRWY74Ne//jU8SYvxtAOe7td8Jg2wZ6+Ke7WCnNK1Q9wAHA9G3Z10RE+HIgUINjO8nYoFmRHK1R00Fk8yOCB+mRPg+CAGUIlZjZQ1zd06jps6i+CBVmqqUkbPXEg8cSa3ALh5mg4ExgHbcFHFLVQswRArsoljADqgStUb9poyozUIcNgMXwL2vDjSP+6UReYgrkZ6okdURn5YC7jz5gE1gwUujlJS0SaXo+lkdrh/wGHGTz7+kMp3dnby2/PT6XiE8TjnBp3z8/HF5fHRIfarZW/dUx1JHR15QjyRgVQc4PkWLqIHIEWmXZAF4O1fXtUZ5ZrYSCkP//Hnv+Is47yWX+mIXgVroP+KiSBsnvaHrWg0cdApdnoDzDAL2iy9Wl+kqQwvAlNPmYLD5NE+exOT81dfvHn2GZyZ2w729jlAzXvGvON09OABN3PZfLfTH3CSlBfDNM+g0+NlcLZL6Adq7FLwqnmb85XcBC4BfRQlKVm7zpdATWCvM4CeEQM2H5r77+JIYhfyrLRcY678XTlkyVnBVSRtS6YV+AQq728TwQm0S05gABRKJS3F7figdCDoS3j33ubpetvrDebcEptffayuXH0FFzzqPbdwmQx8UM2+qKYxNo5kdN6YZspGjCwffh2Wkm6VHQDeDFK3gGnP95hZXQaDySfUopuhlUHS4gs3LRIvL1MGn7ChhqiDMRm83Xlz84a0XX/UY+UcLFLxYa0mR5hu3aLHiDOrYjlJXj+RNUfpN/g7wNMB2lQ4MmKdNsQ1BrgQqPHG+oEd1eReAPpJrO/vfvf5q1dvfvnLX9ET/uVf/uXz5y+/+OIphpmiYTCEei4uRl9++dVyesnMGGXCCueJ+rMo3lvDSQP8dhyRzBUXgOvOi7OExOvaNiAr14CzMJOjCLtgYKKEIhTAXdDgpeyCEg16qQaZEah0oXGkqq9t0mSS5/XNKYXMawxAltkCkC8BZc3YJeFplCSeC6PJlmj0iSd1+xDoL3gik6IgPw+sSi7hoC2DodsnNLLVzhowjZK9GZ2oYneZmZ/dxsX3/D756OPa+IQKpEzNpozjPv3Nr0mUjZDpZMybSDh0wvCAebAU4MbfqnuWtYKeIy+3B1xs6AO4Pm6QrQFbEjc1qOgEoBaH1HPI651yJnBewBkZS3V5T4uBVcRo+VIwytUysObKVqxguB7bu6EMzoY7TIfFqNkcsX3FyyCLTh3byo1nl69Hl43my2e8pqSV6haXcvV62OLj42PGOp3OAUrmbBeHbMgUF4Cwc485Zp1PSTH3pgLY+j/HtSCQgJ5BAwKW5FXurgywqoGlGDI4QM0BD+ySBFAly3U457NNgXa3kddg3s4AwzA0GUBlKiFnkDng+BLyGrYazVY5FqWcCYuZWALnAMa7eF/hdGTWC3FMRrVCXQA1hSDNCHQSgiJh6VXGFXMrYl1XvuT1YbgJKSc8jg5D+01aikbdHOVHHSgEhtR+axQmqgTTpEAl4pMVaxRexawfpNvZnLBaPD3gFXBh7sGpTt8S8lAv682+cZ0+DPw0xpqT6wo/QxPPF093TsQaYo4QI1vrpJLWh8MDlGlNL3tBCw7guz3lDCtL86RjZHkZ+Kc//Sn6Z+UZY8yIiNUCVhDZA6b9DrsNrDV8jOcQxWKkfZy0lvJrQEkDTHoJtil8VsWJiEaIm3FQPXHbBkHGUkHGBkDwlgukA85wi0pJeGwH7CluDtCn4CIuSBSXvV5uvJyMJxHQrAAVYWaAzZtLXOibimJAE5kNoEiglpNVTVhlsnk197hz7puGuUaxcoqbHdBHafKBpMnxNB9nHJhGYTG1b8iq7LK5ogtnCRojzv3R3IE87fU5HtRpdtnheHCwv+QqRM3AZlw/cbCnN5FGkytuqOaixgMO++6zwMURDOUZw8StjXrdJi++ACTH7i6iB2A8snxs8wuyAKCp86UMl8dWEaQFZyDNC0JsJ3A8GpQXLOpzfeajCpWE9th9kmxk3Hmb33QWTLDJaJMtWzE3bRDkDszZ5RvvOnVwjyNvNYY9uthkOuHE9XQ+mV5dLt9wsO5LXgHnMrX2hx9+jD2WpvcP+yxW9xn2DLHUvH65qFP26p5YmdaNcBzcqnGUTJ2ykOYc4Em1FHbL7WqAaRFbPIQgtWvwHuo11tpFkr6SyY38U7G28dVSbtNtYlz+Yh6L8CbtOmtBA+DOKYFLQIkD3qApBdHRo0BKgSeOUO+jgIni9hIkXvBujy1p+jHsqOqKVkxqXFXLq0osrLCCzU6HtnXhTCXxiyMiUV4AsEMjzJcVkcL3cb9agrUevv/mYvAEkT/UH5KYe10e51kwtBsVySgVI5LO6C2ZvAv0UH+SGecQUdyr7BI3ng7YMEJBCOYY59/Sd4G1Yk9bY/zLE4fqmMXS26NAHLIRBQ7E5XVrFgMwosB8jwAzzJErHHF5F5EnlNypwIo0m8HMWB4f70GJJeb0DLvycIYPPIsylLK8kzdpgHfi4sSeT+AAtpmonFC4pnZyggqulCsIShhnbhGzVMQlcxkjfKoI5tAX6jNMdlhOHsmQOWeIl4IV3s5ae1WmGpC6zY1EbpRZEsWfyIAD4RWNjSUzYuMALJaa9Roh6TIEBbZLPAQB02iWLERbiihKUdb1mouebBYvk00j5V1VbV+ybdxtc7pnfzCkclyNxp0eBvgBMXtdlkx1kpvxNmeI2Kbcn06po3zki0ExC7BMjfU2D+LpjQEzcLnaybXkeVsX0QOAk2tym6XymCcXgMg4e2XjDwFSRRYVs6ds0yDB+jKyh5jplZVVXCna0Z5uHlsRHb+MzzrlnQfFTkqsEarLyh3R3X3w6BB9UkMm00todNaal9ebbRb+uOWjw0uZHSwoK8k6tM/C36///m+IyJVcTV1XPdw/fHBw9JBt4+PHH9S5U6WtGbO9HsUlokhVZ1sY+hAvT38tcGAcWItYCkh4aQ6JkAxN6g45EO0iYnlJ3cgn6K8HtnN6A30iOMWnqMygAQi4xC/wJcC9JSRxA+N8QnvBtkTAwA4aHHh0SCVxApqkY9wLARyc0Mm0iaGVLQ75zTlpzGvDmGEY2HfeYKOVZ+IO83vvLa497KtBzOpIk4bujioZSeSSRy9HRcQWWs2yjloE2puSSBUz4KxzyqpNbladq8WISbPNxWxOsmG8gxTAs88znCPxAiCD21efg+LtdQ6B1aGZw4LSAZJH50ksb61Mc7G7apK1K5AcxeKAKtNfOJycTP7mb/6GTV+8H330EdGZpbAT7DPdh4f6rh1dJU83z3AmORh6El/zeZcGGFHIj2uqKJaQVpzboWCCcjtiBDmwHX2bIBgChAsyMAEHTwcidQB35CaIwRDXn4EsAgQVvbeHMcAkJM4+9bSpMC/5MV5AayxUw8osrlhCxmlaAH15hLqlI8ssPrNuozePaYHUDCoQkakxTLyoZ+0Gl9rwUUS+9aErnhgiUlFbfNXHDhTwZOjBqxDkjm8kUke5QxP+nlkl+fXcrnyq6DVHpNzQkGRRVWL+D8bGMeCE17x9Q1LhhfHtVYBsGqwFEeEt3AjyZXePbwonkCu0e+jXnVtiZMNdnr+xLozbUDgg06OI+NwhBpYhE/MRNMjZKysTmKi2HRzoxhVKihtJz88mp2dvVp9/qk9+8DlO7hg9fHh0/Ojg+AFTZDaNKbt2fZBJaHXVYTgEAFx0LnYRcz1MFioJyKnjg8ABqEOeIpCSp5I5yGCbInhHeJczpA0g8luZbikWNCVMkU8xa4F3ZHgD0JlHu2HD0w1T4fJgSzwtvL5ryzTX4TnfJ5qphTKsYxmMJkxJqovg2CHTwLZf7MMdrfnSNw2b9WbqhwpWX5szo0s/hizUbJ5yfGyXpyisAkQ3JsFyo0sPFHks1Le8Ivmvbdt4prKnrLg7JAg4xyV+XRhJljsIATF+iET/BhC2FnX1OsfIhstS0rIAeVPT9YgAxokVBQxzazrP3kFitZnzz3A7ONALSMxumQ1jZaGnGQJ7WdQXY6a/wHSVOOeJIffkvv6TfrvAa60yOMcgQnkjD56YpltVLgicMCdnfpHvdup7C5pU8qdeVHtfKGrFQSGuO+UFbvTGZwM4lateFu2RjvpZxKDSSACcUuehPteWbdnQaKpCS3A2CXSmJtM740s+3sVMZDVlCrVqUmH5lOhysmofT1UlGyRWZ/xIZ84ppNqsx//paK87eM78cVzrHvxgtFw+PT199CDriJFWVxEqCaTXe6AAStaagUkjCbGElL4A6i81RpdN8f6A9hVIDrGRXk9XJy9Dr/Yg9lrCaNYiSr+qP2Rb3EWbVyMd0fJBqBRp2jFuXNhytddr0D4ZVA8POXnA+8On+wNmZ76k6afJ1uOtRqen3WOpuMWtH0C6PARTYbpVItIxTw4cZyq1hHiILHtyiNJWuAyT1xg8NF3PoGArLMs1Po5H6Bmh8DfY1UIQutVDCoSOfe59ATkmw6sSBQsPLz+dYE2Wk3s8w0swz906smeO5Ob2STLOxIHJVrmNRfsIdWg10HziI/Ug8wLCnm41kDfS1UuVyq8OZ/czPiJfXr5cXawuni8v6/UX9uVKFs3oDh798V8OubL6QIvVq3qbKTYfYOKw9pTRE9+i0PdBeO1JWxC2pNNszPX1G9+91wBfyrOaxwDOrrynCL2/wEuFrNdfSx4GdtZSFFOTIcXCaRnEISlcI7+rmvSvOmiKgx4AcjCex2x0mJcoozyJY845iS7iKuKGwx7lCW78QhTRN4B8oLBBnVePElLepr1XjdR+ajinVKGZc9kAfYTH53c38HQQJrw9EVXZ5pkD1AIwipELnDHWPDVHoivPDTRiJxtBqGtaTQycrtOglcty6IKOzNay1nJ56vI4fxpzR31Qnpy6B2s6yGGAvV/rMZR8DuXbMOAkPGY7C2K0aAgFbLh8ErmBROiNQ1XrQI6LuYSgAgC+nMhwqg9U7cvoyS91ktc0mBgwrGALBiPH27bjqwmdOL2sO8wtBDQKGLJczFMjVJ9j2KYhveJ0NefIBiqDNQkw5WBuyvQDB0w2G80WGC5IYJkazMO9B9xK9Pjg4V/+83/xX//X/xU3n/zkR5+o3R3294ecn9GX0g56WoimkEiL1z5dWms7iCB3jQEm1DPpQHiznOfKCfy6Rw6KSoAIyFEZVIks0isxiUUt3I1JifON0ZWQVes8xYwBXmFyR5eBKMpM3mt4MP0Kfc5gOPzkBz/kQ7MUNqOk18+/RN00CC5Oon/TSVa+b79ccOaVzoq2xMwS1hhz78YWSwobi4tPmzXT5Ww84gWABScBqAu0P2s76DLr0WAujzm7gUNSShIdj6S9wkengryR8SObjIfegsB86RUMtp4BGlWNhWhiqOKyKs26Vt7v5Lnf7TdvMlksBMt1hpQKLBswLaaDlv12SgTFTweAzMZFXnXhXlI2facPsjEUSCJampZHo994SBtvWw+LjG7JJ8gCcCbhDSCFh6CYbsCM4skIpU8nQsGx7aT+ol7/u8/+H/3ekOMhXIo53GN0erR/dMxUGdPLIS9NdDotLlzhTDWvcKu31p6iKgmV0bogOrsOd4EwjKQXY8LAmJEvnZAWTor3QbaEsjLQvUeyGWYACN8ocL15rbdCrfap78CrHPALUl55KLQMj49+ytMiXwrddGJFIlaCTuCYTSr5nKb4dBrF38X5iIQYtFWP5zxVczdTcYUEUoDajvKYRwmp1lkjF+jBcsGD9qo2ThSatUVU686nmzK0fb7pJSUQyhM+igPASwr0F9QHDe3t8wZwZpHZO2iLAj+pXT+5C28AeUj17y3JiBxZDkbXx/X67BGJG9EPDw+ookw/xmNtWlM9zBjrYiImnQw637w5wQZT/9EYE9Dj/QNoqLqQYRdhyxMMPbBLAmcY8oSDmo+uihWGULo7pxHeHDJ7hQTv8tPKnLlunz06Iq4be0LdQe9RaDgExdIaoc7cWRExeAb+LYCbDTAJR2IOXJ9MiV5eq8QGSAswcfh6PqVQj1hC4gUfTwtVbS6SGcFadxr4S3XWbNQG1MpA4GeSyFJhuzP46JMfPvnwe3wQkHrDlAajxp1wfCnhiv8cTr88n3Nr68kbFhYxxzLDlCJDLOpNg9fqVMm414J3exg/Sb800ZVeDNWaDv0ddcJqz8pO0PMNANeqnvmkhEgT7Lo1VImo8xdy4DVj0XjG1EhPaNkniJpKlaMiuu3Hy54ji89RcYl7G6c0bnbrbjrrm/Mo5FSgGdiMyEwpuUBS5MzmFyJSodhAnDQbGsXICUG/I1D0hYS+Xj0UQ3OuOsAAsoDNH1O+BC4BTnV7vMZOKsFMrwFQLs6Z2gKBlx2hnXpnOT0/fXn55uVXVEh2iFma7vaGH37E1ZjcWr3P1Vy6ghDL2uewSXs0kc0ThwX3eNHz8L1n3VPOJ581QGN9i+7J3gklCciwj6Rrztq+V0UStpWSculrEqyuDUeUeAKotuYmTcE5Ad/dghlp8cR5SkHgXp4EgTSScppOHE+ndGKeOrFe5YKsFKh1N3NBgFAgEIHnJlIYU5IIcB4aNIWFjWxaDI2+WZDRKtOUgtdxYjGS9PzaKJoykq/NUBMKtVScvwTM17cWWCjiY31ZYbaSyhcS8qkqcS2hckGU8Na5OWH5WaYsh2/4kcHpi08ovLIBFNSiiPRv+tlyLPMSBUvGlCB0TlzomQHzGu7FxeWPfvgnXNVHz3Z4cKQ1y7yxkDSO6DjkcUcVJxHp0Kws1RAAblCSCk9CofRQIkKJc7x5Fxh+puO6S2d//8WLZwx0fZLtaREXMsuHbDOHy4E9uiHXj0AGsA67NVS6ijIrY4suGNF5kkAAmoOlXZAFsF0hJK5X2jSfVAhxFd3r8hqWvgIPmKWuyWJIS08or47ugMuH7OKh6ZnKnduCx+rDuDamT3fU5kBTn06m0V7OB8vlMTWSctXBQ4zjjE+DzadjfQPsAtt8xsCKD9mp1K/e8LxiOKcX8mZKlfmfPvPJzUcYe70yr0kwHxu2a6AX9j1aBDaZTds2guZAsjC2PEX9kXT8ILbsr5xlTRlXTCsg5kfc5oKP+mOhWgnNK5PFKTwgK/jWoHHXrY+Gkqm36WuMaSxEXZh3DHQy2cgUDFG85mpgYPEljKWT6Vtz/PVqGCQW6LSKIKnkyw8Zar6vpF1aMhWAqNMuyAJw2vAGkGmzihU0rsYAgsnt8b5KUaKHIZWEosF5r+HyKCHDk5DNbqlndFOno3rzxZe/ZfuebzRxOSYHQvQGxfBA6xx7h3QfXJnJ+jXq12Ui1NIlH/M4R3l8xok6xwoK6WCSSevy0np2W0chFfo3xo7oXEPJvPSybFqxNFbaMpCDNKt17vdn1k3LQ9HpIyXZqXV4mm4p0ahpcNSfULmRRgNFdgHDynVCjCJASwiaDSBRnylCJ4vwjJthHc4xkoQGytMx5ABA/YVVQsUwLpiPjCkIbZl5CPmHXOLxzFcUyOqGJeOrvoS6+Y1v16MMBlSKy1cQtIFgxYIKpIVoelKvp2UBIUIJ7yTJZ5EJRDnLanonjuQAOAeWc5AAOUwuy3J6EKYTgDrJtTQcHOMtLCa7TGwYgHKGkdVgauYf/9GfcgaKLpOtl0u9CKRtb7RBPaaZkIo1FB0/djxeMdeDCi95wAtDV2Ur3YrrIymwufO2VpvPaDUMd5iiYIlfvqzTlDjhHPnPyVWWRDHGWU4jqJhxkB43gGB1G+CGGTBMveIakKj6hXSCLAACga2c1Z2pkr2VE5NoGHmeg5N1DlEbKBlWMk0vNoUirkfPuxiVFr2OVRm1OvYdsHAM3merxtV0dsEtRqwKMldtNRcLLWvoc8VtrlCRGe2omSwefNSl09I518kUS8yTKkUVubx6pcHdBavXfDr7bHx1aQabz1VzqlbfzmNxCjOEHeP2C1y33oUf77+ZoZJgMlJ0iAzEmFxrcY8pO9kghhqobwAjEpn07CsG7xPnuyBeO+nhvZF3Cnu0Tu/PVEFIjIxxkdxh6yulUlea6ApL0HrFp0BnICQqcQmbpegrzNK5/8+iZD9Wcg47vX8OjDy6N4DNaGVfkAXgFOENoByz4Iem4BMYMtweDxNcRAyAPSpnSE+BA+9By8kVsyrW31p8hqLD9E0WFKPA1sJiNp6MTy5eS8tan7PVufbeI4wyW2gHB4ec5OK0Jh/nYC2Oq0xRO2fQtKmlV1XGE7Mandb6zAFFwAePVdxsdCGDcqUsW9FYWWvwtZ4Bi2BLJ44hjx7bL46gEub10HPmvK0fMEphs1wr3rZjyABH10kRQFfbxGCccjuIZQAPNflElgFFPtYpYRah5DC7MzGGEltAZoCzpGlcGZ6fRf51IPogjXNU3M6BBSk6BLoF07/uH0X4yaWdgbCmIkpdgwM5zYgL6Vg30yPnYLWC85Y5wwCcf3gDAG/jBw+veEJpJSGeDlQQGQoCnIc6EF5XtXsdhsxX4Migdzs8gXGjUfY948vLi/F4hDZAEgsbPBzuM3TUuUN7MRejzL0gmsnYnSG0ANhCSUUC9t6VuC6S1y7JYMM+vFDiVcOxiMAgfXQLHN7pSF8vpiwY8WgUu7eHPeZ5dTWCBuZGm2UcL0w8xRI+kCXAvbd/XjcDduWScADwpcqkuAdZAE4vA2MZ48e58US3KT7X4ItacDivJJaU1VVLHa1lYyJLK2MpmIrnAqnWW17YtaQcmQrwdqbs8LLRHXJc65LP2Nea7cERtoxd3MmidkW7U9lxhGaxOuNCBg6z8iGjXovvX++rvEnmUe37CKB2hx3mZuarSy6f4v7fN69f8GGNydXlmE8MjjkpMNYkSLNqPjCrGDbXlW1WzZGmSEs9JBCt0zLLgSDuTrIOgoGx1bxocVQ7r1g8XTPUW7phLU1WuaJaNsLV10QRW4+pYBK18sL6KpD/qEz53agPJi4iCW+5AhBtdiBFPk7b8aSbMDbwFBPvFDXSEB5tCO8SgjBvVnMgTkpOmLkUfSW+UH/y+Ju/xPIUA4hUboM3rW0I72LABIDCwjnGuT04GjAVsF5syoCOxC20cbi3JzugvkOXBnMfADaZdzhfnrPE13pOT9Pmvv4hNpij1axRf/DRR3wHgvVr1kXYFOZLWIwWYaVRnYZxtlZqd22SLulxllsiqVh8zYK5tIrGapty7IoqqUtxs3qo8oSK1ECGI0V3YIwge3jniIe8FPEBk1Ng4jomAFBBUwTWBEUssJFH6M1ANgPOTazsVLZp5WaYQrMUNCwGGLCFpNEyuuXBQEdP8PamqfBukNRrLLU20Lbv31Cr0Rz/0YOpeclrMmZ5OSxEGUhX4qO6Z60s116o0YGSl1gbWsZf5YqxbkPvSvOnjpXkznKtbJMIhpMs0ueRcXfAYPikEwTKPX1mvpyLAPBgigINBphrcakGcIZSCrFQF9KZQ0wFj6x4kEsBkq4PmCTA44IS2LkBQKYw4+xVjqShZATAK7+EHh0dGrEMuScEsce6xlsMcuKI7kE3Pq+bAbu4t+d7S3plLMvjjeIlCSItFy+edOFiLyXmFVd2oqxKrwoWy0XRUgbzXDqdVr/1YO94j/tQ2KVrtN5cTlg11TCMaSqrdfUuAE1OM0st9a0uWXPjEgbMHqmqomjQxAfsWroQ6aB3BJ59hPn3f8KlC7wyzyjwkpVrrDJjQOz0i89/R9XRFIWR4mzMfVVLnb9d9jiWTX3lFDChnLCVmKroPh+RGazr/AHLjIiD2VYtZDXMOlmZsdxxkzGxtp3UVOmyorGfqoJSv0wC2ejFuu0yHxQuUUnCCmINCGk5yDbO6Ot9bsGoRzITzUuKEtQ7yUonwUcBCVeZLrQpfCUb1w/ylwAnvj0+U+dWGrR/F4knFQZHQrjJXHuBdNskwcFYDqN40NnJSw60U8p4+dKxnQ1Rj0OtXKy0+jIeLc/fqP62O71Gq/v3P9d3KvcPeO346PDgeMiVK7qzuvvm9RmxYMKiDsqmt9fLbIz/zK5ozzcbEsHajFDeVlWULBt5vbCShQ9emoYVnRUcFLZdR5XGuZYgc4fXnXvp/gC8h91SjzfhDE0sIH82EqdwPXSbT91WsPJONRvrFMkQIYubpeLbH5ZiZnoJX+hoVcaFEYxrRk+tgrmV1VxXziZ/Og5JqMrHKh5PO59c77ez7RVnxt2RNHGbB1urUV9BHBtDE5GAvJ2iK5jgHCh5A58LaaRVj+2IVVTCuVri6QA1k/xSuGTT8wsMhndneYIh4/RsPIHB8M0nUsRR5YpR6BzPzy/BUMO5aop1aW7FwBszaaoH0d0BY56dCWIAuHjA/lqX22APUmK5rjxr4HGw4gklghF+yf11jQavG52cvkZa8FYhZYDBO3/xyVkVeVbCRaSne5vndQaY+DDN6+5tuDm9tdFNchOuAr9JdVvfZlZRlvS1dm6D5S/gMcP+t6ZbQzBUdWm06dLa3eH+wyfLens0nmAPDjt8M1Gn+CikpX87iBN37PIycqeh6EV4JmsyuZQaJYchpoayl4xVZrah3V8OwNNH8dpSo9fhOqr9h4c0bGqtvTv/p3/2F6xiM368GnHA63w8umBnGRv8+uULLqkBIJQTr1Q0f7HBhuEapZMfOgJm71qkXdkpQbpSKzKtVtM10AP6LGad0Z0hGLkVVExd+pFbX9et3uJaVyEMqCSQo1c2EEGp9pqHZOOAeM3D7KvPLVAHN4p4rLzZ27qrd18EWD3cof6k6FN4iVzlgj4ApwpvANfjgzfN22EaPHGBnUN0EBZKpekQRhAaUPfG15N0a4rovUsCoJQ1e5qPOIvFq93coaXT+Wxl8FheTS/H44uz18+fotpWh49f6dUm1r0//ugn9GVcjskiNgH0ZjMm3CTA2o9WHdwA+8hLzceHRAB0SzxxLraIfbSk4Z69LWfizSdaQqSx4KiBtAj6NRwRI49wcCRPOlzjWn6QZ48ST6fYeM2mECnXawFloNqnks6qX6Z/Tt3nnayWA0w2j8mwAy0bGRkz26wdANtekamGj1ofxaLCqNWef/WUZ2RNh8pNFJNTdtSoHFDRM0V0JE9KT0Woma/6LHp+TDmWQgQmIYBvMQtjLoCSt4T30GueN9KTC8+I263wvnpzCoYipkt0B6yuxnLtZIBYTMZ4CMB3jd3uUqConOLm5DM1EP0/fPj4ww8/pEIdHT6CEgxL0PCi/4QP/SnVg+k2ovb6fVIBEhF+c9BItrk+TAQMzjGwctizH0jPCEGIjbVlms4g4OjoB4NXPT63QLJWSzVQyBkan7zCOzdnHnAAsA14J2Dde5aiIXeOEetIQEufuzjXhRo4+rEOVZyNB0FsmTP2oUgur2Zkm/lffsZIabgMnjSwA86wGGTiUAbIqaNKHpFycrx70T61RByYc9m7Q/QZ7LXxpAEQigxXvNuhNlEfXU2WLSaY7P5yEaJmxsxoO0P1FDYTpeORMJPRFcsXyMxeAgN/fXoI0zui9tGEaE1czoB9lpnVBZiktlyNefHYZq52VrkH5YLXErq8cFLfq9ce21xZhhYDP7pYca/DlEXri8vzU9w5n1Lgq1hnLxGDCQ9XWNGFE8z7zB88PIIE1VGHXEvoB/uvOUt+6Mk15k/XCc9tpwUwHJvQuSbhgmqZfqnjIT/qVestrTTq5fRmZ0iKAPQs6IkGJu90QutBG6jaVcbuDHiKeDk5Vz0wp3QYoRCm7h1bLix9EtEoJbVpvI3sA+CQyWu1AgCv5NxyN+KDSRY1Z1PGb3L2UHAl/ilJAu9WKrzBlfwCg3fneJgvs/yqD4JCIxSdK5Ey6fttPKRiJpQQ2iKHnWFDjUBlnHtuojy9qM6JFQ2G/C2K5fzq/GR0/uY55firX/wD3R9XU7NnzLcjDh9wYfURvSKnb7G1imjF4HbXBnraM87E1oRV6z4YbKKAtUEW40xlxc8b16cT7hhiPoSxZ18ajpOrMRyQn+U+MshQk5EmEWg1ELCPkzHf/IF+E5H5SLoSD2dcBJEiDq/Wj8zloWbeUN1Cn6xXCCKac6+tiIuGAmI0olpJEdJotaFr07spIwy/99FWKfI7GYw/5zZEDD9ScK5U2xwQHjIxlKhoVz9OjaQAXitMr7L+0qpkEYnkMWfcNLx2ZOCdbLpYr6w4xunRp0fPOl/LO1kG7xmn6/DeA87OnCFUbmR5iUifVtPasn09yVnx9NR9WomNFMFYdz3CkyAcX9DGzuG4/pbLgehm6SJICACrjWykb7A+h0ybhxX1BAFCKmjoXsDz9FpBdEd6QtQoG1PqdRInIDqyQeYYAPAgBRie6MjG1i8jKj55ZGSa01uG1P9AAAfxsdec3BvPIgCMc2kBiGWIDAhvEHhoPJMGOCjeDiDhSFJCZJVczEKmIucgLiJ3h6mjcLLemZ7cHKrOOoqcXUEWFYehqYWZ4rAFut1X14JrVmZrpOsYtBJvUBw6ZebQG3LDVGfv6JDLpfp7Q1gt56oHlOVC566soc745DtT2bHO9GHOs7kvK01WyewsFi8P0iFzcIYxH1OjRmfFxSxojfpxTMun4fOtbe0JLk6e/Qa2vBDFOezV5Or09bOXz778zWdfPjraNymVfc8optRHPHm+9YtKgqCI34Ath+pW6UX0uU27bkvR3KRjSlvkxD6ROWqoaQ363b1Oc8AWN/vdLNS3mPbrdQu0hQlQNm21HmB+2OmVBPD6QPN2GfCq4TJ+xu40GmdX2iN0JE+akNPT9jZkTnggLiUXqbxPfIgRQMjr2QlhNFST0w2l6mKtEoCjpmoRVC1J5QsRpSK6lTpcVia8w8ZoGFqdt6y57CI1ixKl4kJYG89OGWaOr16dvXn65ZcMQXU3Gldpfu+HP+JUV39vj2tAeIENSgwOy9OzRcc7LyREVBa4cb12n17PTTXtC6kIckf1pox8HCYaDQQ03aS88BIEZ++F6f6oORBIrC3nneAWmoSy6l0Kgn8uQt6QjSIG9NRBIZjFmmGz+pPZeGk2d4OOJl1kVjf4qMPnP2vE7GVemA1maUtYF4MUvW8xQExQv4yvHlknXiKQSbfhu6IYF4pJ9BuOEsuQmBavGzzRFU8ctKgOAElwhrPU9Ul5lZ3TFAHXs1N6RIcZBtFZua1FJzi3oJxPNt5ijqjAcMBxygCYKFASiycwGGDIKF8EpmSpADis5sOHj9SWdYGGjqQAQAMG2ciNsZKxzPjPubGrYkCGDNDjXBiH/UnccIF3THgDKPLJWW3UlqAEgEnQAxeD7hC+ewMccjsg0TPtKUuI7rkC9i4Br2M8z0F8y0xqGyu3nZYWSWRNWpNbc4zGwr5qUC88xazNFi31qFEy1s1qsJi49unSJIThBbjLGgb3cYg3ZyaYG9oATC8bUWb26jxDKNUZRuuqwHSVOsGqa25YTNb2iY5Ze0ueLzHhGl2LhO8s6It16jex5AjCNUyabjb6fMWj3q21a8uffviBjnpMLpfTUbs2vzx58Q8/+59//Yu/VfdqPTZSeh+lzKkOZXIbbHkpVKwsrPCT9/CK7OVCJlVCOFOFxOFGt9n89OyCVvrVqQ700+Ss1emqCMa5bT4IMdeSBv0RSsGWu4PHfHGOqMZODya6NG2Avf1j0dj3W/RrU23KqNuVwcYhowMOk52C1BVgKr/vGe9yInMJcK9niufa62v7oGQ8LY8qWfJvF75A6T5+TI3ZuTfRe33FFrMKjQHHKtMQwKI62V/ntjfIDuvpQpnJkgXRKyxoo/7y5efU4j63VO8f7h9xfcIxi9bY2nb30D4q0SM633FiSsQZBsTp9PqMT1XNbZSnUZMGAc0uH31qHO3tyzx4b96qa07TaMjWUrL0sLLhdmcvTYAmgezbTjmucrTkKjT5VDbVpeThcMDZxXpFC+fBS87eWp+g9ikTaw7OLIMBmq1ROzXrQlPV8SLaMyGUlM5J8bCxoM4peNJ5haTdmISUEOlm5Q4mM/OZ/NBYEYoUYMtp+SNzpIhIPN04eVWhIDzY8QRh22hx4CG2FiRtu87BYGgdzzOQwL/85S/dmnqWeeLlSWUIGD7B6sWrE1LEkZY7juPj/f73f4AAdAU23e0BwAECgpBTEbLxhqQmXQtSvxDlCdKDANyJNHfbGOOZPXIq/YKC2DFK1RyYIqwWYc7je6jDoJ0SHg74M/Ae8a6eSQMcqZbrR1VtCWk858QNwIOMW5ZnMCKwACcrEQe32wO0/FzVMLdJm6XiNtiKmQTN7upaKSVoQbY+qj5M9YCcqdfSopPEs/8y75ti6GpCOR2Fpolp7MYQ0FZhfSinKRo0mndorIdkTBlrtSGfAzMTYt0QEhARsgl3aVLpmeFqm5lDWHpdgV6pzutONBX+yYoT0zsR3irh2k5OUqqGsy3I6+SshD/76svp5an6IHWHWXejeY9VIoQV4GrPAWWhysWoyLKPMtRvkyO0iiZgrexrRU6f5eIYxWgx+PLLE77Jxy00+7zR3mWEq57ik48+pEHSzfZ72XU26iDQh3W4vkyHqOokWHJfrs6vNIORtlCX/mUNhnvqSNDV5blwqWnAVeJnuO38wtz58AQOAq824Q0gRe8JBFkASbylFdwcCBnCCx+H0QChOdsMUBiCahzp5Zfhqa/6KEVGr1fVFK4aTh7xqUJEgfpeAHfhGg1HB9QJElV2hdfwGPFNp6ej05MXT3n7DgPpfeje0U/5zjEr1fTgehG50ej19T7x+SXLyCRPBVWiOv9nJdUcDhjP6rZxDt00OYsoYmflsyWIrfOt8ylHlnRS5ejacK0Wn1pDqnLOB+bhVLPQqi29EkMM2c3lSaPl5IQ+Y2UmSlLI1kKP43u6BQ6ZYSaXmBPLrjQGE7GxnkFNnQBzzlx+C17j8xk2eMrHQ4tPj54/r6vYrjriUkAhJ4AbUQCGDxln20OlBbr37OKCbNLJsA6Bo/EypACD0SbXAMTNBZCEEIB3YwwQaQ3scy8UKFNwd8A0bbVucxS3pwg3YhHXvcYha8ueFpJGigAqQejV0+lPKsxHUxCW/sST8V42wse3LgKYy2+uyN8EUB5dkpxsPURwYsXLCtR4FjgHQZHt14STBvjt+CI8GSNuAA5TVz1fnu1AAjg9AE4062rguJufioVjzJidZaAAsBY2Vs2HYKSSJYTxKFw/S1wbO9MJ0UFpcYrRrZb/aDMs3uHPh6JZCbs4dhUvn6PjPSV2y/SuOa/rLXy3TnGgUp2FH7NfUwj1EqRExQrZzqdvnfF9X2vYfaiYN7PPRGOg5qsuqv6z1KNhOEjtlqwW46sL+pS27brWRovjvta+eWW0ztUh2lOx7ok5tETQdhS37Hi6LgYCZHoQusLJ7uJyKtOtUDQP5AJQs1HL0YEsMnf84Y/mzRejxfOL0YTPA3HKRBsoq9qnX56RY2Y+w75eePftH1rpo4MeqqA7w1JzsSITEamjtmKz28oCFdH+1B0yz7frIbTE6g4Cdzki+QtZZX6/ETyJlgQN2bbxXiusSnjJmcKpTkZakF/GGBzfNFQIKajyCrAfVWMIoEe3oqRTsQA+viGLiY7p5bxfVZnOtBJNde7ojK7uueQE1/kbKt6Lr8b09Swa+z4eV2NycJQ3nbR7ry5eJpZkdWbCHO+WwIEgyprU1avaHh4duuuBUFXm3BEpBzd+nXgD5R7ErnJFniQRrt/Nurgcowk3M9rLszMwamK8JyCnI7s4Go4nzZPmQydvXowK6kN3UpkURsu2XPCJMaeXUBS0lbUoZObzcs9/VQLGkV/BGiPlYfJWOLRsKfpkca06FIujKfEkGmkhD5SYQ3KBccXWumPcQ5CfUgbwzgQaiHG8hwtcxLsQcIAzhaimavNaN7QPHn1AWqSLIxQnOeyEs0eEmwOefQgAzIFelx0pqrKaQwy8wtCxFPRBLIKy2Js/pJghLHVgZ8WzCAcyAOU5d8ZB9DgnyGArG0cauOYZlMHwawJJA1xIaZ03JbbpKyZPvhRe6PuMPO8SjLQUCs61YYHrfLr3Nk+Xk6ecpmeq9yYJJeRvla1HlCKxDFCF7E47CtzrhJZJaRo0G4ZhMKBlQIH1aucG2ISxuu7Fw4u7mCDNV5gT84kimqjGfvqoq8wgRPpznVCLfH7g9Uuf3qUpW6lzsS9VyGaNmjTzoqD9ydqqYdintqma0WAa9Y8mo/Mmbw9fnS3mI17j59Q275tcXZzqFUI7opJZX+Ofa6NcLindOr0KmehIn3VAio6cSCU8GdNklv/tN1er/v7jD9r7p2fMgy8Z3bCpTXa4tZPvB4xmy5PLq8YrNv8YnciK7w85f9vihnX6dGwzb6p2uTCz2Xh4fMRbYOjEljK7WotHi/V5T5fUZ86V4E9Y5ejyr+u8mHEoit4gMLxyWSIoeYMAPC68AVyHN3V5LGQIoAhHdJDsMjhbqz0eopwyS7WVEPU7IjCTCn7OtzRUSurMAVT0FJm6f+2tKEiMQFoEDm01dahNHOwZALMiVXtzfEl1r9NvNHS8jkOsVGq+8352vnqzWDy1V+yanfYHH37Mi3b9wZ5GV3abAQcVa5ij3iE8w9kIUtuKYOiyvdQoQclr9sNhvCXn+ikh8WpBKOGIEk6Ulkdt+sjQRlOSoQUxurowe2Dalp5cIQvbR/LqH8zkzRy6pZuQy6qft2hInSAAjeTXNlhWxAmo4ZZSNvESvOHKtRrz6XKgrmBCDIwrXvLCM8ug9RKff6m76wm1ia4ewJQCs1Vl0hzRnRtxeQXICwLYAZonJfX9738fgLEXBtiXQ9wA23dbJDGceMJZnnxP2pGwcm4wdAK8xFDVzCPCXHVTc3eNG2R+GUDwcVB9zVOt1R0c8PIML7A7hFRR6oxDrkwTCUqi4DIWph8XLJ5EdJqIW6R3OXlCH9ycwGOBdFbu/TrPpAF+C6YhVgDBBAxqUbdhTgSmICsVob5OfsR67SgwKdeRPA3I9CWYP5qQtWIL8s1fmxAokr7PS3maqBzcZvxZrSINxHXeVG8D16mEMNbuW21qh2JgxLxO32dQe9UpFWqRlzmVBK9VFQlt68a6Ty06FroEeFHpsVlMsjOViVZuPKvxfhSVtVbv1BdXEy3fIq0t+9DtMm5wOnsqg1aHXL3KXiRcICuBIsujR5EREXyTVws4ZKXOhTGGsncx4bxYny/bzpaXfHVCBarvVkw4fKFNbqaxtDAW9lim50u3y+XrCz7/w4lwLAHnzfiiX7PDwKGx2u/38LJ9vDfoH3DZ4mDI2nW7PZyMT5Wu9d0AIWoRDiSAy1mZX6K8f7zL6VKFeAAhf4jkuWigJssFHYsBZJmVDeWciqj+Rg47CyRt8Pktfqkz8qBfK19Weej8VR2MFT2JKrSlxNKjCNXbmlE2lRL3wfHhwvdBuNnNprD0vGidtyYpdfTPpueC72axQsPkarz6+fMvKT/dSk1JaYWDG7h69Kr73/9PeYreOHvG8eJIF+d9H3j1nuYcX3qGfsp4b7olLOZO/DN+ztmfs6sLRnLANBnmu8wAhRcTmUJG0CQEYH04tZ4WnR36IwW1Ai3VKzEj46F8uRNWZeOlIAK8KilrPYpkkOG1Xe4EWvxWkB6brmR6MwrEJqJrz7MnQ2WNkZalq+lz54b21Zs30GNXpHFrNZQQGKIQEXPO1NbnxM7twydP6Gp84xYAh6Gl6K30xQEh0RhPZ+IWy5MADi9soYFnPD0WXhz0ps51icMfMaEXB4Wbs8FN7tGvx7V09HD68Aqj+VJZm2BcEku8/CDInRIw56kAOqkDZAXACl/oUqhTfv1ntXV5a76S2CtsgYVEj3wUclIguWPQUlTBCKhwa+SagjqNxjNii6tax/91w/CZpZNQMxe1xpTvztARqiZRTfWnGwCx6TJ8OuSFncw6UIar3qVCTA9gNLBiJZaaikNvvtvKGJy1Hx/hSjwbA/Lriu0Nm/WTLjd7MDqtLdRFkqQO61jXAEPVLauU6lvyDEV0F/7G5zY9XJFEk2xe2dUrZZnjMA4Xgo0nOuwy4LqH/T3sLjewP3vx2qbC3BTGi6jcbJ2l2ezt6UJtrLK+bzrlVjANTFa1F6srFI1t55aJXrfGze0DviXRav3geE5bZbqMo5sABgkvNHZNLrbld+JvHL8tABhkQ5uZhGZiNUikltjwSUM3WVArAH7kES0zXp706/Lw1BK0jKodAkCXbBhQdaQl6qHzpwLTvYKBjqcXoV+4cXl2CTXHVLtDFRWnEOivuRzmYNilsCZchEkVrTd4g6TOFTH15t6wpzfT5wteb7o4PyEXukex0bj8zYjdYo4m2L6DdgkpOEqNTt+queq5iE1skiCtnRxjtUp6b0EwxKl6mVMlWVxBb9mXKlAfwxRTW/DBizYQDC1Sx6UZF6/4nM9Mk1pUU/UjyFWKbQz6HAjOGZ8cz2/KrTsZE2ZNhkX0YkKBMa8F5vwjWSxaU+IgFdom1ygBGqa/0ADDQbbUtn4g4OpjSsfLhZeEaFCkQqhzAKAcSYunwy4AXpxW7wqOtFxL0AA4vfMBA+BPgihwN2eOQTDDKIAODxlkkvUWnBiGc1bhdQAxAuO6xutsC6IlwRIlcYPU2bq1quRZJI5YXwfg8+tqkJlbS5IpzvDr+oQ31WLWkmX5yXhxd/eyjoWg1jMD0hBGhojCYPCiLoMiYi6lv4beWuHwkZBEzuLnkA89CVI7ULlKQhzv7ChpSxTWfBzSo+rDQwvel9XcEsuo5U8aGMedfTmX7kTT4DrXTdKkYIi1tLT1EgL7uk0+z8PLGbKg6xwXFcGkFxu6bDAF5YPDVB92Yb076SC6hLAMRG1FVGKY6M6QcwbyXTEIR+bttyFbJO4kIsu1UmteTo77tdOpPjO7aHVGZK2/3+b7HhxbGk9aizFv76AQFs+ntAjNLdfyG6MbHuPOuL6ki2ESxe4h83vSMVXVuKmV1aIpCqUfZnbEWJkb/vemJxyZ4jz21fzsL/7Tvzh+cOS7Ta9fv6R3GHHp1+jCV8PGnDDj8pL+hcqOYQN7SH2afU+Lqzpsxip9fVZrjJd1ljqXp6v5KyYr8//w2YSPYXe6s2Fvtjec77PeiWXmqxPDATdz89KINqGYUmt0wAvbzXHzlNO3jFKarNFNx1Tu1WzV7Q9GV9Nmt8eaxHQ5Xi6uWo1Fu0WBL8/V58pRQygjzsPSYVHmzO5UgOiQYpaTT++wNNmttyVNne/RSAo8NZoeRPbSjB0YO2HPb20w1+tVbkrV8YteFkCFrgD+eY0mpoBZg492yLnVpOpSBB5rOpuyHcdiwzk3qc11dR9rEPv1Du+I87XUia5Sm9K3Su2jkeRhDKhRpRoL/IwpzPz0rEy84/R2GTTNHlM/3o3NJtY0VRpMu8ahYA25uJbaHOu59osGVK+YH2KR6bkVr6Y5UOfir8fn9a++pHzZPOTVzyEHqzvtwQcff8yzzwmuFvsLWu8xw1BfNJ6gRmnSenB9RIxdQ4rH7SjzPzsUqXmrDbn22nZWwLpg1OmOuFzin0fRcSokoRXgZFXMAfOrBanMmzVNaUE6Ej1PzocomqmGp4EWkbf6yTt+m9xLWk/Cqo3z5AneNUTrcNlAegXzAPZjczIrFaWAE2PooTGvzAkWFCt1cX5GgbK5g6MdAat+2qf3oLEkqLEOKPpidEooadK+uBONu9C6nR5rFdx6oUpu68kaxbLhb7uzFIbRa+7vgKSh3jIKlqN66gVl/rQ2w3imtu8EtB0XFRLGeb61IR7ZdUCmO7YwWmON11hbVr+qcqEy62XOdm2qS9hYcFiRVGufVxd5VXPKl3Do6hFeqaiSeveuZiC83bWtg3hqNoapqb2gc9Ri+Teh7GIkagf3lKEdKjKdrMyN9vwnXP6g1YLpFeaGEkV7TA8Yp1gtIE1WLvUkJ6ovuX6kDCtfB1wJDt/mGfQOhFejuffjSNJ7q/eTnKdSyu1tkiYKzisjgPdelREV6o1QMeTwZrW4MsI7Q3rS/rw+EWhopdfTFENFb/5t5vDBcaiHaSsNRcvTvLTCa8HWQTx8/IC+hss1vR/hqSu9FosvXr4Goh/h7vWJXTF2heGYMOVVF6/jadgyuuE+AJfYNaeTV/QyXJ7NN1SePz8hKQQixX6v1u/UhwM2knvsJXPIi6mWBtHDOU2NTz8OGrX9Zpeeaa6PMs8bnS59uZosx4bYZ+I15rnu6OalVHuPkcardseogM6Jyx45/KaxuGURwUgUDRCbDoRypn/ALHPyCPNN6+aHl0ZV/Py3FgshUaU5jLZZODZlGXdAqYZecGCcknieCoFKNutevRtf8pbXatw4OTvn9CqLveQWPUHJQXTORbGYoJu/642Xr98wm+FDH7YuI2ujJM3R//I7no2cOf0Rvb1S0U3dNteRzMy8ybfVYxffxCOiS1UCPB9iZAR42TYgvlsEzuePx5OTN2ew/8UvfqmrMfcPDw4fcv8Gr5Oyf4yoE66UabAloWGoOswZkXRoiKEGOYchdQFtc7GUKT8zVNQfr1Exw9Ngwx2Dn3xQJalyY+yiFp8hcwmADWSOdBgvQnhaSAXsyoSGvWWn5OnO4yIelB7diYEV186sgSlKAoxJIFNslrvzMStINEIsFIKDP0m7Y5ibJ6hfGIIHOH7wwCa6fOZUN2AwBNLslmkGlc8ccwun9OjMU1xglwckNXxTOG1m4Gh2PP3rZABGyUBaUuHNcqRahQ/Hr3LPGp1Mb2PVsbEsrKjnjHLoDbCHNA3ePOSMOU/INQJSE3LR1k/YeXKwdaxhlIoHkZIIvJGaPOCvccYtK+IimeOLmHcNvycDTMaycnFV5qX0rrMH/9AphYTzFEFSTBEEshAoEgUZbZHG48aTKNuhqozv0pFiMVH3Bmbt3aqLBHn2A7heTCMTSQCV9HwggmklQ1me+lhEhw9I8a4K41NaEoasyxEOGSHrfehEPvreD+nIuE+HzgXHN3rYlaS3en1yhiG0WZwM9IShsDkdM1f3J6PJBJeJvhbLGrUL3quYrS7fjFavRrIgJEYf3qgdPGkOWvW9duP7j4//6MMnzAIoJe6z4euPTBsxk402d4f02t1Bc7XX6GM223T8zO11Dta6AGbhGgHrmkbsiGYbOqBnDlXMRtkerYvHUBwnCc0cUmfovcCoGsieeYUjAc03URLMWPJwAllc0UAqR++Kx+0d3uh2kQwpXr0+GU30tcyPf/DDjz/55KsXz//hN7/GUu7V23sHrYvROQu/jEYmb97wvUKi221TzjqXQolg45Ujt2YYNYSUnEpVTVSkdIqaXmSCoYpcQG8QYgImYy2f2CmaOaaqaMnO22lSzBSECamYN1pMcE5fL09OztCl7rW2Zc8nH/8xFoIDeb2hDMZg0BrUdZXE1WTM09RLfTFTZksOl1MuxLDXA4TVa0KQkT5I0sUxR4rCwmuhWaN0LxhHhncbgCDnrKw5gdstj8uTFAlqNbOLL4DBREQyFRhHEopjzEaBuq1loQJDi8UlhzjwPKHxiEbOIQpNeS08MqsMMn/FoTEWk92pVutdKV2pCMA4EgIk10a3SQssLx5zpEIm2R9yr1EJVN1w4ymfamxEwc/XsWCiEHMGW7gxJp6v7xCYkTEQoVAoO8ywghnAsqTT0Ne6GG8RSxKstOjIwK1RZwHMHXl0oJCQwEACQGOjxSwtMJZCnrTHTDwhdm4R7pgMWcxzULwDQCW0E9vdqDN9Zc01y5uNYt5DBslaKXfUOVW7TVfCZLEUk7aXLEtiOXOnz2JZDjfZ3+DjdZEbKDaDI1GyYYlq7InbpDKxLaNO73kEDqBEH17jqYzjFNcgZ6Ig9dYKcj56dhqrdhPTOa2xtjtnxM6fvwOj9XuaLx5v3NpDxCSz48S3afuroewEWwHeudJ38fULzmzGahtTZLqeMz4ygLPDXHRFHEJjBscJJDabteqkxWsu8ZZMJIJB+s1nCzYx+fhPq3728Ycf8a4TH6ChZU9my+cXlydnlyzOsw/a7bVZZGP2XFt10R6Tbj7PrNmfFqpgyn7b2AwliWE3scV6MYpUjls9+iQrM2mpkNPMMGs91hatITZttTWw1yqyOhQ7ySyFZLqEhTm8Uqb1ObJu6srVESuHrIojVaPJ6aiHT578b/63/7uf/uN/9H/7f/2/f/vlU66RfNQf8uZPu8M3eepn5xcsw11c6spAOmWlb61bnPMiaxTewfOk9VSCMh4CTSLXKF6tFuZMSgChmdiKlzudGGMlwTYZmL5yrbXnRlunDeZzrDrYpM7YNhvP37zEYLB0wm4xt1UzOWaerI1q+mXGNPpQnV36n00CF1xdbSll/bQGY+qpMwNslQ2hVD4qS93LmbUvR4J3oPgMpADmeBHFyk9efUhlKavWyU9sYETszz4muOZJoh4dy+pqd+YuDIX56e8+46kxprmwrG56CULmiEhcP5WNcUUbqrf2DUrgR48ekRDZB3anXgBVsHdGJLNesIIh5p7/RFRJag1eDs5ZzbM1ece4qLKkWnh1vZU7luZKe+pZiwYypxquZZPMWQps3UgtTa7DtzOivLOoZOkExNsaA3w4Q0ivwOLFbMqfVou4b4j0zTkfZwpMdnh6qOsZL8Lq6Wq37tppMlESP9C48/AsunnAu2YAxDvB4a7Q72kGjLien2Lduqs8XM8nUzSdtjmIpW6TJwsyjAnmCBtYSVx1nCnmCi8wEbVhkhFSjHbEu4gRycSUwI53GSI0APCWwayyBj4FiI/lRBGNaJuzM2SNiXvTVzPdWM92O9tEM753xLK0J2jbnW5vkBLffKTXJ7A0cKXRQoXFIm4fc8gq9P7eYnmcdxzqjLCa9E2649Ic/dpkqqMlbO5gMrSJxATVJ1xWBocP+Dxk4/zl+XTBnnB/qm2eGtOK0XJ1Op48O73kxo8rzbn57krnYG//cHBMv6rv9g1YPuelKI3NkZAZGqN27mBWw9dVIXwPQ1Muzp0jdCgc2dVTaatVBkxHoahd6giybnTBZpcCgkDGWbEMRUR+pYq8pqGY6IAdSRZhSVfLDOji8uqr5y+HD16enV92e3tPPvr+f/KTn/7H//gff/CDH/A+yd/8zd9gdy/GdO7cgKz3bt0+eXcmMWq1A7tZzDUcTwy9fzdaKWU9b1ZhtBNsLhcwaxPhJSMmZ0bPeysEgYE56lJ/C0sWITh0I8PA5iMapltXjcV7MTudT1bjSw7urRpPzRR3+6x3fPzxx0zl2dKQkHMdG4EYpoyoiKhZmjlLSAMjjBDzLC8XvBpISUT+snbquSCSA6knHCKoSIwYniLGjNrnlg9KSyVirAH4YGGprj6g9CcY7hqDAwYYJs7TZSbII7uXp5fdo4csKeteFGwwx6ZwboyJ6/RlAWT3suEUAhMXx7KH9xDOvJgvvbhguTCkylqdo2pntaJ06FSOpxSlzVhSxKPlG2FV3CC9GunrQz1OWTKS0gIZVZmhkpo9S+7s93KMVamQKmtTDKRpcixzqbvQwEPDCEJFYNWSKiBAkpl0SKJ5tcoFGuEVkgMWcfvhHJwe/mqrBScO5gIoBL4T8M5mwOV6kEtrOZHWIqPAVkQ5xbv8VVq5TpHQXSQYQWAsSI3VYqgYVYvkQvCItwaMNwRkUDygFovd3G4RlETBSUDqUV5TCREGpww4lPWMeMijExQYlEFFNWf0ggzDw343leEMRWPthabug3F2QE9PzzhsQc9ONDpcnMVfHjcfAOAlLlPLrB9p1EenpzQ6OHCunGNVTH741gQrZN/ff2JFow6dnktP6/G//PJLejEtYhccmI8//CPuoT57+QuWnbkjlCkXJULHrM/GsTrZ7bXq/eVi8ub0zexk2j87H9Q1INAuY5uZcWfQp89n3bpxcMAhrwYwx4Z4L6PFFV9dCHnv6kxP0ySXgSMSJ9Ix0e2OltRQhGYPtq/mNYGZa6Z2mSVmbzLYPgEAr9qV9wGMSkzxVmxigpY0FVD/2eDraVdk49nzl/+X/+u/3vu3/93rNyfno8vz0eSDg+Pzi9HB4TFBJ6fnvf4QGR4+esI3VilJvd9mPabrUJJ0dWjF112UETRtk0T6P/DqvNWLyhFov4pqgNpICcDryAgiD2QJhVDAqoHiSNEzcWSmo94GNHN63caqs5f1/T3phyPGKJIi4hXWq0u+INs4PBiQcY7QU+CoUzsPujEzk071Kavz1AjVJV510wFwf63FBHf5kcFl5pnnSACU/nQ8XgeyMvA4eX7xSUnck4Xc9gIuG+5Uakqf76UgAda0OKkF8/nnn/N0tqrVVv+xo+dnZ8RyB1vwHsqLXLQd5qnYV5wbWoaGfMHKZcmyY4WCphBDKrDoRHQmeFleIFH0wX+iqO540zOv91QMKeEJAU9IMv6mKFOXdLDWmgfnT7TgphdrC8s1nSwwkVTHNSpe6pYeInGWjMrGNpUfxQJDA9BJt1af5WNOh7FjwOdkaHltbgeoN68ktXdpyp0n63nh6QAEAQBDozjmkCcDLHWPvv3MaUTsoQE4N48tsu3IhkGARMhu6PcxAyYXIazlKHy7yfrW1ErU9KUCzBVX1G0gi0lI97Z4UkQWYWJRQWiZ/DlAqPe9RbIMTmSajraC+FpUUXII8ZYwpdiEegYDKBGUvEYmnNjm6irRwDDjyTBFk1CZYOYgdnKaw9NsC/NxPL5IMZMWeb+FXT+aU315OTuhs8A5c9qrOohmY8jiHpTcv0E8dc2WgDo9X3KUH3tE564Oe1X/yR//iA4O26dk9D7GXJPjyeTV61mHT0VwyHauaTjzZDVxJMO+8E53r3u0f9Aeci6rc3Y2WjXa53x1StMlZlHq33kXgy2peDIh5nVkNtfoEO2WzeYfPzyQvP4drQZXT1BFkBBB0AKCkwkdq1c/ZI6TX3QKkhi8LknVHMDMCAqQ5QC2RTnKSBEiIvHQjKyvOoM61462esNVe/Tm/PyrV6e64Kxeu/riq//mX/9r+l+GGr/61a9evuEythdYAg4Wsw1IAu5gK2Ngf6cXF7DFaUNdTudKEZjhCcpd6J0w5iomulliJDQhXbYsIDBOF14ATKgNRD1lNzwq65Mz0uV6cMZmmiuxcku7QYzTi1ekjommgmBj+RwZO/ZopN2scUMLQx8isReBEvnzWuHZ4akKxc2lWqnm7icpXAJozCGHV89MZINBOdIozVd+EL+MMj+sqFWoEZkxkCTKMaiXL18iG0h0rhMN5oCh5L4wAAaHTG0dE9FROpZYn72wd7QwunDjxS2eWFxCJb3lC5hzjMAqMLOjwC6eDuWZI3UczGkFINo97k7HNLIfIy+OoRVxpCvPmS3OYJc9lDtDnaHzlQ21nSMh1b3Zb5ame2Qysn1lQfR4MG7w8TUAEExIfSsFGqTV54QoOxW1eght2bAQxrs3w8GUe/P1CoNeH2l1D5rs/utMll4/0bCVvsTejHI5Kb8AgCWKxrmUdV7AVrLWWLys13gRbzpXI0+nLwYaUginKQa9C/h9GOCQW3lLdOhBc+dA6DErP0sgkPhcJHuqXAlSaKHppkTKKDOG+lHEHTNYrNuphIp4JZG7ECCAPKT8C8FOmlcqVRXY8BsBHKxQ/8dxR9aTeEmQ1cIpr56tWC5kv6/BNye4a5euXB2IpOK8Ft0Kd26ESGqazG7oNmR3tSrFYS69jsLXxBgkL+0KQ+RRe6Z3tHaIPeV1C+vBe61Oo9Ob91ZcOsgB4cuLi35rSffNS4WYXiZXsnyrNpdy6uNU0xVLy1x1zEY0H8qbzVe6MV6vPWh6S++h/opNM+7iZjF9slroxA8CcFL3nH6eLPyGDeYO5nCwP9zjkwO6LqTTgh2zZyWkHoncow8bmCjHMrfizSRNcwTshE1vsyCVplSxqW4QINEMarEZClP3zvTiis76qNnkMxhcqd1hqbbZHJ2e/ehHT7744ouvvrpg25e+/vnz1Wj0DGnpviSbXWYEpReAdovNyDNzX83mdb7UZYaWrOicNtqXQC62FZh9zSCvPFGLRIHbxjPIsOhZKPIs0eNKb3V7hy5rYVNDnUlfMfBSKipWveJCX75g1R8JpmNu/OAlQmjNIsgqaYjA52RlcGRdMDmmJdlyM8PSpNItOOWo4LUWmvtdeCnf2qwDGh1tOg9Ff0TGhPT62M498vXm9emv/v7XrPdvkme+zz77zCNiVn1GS0EAU3OwsprkmguLi/kkFEcxkT3iIg+8IABwhxe8O2w9XsdbC1I1wctbB2iJqMhqLQ6QINutIQdUYYgIyI2rtG6OGCjeiOVHAsfrSRVxxWpDoisN8ZYnWF7qVCBIbgeaijfapuFyykNDVEne3z9iANxtMpTlwrsOA3EWuhga845BU99rIaU518rPat3LKQ1eq9XuiAvgTwfIeCCBM6RTO2W2ZlmuBDnJxi+scOSBpzKx6Qy5iXo3vjy7t+Z+o2SuGn/CVZWJcuGfKoWSsfxqYYQ2RCWEEhp6lkt7fSUlCNHFgehbFODRn0LNasAQJ591YSRkBEqFvtWHl1RAi6QK7WR0mV4Vnd7aPfOK9YwWSlL2p8QwR3uxMbFkgqElWi5LheGKFdox2XOjg9gIqfKgLVopIeSF0TIKpi9wqZAIfC6hC5h14jnStGTCo40q9lm+PH88pW6xzTjTH1LJUShawsFBByhWcw4aYYSXkzEXyw6bndl03Fmy+dm8GF+wpsgrRhwz5vgNkl+O7bNlSkdrrzCRbcBUdLtnlxeHxwf/8q/+6rPPP3/xmomRPr8z4wPL5Fl9sxHTIbEWjHFuNS4uzrn5g0kCX+jhZcfZctJpLPlSwNXZKR1Dm43pqwteg6bjZ9t4yHHo2YSrEjHM7OgizT/66U/JztnJp6iC2TNbzKiEGQgZxDCzH6wJoaaz3mUpsxC8ONfXZmrLy1rtklVrFp57OlfGxRRsznW4aFOzmm4Pe8mTPpfXqPhYI2e++8Pu82dPh12d/WUo4NXPFesF4cWn9QQ5NKzBHEjMNT0n0yhd29lr9XivsdXkoBX7wvo4woyDz3r16I//+AOIqRVHR3TmWhHlycLAaEQ7Q3VkDZPc7A33UCEzS3a9vSKRHUYdLKSenJwSRK6ZDkOJ43vpl69e09fDCi/0XotJCIxkyx06BIPb39PFHWQAlULAyoHtbjI4yRoYuWIeZItHVFYqTV7lLJv2Lqcv4TPK2VuqV+dbDm0mmvTrxOVVHWknHNpZV3uSUAAFlQN6TQjH1jiSshFLBnEcW+PQOCoia0jIk6kqGMlMlbY6SWadmCcYNOx5pcsiCu9es9P+7KuvmMlJgblDS9AT9/Hjx0SBEkfnhsMLHsAFlqC5I0cEuQ8YJgEzuoW38pBvQjslSCj9yQ+heHH2Pi4wtZjxjZTr2ugMejDSbokoqVwsxqi22EX2WS9hRtWLtYVl0jgNi6umD4/sZ7SQ/KRN+pzJZz+XrRKGwnuH3HwnY3z86DHyCzg+pugvddpZ7wFr1LlqMGpgosxBvd6ge9BnYal3tKebX06efnV5cjLsMAmmCGyuzOsK1sOoirDupQk0IinX5pSY0rPxinJlGkC2nEAZCLwj8ToHAGopT8bOKJv9iwLnYCAghd8g+hqenQ3wLdNSIa2VlYwEjTvr4ZNkdxtAitvJCZm362JyWX8Iyooei2SUZpiIAKfiH2TrClBkcw0cVeoamo0gr2RR1QACdjp5d+a6kcSNnkiRhSe9HMuiIq1Q7xIALjHDTDB5c4gB/5z7Jum42S3TRzDqfOYOjalhMDnWq43M0ujZm89fPseUH/3oe//yX/yLv/qrf3k51Ss3l6PRi89/q2Z8ecWK38nZ6dkpNxO8wjDz9i+9JvNa2h0T6v1+ly9UsA6p1yRqc6wli2Kk2lzOmbjSCTMAqPHtvaXuFtnrdR4/OPzBJx+wS9xocVpYX6vlc+N0xJPxjLQ4SMz7teCVGa3d1lnGo7dC6uPHD3li4LT/u5pfLfjgPKZj+eLNGV0NG4OMyZgs0QfRsOkdPnzQ4v0a+pInj49R1WB4yO42r9jQF2Ld6Y7YB1X3puVoDTIY3ahFUAD0bcBMm1mrXaHDNp2ljFSz0Z+1l3yNusZogmXpISnR6Ss9LREsMZUc2OX9ZpNTlhjJxdPM+YvXp/B2e+wWwu9C0nLoYkjnTsdEFL18amXzv/8//B8x8Jzw4nYz7BZvN2GrYMVxZSg5uA4hSRORbXL2z8fjMzBwHg73vQOlh1R1kgFeO+sehGHP3KZTNByd0WMyBSFzYw7AaRlFoYwy+UNLwPwaN+ckGLy74F8gMGOGMJhelICWGGahJWwDOUJsRMUiAvDEsiIwX2KEAAcyHHgcSNekq5RQlKZVcvtcARzCgYGbRydiJqyNV9pqLXlHYwm5F5VS5DZrAGt2U5MEGvKa3imdm5WPxS88CKJiioBIoQ/5V4wYGFFQSHYsnSJBKu0Ps3CjVKCgqjP00gkp2nGdU98kD9ooxUL0jJs6WipHpWSaASBtnEJnsks1prrz5BTCxcUlbWlR79fb/RZ3vzfag3Z3rzcY9vZ436zTIyJX2Hf00bPGikN588vz1azBoBQdz1kvsXruTyVLRaCRsyhNU8xzpW4GcVCUKVNkVc60WhXwrcHdvQGmOD3bDui5ldtt5HvTFBWXJqHkVOsyV0od8VSym3JLZusYC4Cii9hqsPPSckyVs6gVARu9RUX4FsrELqYI7L2DkxaDtiJ/XQTMrc6v+fj4Ay1oIdDGIthglMfNzsdMTx8ccV87S8HqrEeXKJBrb7CCdGEcbNZBEsbC9r3C2urBCZf+jEez6Whw8IBFLLaImDP85Hv/isQoNfo+Di/LRPIt4vn8N59+xrcQ6Vh/9Q+/saE6S80ckMFqYqOw9OzNa71Mk3V2aVk4Y77I5hJmS13UhDWwfk/dLjuGWp0d9h8eDRl6Q8uscTJmaa0+ma3YVh5dcpB1oiPY6lUmp+cjzRE1kpZDNvoH+imMIBJOWBSdzjHAmdVhae6cicgZXdvDw6/6zEH/rIn5pyPXQWCWAXXfmLY24YPcznCtXJCSX50MVl2Uql4rbpNipZbM2aKezIY7UxQfJtIdWGDwAiAVDo3hUOPwgWZ+TPXYnbzipdrLKQfmKFS2NZnoDvZ6JKhSrtf5qPrRo0f/+M/+KcTombxjfTHGjISwwRyCA7NcjZjccz80oykm9sTCHLEicnmlo204BhmyzS2ulOaQlAQkLc9mDqgL4pCOpljW5dsPwqv0sAT2D69eb6LPXbfaoiVet9WiSYax0mIOyhgOJTDHZQzx6tUr8MjGtJgn68k80QyhqIgLXfA6QQB4tZ+iyRb8tJfPe1OPHz358Y/+iMNh1CKb6OoeGHKK3SViViQ+h7NoIOXsWOI2fwRwJM+S83ggASLIdcjTCzrwCaC+d3DgX/LWxJfPjKFOFpEarfH4hAoKZ2tijPcQQ2uW3PLNYNIHVuSLDJMvMr5sf+RZ01gQUmqw9vZb7DtxJziaYFBSa131F4tPfvhD1NJ4dKThKH8rHa+UAojIP17+VYWHA9sJbT6FzvsFUy649wu4LBueX/JYzHhk0JFimPetopSS5MA5vhgacb89wJ0ZYPJMrjy3ln/rU9TIlFmFohQb04nQVAaxaUm/AFC9B71YYrK+JnC5aEG6ixZt2XJ5FUWSWnaAs3yZ0JY/i7pjHtTB7OLWCblkJow3wkzyAjcJafov4N4ShJWVkaKLrTkAtoJol9Zna8wqW6iJnWiODvf5xhHbuGzAYj+5k5JZ4yUXFuoO4flYxu2KDtnnDQeHg8Yz3umdvnn92i7d0NtNXPn05uzUZxKMsqFk+Dxk22q1+tN//E/4JO2nn3721bNnmAebUOp8k+ZOdCYmIDKoe7Cugnm47lMiDxy1XXJ29Wo+HbHe3G0yw2W/WmdtGUXQSfEiUpu7PGptJTTsLR8cYz4RWF+FnM9P7e1kv7MIo4wp4sMz2Dd6J2wMVKYCxhj86XQ0kzjeH6bLw2rvcQCZ9VetHLcRQMuyZoNZXPeJn+RFcvuJEOldtY5LC/w0KYpZ8vY1vRjL7MyMvSys8alPYxoBOSMW2FAu7MVRQhj8JWab+bGu2m4PujqohkVmY5y5DvMeYIwFppSrqZCWS0P5mBU3WTLggJLXdPcOjh9/0PjYPrODHigUDDPzKi4ldscUGczV+Ipul/uecKTO+66IMboccYSNmk5+KSMw0Y5Aad8xLy9yQSiVtmBpqSM+fbb+1WoXKkJNkXGvb17xMmQWheFRm+1YziRTSRAMqahFCM9rTh988AFiYDKJQo4IlX6mWj8PBwEOr5TAuoJrmbNO7faDBw8++eQTDDAwM0J4EgqHcG6olB0rUrzAslrmAr/tLQaROl53WUyTIYRxJIk6wLt3phx81CXrdU0A6iKD3vlirAvfatyrqSVdBgwzrmRlRsutlVojZ25KXnps4lIPqTs2guVXQwo4Isq8+QB6UnfHFbxEYweecQxzX2+qXMdGu/jg4+9jjE+1hk79tmm1arLEhPkVE12dx+LYoPaWaBSanTPfpuAJoXIboGwAW4VwwBViGCis43HZnLXYWwMqrLioBC3Ui9JIvi2POzPAniHKyTMZgKsDBWRAriYvUZDQK0q0Jmf0zp5eU2kLVkxZqhLAHFJlDoGs1GR3RCo5i4AKmR4LA5T9kQVg0TmrrWcKH01yK0YC4RJGIF7qPU/HeKiaz13r1DjDVAl5KgD2zq+OQ2EWuALAWw0NiyM0/eHg4ePH7LliabBCV5faHdzjdBOFzmX/zErGE848IzytnsVMru5h1fXwcJ9dMy7CGvMGUaPeGx54vmiV2m3CMaLWV6F0CmkwHLLCyqu+vPhKE+4O+rWTK85Jk6Q1eS0GY0pZSOOsDLNGhvDqcHVkp8G7Rvy1ZjrBjP2Bhpk5HRPzSoJns0uKUx748jZMg4mdDNgHvX2eZATxNR0fj3x2xewQ74h/tmNqBHR5zNr1rcqjIw6hcF/uvNvbl7VHIWjK1OjVAn1qMoABxdkJF7qWbMpnBavqJwNE98lrG3Sh9GlchK5fxXBHlfVawGoAowZCjDs4VVVNuhvnl5dMbDh3yzCAjpH9YTfAU/bBbBTSQPEMSlZcvj383kff49ARV0vrJajRFSWFFeElrX5/QA/bGQwfD4ZPPv4EAcyck+n5f/t//2+g+fLp5xejM02lGjXWezkVIGXS8VpzIqsCyJFsBF2tMg1G4uupjUVZU47Ea/4vPXDcZ8WhgorGRWTPc85EvJDfWJlF/O1vf4uR+MlPfvL06VNm8LwzjUgIic2gKBk2EQNhHCCpTBjxy1RIKDN7WRrf/MbPgRV2EOlH7NiUKHPZ3NBGkwRPKjgAac+WoI33xoOg8EsJuaN48fhfjpMaONcvrxEq6TyGjvObk3WSEuV4cuUcd13wejv3nPT6BzQKdRtU78H3EVXS2ktifoQKYTTjt4aAaW5hZPl0t03uG72HmF5UwV2XPPWjU9wShi0YTuwhCx8i1eH/VnvO2QUGmjrQbGdlaIyWE1RsulQrZU2coZ8OZVK3qbQaxsihhKIegF3DgXQgvB6rGBF6j+JBRTiIv3HgzgxwUUGe1VCQmohaEe1OlRsnYgOkobUzMsO/u4enltXcXIZicsgmJyGFZnBOFCNYA3g9P4TGn7cC9Re7uFwTt45jPYvL6IJZq86qrQm+ZlXyrgPuCIK/7oLWBQj64dohO7ZkU5ZmY3C49+iDh5rxYOIWM5aasVnzhnYQZWu4SoMNRJ6ojG64030wfXh6dUnPgg3jVVA2ellpXXTZo5VDr/QS6iqsy3v++oQOlAsm6XA42TvjjScmtqxO85WqekP7q3wot9HWm7AYYBkI+sgOhohvN8GL9KeLaWNea1xoisZuNYwRjLNODA2YvNKpaFOMu+IXVzKY5uAyvexLEKRg47DfPBru+yicaaRbZRnm7KZibb6evGTpdd7vNs9fPhtdMu+fc1K8yUIvFUf1SjZJG23GRXWNbpO6xT/TCxi0Kz/rnzJLWoRlxUEHxLG+ar7qYzWLl/mSotxxpofsuNhgSEU8a7Unh0NSozwkLe9O2Tka9YOa/Te5lpcxSY+etz55sH/8w49/0Gr3uaqel5TgBg+eXH5ML89mtuxt/rEdrZFTLs3Vv/pf/WdMhRkeMeShHE/P3sj60zdrfIORIb+Mj/StCpPIJMZeSglIBxbYEmLjXvlFdN7kpnojgk7K2FK10OaIk+XZ2lHGLQuTemscrsPo/pN/8k/+8i//8q//+q85Ls4SNIvPrF4wf6U6UEZe9OQFkah+PN0R3XXIEwfSKQHwEhdHueiJ/OYc5onPmXhEvKKDcqFT00FvdUAx1xg8JrmwwquMlbzXhwypzwlYqCeyftqGiCoJKEmsYuc/zavV4fz2/tHDxx8P+kdYyplMNavHdrispRPLmkDgWB1uth4O97G4fOvMFpUHzG7N0Lb08Q3LKzLaIIRzcvpiBwsMKIf6Rh5HukmWZRStx3A20SoNoylW36k8NCWKn6qLeJTmlD2YGlexzMfL6ZhBEANd+DNFtim45cImQngZ1lOHPNekDggyO7rmMuXKIROoy3y56tzz7XvemQEma5HtALbzS5B0tamdUNY2/Z1jVJHz1LeZm3gu4ToQv0cJYB1WgLK4+SCjEHL3oImYsQWm0uMpIu88SZgXx0+RHOaFcyWYXnbGaA80LV0MyQYsC8jDwf7xEWcnWFim8c2GzHLZf9WSJt0/H1HWRIXVTxrkYsUpaBCzlxxznbKRyNR2yMZTr3d2lR0goggwhDq0NNPiars/4OtG9CsrW+5k7ROufDMP6+uDAACux+CdUtYLEcisVgcpmUZjqrG+bPUyAfxgcIT22GxmLQ2TdDG6xIAy7+K7T5yxUotn0w7DTx/G2xONRbfFOjIigaeTYS0dSjV1uirm03ynySbKPd2/Za7+ww8uzrnsiQkht2qOWAHF2JO38cUJBeY10XRL3rJCjMqmjk5mk+6U1GWSAensEZizXmibLk/zf4yB268sTU+YSFrjYzneGXpgfTyBD30t9p/zrEzsOGUMV41aFnU+uMMNGXSNk8vJ2ekpH7J61XyNZSKur9wCU0D0s6SLwgEI0pSIf3Za+PWLzxgF8VGmf/pP/ylvoP7b/8+/ubg8o3Q4lKSOVeML6yjISaYimxarcpEfjQesohEGZ7PBRGMQp/M9TcVZxpK7Z8ifEEtb286F//nPf47dZSH69evLRuMFWWDH98c//rHGcLae7HlRC8oLzlkR6g4+GCRHonZKwI9Dc10MMBgVojlofNwDhiCPAoCDLFvkcGzhqaQrncyPnAcGT5LYjgKZDrWp4K3SeBwbwmkc2m3393h77qDdGTCwZMGJu7fP60PmseSl19eHI7uclOoPtbzc1dIKO8XaUtLhLD7zzceysJXZe8nwNqlsWt+oM9rmcLvZ12XzvLHkQtrljMY2veJKV0w6BpfFJNadTAHchMW6g8YJtS5zb33WjD90uOR4ojUEckFN93kFGP6g1YjC82QYmWqmADnmul9XYKjxOtL3G3aXBhjJUdB2JoW0el0Z6vm1WKHcd6iDonhWnBRtVPGkANdI/g5lTbNGHlx0FoI3nQWXkZskO/ssFakLFynShrAEWF+ZPYdJts4OIps8Op9sM1L6J9q09h15h2PGRHU84egFRzOgpaVqA5IzUu3O+WRMJ0UZQTlmb/X8fNU7Vl7oynRH1np2IhswX/I2Dn+Hh3wYkdbdYfC+uriwnpr+m/bNKR56bT4Fg61g2medv8wW0mBQunwwZvxyRAfENZT0TDISDd5MaDJZ5yj0dHbFkjInkDiuxPEvem3MLcexrQph3dUdYOFsSlcbnV3poIrNJDSosKkAdpJ3WcmdbZQzaWZVrzOfXfJxJ+tlFsSWBcscoCZ4mvAWOlyVokym/qxLRK8AGucwC+Y1aySARP8LDgHwEcTiHr/I7I7LLsAzzdAKL1/sRC0zZsKs9sKUY+OskzNVGrB4vj84ODo4HB4cMFlkvsWsns4OJprP8q1MAygXkgCDvBCA/PWvf421Y7EX28YW4JfPvoLh0YOH49GlycPqto7I4dT7qpOlAyU7yoplArnwK4MmIzSsMUnYxBK0WF3jKOQ/+ZM/Qaqf/eznjx49/M//8/8/e//9bFuS5fdhx/tzrr/Pljdtqs00ZrrHAIMxGIATAIckhpSCQyNQJhRBBqWggoFQUFQgFFLoD5CJQAR+EH5QkCIgISgOBxCAGXT3mJ6e7pl21dVd1WWfN9ff4/3R57vW3nn3Pfec+0y9V1UzUr779smdO3PlypUr18pc6T4La12/fp0+AQuyqHq4wbUj4eDPTVxWIuHIq0NWeLy6ygOJ444+JZztQIgM2T0CkD2tWMGWEBKOB4urhz/Sk7Q4cgyp6KAGP5+Cn+s2YaGki151LpyuSKKnyKXP8GFjZX1r60J67VmB1boBdXAFyQ6t6aMJ6U8bYNVUrNFRlorvWdh4VNFQqN0eh7kqX51rRq8QS88sywo1KMpImLrVzgF1sCgH572ADHMxbKKUgufONNZW9jv0vlNFXW4RXCgynmQxQwQ8IU4IDCHLkoSYH7tnqQIOZZhH8aSuT32ZK2pIDnPDAbRRCUS1q8h5fL5iu6CRdIeIYPXjpaxFU/3JWQs3H1VrQR6ujh4xkbEMnCQDiElbBWzs1GzdAZPbiInNPNeUtSjY72yLCBghDoQVabWSCCalr5ZnQS0CEjMmb3ZCXwRGPxFagKJNkj33EMCmkpoIWTFkhJ4nOXnCeycvCR/SJfF24qWtnrwkfBy3jNCivUA6ZgIZq3GziGZOskU27jCtR/tAlFBk1AFbbwEPeXCQBTD+xGPJBXcuPDu2hq3RLGUZM3tDA5E3led2BGQvSoULxLBCcuAUl+6sFZ/BaAkhC+NJFmsevWVZhzh4Kp+trY1ra+2B9gZh1GKFkinXnkzCNa07ooastsUZZIQuxbJ7PJtyFmSJuw1LBczUGImNJZSjEddIrGKi2jMcT1XNl2d9dhpp1yCW68J0ymYZHU41HaQyKEVmYTuM0aiicboz5FyslGzIsIOsb+NCdr3HLputlcZwfJzqtInHSq/uXp96yU+GjVRqvVjP1Ndl8dUBA/lMY50ZRFYCtzlDq92+d+8ei8nwMBe4e39nbVUzZM39g6tXr7JACYWezR/nK9yK3MpnjodqCh1UMCzHmV/Owdp6A635r/pJ5+iPiDbMTbvteZpnrpHjRGbDajFjY/Rhmk2dtZXD9oBFo/QlEPlQUA2A/yIWQm6C2JN939nXWgIBBDUzG5CGiDLgcU5RnnFwppTNMPal99Lcb8M6GCynuVFtrZSvcYWUrsQDIpBJCAhasgt82IxpQKzy3NLEiiTGiO+98+53vvl1zBzQ/1/8zr+wzVDsmiqz3tqLRwnVyVABVVq9WL+NoqrtgxUtkS9MHqRL7LVSttKIebZOF6ZFrd3VkExc681GT1caag5yqlyecTvNlMabW+scmYJOgEPgQL5+/vNfYA6bcXu12qBfxcpuDQHZ2KYV3SNxqeCyXVWGDvWxxPaY5lEwMzamk2NBAzrIPqpkW9hHkBrMwRBXeIlXMJdQIh4wqQhGQcGIb2X1VGwwnU7TrZNaxPYznWg6QEJEaUUIG/yRLFe5iFji5BguMGHLU7ZYseO0y1h72AOQmw3v33xvvZGbjY+nk1Yxn+qRkXXO7DnF3M8f7FQpVtIzXT2SztZnpQYH0vSrm6PVSywIoKg48oJyFMDbO1AU4FTGF3skLxc5pi1oUyZnUKh04ziwpKrttdMys+WqXJm8Rmyio4ywOXuOOBAsNS6wsJ/WPuQepNmkUqO7SOkZcY8Rr6oF9e2w4siyDWtQKNaPaBYEOSO7myju/RsJTLGPxAX8j189CjCge0CYYFha5924CJI/hCDMY/6hqFDMyKFIxm1x7If4JdeHiHUSZakCPony1HxqJBJFcmRir4+G/RNEzZAwVCKfqgC8Ysabz8pwntdqCoxb/lwCle5JOOCcBXU25ElktQAGGXkNhRxhayOFqCadSj2KBnNOoWoFNloKcOkiK74pePWIWEVV1DlNLBBSZ4i2yM4hSSV0i9o2meshzSHOOXFSMLwTc3GvhZiGpBBTS1bLjhjP59KQvmTJlaWj3oCxUfvoiGj06ImNpC7Rl6hVO4P+3dt3fuqVT3M8BcoGQQ80IpMvQhw1zPwiQ2ZmQNmVwr23x60m9uahBo2RO0H4jA9QREoEUxDwtMJbqXSUNVKMfkkRG3ueTcv0JVEU5UqdbiXbgDAHI8c1McfUHJuMRVqd+YU6MfsoEkhGYw63Bh4ijGVwDMzGDO1lHdAIj+zoA6PkCEUPIemYOBAaCdYFSQUh/MwBEFIwCoQC+LFL0/9gXjWfVx1pEtFOnzhTOlXE6fImiv4Q3iRKyehnw0GAQDBhKYKq3rqnlJjcQZVwVaU5QiiT1lXZkBim41VfyCBBARQx6ehYoVn5iFQXNWx9gjrGRi7qCS2rLr0Sok41w4r2h77dHvMkHEsjHpQZNosNW3t/qvVVLVhnnF1gkoLj3YQbeaVyDbq0dPKZUrVJG86PQqv0G/VVTn7JTkZ7d67Bouwtp4dJNqqcRc4UGA+hJGdtlNdFcT9smMAvcfCYNb4ln/9/L/jjV8DeksWmH6uj+cWdAXnV7aJjZH31ZXh5AvAntnpXSfm5LM1TCHd2V6My9xRyiEAC3n3yxIVF7E7QCCgHc0SI0BBV5GRKJLaNqIlExzdgyAdtPLB+NqoV/UsHHaILmtKYlJBAsVZL28XZOAGwbrsVfHdaT0ugaegYz+iT/XhMSSCcdQair5z6xBwwU8CcRYwRBlGn3rrUA0oFgViqlhrVGgOF4VGfga8GSZQY0chqYPZt6ByGommgsS7wKepcJC6i2NnZ+e3f/m1GxqnimnMI4Y6s0SfQz1SRvVmmSZTd770KyU1OHUYok+1klmeVS2N1u762cbS7z/F905xUrxS2+gUsdBGGrDxHPzIIwZ4MDmgd/qbpDh0HVDK6Fg2k3UGy2+j+XQLLRcaaukSwVipucMNDo7Z7rEVDVAd17wUAE9PKKk/M/FoW7l/BEEcc1D4eEduihYJZMZVj8IRPSQ9fcQvFgoUrbvB4wvAaPISDIa+U1DtMARm6XNQdGIInSHoSRZY5Gae91Brwi9PE9qKnOQZu1DjpKCIGsGJlldiUVnlpZTSCgOkJxqzaUU051aGB8HRy6B2ShgsmMAAb21Q3OBmrxJEy9KpoH1YPpJX1KZoUQLFrKJjJl8rc9lyolgtltmdXGQ7XWcxM76Hf6RzdbR/dYWDOXE8uOx2i1Bc5SkER3Hmb8ZDQgCKO9G+LIERhSyKorcIjfKXdxn/4l0Sfz8AbN0+R/QSn+Wh/8d7NoL+oWCLEIufsuOjLkrDFYBTZQYk34VobTzxsdS3J6sMHU2p3akdqvZLEC8FaNLELDvyNbeRfVoRldFOSRW5ZfFNKfJRQCE7v5gjhN4Q/QU8AK4+hbBnKFDTVNQqSs56dIqj3giKzJVASSjINyazEbsC4uN7kMHi6OVKDWAzcSCKN5VAXWneLaHOxxEBYKtw0OeT2rDXwsAUiKFTpCLfe+TcjUyi+KsYkKQIImDre2BxxGWiweYIM2YHEwKbKpQucXIVoG44OmwdsnlhfXWUe9OD4gI3LLFDRNYjWO2AhFwM+5nfZsoRW1silWGTlNjBrjVqr0yIimys5qyKgkfQITak0heENHnUhKJi6EvCSDIHSABzmly9ySgkevGwQYUnpMy996upzLx4eqE/Q7bW7XFXY077kIYZ4Vr0xf8CeXrb/iFxaoJzJV9l5xECQqmEuDrxUR0yfaAm1BsdQEyrxf9jvMQrW/mEMzuaEIXiq4yKM+eWwbY0d+SrrnQ6pIA5azT08oTNPIqs2zYSohFZOK6/g4DmnvVg0xcER2V3wz3nmXonsIXRBPFMyJxBMeMWhkh1DormL4VMQEOdQc+gRIcxY1nsVQIUlKRsBePlrDzBZi7sFXMQDmGzs0JOY7GBTP42NtVL1OooyV2INlE8Ps4VJa7KIl4FzhhBTG3LLbPuhZ6dVy5pVubC6RV+PE9uwpmAD59gpTOEsl2AaqFpI91oa31Pp6Uk/n2Fl4SCVq3lBlj29+NBhWYTzw8luYQTAEh6+4sERqB+C+Qhd1GIVgCdugguBiey48E0w/oK6j3ME7CRV64QbdKXQJ4LKXvcSRGp+56GkOKZ9wd/9HwGTBCqRtSNHSNIlcVCc80qQjPtQfjLyZoHHE8yBtzbGXJKEk8SY9qoShnZE1PCq09vdaVAlEksSmP4kmFGZzZ0xm4ROZfpHMJxFfQ8wcdWcYxhSIdhEddaslLEKG5NCUYTkSSsOn0iuUI+JWZAxA1igSukN0AOwjROpTqddGetQJCpXh3ZhUeSu8EIB3ePrUKAtclFHA2mCGUTZ9ZRusuCZmXgONu/3WFhSK7LtWfkEhOc89ilCBoD2lbbAL3u7Ynu65hTpurATlim5HPZnVrAOZqmNy89sXX3x0ss1RupaK9bDRo7rslyMZedHh/sal/e6bLZmzRv9CRwqE5u6BsIs4WaULMVBjUJD1uVo7p+zpXXm02SAcmJozyfGgqpHG8jC79LB1IwqVsgKZ5VPjRc66GhGO4eLT2rUZxzRvJjBcyZKFEAEcwLvQSTE+VsIDB6PE16Dx/FRSrPBEE4I3QOFxI2XtCE+gbySDc79/tUVthY4iAZM5jOihmjF8YxrqmSXgCsYG0M3FC0qkwXGUpsaYIuA+BUrm73fHLH4mJ4RwLnNQ/MM0zSLk7c4No596WyuLWk1MouSpXFzuRUmpG23Pcua7G4K6mc6mA7RYFxCovrudqlZVhFznRgr1J0O5z/V2WKYvqiCzk94/tdAw6QnYmr10NV2caaM1eQVImJbiH7lRPOQJqqXk4rwOH+Rnk9/BLycWl5PcAOOpU+h2paneIpfyD24ZDahESYD8ROOg4mD41Vx7DEX+TFeQWZhKlTM2fCAefAgUc5G+5AhjpJloWKGvJC/NCf/Shb4Eb3IDja48KRy9UmrUbT2TTuCzAk/G/b6a7SegqSMunSwNCpIQyg2HiqCiuON1aPrTQ1Yn+j9oNYRi5qFDmgEjyfgFQdAf1U68Ob4xD6LchmDoEqZC5Rc5qirjoaSA6bZUPDHR61MvsOhIrVag60sOsLR1ukgviYZaVddEZBmbxQLugrVeo3BCyKYQ5PBDrWIQo9GkRojKnPxjUl4FzRA4NXxEVIa+RKR9VHqxCjcXrEia0aYI4q4dwoT9HRSWdnKlmrtMX2C7Ixjdmu6KFGKUUZMVujo3DFmcxkf97ptHeN8rO1Ak+Gx1qD3ekjtGbswdUMUuzBHKG1xchqdwWiZTUdZTmxgxtnUBgZtERnEGVBLbdv2WShACjC0QaXIiW0WRUWgWrSVVBgZ2VVqayAqb+whjteqPKcd0cxFofjdFzy8Br/FjCLMhSczMBTEIahC+iLEBFVHJqTSlC4meHX7/OxJ1Z60qNQ2zGOK3Ma+rBWvrXCVwDZr2/2rxdTBLbATawKY26Ajqi4DvUn6klBxll67/BzdFBQno92LzFmUyjDPSmMtX9L2KhtDq/mI0JyYOkntNNtS0qSkhWhwznCb7g76XDg6YnBcMc9uQNYzaU3oMudUciJ4xRHiQmtZkrPhJDkbeDYk5CVWFgPraRUmr/KN0wTfOXAfMtMY5J+n349zBOxkhSECN3yMlBNTmAOHyGO8GdrnWdyclcPzbISnEQJuASX3BLSfRnYBJrkEf9KD0MEhY9AWHkc/NC/ZpjFBS2ojTnwMLA/C68RJHdo0sH61uVROIkQgJe3EHHgApzdZsPBEESQLXYASKLkaaV8hcAZZgsDTQEVi10N0QC4bjgslVBE2QCyyLG9qdXpodyyByn7ANWlcKsG67dxRpmnrimRoRYEBTRgajoQgVcFeim3W7vVZ7axrXykKUlIkkvP8oyfvLv6CJ/FZejgycSgbYlJIaJwfag9HicMyt6++tHbh0rhF60HhsaODo0tYJc0pm0N2aFUr6/RiiqlZnbyFIfuVRN7h+JCtmZ1Oq9tp9VhHq/stDnllIIbtGvSJOUgfC6Vcqcu6Vp/gFBBwkFoVHalEWU8pDySVDpMRHxT5ZGNlK5Gqwx2pyBo/MQUh9uBf5oiz7JOHhwjBszDc8/VPqiqnuC06UzkSLvqEoqPrR6nhVCZgbQMSy64UmREsh8owCcDC7HyRgxfX0/lBYYvIjH1Vchzq10a69U31RdCvmEM4UZW7p7TQj95JsYHx+/6e7BOXLl2EhN3+FMt913Zj2Sjd6t1Ln05V6jU0F+EwQ1JpUeFabAfjs9h3OOqMWsX0gKsz2ZC+kG6OnT9DoXkNJ2dF0EMeC6FQd0vCSc4nIAOA//5HB4RuRDINzYo9S8scX8RF1qxCHMGMai2E/cXxfCIU8CeKvtS3V6+x6dKadpx5uiMenpD2bDK+ng0k5JwkC+MvS2LYLmftZbA+dLjnS8Z4kGeoCKQ9UBF8OCZ0GV0RwHQiE6bsOJAxOnbWWCMFi3IkMkoCbSKYyDqZR9l1hZolgRRwPNhliDdDrMVgTn5pqiQ5eU/4kDW88dUrwiuLQCbb0B2M1YWttuFqXM1y03q1quWq6XS9scIJlyjj7GRaa6xIFGpEghg26Jjy7EpGxC6blznRAPshg04GRYwHQZqBCkDIKImY5a7kFniKMUyhRaoXpaiuiOUjncddMZyHwH4VTi1orF95/uWNi5c30g3wl+KEetK/XL3YRSJzfwJ7K5m37ulUIvUVID76O1PZLJZnxY3pBQwMmJi5I6Pb5sYJhHynxUFkHc7NuH9XFy2sbF7OV1aHvQHI48jF1jGZWmWIx8ayHCeWQAiKJssnZcERzSmM3xCP+ijuJ9C/+uv5T4EDBkmWx7MYCz6HcDyOP9UGbo4ebdHVJJ9CTKCAm0JYj6JBMAYOVK4dlco+IxCh0wF/w5darsw8bJG/UvWyT/9L0dqVSoyM6XhhgvaMYWUV2e0GmUxvokocsE1ex7apdpm057gxCiqG96LYjxoKe5ZYjW7cT8Oiv2nqSWzK3nZ24VAK9DrZsSXHT0I5VlUvcIEOobx4gn9BgicXRP1RFJXJTT1ixWhRZsjEmdxfH55DQvI/v56lChhGXFgqbVcz55UXqvB8qvFVxHfGMklNnbCIBQGI4KNjyAzScbfL5VgLMyWQjFgrggcetFd1UUGSuTI8EvnWfsjFXfweverHIjjCHipRYTv9iIzcgL+9WTKS4KwC1nLSzVxGIDq9pKLJse2ETZDm52oRbJKL6eb5CqvTzvA6HXTumzZixs41PpBBBsy5qBb5IvsallWjFR63HALSM3I0eBLT8wkhp7Klpryijdp8opqISc4a0tKiZAczacsO69GoaNvzkdrMR7KyBMsn5NvduXfrZp21JJJ9yl/JGcApoRY7S73BZQTaN4VICyrcpLxJ8BxaWxl6oIzMsrFyxY1NN3LgA7iRaaUs2+/RURNouGRZlIst5gIyZ35w5V6/1QTC5uYmMXOpKvCY+t3c3EZv1SolzLPsNCEuplhGsTAn02u4cq2xubU9IdqIIQsHWojaHABFXhBffl0ENGN4KRvjoMsyGiijiyLGGnoSjQKBjPBBzXMQiUQrxeHPfhJIQ37vyDC8UUJpYppIke2NDMRZoEbfgM3fbGZm2A4FzWAPN6ZKNVkYNF5iqME2U/bDjAadZuvGtetwKYQ67h+CKioZGzRTxXR4JukqV1RxZWK+sbUOLSaTZ15ugnmjVmmOMqwFonB5zN7m7BoncaCmk6VaYCPOW+BMLTaBay8TRNDabx32y5Zs1RSkc3tvonwnXqOHiqggJIScWEI/5jxfnv7qT4umCMGT/Jr0E4G0wsRaewwtBa+66qL9gp6Hg6q2l1oTVoihAATCKQ7r4Jh+KFUrNoU+e+GlFy5sXxqWnw9oiPGirUcp6IqiodocjmSXGtSErhtth1BGxFQbeFW4eVrNI6JBLCYjFQUKoMGTfo6tVIUZoI9ZHqYpjC4UhCzYr0cZOcyCBdPAEuJGMJA39EBNVSZkkAiTSc7OcMajxRYJ5/GV+jTBQ5Rl4UQAPrddcJYamEBbngFaSI4HhEAtz/JKDueCEmzJmurGKobzaiVUAbsM0hO1K4llAYH+ni9PWo81GO27EG04DM5MO1YomS4skSrbSsfqBpNU6C2llQjiSUMAW+qUt/EEE1cE3/J8qIfjczbqsvCFpCD5Mv1yFvKpkGXgTkVa9GIJjTWs8VAVYByQxiN++TjcY5fo40D2E5Sn1Z21d+N4rt1FgNEkRv0e7YoBA1tUeZOEQbCh8PJa2SSjmklnmoUOwmK7flmDAMbJOpbdpAYJAMnFaEhZ2prCACe/Lq/jfMdKte6SkQlMKALvnMM9KAZ6S5y9SKtDTwgT66kwJETDNjYvrK2uDfpdTvYoVLhmoIYepaELEx3lZUJP6tNmu8EGuaD8JL3Al3GTIUb3wf4xtrHTvih4Ol1xdvdCLas5Sx61ixBHolGkwmhH5wWPio9TYZHD6AkOCuabdU9AEYf4QWPzAZnO+IpLISltOlfeHEwvXbqESEpXpQ598U6v09bwt99FE7ebR+wnTnNEIedPlFYwyXNwcLbSSPUPVEKpdapaW2aZxRcSeCAIHSI7q0A9KtkGiCbHV/c89tNyAIhK9eGhLUTDs3DgLoLkF1UpFseaR71AF9kQzbo+Uyiu2wjZwFWuF6u1IesVDEUUpHpJZpeHMoyxCRZN9M8rRxHhcEhG/8ufsJC2vamYRDrRhbxr8ZXcSaC/P/bzKZHR8XFinsVtjg/mXs/GPz9ERYjIsjiiNEich9dpiBdeAeJ1yqdQ7+FriP9UPWTn1fE4CthTgl/wPAyuRBYveirL3ikFKnRzPPxc2j5MJk8sjooWV+QTA/oXFZD0HtoK+ayDHXhD/dAxR2VwpoC27dgGJJ1zzwCanRKSUky9IukknGSYZs9Mty2aS/qJ7rAEkl1sanII3kGWiYF4mp84aF9GXFhDObKZiCx4kWVUCmiBQ+WgDmdcLGAciBpeWVlhqDrudoul/Mb2RQaIHJeodc7IWp2zoS4zZnGGwiQBcyy2uANmSFM674txMLt3/JP1tdUJIKEzMyufuOyJcHrkSua8TgxzvAZWN3/ULpShBK7W7CicUslsJJ+sdtAgSiZUUHf6O+3YDsxSaWkCgbEVbKhtOjVaVcXYIdfjG6OkKn/11NYFZ3LM/xzrxRXNg36b8zc5t6vL3cyD3t5x75LujaBeVFUc9uSIa0WQVompQqKRmlFVUc2BFL+nUXsCbwYzUO5DAXQ8/RkA6VXz9fpPReDsYR4Kq5qg4FildQ4W9wBzS2NqKBEqnIw53ePGI4EVgUhmEfSOF0ZWFUEdqygdoKUaVhSJwTk3TRUExGjJQ/DtZyF1QX8ueXjlk9KedgTOM1Ac4Wzk+MviX8fNeFcdNDw8iSo7gEggtMXDpxEkCjgYGvpABNo8MeF8nBIZEH96CIFn3fmfPHlIRXUiOngqD3NzEULMp+Ehw1AcPI+sgAOuZz0L0SUaNPTqxEf2RJPHnngQUpJZFi7kFkL5SAJBJnLGMIYnvkfDCAgfCbKflEwoL9rARRT1KFXE/t8Ji4ePWHlrB0WlWBaEXCMmI0UOZ0QlQ1XGSoSgw7jLQclZx0n7Q8SoQ6bjGGmPRBgOTNArCxnqCKKZavmnhrMaaqPp0Xp5lh5jbOJwuyUKGJRwXHTr1icGxJwoyZ0BrWMpfuzLGZQS5vsC54tOCqDFKZ6oYGSirSNDmoA+/4tDdh9xbRqwNLIfpXWZkr3JA/I4BCtf4QNiJusJ3ImDo5TRjzUM/Mb+9lWDWG8NegUZ08aCCZ34YkQKmtgOGoTiBid6WpYyc9JLYKk2t1eMxmxO6jI7iITX+amQXpGEiPE3QzJd2J5qpFPbmAbHXB3ZbDKDcHCwl6bDYfWrRqpZY9CTVdBUkaBoyBedJWXB9jAUnswDeEFWkNuTAWpQgAzf8QwwhTv0p0TGkvpqkhoPMynM+I5mWP0x/gsl7Du6zJYqMgChClyLOlR9ilH2aOE1ZIpH4BQtjpr4FpSW2F+kh/3BzyrOFJuzHPPHD+lURGl8OYFxRB8y8SNG8zzE9TRmsZocPVJ5rKyiNLVwGgdK5PnwKYmhEp2mkLW1E5xCQqcJHywkapXxq+IDCreQ4Pr8FJzjZpkKq+B5ZAXsuHl6/MFzPs5Ec9JZfHnxmF9jHeSXi+PzgTzVr47MU83iLypwxBM7jlwii7do37PJ9vZWvcKFRl7LTJHa6FBLIBFirD7R6lLkCU7nDBSyXKvrVcDBQQChi4qO1FW8HL1o4xBED+FAJzuSsFmIBiTVWa5yekFjdU2nUE2z945vLKQzg11M0ORGjoABeKPR2Nraqq1scopFtlxhCzBrebkyhtlZuoT03MHHlD42Qh1cwYoyBvMsObDF2swc6ZoFoWQIJ2aS0N45eha6OoJ7kWPz40IGs8CoOQDIMTfxGAkHE14YLaV9bXM00pc/zPzuMeikMx0scYXDz3G6OpWJ4ThEVnOHahyeiGPwRT78oZvtTGrVgaWa0PeQaT6bKuUqmfoMIz9iU6vEsV2kR3xRWdlWYwuV1C3SHLDZG5CjnocGiHIA9KcgP5YzMIJB4T4kqIX5O3zxknWSKBkeAkVcqVWKYE9T0nyCIUUMDdKk61gHyPpC9oYRkaiudD0jDDTqYJ6MLb1aIz5Q80j+WT2LXnLxrwodOeFhE8hkAetJ+8IfmvPwCGJmd+LWODROferXi0yQle7Upw//4sBFDZFOT/csgxwzuxRz5KzQlOWEDHFiB6VPsfMi8+ae+FW61iiqeOaPEyReCXchYJgq+7mYJ2metI+MQlnc88gK2JM5YsEfPGcRtk8iHB4avnuoInzO+tACR+Pm00dGiLN4ekgoiOrGWDyEzCVZhuqjxp8D++fu1aU3pXb5JelgCz73d3be+NHr93fu8on6xQLMfCNEK/Q62GkRZ1znwnXejDawBpcqrIHaRHwABClDEgJRmSzlyFZWteuDT4yrga4hALykU6Orlcb29uSFF15q1NdWVtdR2Sw9/fG1xQqYrHGQl6xJizriuh5cKVvOHLOWdaVYb8wKXYzaHCNFByI7K2GYVRLpXt04wAQov1rX7ZMmGqSDkPiYEB1kKQ9RDEEtmqEbkaGvQXFwyZrVK3+GD/7QLtRKJKjVOOy7D3+R5YRrkGPlpxPAJaoEah5RF+0KkP0JXakH0lNMEqNcOH4M2rG0CNOEPnFuMJxNGAdE6BkJc4ur1XMsm8akz24qVlOzSWlrBYJD/hwGcNucjF/oW03h0YBYcKWb9AGnV3NJfxz2+L8xbCvG44M5SUkRcAY2wtkQNgJCIWhGWfyzGTm0zon9SxrCkQp9rD/vgSg9dWA/pIAohiXMSqAjHKGtc0NFMgSvrNxecZZbXC6bSg5Y5uj2yVRN7dsQGK8hS9UJuLFQiGw5h7dTHrDyDFQoL5rJ5LPaLk5m2cQvj/SrLGIHKcRu0FE58cWfTh8LVZgcEa04JwNWJY0/uZ+AZImJf5ITMRLOKcOT+MAIr0RBHCGCaKgcmwID4POvidRP3ksWoYzBQzaPrIADag6F1+AJn+Y8RAh1nyyqJ5T6NSn+QDhzYJ/4qyPA03hF4MUVcbt44tn9xQAIH7NoMcnfRjedi8TtQPzB4pzpg6GY9cCQ9kJBkVlyldG5wzpIL8/ic5sWY4zB6iiM0yTJFnNF7jLTyLjPeDWrewe0EhuRlk1xaUKGu5JwnKOLkiYvRI81RknPhc4tzzM7mkitbjzmYGcS9jIlOgcbFy7UUJ8ZNn7amqJ0lqVJGsowXPbrEjXliw7iOjmNoYEgNmEyWKEaB8MsBFqUaFByFg0xljnnKrwuTYKHJBSTfyEcmSTtKeWHxFU+aF880Xww2lN7suTskCaiauhOEkhoCgKTPCiyDpuFYSrCVgEFQGTdAKC/kRLiUNhY8e2GuAn2/HI+3eC44VqFjTOUSzoYhWEr8EES4PEc8EkpKUVSB+s1Fg7A99eT2A/ns1SSHo+X/JxMAIiL6/FEk4Gq1OZpR0wVQQW3TqKIwV09utwK55WFxxCVvCCydSTxarGeQmI5YhuIeFEV2R+fObIVQIqWdHzG2TwxvSo4QP0trdDWAi6NgJ3XyQvHTADIeJIkkDk/BRHM5HM+2zjFsvD4+9lfih/+rMtIRt47O4kLhmEfsGGhOI4PkUiushhVT9LECOtTwvkrzzkPUTwkxE2+4qftM3eEMCloIYo2FNIuAg4h1RP3kLXnEjxk8fgK2PF7SLyJdop4TlPVAMjIPfHS/v8BfjQUkFJEOyYqkbrGceE5Rz2wI4VFx41MA2Qk4guFrdU6bZDR7erqKuuS0YsMeZmv5PJE1CFn+ZGWyJKNyBwmbdkoxKjQhI23WO0TSae7rTbnMh4cHN65zSB7j1vEGQHfu3t/WalZ84zjgmLSkunhUfPatWv3799vzjRyxYINMjRLgLOUjGMYkIfSrYwb7bADO9QLLNLFxEX06C8OCPQxDl1q6UYZqjkyUtcNyUwpOTTP+UkMrbALmF+pThqFhpg2HGfIZcNNAyHJJWFFCBC0IosGrs48STH0I2LGbPOjo0Cvg/4Q5OXM7syo1wYYq6j0BRXNjZvqS+g8YV2rx3H+7HKF2lzhMBhynFZq1cYwKsV5StQKciJJk2V8PL8D9LRJ/+NBW5YKyEng+EVKqdp5ByVhYF+aNbGlM1CaiQrRxePaXkhJtEi4yWIRaisydxDTYkeqysai+C0VD0WPFatsPTjxlNIIGqpLOljBPFRJ8j60S5b0oRM9bERvm+JJ9z1cuhBXdH9ol2gaC9KEr8HjkcKrOqTxYklrIFi2plTuAlhPOggc5uijoyg9yJ+OJU/3nEWAocbC+OKcRS6dHiCFOGqS5XwSF+pg6pD3GYOQaZ/733jPpjuZaYfD1OgK9q1X4nQBHnnhx2Eo8CGAxKJk0wR5SptAYti0DU2DStS5tkxuWUeKWDpwlWUUHBevZatckZtjUQ84II/gFG5cHc84wQCeRnyly5gw06nyiKPui1qZONRiIXqyWt161jHAQMaVuUOHVT/TIVexM1Qq5pGAZ+MqhPwWf3jEUKQlspN7e5gyhW8YfNKLQ3VBImhF5pSeU42FBsMZzmywaaFQm8HziNmqIs4WgDCWFXORENU7YjvujBVKrBqacMIhHc1iuTCdFDmVif2JZc4aLrHJtjxuz2rVej1dKvQn+UGrnpvVSvlSYdIYNXXd+4h5SP5Qk/nUYDwbDirVia6xz3CoBZtfqRYGwlqMW6tWhoPjan406e1yJuCk1+aq3/TsID2ppifM5lJ85BW3BLO+gN01mfGAm+a4P4BrY0awA9ceptLN6ay/1deyrKvpF4tHIJkbmurkoD+EsGpSc7k+VrRmV+UYygABAABJREFUO5320nXMzdMM+zw5j7k/y/BkudaIEDQ3WTA8GYxYF6ZTKkmjkaJ4k3VKjJ/ohVBJaEj1IYyepurFG+CDSE012Xs8HZUmgxKdj1RunCr2s3XO6afbkJt0y+leNdXiBuDOrD6alTuZivE6rUp/tn5LFQto/aFaOWIE9h5zRCFHjWB7SPVKK4SgtqkomqGYhXlxjjYsldvsb84X6fJQ9CID5+FotVhity/zyDS+CScQx/rEBuDaJT1KDXSolM0uI9RklGBjZb+HWYJN9PzRSDkPhEOhIIUwUzlF16C2aGIihPQYPTFDm+4BBJLJnO6EKRqY2qJp7jsa5VEGo5/xPAAjcpqPR/SVjLhK2TIQdQEsNIz+NvYgtcSIVtWpDlhFzwUHsAG3FPXH6W6mgmyCV5gOxjzAbYvIhUo+0+VCQPiCix3TBa4WVHYJZ2gBTb9BSTIk9ihqsogt7TLXhcLIq5AUGRT8JwqJLilUgIcku7XkjfMmOXacts1hIdQKE9FFOk6oZl3XLVkooxIiTWU38JrBYeU2h63ikekbgahjrJEimtQ5QSDkjsepmww5309eOquGrb0ciTkZVSorNrce9RSQRXFPEcYDEscJCEVoNxmxym88LaRHeQqjsuioidkwPx3msPdTclWxZkEoDN1aGEEHfJrRjfKlp5z8pa8ZJJD2x1t0dtNBLfEleFHjCA91bGBR7m1E11LN6lcTQOOe6szRWDBajXll2VNMucjF8ee/nfChfVkWLST7sCPgAOhRPYyNYHNSQQB63/mGzjbi8L7pUMs7HBqfKIA4STSWw6OhkfUj0EP8sVrVFLDOXGXIAd3hY6Jx5Qjn/zBCUUumoqC9bImTDGt6TFcJWtzVxc9B9ZrnIiZuSKvS5aiyiNoe00ct3SckPuX6uDBh/LWGBbNU7rTb7EpqH3HeYYe99l9+7QuD2eSImwPG2UoRjZShvY6H09xYF6FTJdaYULd0SFVBJng18KDVIXuoMdWf2ZBt8A2PqNYWFpTiBwqQxEnhgSTBEcIT/qAx+qviy4orh8gw/hOXqFkb2yD/EYckRRTwlTGyxkEMdzVc58FJkChXhPWAmCzLHiqTJ+NCWbwIc0BB3Aq0QHQujD+X/CFfAeWEgiRzSZLozX36BL4GbB8Ptw+Z3DMV057Q85ERSfI8lfIYTT3wzCPnfSaB2o+xunmih8V6MF5OTD0fo7Go10YvzrPiB00vRBwb9dai/EUf/sKnMyX4eAI+NgXMAlRK3Gp36d2jgOu2h7LXG3KPpglDFCeiTb0PBB9/0tZWwwqNTPYQecqJS5AWkUc90HWmI2tCM8WVaowDXQPT8eOMK4QoQADljvqmT4WwlFzlgHtfDcSqn0JBB+tpgoy+0aPv0/p46vFUrsbKcNpH4agZdCPORyaaiDKOZ1i5deHSpQtbvQ77SrFl9ppHR+xKevvWTWYk6bqv1MoXNlYvbKxMucgFxdXscnl8kWP8mP7Ns/2DvcEaqlFt1qbUOqliRim6EYha5sgPTBTaZqOasrN7T5iZ2HOOSheW5vgk1jHu0oV84quTfq5FIUgEVMO2P3JnGAEq3FWvYQvKlulTHbSpEyBtnng4sQ1RHCUN1qNOj+51NDacQ+XDvYKeA0h4FGCN4xRoL2syPE56KtoDXwzOGYJKdyhQX/98KuOz5XKpvYwg/tVTqdAxBZbFXxZOUk++LMLDhDsEUHKsSPLhYT5MvsviBEzw0LLCK2gtS+JfAiXxuH9Z/MXhZsBU+7XRGQ/+4dR5lxVAztXzCUoeSpORtFLsU/nGtoo41tP9PZFZTzefM9AZAWPCPW62S/U1RqdMGU5zlWm7x/oWJ4fTCz8DIxyVGsJddLoe5Qx5WYo1ApH51ybs6BGluG6blavMr2Nsw3SEKc40NHZNLcFFNev0emylmsMDEihgbCIHDLx2QBqWEll3OOnv0eYGHMkzxX3qAULdnOeEN+K+p5yza984E5NKDF4z6dpK49XXPvOXvvgFgsYYk7llaGDX5Q2xFre5AGDYbXFKVio1YFdSZ9znICqtQOZkZszU3GGQK2IuU5VxDR9VoO0v6GPqGeMEtlQNjGk4qGCs7ehgsQgVKcPkggZPIHUcKCPimCNEvyz65Vc61huvKOdeHSSgNS/S/AyISZgdD9Rvm4wyowEW8smgT19Pxv4+d8QNWOw0HrBVNDfscO/QSCfjL0AnptaiX7IITkJVBZLzuMETkvIlqNg4licLUSJPMub8t4d4t8050IfmKXtBjAlUPem7zIHxODzdM/f14311rMLzgcjAJsTxgpx9PjB5iOBpw2uAKU8iNOlPBC/2Om6CEDjgTEQ+nThjqjNRPmyAmtJpR47nA/XvJ4g9KP5ZaLJCWw8dQYQypd1q1tysWhYOr8p8xhfCaeh0DRxI/Ks3sD6N6gPQPovGY4d8bApY00WcOsSpPJXq7u5u+ebNVKHWG00LZS2KwUERnshVBq6sxBkO+hZsD7NDInEZ92SYrvKlKRLTGn8xTURalDRyEXkoK7/dpanBC3eGr5bKpVypzEoTjv5nghKpS3yW4iLHZ0POp07lU5z9W+CB9MvZZMpjk/djSOjc7CLS+PnpKmLVEWqJzYqxQ03Bzvfu73KpH/NdEHCQ0pnJHIvIjeKNWoPxIn8YJzjujytnmbql5RwcHKitsL2S0S2ri5mW05wpc1Y65cFFi1qWVK9EPqZrWokUoo2AIbSi6YsI4K/ymeMVPHl6S0v6rV3yhd4yQ2GVAj/6maiMdEVGCzG/9D9dNfzZsaa8J1xc3xuk+z1xWa/DlYQ2r8WMK7NL9BJo+E+sJYeChKKZh0fkKLQh73SADPzRFGSPkGnIOxFx5If/hcJM0YX4oAE9ZO+wtRG88sJXi+Y4njxDqk+gx7E0zB+qjsQ5VoyT4lnBH6NoRrM4/0T/6ZFAkd7jgxjOGU2BD93cDYNHyvOhIjs+QulcJ0wtihPCn94wz0135iMyw2d5bZ0abE5D1nBMOlgZ0OxtfBbhRYh8DNHMWRzTz3qViIjcRzUO/tgUMPdIo4BRkzs7O99+/c1R7neHqDzunSnpLgF3Eu6xY9EsXlYbYUb2MC2a5Yj8UhWSYQhl6IrdklVZpCEh20yxgjI2smMedMUp200LuTz20GymVGSli87P14iKJ8K8zoRlvc7tJkzaoZiRXAzkHoNH43YRV+QT/30AYz/x/B4AUEQKi3/iuChgzqwv1apcmUAVMBGv24G4sm02O8IwC2nzOZZ1DsaMZyeshKN2ylvbjEOxYaC+UOHajJTlbKxsqtd0qN6AMHGo2jgy2hYQYO/llSyIE1qy+WNUEr8OwaN5fPgEVOm0afgrxSsVa853FiHWbFSsLOg0KN8sa0ZgGy046826/Umngxpm6/Ck30cLYxsHGQ6SBwgZsenJdjUlkHiQN5QCj8oUFyuks3C9uQeJg+Nh0sbEioXg035VfdDSWf6w5PB2okuV7iGderTKzhb2qrvl2lcgsWHw1eEk5JcFeCol/OS5OdweiKQktY2Tkgndv7BwJtkXfFmYkVMoSaakfwGUiDP0ZVlGC1N54Dlon5Pq/E/JcjlKD8yFUtufR7Sn+hHGvudnlviKOcr/NPwVNaTY3THepfvvcWUPtBpUV1Stmg9a+QEK6tTTUrRIz9wp0hMYrQiJvj7pn49NAWv8ayPgdKEyunlv/3i/O85UGmsf7N5FtuJCb8jrh12bqFvCeRLiutniyERMCIMlPiG19dSF4VMUMJKTXPxIh0qZuznzP/tLf3OFnTCTOlfMaZEoOl6X3ZR29w5zhdXaCosOUxzaxxJtqoz5ZmA/aZo/FXhOJdHBHJ5HY+THRcrztdTiYIl642+UEXeu9bhklwEUTYSF/nbDOUs42fYLu9vS5rxObJLiS3WpK2HPWBqDB9fCc0YHfSeqSLoQFyGo051oMjNuPeeZ7XKshLQdflIHVUCggJnDgyOCRfO36BOsYpczueGEnjJKNnKeVgWyhFwpgbWE6wom/SYLKDkDM9UbTLrdcas97vbHowEKWrfbp9MdYpUmvaFuAWRJ5pNqv+CdLFRcNJNYMW1inF2uhbcn4lHlCoc4L4jtIXGA56LAs86RPxv+MYYspOfD4EPC4B4m/gPjxHz6wIgLIsDVhPKETQ0rMexCF3DGsyzOwoQPGejwHR9lYe4h8/K0ej66Aqb12x/54aG9IbdFAnqcrnuBClMGLWoYuqFLKEr5nnJJBk76T0V6gi8fmwKGLiyLZQSTrzRY/8zlVZ0Ws4SDaqmIWETREgFZyHACAci/rfU1lKXqyAQun/DASuhP8R8cqNXOWjvFcJi9E2yDZJqZLJjd1VeNkrWS61/+wXe2WPtzcWN9rcZom2F0sVAtFtmWevGzn+c84PVKg0BqJqPThkjG0Qx/rpyTCJTN89SR1xkN5KXJFxFNpLInt5dLr6JEITpTtepvqoFMuGUXE29mguGZP6KRkApK56E3JltN93IPzWjI8Ra6SLjKAYlUs9cgGpJEmK85yJKdQtQ1lwLKSGz1m2z2wuNEB9ubgHhMJxFPeRhksyfC0hqxWMcnMzclUR9G7VOp4EN278NLHA+F/TyNtbk/GMNdne6o25kORox2+yx9ns1aLM6ajjvDUV/62IWA5/9hn8LWnCEe+R8INE70wIiLI4RMqTyXcTzpJ2GqUALVNcItGiI7Yp4kkXAx5I899JHwhAeg+BMs1BME9XiUBIEPyRvJfA3aCcT4FQ5JxjrPb4kflqsDIGvRtDJraGpugmDTSWq2VJqmnhyqZoftq4kLY92F2UmrBPhP2yM953mAbMiMQNQVIeg5D/cQnsiqhfFDWo+fhBY+JT0OEEPxcbeHxmU3EePUCUZkrojEcQWN9VyQt97X1hk9tHPQkl1a66WQiZpT10G12CttxCxdYPJSZ1vqfCWEvxdE5kONb2a9YW9/n+VZnTu3p302wyh39kCupNKl/+J/+xlUMhA6rWFltSDrpuYFF1aSmiJoC1PrJfCKH5SS8wiEBBeTOQQ8wAPhF8eAieykRjorJVTbpM8yJcb6joAKGTlxHYF0YRyORwgwwyvRCfQnHsilOKBr5Q4Ph2rEtc+kN0fxIT5VwZI6COLUphqpNZKgd5mGH45HVDQ1sL9/UF/bJpz9mN69ASAYWmF9Cl9rKZhOUMfKaNwfHGo2wLfJS1WjMemdoRmZcQDHDPt4uTpPXGFGaaqASqfmSAKC3HjLWBl7ht87IpSMpYmPEyYgqlts2X/IxX3sN56xxZdAkkvr0hEgUyzl42mTldzcVj84YK6XnXPTTh/ty/B31mfoqwmn406nWK92ppPj46N0qVTfWGcDMqA0V5Ips9SfDaWAzGs+BJjRiUCBwqK5VRlJ3Hk1xH6ViOVsWHQ6w2G2Fl+yS+1FPKp6JHJ4spxQu9TZMMoOeB27x+kcnCFhc+dWwV7HDp/ODI4GZBAoTcR+UIIbKuh5aITPCYpAIUPbPkm9QUP2AUMm9oNTTHhSUBY5R2zBlyif+S+OFaWx8imScFrWGuOCE80zCtm59OCVtPFXSRHYgFdYxfmEVwLxO6PxiiOCCmhZMwggbnjFA0yPQ4Rz3Bw+0BnAnpDSuVyibsRmSSjxC3EEQTYaW/1g1etRlcQcEeQ8omGlSRWcDIFax+D9I+JSPDCfDNkfn4GRYtmQzPgx/QC39QaROcoy0kEF4ADKws0Fip7ClVBIEYpArqCKkNciEJq+bYpBpGAKww9wKOYrKohGx5hqsyRTphwxdHq9cB47jYRPJKTYOeaw6CzLmDmWWJGEyPNCK+8dDNm9UExzph7HGJQ6nRbUEPurz60MYXt+0MRkLc8ZpzIscg+M7xFCtKUjYGLgQjZ4eF2U40OFKe0SjOfShxwJd78/PfcQgseJjkfOlpl4DsaWWswMORHqlMGWKFC9snwyAUlCHMOvyEmFUze6KdY5WwAfv6xzBXrCr6DmEIVk7J5wHsbxi/nrTE6gEIdJVJm8k8pXy0+lOJABGUd7GA5G3CfDDYDs1aEqjbrCXknoRE3TlUqZUOoI8aRJYwbAyKnZsLpSIxrth6V0HGBDb4cJSKaKOduhwGkJtb6u8mYMixIgF92/K70L2LMuGY4fBwPoqK1ctlii+1el34AMwEZOWhMHSGgdzcUhWeVqpdaok8Ws8Ix6DP3hBAXMKrNOf9IfghE7rA5bbbY4p3o9DpFqDfpHh81ypmLdmbO4PE4ICIdkSX8IXObxyCeJl8U7LzxZDvz0HkxCJZMwbvgIhw7JnB/DH/Hg6ZQWKDr514huInskDgLZFyY/DWzxW4CQ/EwOH0K4nkB6bKxOQHwcvgSdJZyR2DwJdIcf7Un3DpmsjiAqPur/SblCOg1rLSqbTdXBQPnnc/R3WU1rL3RJNFpjRozAao0jVZBJDMyUFy2a/TGmbsXhyBxvZFByAYc/BeIsVcBxH00l83zBCSeT3SM6pTIYeCJYyyGE7M56PJGH8/RKUh8hcpFIoK2wAhUstUYah0LWXC71qsN1iKtAuRTjHXaTqGeUznGOf71SB1OMa4ytvD2cmj1YjvNH+cXLSo5xqUVRUfjJuUeqL5TfCTIxDigt9pUNBsP93T2thuOMHutx1ypVxrY0BnEXelespT4mQ8w2R0uqZjQiKeUKNv8r28a94x3qkUD8GrupWlWxOtajXGbIxejfBpfFEgPhAodLa/wa82yMUPxrn/RQ/ZuDJTVvTduW2gUvTWKQQ7FUpHkzeuez7XSe5QpFnTOZlwFcxzwxqmZASWw81kMfcvzVDBP7mJN13njrzYM/+3a+wGyw8o7zjfF4Er/AdDB4IGHyNQk+hCcDH8svvSsVe8quDnhVoufy5PJ6LAQfK9Ec5qEIagVWb0AVeQ04X0MEAuR/3JYX4ATPo6IPgiCGc0yQcu5fCIdcHNPHzm4h2CcV6FjxlMee3jzNqxDaOJyH6KCjjSWTIT5NVeWVHcxUEo2YFSOwoupLJ6CzwsdAceqXjtbLFor8TUZtg+G3ewkgU2Fo94iMWgcCAGFxDjGfVKmBownUheAIFxaOSyziFXlJfI98FpTBFxx5DCAenIIWuQAnmTsR/ZWEwU8Ir6w7NWc/MnXIrkGfhgz4Ti0i6NU11+w8eZI84kQHSDdHO8dmKa2Brq0AHNVAN4ondfwYrcuKuKhgjxjm6C1MJPKZ8zh4pc+ekAOaQzrjWVZjorwnivuMkHs26A7+7Fvffu/tdxha5tmUbQ7Fdml9E/j0QAngU6VcQ2vSZV1bacAUVizKYwdczLiGYbbaYARs/WJeMNjZcgpaHwqeRe1HR9RVrtttsgOXyfoio9g0ijxC1aGRHSEhkFdv2yADGrXqChzDEBdDqziE5qyTk4sctKHROPvQlGuEGeqHEa865PqT5TrDHcI2/c3O4DLteDrhTOpKrXrj/v1Bf4QCBgcML8Z2joVhEtHYKf0IzwAiAKCsZGGMfQKHaMqXJqAw/6oUMWFOYj7Yp1Zz1qGM1dGxrEXeoIPPRv1khjjmjpvwN4KGwOCxoonCyAL87gK1eX2M0pHIE7on5PUYoIRY3GBJLv9yjGLkT/Ix9BcnSII9SfAUfEYKZ04hGHKYQ8BHwGq5TPdysGZGNirmKUf9npoq3M4wFx9ye8ryjFE2X0QmYhPDbAZMINOfpr++UuM8WqxryBQFklCjXkksGF2DNagIForgPHGCUUBNHuKfen/cl6Uj4LkMeHW3BJ8H5E9aivtweEcjKiIn68PIAQSUJU//EzzF8Y6O4Ed6SNsvbOoT3YoqFuZpVtroq+YlOHzDOk+8QkiGx9g1uZluZWVNa+e4EzabGhFXiHwSnVeESGpOg7YnOJljMEWw2OO5LHtaHWls5JVFVRAT4nFBIOdtHOzt02RoPDwjkBOpVRSd3fWL8tWaO76uNNZQh5isGTHLlXUXISG5lToP4gGEXWSYjvAoSdwG6isN7EjkjhEYyIDCb7w2j7KH2lcbawt6rtflMGdgoUZZQq+OlwzcLHnWvDSH5sJw2qGgzpitxy7ZGcJkhF7WTULabg7TkC9D4yyr/gQsm0P7MhKm5923WVHP1J/zaD3c+1xaXknHfxBDnktsGD8oGgEeaNKYb6Bvfxbv4bI7FSviBcKsScjO7NUNqb2dhFfnmlOpP8kvTlWecWOKZI7qFy6l62R0xm98ra8Woq+UK+rhPEoJz3JmBPJRgChrdbxksYkwOY3n+cAsRy/I+RE/oq8BHzwqzxmnJoXZKy6v5oAwRuGGwxyrN8x4RcNnjYQJE421+l3d4qX1JKzuNAkjKZHtpbJDLXRgf6AmTNDaiHpgMcGPKnSJKoXhfobNxvFPkQ5LR8DQwrOFGiF/ApOvIRxPiJ8MxC9i6rOC8TpQec5tqhZBeQWweNxPoGCZw6+eKYJF7GjnSkr4EwFxjxxVbdJRJ65C1a/RXKJSAZmBj2yHRCZsyhQgN7QTkwAm+Dnm8PEU8LJyJbB23B/wXAYntHkniGhifPkAcA/92QjyCPUlqkJgoevSKpLOKyur1XKRngEAeXJIFChIiRZq6rri9IXbaTvoPNrA7sEejc87ExIr8XajVLFKKiZ6cahk1DJDZ9Qw+8IZP2N/brVa5Iy2bHe6aqXm5oobAkUuIRsdr0arS7OaV0VAr5cYETOM7nIYy3TKoZjGt5jKZTCXttXsE4v7tIdBjJOltx01HwKIl6NwLPFIzUCzwvA+z2UUWW2TMhdw4A0IUeiH+PGyBABzr4R7yNnwkOQRPKrjeQfkZDkso6ctr+ZxeOx3MV8Se6sUQqxQCJUFFcQnd56p/C7XHgsJS652syirh4V4tgjLUpKdJJ25EMdaQ3g75fkwWJ0CtORlDv4cYp4oWToEhitgPknXWj+b8VJ/0GE9Fcnxs0+B+S38A42AtTCTyCzDogF3e70jc6P+LqKDzrpmgWW6Rm2jLOQsU4SYN084wXYrLWB8RYzjW6LEg9wTbw/2Lh0BA8iwijBzuAp8MMzFMYSxIbcMdU8mhRlFE7uEepJx0NvFKRw4SEnwDDjfEery26S7ZhrxyxlN9A2ixrSLs/FGlEG+5yss/yE5FWzX26gqFpflYwxFx1AgeAunnp0Wukb99CeLFblENRJ7lsH3akqSisaw5zcBaxE6R47pFAshnM5gLjZ80wWu3rCxLG0AyOwZU03Z+TXGedLP9KCm2bJqKp0a9Dq9TmsvHutjTXKVzIkuaGIi06jQ0DrUzPgnYBte3cPTnRNwc3OLE7vQn2qT+SKrrnSoCwYuNCpsYyyHSZwItEjozpyuezgak0ZPqeEubMzN5hFj6EKRZfRiQy6vyYxS7eNmqrACJnGe+hXQx3UBjoMAlphUfC2aeUb2jDIwxpff8o2e0bcP+3Oiax34h4X3MaV3koqO5rwsEFNUVcNSuL3ps796iF4fSwEDMsAR0Md1YGh4egc4wvMcYMlMk/5zknw0nxwZnnIiqpwER7wUixD1rWlYNH+dBqMmqLipVKVeYyDr5/MMuXQMbTvmdJze3bt3GfJ225wf0US2oHw5bxGD3Csvbrz22ms1XXSdp/sOAbkvlYxU31FrElmB7SGPVcOPQLbzFDBgNBYxzPA7OT6E9BBaAZpelriYFqdkSogbILjHhrIMgYWl1Ygi8hacJWReQCMwpCgK2Or35EE4VSsThLfBeCGcJfzEPegcUGTxpilg6QRTx08JUch4PuR43jeKZa1CHRcWPGux4kwHSXJXY6oYsXi5yrSoaViGraxnxMmCrptrVTCUnoIZYZolgzCOs1FHI5PndNBQd7IVizP5wmQwY2KS05zUStOK5i6JvCEWhXvdkwTPoD/Y399/9913bd6IQ1vKbErLFvKFcokIIGWZSy+z0QY/aACWm8/QyTx9Rw5zSlyJCJZgNRiwN6nLwR0M1jnqpR0j8yF/k/gDau71fOAe2bn7/JiP+hXI4o9F4+NHBfURx0/yxjlZJ+mM312IL23xJFwQXA8PbGGSRyoUZXn47D6amKCE1Rh5FhHa1LD8mYw66Nr910enDnrsPcTbH/b6t+/f4hNtnx45/V2eOKxi3Tab9Yb0zkf9Ac2Ylouk6fdnG6sqNcZORAUamnlhbgHgExEIh4Au0OT5SMiTs4nRBeSlG0CoOvcxHsS0vkEkkSGLIsSfET0OxZkgsMIgXdD8E7KSqMpMc7PoOE5eoMMySxdGOYz0pVmK69vYIqk1yJDD1CGWBN3dWGTpmhktAeDOMyJvcuHiOlCgUwSVEd+ECBPMyBm2vXC15JC7WXUz+UQbyhjWZGct3c06LaZnlekky1n6YIFQvXTlwqA34FQOTrRsd3vqFLGR2PqYUXb2E8qFuZIDLTu9Qb1Ww/TB4l3WyraGQ+5VCvFDZELgLJ7JEI+WsQkMBk8UDcVKIDTiD9FPuZy6SSU4YJJypKsbb73/Tv94f6tR/unPvZoZocCYH21wLDEX1DK0ZCcMVx/TtxvGl+t5duFJdu73ivYnIbMUu3rscG3biOwUgCuhKWd5YgZiXYNOs2IDbqaQKtZmk8NibhOaN6r5H/34h2+/zWrkEvZ7rLB0W3UOB5O9bPQxixBEXalqWTC5q77iJ0ubjX0IAC1iqxXICyl0sLKmHKlbkIGTZMOg5lTdIDXEHp1nMxH3eRTSw16zNLrQ7GfLRJp0U6nDfJFNQaMSlz0PivX+cXlaLnVbK/XyzuGtfKrNurtWqn7x6pWdvXtwHY4eA70C8mVwLl6KTpSVWcYQ5gSuitQwButSyYfgZhJXR7a2sdHpdzCUay6jUuqytp7ZazaqZ7mEeDCb9gt5FlR3IWGpkBmPdOeWxq5qBWxc5lIvHX+9MmAnsgrdZU6ZXiPLwCbsjipmxgVYqJuq9LKFLou7MsXSJFsbpTp5gMgBiqfXKk9ow5Y7eJHJbBCmALoYkf3I9FssIvNg0U5kUqVSBTZEsl0Y48pkhNF/zEEjmOPzVTZyIhBTzLNxiAr7feENJm+4upkz5vIcxyn6M/eTGkwqVTaSqfGp3rlIQz0kiEZLkoHebE/O/xG+Z9uClSN6QG18AgebmEDmlSTeFpw3PKr7+YDHXQTC4lNme1VCt6/oHE0bPs2mXGY81mnk7FZn6To8qm0sjKI4NH6ayWN60b4XGqK2SWglACghw0CHiqXPl9Vm00jshTznPY5xhDfpuTKce6PTWh+AtIKotvKAzfTzCeN3jHJYWGB3yOHrfRkDAmFWLnIDyN1J6rBc4uKPPjbVbLqcnnEkAG2FOiVPkEWS8D/H6TBw7Syf7c7G3dRMywWL00FmVGVNobk5PJ1qMQ4nv8sPOoA6EmDdQY+te1xEyqVnkHOSGpIBJjH+KAGVgHmSlTj4aWLg2W8zCVXZqF69++7XqsXy3uwYW1SnddzvdjhgDjk2HvbHnNvQ6cByqMz+QLoW7TvgZDrmtvoj4qve7UYd7gCGc3r9vqqG0/RyNXbkq1efRcvQuvt5xr7lYpeVWcNZnpuRp9kJ9xGzIcaaj5hN+5poLqCrqg0Vd0IC8z0wXIDUHvXELYu/dATsyR74BG7I45xszsIhIc5aqXutgSRVzdk0FkLskGPweFw+LUl0EhySeK4hIet+imVu3qYKUoUZwg6hQy2oyZ0kTvg0ajOTCL/A5KnrAUiBkj/tohwNt7OwWFgkgch/awg+9QwInfnvScRaBlFDPvoKjAVnezv3//F/819/79t/9OyFzf/N/+o/5Xb6nRGSVsNNOhqwIBKC4aekGOJmufMsTn8XDSkZ+MCE6ssY0qwwTE911S0iA3sQGoJ1DPQas/V1lP7h4fHd/eb+0SCTHqSzLXYKUAQVHLHFqmI7wUBJOQ4j01Sw9UlRDHjcobT4SgiYu8Ov4mAQVqo4iUIAxKsQpNuhA7VoSgDkWkN2MA0K9A/o1Qwm6J5MEasyN18xP1uusW14OivlyjWk3XiWb/fGXEqMKKTSC9r7zx/lU2l5DIY9mBNZTJvnio4BUkBnPU+aRx2Ixif8PHFOQ9Qw6/iQCyjm7e3tW7duDdrNYqMx0fkNOhIGVQLnSgBLKaJcjaySWsg6/vBQIAI9/HSdfOxvJoySWFDqqOsWN7pIvSUjfSL9trjD0DcWlQHTPKeQdU1HpZiutsYp3jAHW8pgI78q68Ey5xTkR32RCDJn/SbxiRolT9o1z/irotgnj/ygp3A3F0GNi/Hgwjw4xoK8lUvo49t3lGghXSzT56nVCuns7eu3/tXXv3Zwd+eD5nVaHIfI0R4wOZWxnqlYOl1HhjWBMfsZbdA8Lj1UFyZSkCDmpQ97oj4J8UbqnwREzGvQxAgnCiX6FNHGEH3Kj8dXwAHvpMexJeSBaBNHwisipvtlfIx6ImfSQzvCPK+QIyGEuwspeFV4LMiIbM6IboEewaHxSXWaSiE6mWTUqrdctjfsCy4nKE0njHUccsglgu89TK4ZQB+oDYvHuCqCnziCoWHJgID45hk+uUeQUWoS/DQoVmwLb/6DVI1Lnugo8tDoQd0C+waJJvTT8+nx3s7t/Z27tQzHMPU5rnE05oxHTsYXUaW/WZqPORQiL+nKAs/KDtgTj/xoUJiTUtgIGHyiaOx4BbCUhHrr6OfZtJea6sKj44PDe/fu7Rxx/CKjVxJkWKSozrCYXCPV9EhCT51Kut4FXZ5BeuwQ8lAq1dfsxt4eHm9Orph5xVVzKCiF2y0bUtgMjwlH26F4I4WNhUlqWgDZm8RtgPSJwGWcznPyk1hqkuu1B8fcisjlhowa+90W5uNCdfPKS+WONCi1AwFN17KbF0U7WqvX0SjjcUHd7UyGcUNPu3zHbGSGxLoCkU6XzRSrlLNZ9+C4f7QHF4HG8f3bhGzWStVy/u5xmgXcs1SB5QUMKzPpIpXJqnvGlepi85QG0DwC1plYmlIQ98YBRqiP62EVp8yNwlHTpoCBN/BrzCUnqffnziGA1QBl5lFd8OpFUHk1eooKxKtCzCX90een80N+EcUFX/xgowJOg2JIaeskFMgLPVKGlTGui5Bx5GlNp0Cem2QRmEcNc70pQoJx5LCtFItwkNZPsRoTkdsf3Ll99923flK6pJ17NHOkXLlUqJU0RUuTpxkCiCJEAwKTi8RkBOxCAxkh4P4iYkQ9Y0sSZYsfea4Q8ubvjCOcrx6c9J+J+MQClipgobjIgTjBjmVAEQ/Oo897zmOJRRk8KAz4TkGe7s6mIBw0eQqtyCkWXn0yTxxOiD5xKRPz9jRATN4DhpJUJDf2oDDsXDTPyJ9ExuMVychIkocTm9huwv3BLMOzrrHHUV7+RxK36JryiIMtY3v4bik1dr5xQkiaDgEmSvQgbIRmls5juIi+4dadPFdGZSe1Qmp7rVotZbC7drpNrGUY+7CXUS4WFYu/BEmvKt5yFyK4R60abEkoBaEOABjxSSsG1TGgy2TvjP/HGIW6P3nvfW4SPDhgcKk8spwlZ90GKRQlDmNKLvRVUTvDaM87MHGE+BNzruKbs4iRKG+NGTHL8YVVT+6xgF2eNLQQAnGYpH3+2W3IwdHMqSw22vIo44PNbGllPVUcp3LVYn0NK36j220N0nd2WpXWUaTFta1XhmUHyOwRg29u72BPsC2+rgwq0tDjbhsN7XNOCAUcr0wIN2p1FHOpUCSE+SfYgD578+g4m72Sz1Un2uuATSKHJqaS1YhkmfChDPSUGmZIj80iIoF+kv4QHDRc8Fht6btVgBFKb+JL4y2JLP9T8KM6F1YiuIlPecjCcvGKg0FBhc6QmNVxeNQ8PvL4xnaQF3ZCttvTOR4iimZ8ooQ6zFyFNTKqi2FxxLXGt3xioV5E5IcvgprPor9lENQO+cZ/YWWxvGZ5im0cH8OJarbeOow1tzRDFWR1ZunVaKxL7KCX5bwwHMhLHcWyrInj0chSIeTOjznCKT1P2lWfMySHQzF9qUDzZzEHZrDBTCflIRSwMNHc+gx+afapGRFIitM4wOxPJuMEl5LhaHGEuxr2V6OJcuXVMtd6DjurRyM9KOCOaETgqdc4pl5j9RHFezo/SxXw+dk50sQJHo8fFSOWqucAocy4iHgJ/7ImrMgx2GSmHp7MyEJOgolsDsCQmPCoMuJwVTaO+mYuQRp3OuFoQXSdbiVMzQac2WtJlDJ2nh3VSUVK/rJGoKlVvvgJ5GscMfolZFl4yRYNqUlKuRgfIRE0IBoyR1JkVpxXrSADF6zSozyDulR/tZxdreYzE24CYBlBZ3Nj5c41Og0MO+FO+IklurpHGZQmI6ZCF7pAz1MeLryFGnFfVQrBeFwl4sBHmjsBtCAmAouFGfO+a51MflpqcAIVuWr+jOGjpAAdCOKqvckx8wNYoTEgaVwFRCPE28ywrQM0/BNoK9wciiywBCpL88ByaoQWORJA+KEwCbtv/SiXHnI9IMZ6rnbGmEVvmxmg3jh1yLVFoFTlMMzJnd3D/8c//n93Bq1nmIszvatDQWynEzcXA+fixYsApLmiR3klO7c5p+EKOQwCtsGwVHIMUMngVqwUc+VJqtMBJmnR4oVUnbPG2fzP0J+6TKWLaCwmEqERdUVJqDTRS5S2YlvxTj8ijk0EGhFIBzz+RyTRd48KXaGTnoQE/evRFOsRneYUldgIjg/2kgynBBwGhumBbhbDdw9+RNAfcXSIrD6lUV09BpWK5sWGEziLWrZP4jIKpQ8KV7HTLNXQces46p4JcIZRztsxyZXyYRy0UzuJ/qAZfq/9ZakZ7FKHIKOGZ04/wj6qeAoF4vYt0tanQEUtxsJcxsQFVJAZ5N0jYqi857sYiQWxRFAJsuhP4MDNwYoPRV4Hz4i2P6LcLCthBk4l4WJvhjEc4WyiEp3L+el0f4uSgRi/tMvfq0vsFzmXLfGLIcSL1xFP/4CHGkQq8p2G7As2acuhN080Sxo9TlIlQ5+af6kCnkMrICBOFcVU+ODx15M4cZEExBgjfEp6RKP43f0CGKeNv0S/imCf/MlrcB4j4EN4JCvsA+HmnLeEjUU4qTxHEOGL0OTaAI9AM6OkKD1dh2dwPLuQnCqkOmNQ00FP5iAcHOduLr7HPPts0b68XBJocpi+KWu9WmTqsTfmTp3BZMgtxq1287jX74wHwwtbm51Wc7WSef7KVimb7ncOB/lJp3U44/5j8Kf3oIlTgQKwA4+RWvwb4uChyFQZqt4bPWozqiSK5nSlDBqjZ+u1cia9Udt6HlIAl+ARayJ0B7MaWn84kJ7CSuuOFRS2J/j27ki5eB82+ibSTbSdTFYjZLn/GfIajyPpcEYk85ifmQEqxgPFiHbSDZ+PB9eqLKSjEH6DL0Mz5hVkxsiur6+n8nVsF7fv3D8+OupxlPNwZidFSvVZO414g6ZKR4K5W1gCZ6pdLQX0KDSOEP/kJ4QQgp4mgjdvSse9CyX6MtPSCCO9rAUD1vhwjgi9IxiKKWsIpjl6AcWLFBaryWaRlF98TbqT5pIMfcp+YSXyejbmcXorECe24Adewx9He8o4PQZ4KB2pXqM4RJd+BW8NgvUH44v4Fkg9q2mrJaiGvOxqmcwSODdSZLWvx0DkkZNElFcLC/mBKnYmOm4YzcES5nHOMePTwhxUVVY7RKZM/GmZ48KoTzrQsUY1n+BPFrZkBprqyJtpBrsRzQQM7Th3VQh0lnjAlkxZZyylKkke09cwmaY05unrBrJAF6HOK47qcXnipSYcD1V2MgK2wntkT0UEJYypgt/DeZ51IdrZT48UslQBnwMlYBk8yciONyHBk/wa/HyFiEh3d+ZX7xJqhzgf0uMEBYhhIrB4nHBRrvEPn5CnalFGdE+oj0jFk1YGpxsQQ0sLa/hKCzUpqmPRYGssxdaJU5S4vTh3UDRLJz5wjz8nPY2zpajUiJQz6z8A02lm7t669sH7PznYvcMi2hlr/bqst28y8lpdabBhOTNqvfrcBZbqXn/3xz/ptm1mgyyUG3kNedhQrVxcPglsGKiUhpF7mCYlmPYpVYFPpXRGnLDyyvpHE+Z0IRVjvWw9XxsOfIwockkuaIoXBJiltRDTtWY18tfUa1oyTcvCvoSzEbNWM8mQYOF4kuG3mpHC1leUsZ44GqqSstjKsSOQSXb+VjOpWolhKiexj3PTMYZvlWE6aR4zQcvdvYO95rU33v6gyXrildRmLbd7oIrDWVWquFQoTZXqYiUsBz5nOHtS04LiHNxuRyrby8LTEop8gXmghhOEEPT0hUK1Ui3VSropqM6ENl2N2ZT5LaYrSAWFgYyQsek7UV2C0Z0wxxF42lm1WExQPP3pKbxRZIgOYHmUq5Ya0lNCRopkUl3Swcg2qYKTxvIUUHkskFJOC6gEVbVCmMZmf1Qwg12UK+GUAfZVkUR8tQ5i6uQHapN+l6Q4qpildTIy+aD5sTB7lERzpQAzKSjjFHWZ6TaYXSXSxQaZ79K1Ln0sxOsHYlBlfMJj88GPggdxI/ZblEoUA671CzwaHJJAIJmG5TKIf/3R2mfMB7MwW+ftjLO2a0iLLo3faPCs/jRGB5aaPzsTzDFitgao0gSn5uRC3tg14Y+aKhVHZODAF0l88AuapZrzz0V7sq+Po4AdP4pxDipGmnO+S5ISB4fH/dQFxAskWJjYkySzxk+gR8bjn3gG7PyrnufhK6YkDg6PJLBEKkI3lRlFAppPYq/YsVoXtBViWgGVooT5BMMnsCIRe3EMQpze65sxU6UCHGYR0T+WPfnKtre9vnb7+tt3bly7fe0npXxqrVFmpdVKKbVSW6Gn0Gq2XryygXhA57A4f5pFpvs1d5KWUm9cHaTlu8NysXKS5XJfwI0DlYll9mKak7CkzVEqSmkNnTkYrUVCDLPeMMMtdLNujsNQuLyX+W+7JxH5RGeChuJ1IBEdr5CCQsPhkQQHfwXR3NYe6zOpVPyYH5wlyL9V3CQQZaurDkYTTBRQirEvZeR6E3WcwY21GGMmx/u8TjtDLL2t5riSZffRpKgVT4zMU5srtd07mujfO2qneqmNSootYwe3xqmKigjRvHrhAuawo2lsyqDhuB0mafwgevh1mS7FDG0KYfiLecEKvsAsDTIQnwHx/tHdRj11YWut22uxOUu7H1JjJpc5mwR1ZsoNj0kuYYDDz9+5zCo9HRypPKE/PSFP4jgcPP7qqc6HHMBGHol+99JPcNyEJxoLuyC0glk1fERFoQakCT55zpUNyig5CGaVoJFFhbJetnngFtHK7RAUj0DKqIcNfzUZoQvVmItxp/jJunjIwisv+yO+U8yfC5LT9Oj0KB4VYbHwLHR89E98nwMHEP8UNUl7DYELoS0LJNUyxydYg6f/EQ2PMx/+ObSnnDXIgptspsDRdtpPoTsE2Ws0rRk9JYIlE4jAJ3ZR0LTVX1JlKFxfrZQswsLvjnCrLSFIiKRJ1KYkIjwQaP5pnkZKFDkiEyd+e7q/81rh4XPzIoUSktCRhgr4k1/xu0PcxD0iERFrAEMpWi9yio5Jjztz2JasdcW6MAdo9Il4uh8IngUhnoXLaLe1JjP1aNQRHuJ4NKrSGJhZBx2kQDWYR1tK8BPzmWeeQcqzEICxCONyLtnhfsLRmAspVRycI+BPfwUx8qXXxsiUEE44YzFSCZV0ptYJCYGCFUPDQ3FAQJcAEYOsScv64Flm/2D3uatXrm9vdA9ra3Vu+UHRDqe5VH7a41KP6lqVe3dgMczVpK3kV6ADoExRTbgOALNrt9Pn/BdmQ0HSy+g4EA2nzrs5Q0GJ+SqXKTPdstJYpz+AEhGV2IqazzP+RhKDYCZdSrOOCpkrycy2HjUY2JUrFaxcKCHVlOeIBwdUaGXQ0V+qF/zJJy8aM+NISM+enrkMDHKTjE5XNoVNpx8lXZIYYqE1MGjgLpWYMY+ip2ZtOg90BdiDOGYHUh7LhPop6fGw8/zl9Y2Veqs36FGvk6mWO08nR2l2r4puOGhlv+oJYUhntheh4IjLijy2QXZWHTLli3DxFWv2SiDY8wf/cAAPXEO3YsJ9DBlOy0vduHlY1SlboDxZWWscH+4gdDC/SSpppAINwVGcBt0oJsSQ+NJoWf0pVsNJ4Kh3ohkLRdMmYw1g+E/ZIS+BVLTREKgzeiu8wo39Xo8V40i5LtcVM6uWK3hNEF/MZ10A6po9l+zdJE61XOr1u5wigjQEGQ7VRPZhqmBSm1EglAEZVEIuPdHRdKUMRyJwBmexWBgOuQWKdpxjRx/9D9KSHSxmtQruQjvpPFxMdK5TQksrZOXmYztkPuDBeYwQz0P0JJ02F5Ef5IY2moYsFMv1BtdUtyhPuVSlummOnMKSYv92Njfh4ODhkPO9x4PuaNC9cfMaFGOD8P2D5tr25c9+4Wc7k0Exow6rMo4L6FlT8ICof4qeaH4YdNDn9hF6ziwwgMWoSsw5Hn8Of8gI0takIngwgno92KCHMFqnWMgPOq0yrW8K/RVHqNCO4Rf16SVTCKRG6qUV8duEjRFq+xStmi8mukxOQMlMSx6JcQOmh0NGICiPOBf3+JP1KuKUYb9Rr8JOLKpQq9coRuJFXXbTnWQPrCHz6KzY6PVLkD6bO9zfZdIXaebDHc8OUjg1QEm8HZVMYzYiBPsPKBGCIzIf8KA4uJ1FSex8Hu4SpTaQ0levXoU5kW+1ahVSiEutxVjxVEDnRhhO/nOdY0gUx9CfvIZw94RXMdwi98gKOOQ0By3kNBe+7NXiR22GOLy6kwxb7ogzh0Ccbp5ky8KBrYqxXAIopMaLL7747LPPcngHW1jZosJgioY4GA1zMzswIYCLGRE41DRP9CfnsiC461zZrXM8IqMCKbwcc54EpMgrUPpzIcEPMnxS0KrhKWuPYVd2vmQ4Umo8TE0G8BpinI8MC2E3WcGIqAgIY3gUGz5gWS0lWYkDQeDjCYUNWNn3E7r562hS3Ds43Dm412l3EanlclUKmFuDajUgYBuHkVCiUuk28hsre3WJ0Jg83RGC2PJwYWjMxyuo9LpHICA/zlo/v45hQCz5mrNZWsNNeoWoqD1cuajtCibN6LRRF47+rFKu8KrBC9nqoFKIhJ5N1StSQit1zFnqs/CHCke/dbRdQXyoFozT9QrURoZWig5GghBiX6XkqOX7Pe6ll8EcYe0OPyGUQqkVh3vvZQxH+ACJuWpKyetgiAg4YhxcLaXZsIyKVN2AqpXfaCk69JtcKs5qbaYCgClyQvzMLK8ZY2jOBIERjUwRoNp3RgAwsqyvjoqgNOlMpVjqDvrtXheJwz4OtOmFCxcAQIGs56S8lL8RkwTkwrsTEQ8nHXzzm998/513n728RiAgITg8UK9Xa+UKkQf9nkwgMn1m6J8Vub5RG0vUZXFqUCyH5sl5KsNPgLMB5Qz10On179y5MxhODo+bnXZvOIHne+lxH2tKezBq9Yatdueo1WS9BYQc9TowPMO1Uq3ODrfPfOkr/9bxfgYFztykRIEK5mX0p+nNk9KG4iN6YBb/oIqyiucVfxICNPQkVK5TzobsigNhs7NxqYze0v0kBgRGYZqFaZ1JqrjYqAkPhCzgOnShGnEm02fvZcIJvv3BXo4VqWA8zwVsMTOF6IS6n5gwpLrOOvZOY0zWLoIaalNXeQKQHgTdNpZiMnOkQwK0oFRTOxM2/42Hs/LR4QH+qKiAMwcF3FHmOGzxL2g8kL2IA98GhBcDetxQ8ATyY6ReqoCXgVsW7pTiKx7HI3gWosVX72mFyA7B9MXCFBF/842YjoYn4RkS+CeeAIeJPXL4ioeExiEilnMkIfShtra2qitVcmefJmINmYICLk7GeTvIIpmRZ0cqHFnAeQeZI9oUN+vxF/o5Hi0gECB40woNjB27ikPvjsGR97zSbCZCgw4LqTxiAtx0AxdTn/yNhvliWmcmSb+ykZaDeAwPlCQNChkNZWRm0HBa+gU6iP31B3s4mfDI7xTS2Ms/RuLyzs7BO+/farU6zVaXmdRipTwaWhxZGhm/imYYu3lqrRjbCao6q5l39RW0HVdeQhqNmsJMU3uoIZquKpUiEVlRceofR7zrnuSzODz0mqIgflio5I/wp7naqDAyTKHKnDHol6hEGh+zMYwwWcJm+4eHZKvuUhazNIMKNitLp65yCJuRQCu/qTyUnbFToaGpAZwpfUHG8TqurLgHv9SzOUJkPbMQ19M8cYTs93U6Sr/XHo0GK6t1svLuGqeDkKFGM+pUSCAKC7t4nPH8jIN7ON4LB+r0MwzXLGIMdUf5bfhrp1az2E23vuCIS3YGBKgsKZgMBujIbCFb6Y/65M5y7+P2MXduOhvwZNBFM/EkIOljfYBolDOZfPDBB1/96lfTk7aYSyJVU3RaK272qnKZwTTZSvTj5fqqUlnH3zOqAII+mChwlMji43MQw0ucRCH9/gfXv/f9NzjTsNMe7B9yhB2cwvzIuJDVCapszmfv+tQ602nbfbBWrxq3sdJx1GeGh2XtLJ4v0+s+4Q1qB+fZhLpI5oqfq65RrXQK0UgYL9CBUk60LLanSbJFOsjh8ERHic3FGmq4xFHHN5Vjeimfoa+jnhizojmOHsOiog6k3NkCUyPiMWUALHrSMrqhqzF7EF8VbAlD2hn76a0sPOWYJbPfCiMDc2RFIWBx3kgFt7PogkOqhuyxx/oyHfXbebUBN+3Rj+W+UJ1kw9pIdnmSdEofl3NuZoNRuj/64Q++32k30dmssDCAJ3okxmvxryPuOCRjWHhEBsNXnUjJ0pgznZieRMlD+ZNQHsJP2oBDgorzKc9i6DGWKuB5AOE95rAQ4J6ARzJcgeIbETWEE2hShYCo0AowursnxEx6wic8ocBEgOd49UCPY0/JSjzeDJJw8Hs/CA+VwZNoSBb5wYKbeTR+FFiEtDKS2UqIzlUQos3TcqJogdOD2WVSbwSAfPKvZz1k584jaCkCOtPksPrmWrwwpYfLhUz51HB1ZV07U2c97UvSkjVUK3Zo1kOp4RNT0yjCmmKYXY2mFWGqVYKmg6licEevEMnZmmLxKtqYxKaY+rNa4SQ5zodCYLMQOjuCJJMsx0UhgnVuHNmPGIBRp7SgKeuLgXi4H02RAtvrxVq4/BRGPQVv+UQ1NEtZ9aAVaArYn4RwdvTC8GdKrRCu5cyksillq0RVEJAMGg8ySw/Yb6Sc9eqDctAG50JuZIipVmXBRqxBMPoHk2OqQ9iZM7+4gmNn8avezUWfIUhXCtWdpGfUpc5MSidz7UmAh9k19CVSklNsUe7VCgqfETKXL7HvWVuyVE3mjJwgR92K13jCiaoVtlZTAVZh4moJMfUSwIHYGJmj9PxgoFBKkJ6yap7OUKNaf+fdt998881XXnmFbyyhH3MwH2jH1U104Ij3lYMqC4nO8IXBFZ/Qpi8+8wzylG3uOE7YPTpqKevpTMeTwGokUxWzFkATRox9vrBpNa5RkPRBhKfQ+ngc5DUFpiYip5lsUXxr68LO7uHtW3fZm8ZUC30RtsTAMhx6iEJk7Z2KgVrTJjRWX0y7/V6l5OvhxTwUzXrEnBGKFhNn0fu1DKIHfSZVUOyCX5oHI4H9kaXqwkbE4EBcj8YzeEY6KVFmjwydbVMetFmzkFkUEgMRm5kGtJSL+oizjH9BizAQxhFGedxupOMrMyOECYH+laeYyzpiNDQP5BWW4OmvZvMwHlREOcKpZe+R8KQnSiCqDvww0rAvE9JgKGH1aORs+MtZ661OW0j3h539ozu3bnD2pAShOQAG5yHLnuRCTCeX4sSqJcT3CMShRMhSnh5ZqWKC4zcihUQnHvt08jrnOwFldU3k8+PPJed1sb3ibLwQAhGDf87jeQeckqg4FYgfAvEEuedJkl/nIIdX0gT4Hgg+hNBYTgOJWOQkD4vtr9QBbyQkFU8CqRj1+mVIoSOHKpPsENvRWGxuBuA4zwUPMVkKREJLjnZjQYqWUxJmXxXB8hJz4D+pXdU5rUkR3KFrYQRa2ARlQBDf1IimZSRAaliqcsQ/BzvrUjuOS5xw+SUHUUnS2vpLTpvQ+E8ah3z4RStLQZItbRn57pkbMiAWIyPcvOAhBPj4cVpzDBJM+LE3gDlRDpOilRRyfcRSxNy62MLaoaYwJ7VVpfDyQhJNV8nchEFSLd+0Cg+Fm2ufLJpAxIEqskM1+PbNG2AMhakdHB53P+o1LVwTw/6HtiCEdoT898hIyZDETeWSnfrDaVzOKIPZTzORQV+ohSmKhR1FvMNZtE/aKg5Kqiz4GePwFc+cQ14YcVWDwZbocQjBOTWChzVroFDgX5FPmlHGYs2xnjqm2ajoaaL4s0yBNaEa+9LBgSzChDLQhWARn3gE8S3G1JI3lDpkJQLyjiAg4OeJH7faWB1NRq126+/9vb/3e7/3e3//7//9T3/601sbW20EPblrLbMcsEVPUmGap39TKg37PbYv16tVsoCwrGAzSwoY0fejQYhJid9oqANBKNProIRdXL0ZDhqbRCNyr24if+yOKqXEQsP4FPRBmg5Et9vjnHgYlRUHnQ7Tl4zWUuv1Oqcz6VQ5VgZw3ztNmgHbZMTdHEXOqq1U6TqNsrOSNo1jBc6RymucJ/QkE6OrKsX9yjfhZDPO6m4P/uiZ0WWSR6SX/HEIHj2CxguSQPMO2jcucSUWA9Uso06dt6wvNDgWJ9J2YQPNSZ91rildIOCnf7ab3UVkTXtHgEMaOBfBOR6TEIB4Wdzjz0GPMeqJ87LzDrfQA4NXSU4qEAFTdiXS8xWPaJSBsrG5MRGJWXDU7bhWqeVKBU7k15IIWormVOQA685fz3kGDImfjObU8xA+URwo5gpYQuG0s7RC61EdCT2j4HlUCEsVsOG0AJpk6SIXCjyXUAQifoI6RHAg9snki3wPLn+AjMfj46GaISgeXMALf1AwyXBDJFp1RThAeOJohNYOreL1kEUFZmDyEDhCzpxGWOaAI9mkPOwrzYgcuRmAOWNO3DfnZQzkElBzAmCM7a9AoIHxzXqJ0muwPQvGRuzzIRrL0jR4hTi0UjavcCQW63qkf5kjpOj4gQcigoNlSl0KJaPFop/QwSFfz+7skwhA4OmOZsc5JP3xqM3tIj1OckT8ZlifouPKJQaYYyaKugigBbSemdDpNOAHAiMrVWQ6zZ4pwEIX3nRMkkXm6dwvGsgplbtsLjKFQQCbBAcGaSfIJ48sojF4MziEIHp4AgaZr6GvLUzlvX/3ELVsI1MNJXHo3Ux2mkfYsW6ID0iddL7IIVe5MhNVhYIOjyQvEIsdOUm1IygJwU/JePrXWiE22RFkpfCnyx3KYsGJTz1EG1wieyMiWef+ZJg41Eo92ThdLyChRCe5Spb7DzBNIvUZ2Wh/miLGpRYOdsY8/Ql1GS05YfjVIXFnQ+L7u/dZFdioceuL1pdubGywIg+j36VLV8gq/JEC8zY8w5w2M+Ycvct2EFkL2cVmIpWBr/hTOlujQQyKTgcG06Rl6pnikIVUAuu7ilQWB3cKLRUwrm48jton5wn5bMhOvxlGlrahFMxkaypBqjdDeeAbyqzKl81f5aaWOfmsO5D5FMcV0tyc5aXzZ2AJ2McL60VPUIAlV9QXnAdEj0JrEs2IY3JLrRlnr0yM0vmWfNMYUr03KUjWXzGMnA5ad+/d6Q+6utWB7ywcKeRQj4BzdgoesiGrqMlRs8xJ9KedHp047iLQIscor3iYS+7OzxRHxY4dMcGayI63EloDx8NJ7FoVqMZufVxUMOsfpxzPVwA79Zmdh73No3uHOiKgj5Sgx66eLM8JC8pYfuDAHaVkXh4+9/TmRjQ8ycjRq7VQBwV+cyPgmCQq/hzY8HrOJ7IgGhGCx19D2ofxyIK2MN6yjM+Jz6eHx0ZwkDvmqF/3KHCJC/iczYIQXEiHHzjGwwvUj39VAtMZHo2myDvqQPwNChgm1Y9DNiHcxVUOHv4MGMKZFkhssRbtBQ+Rzd4lXJw1A4MGj1jRPiuSGgZfNPso/Yl8pi3Km2U5YVFdY5+pQctKCDIC8UlWWrcMqFqEBTwwgIB4eJnp7HI0EsjIdKWd+VAB0CqgswvfDQfew59QseqrNWqlSpFppexklkdDZNk5kytUqkfHLaGlCiIziy7JNCvHcoYg93rxJqY3UWEoHiEm+oADM0DSD45M0qMBP+EWzcOdYrtsV5KTqlY/xbNmK22BnK33Y30gld+jYSecFdCzGPKtEw8xUvQi8houcwGQskGSYRUuZEdoraP4hCMTMmLDSCxiaUCIWpFAG9j2mskN2sLEnIc49+K3tPqQDN8sNmAqksiiDM8ZWIY+RgFlRxdCCUV++VF4zEVO+eNUTyWgO6KDf5nuY8hDgCqAbVfDIQqg3J8Mcxp5oH0hF4u74WfAgABrGsi33W3jX1tbQ0MzYltfXQcV7K2wOXhqCY+Ipu4mqYBZzpeFPOesWKcWyJwLRp7YI7EmMsaSZFaPccraGZiNukGdkDX50mi4XNXLDkxCeJILTzBR4T5JjuNFdTsQRgns6ygiLZejtRVKVUivDrgKqaZtzZN6iIvDKI/5KrZL1Kt17S6kOoim8krmEF9J4uL7a3gS3mntQ6LDQ56QRPAhO7XDtbUkd0d14PDzlYG64ql+RUmwYK0VW+tYLzzut2/fvg3+ZS7/4sg+OMR2IpxPZtfNwGeZFTcK1W0NhLcquhvkCBwgkOVCOFhjaIMnJYp9DGDUur1/YBwo/DkHlj9ryQkbvQi0vtqQMb4/gGtK5cpKo0bh8jRjy9egnkiJZcg4nkpjlFyGtuMIK0JnnkQORUv6Q+BDegDryYPnnITEWfh16Qh4YWwCl2G8LINlcBwU0JLunMhzn0J2eIKfOO63sFPhITmh5DgXkyYB62AZ5MniIqpI9SThof6+R3bI3ioIYXwQMJfQhPtsVxJclczL/Xw9gWPc6zB5MopBptLUpE+R0jIjM0njhz6y/4fDiIvTMdPBWtvC7hnEu3OpJuCkdM3ZdkR8dBbM2gYQlfGE0QwPL0LI2sJOVSgQOMGqy0pHXVUtlYOyJdPucFCuskFPPKR+E5nYUcY0xhJXMojs/MnibVlIXbFNhUZBBHXdJaDtC0lNmZ0gFvNluEUKHCyXCLFxnksXLERP1R2VAmSoLO2EXrRl2IziIAuvvREqR1dAUHWmrbS+ibFGj8sZM5jxSSpTLhILqUvrb+arKhBoWL25rqUSOCggi7nB+hqUAOxNtaTKdk8HCTQ61zWJyhRH7bsnhPhrjWvUpMGwXNIbkWUS2gFKxkcbLsAYCH6eJATVOl2s3CBbHHKkJfKC2w91lyJ2DiOUeGAwaLbHk979a9euHTRT3XSffj3J2WWxt7eHHAcsihMHixIfsJubm0QGDWI2Vjex60BFVkevrK5zXQSmDQaxHPLU7nAUq5Sldwrhatwoq8k8SmD1B2yykgMUJKMcmBhhfPaGO+PzySng8S0ttPrEOUa6tPicDdyYO8J6RdVwj2yxWoJoOkWN7g7tSyxA7VCESLlCn15/cnh4eOvOLa4tzE80jUVaHB53lJbuERTAERKehHMLLVXDsfPHx8ckARpEpqKdQCGye5SQ6ScbiNM50DEgIihxlY6dUbADmo+Lp1lxNx70WTmSSq4JcKCJJ6mpIA3rsV1kC2qaNgI+ieI1jbzCxB1Le2Uat0FYmHBJodMdTVganEGLr6Ak+7wtOZ6w6owGqFkydd9x2uepSZU0cSAqy2dKmfzRnfv/sljsHDVTMjBFTuSL5UMcdt6vkDReI5X8px0hanfmTn9xqTUX9lCvCzN6qJRxJO6NkSA466zXcjbYpL4Fewn9SYBXGJ6IZJAC2lG/tE7NrJ1qhHxSFTLgCxqE+rQaBaAzH6CMVqJjBBOf+YFpXkVghEh8QiAuwpheOvtKmJTtp2bcJ4edY2Y3wZkZUAbIcXHIBTc0m0G1eH/Yy5UL+WZvNVOclHL7416DFabD2dG4e1SY5qaT1VGGqJ5XnGOkGGhWNB8hoP0bmIaw6mhBPzY5R5unlSmSR5xLlYDjzKEnFhsgSlnRxDWmUyxKwpwhG9VY94Gxhg7uaDholHPNzrAyrbJFkQhTTJTYVPmvRp4qFQssrwVUjhEgq7RymfyQ2TmWv0IhQAqXWCOTh7JxPKEtZXE/wqYyGZY105dnu2CzP0yr1cO2kxynV3BHGF1vTLqc1KgbUyGnpDegBIQ5QorggwaA59kOJKf12zy1TkSZ2n2+/MZ1aoxBBP9qCRJfmWs0AeHo8QFOUkyZHNTULZWNgw04r2zhRO+zlogdNxgBTHIiXVkyxkQZd0YhBTRwBnlsvHTP86MumTrLneJADPHqaESOWlTp0ukOO4JiJ2zjtOy/EN6xI4p7D9N1ERsC0EmTJ9JkIxvrE80bhv3Ku54eN8fZSeVSt3d8pTb6q5+qrpa7/eF+X6YIlpBUMt321mTcTbfu3Xkns7a5fzxcKU9Hnf3ytPPO137nD373d7jpmg4EQpAeRr5UvLSx8fJr1ff/+f/1vQn3bWca4/5Bd5Bb2RqXNn7z7/zPR/lGV+daaztvo6qtWozwquUyN4OVcxx/z41wFBE5CTHGrF1TPxM6aEkNioSOgW6+4qwSDA4oalZwDdENtrqCUbQk8hQLqnZ1s1bICQLB8AioORna5REl8dmffDRnGjUjcA212e+ey9GfoII8eSAvKZN+A3nqMWa7mbhOGSD1ob8Wa1g3V6cQo2bBn5lHTnlD/swyPRb9U09qLLRrm3bVokh2RUD90qDVLddqmIYOb9/8nX/831x65lnWYKJr0aBQjrEsHVO6JrQY6MRGgEFPK5Kw3DNsJQ6lGKeqHLp4qVFrH+6DOXPtzDbMevqq8psDWVqVvdETrBEN1Oi9gaX+MKPQmrKpHjvBUulSoWR3FrAXe0VmEosCUUhAF5oKU9E4ZpnWaBXCzescFzQa9gr5ysb2ynC2hcSSdDBHVup28GMnyav/IbGs1Wdx1yuSb4QTV0/rOGJ/Rh4yzYEOXltdrVRKiFwqq5yRCR3WYVEnhn3+YE9hRp91xDZyri9jsR+irzpAUGOWyOpMSjgHjWJZi/98tHOqXuMXpt1QMqpInR5Eo3Ixq53znFiAMhDNmTeYdBqr1UK5m5nVqHKjqMQiok5tnN3YZkhT05H4tSbrloDMqXYdZyt2iv2kwPkTTzSNbYEPfjzyCHgOJHiAP4EJhCJ/FOKa2JIpJMYbPxYKRCW0dkcI7qQgczmd++pppUli+MnohmFEL8eWr0kPK1xCKWBW0ylRhIUAk8Dd79EiNIwmAPRXzytkN5cWbkMiYb+DFGbc4ztUYqQ7ZKaHZagORMnF7aG3ZKoBdQf/i+ci7Y4UQbVQgqjvHqsBAU3UVCishweUiBP8cwjr05mvIY6R7CRtAHLiQSAYkiFrPAElogVQ+JPhiTw9TpQL7cqTAAZP7I86uaKUOjamgAXdZqEsmobGJjXsjVU20VmyvCJlcHhAAOeY+NNfYdSiLe8UyDga0PjKJ6UxT8TQzo02hUF8nEwIsQJmiEFkBeopCBYl1Z2MevR2uEDJBJgHkhfCnb4PnILpkYUw3WwBuc94lwsbuWkJalTz1ZW11XKpUmIaV6NW+lATNsrYaqGiumqozGy6NOHkEU2YG8xxq9fklsbBcNpqHwNZiwzQPBgMOr133/vAc1/2dJx54lz84FkW+fHCDbYz3jxkZZpg7zn453+di/wwrwCEN+jWoClplagZxtCYHNZrDaqb2iEEiyval7qQYDOjGi2aVCVu8pmVbSV8f5xRcs17wkEs8kLX0IiJbB0y50zTeqogXLSu099YIY+yoeCzSb3W4Bq0YyKIT9xgJhUZCYIzRQI9oIEb+NAPZRTOEoELFy4V61vKxuZBSGQDAPF/bO6SlkIeBkoPKZIxvxLJI3WFI4m9SiD5J8LwgCyg5GQLU2ERTry4IFMi/ckOb5EsYgwwhqO2ZmELHgIc5372s38lHAjuzsZZGBISLvx6NpD4wD8b/sCQpduQAgbL4IYs3RPiz3n0NYyKDB1CVBvSlkwPRsLKAkXKR3VCz6SeCSy0p7QPlFAe5ubxN6NiCCQaXAs7KgR0fAetVRicYeOnxViRBWxJHGOo6EmGni9Pz4LiORri+0VOK0vVH2cAKeOziMU4g33A9IsHAy0JwQM0k/IAAaypHDLXfDF+VDdl0CtOLZixHUDkaG22iVdYKZnRxD3uDxiFTyEEDzHDUxFOk9S/njQlN6XEcTwhqUCdlNiMPaan8lwck5CFB4aE/gr155KEaI4zJA8heDxQ+cb171+d/oDSmixTPzz5VKmueB2JXL7S5qSXk4QWQcQ4SSpHyfPw5Poc8yGsiCNcIYkepStgR9LAGHylEo5E5omgZN093VLwsToXWBzjECqYcwy0EAh7TyHLuiqulzjskgGr44vlSrFWbfCJobt2sWMkSWWZVKM0dhiChKiXHVIMuRCilgcf7J+dzrA3HJWKld6wOR4OGFR1BkOGMshEspj2j5P9AyFqzrHiKbwTLv7+YX+TlQ54a+UnFW0hYk5c0p98nQu3uA9+kIrqnYtHILUJbXlSPwzI6nbgl8wMHMsqctNpQdeWVhoNXtrNI9ZIwU61WgOlBukxxDJGZmt8qrAObbl3j7Tk4jMFKHX8FBlH7VjV68FroVyxYD0kcqQikRZTrmBpH+8BGWRsuoc+lURZJG7mCmBU4qu3AgqCjZAt3cxNdLWWDvKqiErEj0wGsr3bm+wWMLKCNKeDKSAas6lBy0U7jx1dUIdLBdC++QOKwu3SvogBRgh8lERQXog+cEE4yGIvwScHkTUJYo5Siw5kpnpZ7OY+CYTiJlFQQifjYhAWSjIj8jlRFnwKqYJnQaTlQecpYMcmKo3JYuAkX0OWeHCei3vCqyeJX6PmGqhDuLvlGD7UF4BI3qGFJPFkvkmCTZKecFtYisUBxpKjimkzsCBY8UoEvBiDpX2pNefLM1jAFjAfwYoZ/ymBGok5I0hMFS2ojsLP/JCPuJZlT+xNAVfx34Tlz0OW6ndaqgUKhpHF5pjJVrxOaxMPa0oYhL1jieh06wkeGgvSlvZme0iiqjmTc1SbKnJcv8TBH97cx5Mm4BwdQhxapGCEpHKhDxFxvrVpE5zqep9oX8PFgXheDgcc/NU9Hmgh8ob4wY88jANPlQ4qEU49Qig8UE5JVFeKJrrRO7FWjZ8QbnSUPNJiIg0OHCafgKCERgh/Rq+2WAy/O8eWp0MjpsMhBEcc7TA1p3eIFAtIGVqsNYnh4DiLzBOjHHsnORoGYY/ZlThSyRz7UCqzgJqleTkbGbMG6NVXX93+1KcP+9lxr1nNjDZK6d0PfsL2ytx0yBJpkgBe3TIWo0ITZCAcqCkhxt4sPkqtb26yUCs/zBa5UGY47nd7nKySHVeRdrzr2O3hkPNYtuuaYfEiBDoI5di5II3fopge/8M8k3l5cwYauTgyya/4LVy5eTivwRNx46OgorKIdFHr8LZAtQIDVvGx2qVLl770pS+tbm7RvvhULjBhNMXUzIEk7OK6fv2DP/nGH7/6qZc///nPr9TrTC2DI5VCrR0Osvfu3aNnwwA08Kom1I0BHM1QOr36YSCaP7U3Wr1E16SJnSOnkz5xYm/n6uXkB0lnUeLDG4gYcRXz/fiUi4apah8IE/EoDUCw1ObVu3dngolJhkWOzoJFRV+Cjl5cEGisb+zNjDqDZ0Dzxj/AAxZxy8icSUT2BDsFknQI+SSrOATOec4mT4YAAecUmEvor0QmQvJJOK+4ZfEJJ4lHC55l8RcCIXCpCToActBz6fkaskxG8FQhrSMXt19jaL7pswvGCKpDgDrnEGgOgYWvEWyAu89oB3B3joYLO6Gg6qdXy1oqbQJW3kon7ezcDG01XbiY/hryOodJxtufi3hMMEB2+As9c5hLWUppMw2MHYlBsBo+LZ0zAJj96ndsDSSBbIEYDdmVpKkgWJyTX7UzxLHWBKe6l6ZcxOKYy7TolxUm2ZS1F6dHMusQElB1bKGVRzvrCcn9kxLSXNWOvLxKSOokQH3S1qGouikYiAc4Fj/KLqQCOH5/zsW0fKP6CMqS6ITbJ7UHfzHI8kIqfaNHw/SvFY2HqtocIQhBxhCekYSZ9cCI4FrZwz1h5DeTm2PrTw9f9kQAhWjJETCrYAJw4Rc7lqxgZ6TyqGuwAB8wEVo2MEb6s3yMEIrP8DefzdermUlhbYXjSWfdGtdkUQVGIYZk2OgZCLOmkHlcdCvdEpIivbW0a5K9cuXK88+92EkVuhPZPykyq6yZ40dho0j29w/f+PFb3/72tzGvUvxQBPzgkiRIjPiT//VclDX/zSXzIECFjdnVPxEYUs19SqZ9GL/BP2nLDFjRYYwAyRJxwanCP/VTP9VY2aCufBkEx9QxuciVk0dHB/d3dr7xJ99Cvf3sV36+tr6BdYIxgYaqhWIuXWT1U7ZYYb013SmQVN1zg4f1Aq2U0oGWu5VbIgEX7emne8Q0ASuhqC8EjnMpfgRIlMZin32EDoTtHNNAGeMHPYNadQUccEZL9GKmYBwO8ggU4DB8VfbWzOFM08siuyU58TAIoKVJEFmLcwQsEgyrgmgpCHCk3RGsQIqcLzgFE80OR8Ig/vYQvyq1OeLyGz8XSBVx+BIzZDIfATkjxJIRkn4iOx2CJ/n1YfxLR8Ak9vLgIY+QwUJPyCnEDGnl0T9zoBv9grd8fHVnwcoojuoBD/skIY7OF/IT7g1wk+mJwKs/ZbuNNQcVQ+sybcrRfEKH0kqnEln7fAKrJIHBpKx+UtvQmg6aBKuTrN34nIgX0xJE3sVQFMMbGCQWZ8Ly1sPVqJoDzbsc629ogyQMzI5La52sMwMsIxotJ3SKESsqmlEADYPh0USGLNiGyYJH+OSeCEJMJV6TIf7iIcQHqIbeEWwV0ysvmcTBGi2j3OcyOotTlCTGOUDzmP7KU6o/Bhl7VO2MQP1V/gTfYusjXI3QDAN4cEQolatoNT6JBzKcjK+Vw2G4ABBeebqHaJigg/OMeAIHgYUnfAoen9uLPkEkMZdIpg6T1SwvCC5CcIQgUiX6gAbzxU6IMYrVbtRcKZcf9jQHSXwt288UOt0+50MUtEd0ilS18561QVyHO2g7uJYe4aF2tM4fscrKqeGM0ViWmxI5RzIVrTMU4TgqpM+2lnK9vsIQrdPp1euwZFTHQtEceIGqOz7KY8jbbxzpw/06KCdAICsgPWt/eg743ROSBM9joGDQxMw4hyzC2UlPWk0lC4pqEG5hGbnicF+acQg2A/rrGdYq5zhBtnbp8lVW/tzb3UPow1Rss+F4k+nRcXrlSpfjAnRIagoPX4HpByxbnuFhONiFNBEa5MSfDmSl1Y84nk71bRYnIKC7wIKaCOnPeoAj3jYjjQaotpWj327iEQQ5X+pFqYik2x1IQlUHRiQmW+hFXjVpS6EslSliSOMCaEMPUksuiAoUwlnZZoICQefghKTkBiWXQUaTyjOIY+AiExSpk450ydcH+i0fNSxielqAU0bcOWmJqaKddsuyDpGTnpDdaRjnvS1VwJ7xWYQ8Dw8PeRNISEiSDJffemeKY+QgRPQUN6CyNAzFWaAeXp3nobzomxMXFpDFOIYVIiZLoZgseouqQ2yHnMIEhEcjBFbKqo8njkZSwyjevQ2ggkca2rhCTxlZ7KliLTA1C/h8zcaQrF2xuw9UlLMaEevGJYaHGLO0iUUDIE7OoZ2n8sXRtKfZXpZiaDkGS+6EBBGctyi62iHk5LyJqKVJUp/vRPYEfkm/JyQkclFFi8aqM8hlnUqtFj3tiO9gw9O7xjPWPsbO4/AWPP5FgM2ZR4QjgocEf4gjksWOaM5LFiAEfYBBYo6v4CtUmlPAxCEJn/xr8KCw+eRf/UlaPCzCCYGerSdBcRLunwCI81cWyXg0vYJHrIB9DphPqvO4BEQhhLQUA3sGDj8hnoULa/ZHEeKDYEQwextBlR2cqXGfZbYohlopUynkOAjaeqQIRg5uYi7YWx9IStmjp4GpvMgNaCaPyZvpTXQ64RQWh8dQOsUeBOIcJQFxXxziXz/809EDjhAQJUQZPU0dJr+e9RMtBD4GJpaZMko6emnjMf0fMZgM9O7Sw1SmzFJbNzixmARy6IToxsq/++/+1tHRIZ2YLvb8fg9zRVE3yk97lAPjhh39TEvVLaK5TLdL3UWrbR3zgD/qyQlAY1cXX/YvKU+VUTziutMkKYF8W+ICI/Ed4F5tVDEweUNFKiVriU11whIFLR4Uq/BBZLfvvPaY0VBVRJoML+n1LhWuPR02BRihYaWgwSkL4igafvLHA+YCSjOB+9JsfVSdmT1QoKy6iU/WYE5MS33eQ7VmLkSaewU8Lnyd8xCZr8knEex1LuLJq0eOoy2FfJJgkS8aLpz9lITO1znUw9dkQo8TPuHxr8GTjAxZafpSFrEjmmIup1EyedJPvjiqjbp1F4GyFz5Rff4kQFIMbuEev2hBk5lqORkBjqA7KZR9VkS6XF1ah3jmqXDVFwwL+xJNf4SFEY9yDHQzGXIGxkmA5mwV2TezYotGAWNy5qT9LgwossANxuUsesxOc0zdEEaOyh/D4nTCFgesWOSDiKXjivRW1+LcHt9J9gmfo01A8PhHXuUs3CvWiQwWUAlREHSw04FmJmQERq2JiUfSEplUFnLyBL4XcKHHc+dpmYc3rV7xEMDHHhEcioSYeNzRouEy/EQI/pCpA4VWsAQOD4LWh5h88miBksNBtFxT7GsuzkTAA0zPiyci26PxSUIsVsB0+zy+atcC+SEEuwVdGpXBsNVHwkViGRxRA8O0hrl5uzKB40g6Bh2aayUuSoIFzQyRORef477YisaVIjms0OzWRT5qN8gslWfScjbrqkSwPIdrUClUn5ZYqw9heGmaED/nbbEHBETwG0ZRWbxEjmEUbsh7iH/9CJ6OVTKjsyHJr4/kBxRMRxLzSG6Yi3p4+OFAtrqYHsHWJPMEROuNBkfHBxjwL1y8CIVXGmsENqW6C7ROLWGnPQM1KyMw6k5jAemwFKe+zqHn+ZLQxAx9XbEJjcqRwgO1VamGIVBBNHDpHKjw6hFAiYM2nYFtGz2ggGvgkR80EUbnzWOZCX0fko37aRbAYVsiT3BTfiZwlbWaFdoXRiMMUHr4H6BNiCqqSVdxk/zS1jAVmSJ2OdiO+TUSnjRz8nYKeHbns1aISeRz3PlAPKFAeZ/jHEDxJyI/DMw4+oJfv1X+VLE9lldVEroXUn0m65X4K5GJSbQgj+YyERxkjFUStCcmCZXWLIHcRlLiFhVb2e/LDehZOgRi4vAT2YF7CH4cgf4KfD57HB2Yy8IVDmfr9zudLgCZAmMEAsNhrZEygLG0XTdbKtSQYggsTiTH3nf1mcscaj/JjtiEZgfxogJJnOF2QY5rc1J4FjQ8Bhk+PPK99rzisDIRgZiIOzw4MHcM/ZVy8Upy4uDxMvKJu1c4ww9uBgI9TrY5YBGfjTkcNX3n9i06CxxmBIWYCqTbPaukMZcTGesWvUR0vx2IoayYT6IqMJDSH6dLQHnppXPcT6Ggs+soP5QRrQwx0ICGjkPA0xGjukAPvIgZ8AcCAy+da2OFsd5O1D6AXCzkoCQXMfKRww+bR4ccyKy9FpwjRqtSJ1s1CDSMnwZcxHFMPAue8ACgyAgXAokGv/EkOeFCyZZ6gn8i2gmeimACIkCQNLDkYQRs3CSDAQQh067uKZIlMBBEvGLYUstE4xVoRAA4McmXhB6f16hccRPwQoXIpOWWDgIdeQQPoxens2/n4BOEppR4cKAK3binoTMacekfIdhmRoPjIrvV0/kupC4WW60jjJ+gxB6kxgpz/6mVSo5NRI1K8b2339H+b7bnsi6aihQ3aDKZ/+ojsVUX7swVjnsDFgRxlARWbsjCyAqLNmIQsVMtlrk2gmuSqG7OKoLnsHh6GUGGTEEejxFfzUqN2Bu02qCKhnOCe3FOnhHLqzoexgFHNYKcENtA/BOGmUvuWXi+fCJ+eOIh3Vx8fyU+Zk9qsFSuDbmYdzpC1wAKp1LyR2uyJkJMBmZUOp849IJtv0CgInilF9W3s6A5A4P7SYhJ3qwnZ4kWJLp08WKjVmOJ9IWLW0wPk0TUGufvzm4zQcDCt9Valb3aIMEaLmezgKqXgldflCx1JVucGoPmFNBabG7tcxCHmpVVBLukMpyio3u0LNDaHEQTQWBIcAsSgBDuPIa14P+i5pijgtNKrZaYCFbvjXysy0jPT4Sgf8YzbYu3acaqZtLpV4nIhxEDq+gZUxkHsMmec1pYaa9hLcvsaVTCxIQfLKMLg3Ni41K5eLy3B3gGG5VyqdnTxnEBtOZgT2KZfjEzG/DdEchXMYlXuo3lkDkoENWM+EeiG5IzkqGrQnM2yqgbgcfh001QrRmJAlgyJ8Rfo2hn+Mq/hudctBAePCFCCHFPNALms2cZPP7Zk3lRQwQPTH6aA5p8VTSAJ4PMT7i1KX+EN6vxmPSKYjWBJ1BkDhL0hpaOIWTFwY60K48muJaDPqCX1GWMOhBq3ZYQvoQXVVuc2aG1L7Ln6bJzoDKza1rOclddUS9ShXYYOs1A9cppp7q6EDWqisRybXoksuPRkMUSEzvz3WrRajYqF356zoIDM6PhMCppOllSY9zvdZrHnDMHf5Bc4eahtDb1q04arVFpnVesnIy/NQT3uRU6zZTZ8zNC+MNJOkfG5KtIlnAGYT5EmJs4Ro6ggGFuOrGMeLk8dTjodbleTUdJ5fgqMcfK24L2R1I10ZE/CfjupTeDhzIKVNy0rNJMCp40AOVrFXcKxBzOfItDItZD/EFGAskCeQQQVB27vGr1Cp/IlEDQwxEBwRRYyLMjoWrROgfJjAnHgSep8IRPC/0WyOMkmsefC/HXuUCRwoYkZATt+QqqmJBgSsYsiLPULH+wvwt3akhBXfgiL++ABLQgY2rKPYKswmpzZhaX+sp+SvXoBBtoRtdCp4USDUkp4Mo0kfrEC1mc76Kfky9PxjdXfICeDfHAhQgQORl+Nu3ZkPPxhnuJAM2dB+AWamQyG+bzNTXESc5PGmH9GszD+RooLbSl1i6DCQS1ji0NWyvj0IWmwAiHadSKYUtrv44zzyAwmY0QBGQcylE9WYYz+LRWTht6jJUsfkhxfjlOfVU+UmhoVklJ/VHdsNdUR96KVxyq2aVdiHMyF6ksW02HGzjpae5uxXynnil7q7gUnZtjQFK7pHRQC1dhMnWNE9EYEk1ZlEC2XHDOGmjWJ2AcGHPWECU8hd/pF+WbaDtJ/+mI0ZtKZy2Fd/xqOCd0XZjiJBDgCyMvCz9J+Yg+detIQmZzngAnfIpDFtCItJ48jpP4tTLzda48hMBJSsV/c3ioHq9b0keBCc8cBM+D9gAUiKu0MvFI+zIgo0weIYLODx0feJeS2upBWYSsH+QKmBe6UMDhBB+WKg5yJpeoNrWgiER8xc94Eq5CZIOvHSKoDjNdTP4AritOQMh6i7RGksDKmKpk41ZhNQ/kAB09YvAOUix6VUPDIpjGnpw+bh3v7+1wvyaLOjh6AY2MSEWhsnyDcoiz6BTTXEQv2rD1ENlJbAoY+KpO2dTV+RXTm+5RmJXFC+5+R4Pn3GsIx5OkPH5zUpZQU2NfpiC5RHk8KucZgg+rjLvLRQZYFJrz/bBplbkpuVpDrnPY4cJcPBCw5OW4qVrNFXQJRIQAObqdivhWDuFIIl48rYWrsjycZxxvxniRbha61kaDUuo+Ami2jpCYQMZRpzw50xHkOT+Z/AXIHPi4E0cYnvEXZeExQ+7u8az9GSLjCSHBkwxMxgx+YpIFT7IWolQ34ssMGxx6RpeNXePTdu/u7VtaOMNpSOqkwR2InwDD8gXOhE1OdDfSh7v3ht1Oj3uMCjVuvMByAm+yPnfGLqg8h3AqAyny6CzuEzhnfWCVyOfs96cS4tQAdPB4NuH1lMe+EQJJAja8ugsheAjhkYgVfaSMVAE0JwISI+qxTaaDrs2mq81Ddd10y7VbNAhM/8AiBS1Ro1dBhcDMx2PJZcEmxgnedSEBWl0az1ZEi5txPMnP/FoESkRqHy1JJhJ3CEkUFvu98eicQUkXNLPU1wnHRnjHPwYvKjuY4otChFX0RyC71kyuOJ28tRFIJ8AkDcHCw5zwk+q1IY0yh2WQA8gm1LAgaVwBPlPkgzqN+EzwaiTMoeEMcWA5ik83RfIKRvT4EfgP+UMZkxDUaswlAxf6ldDoJCp5dVi88Bo8C5M/aqAUMNnwJGXwJKGcyU8xPf5CTzKtIuif/fFiBfMIEiiWr4cJoEkZ9bvMEaJAywuP02IuRDE0sj2VxAUl6xwsXOk8bQCrrG2+QcktCzQlXwUFPiYrcQ9L6jVxSWoXfnw1PuKXAAaaYkRi4+EP3qHXRyoknJR43k2aBsfOAUZReXbk7pjAD8RXErrVWtwoNUrDkpU1nTnY3zs42JsMByx75Qx80KTZA4F7wlG7mj6x2JBLzI605MwdNQ7docY7sPHTXSaVE4R8lVdMUsfh7NMjnA1fGAJAlndifOYKOwnrWbbbalWK2f5sVK+WyTE7zvYoeb+LwZ+FJ5wsGLZFATDgg8dLR+5g6ySCPgTSf/aYMbnEezhS83QIAQ4xQyBew9mLDEm1wBhckIXAJxoo0TupN6oIU/ptHI9AIFoZ50TjlQjkiwcXsPJcCDH4AR9/09MjewSe7klGFsLObjEQT+Vxgt/TenL3i/n83agnONMh8o3zrzp7+/fv3tIaAO6JUldMlvyTtEpoEpqxMuOv1Oz46AAFXCitIDHFwjoQRrPA2AI1D8gkjHQ5lqRlNouArDzk5e5U6Id7EfJGHxXZiB1CyAvYvAaPxwivwROl9MgnNCdYzhF0D0l4N78+kTfFCiUQz5i1CX7AwRjs82JvEFO/lsRC1XtkLYgmShjnyYYLnurCA0YKkseMcR4VRJOGHe3yDOklxgQY7SRs5NyjJxjkNSZBLDHCoD0LpqQX8a17JCEqZ41Eyk/d1UWOOGeDCYStCbcCC0vr21mzAqtk8ePENmetF2XqIFVEeoBDpn/QsEhNGjENWpDT6TKGPcptYy2s2nR1UdOMYDISnByKSYYTzjsYc6cT97+pIx/ndPpX+Ft1i4rWfgFulD8dL37jk0onDpLzKuNJWeMop36Jw7tycVaJPR7/bLjHPwUiejkP/tn4squchUV+FJJPnrEnS6KYDD8bM5mNUqnpy+EPbIDfWPwkrkIIi8kqv7k5T/IVNHDEc0/04y/kJI9oTmloMIqlzpZWE4tTTf+BvOGvvNQM4HLJf/XGlDI7ZdGBKzBgYbkkkLlU9CuBJCFzbNe2mZh+MR85U1c6j+x8LE405DuOEDAXaDFBlDswCRSayDxsVnZokYzes8m9e3d6dgpHiAOXi1vRsHLKWWsOBUHNRj1zGowMRxLuau6M1dQVkHZxg2qSdPiDM4DRYy6Q3I26ySiqRzLzmNPRkMOUO83DZ69eWatXs5wKz0GJLTSucOG6z7XG6mAya3W6KD8uDQCQEpvDY/D1AqGgDK8QyossaqL8rLF5XkTzT/aqVAYqgqZ3cxaIz+rUQigElQIdcOVigTlUIGP0xjzOElVORWDSmgg+qU9y91NZ+B0f0OM1YEs4DtiEEI6HCPhx+AnBeQR/GhZ6nARan89DYnpEEbxdzCUUZA2E5FSvtpwQQ19m2i9w4FW2cGvnTvvoEAEtluPYLAx9dMLE0aDiZx7LLkKPhr1IjFyYset2jre3rwzTuf6A0y8Z9WoaBfCsfiAtkwpaOLfEeVnDc74OlqR6+OBQfDxBeoTkCjRSB49/Cq/BE8ITHsEjQnD+KRHBvlhdeS58goosmIIZ/CQyGhRkpJXBsr1eh5uF+MSojvhqv2I3+tPq2ujIYxs3q/7gf663GI/yhI91tDVSifrSQj3r0Bs9hV7w6OYQtWeYQl1tddPZQwskSTGJIHDlv1Sn1oQQZbELABXfnIf4awgUsZ3cp8CI39wxbjVEeMPGouzAA3xkF5fmNcEjK50EEg6Wo6sxnmg3ea7ISm9sBt2jg320bjGXXV9ZZeFpp33MLdTqGJ9ZiRblaj9AAGAISfpDYPDYVxXRo1E7VAkEB7EQ56yHyCQhPHg8TngNnrNpHy8kMkGTOODqHnIKEE/nelJ/ROATLsQ869G3EOd01ZJRcMmEBPLqT/cYgBNhHXLEwwiFLEISPNAaF3AiBLpTEYgt8UiMgwWqeoiAloJPlJclEy9jSubd1jqqndjSHk/iedECAwQiKg6zREwf0v5wIDakxamb64zF4QcWrAyA4A7/cDhistRWcuhYK93BMx522517t28hQ7FFC4IlQQIgbxnXUhiUsNZkUCipOfEUBl7uNwEsrUEDYMoJztgUCzpnFfS8pIDCBQL66yM9La0eJtllFQDn6vb2f/R3/sMvff61bvPw7p3b/W77D/7gD959/9rNW61me29lYwWLbl9LdLVULSAAtp41gUATDc1gjpoEZwpOXM68NU8UgRAimwscG6pawPjEE8ju4cX96Fq2t+LouGBkJgvy0mtmxr00OHiAV57MDRMBvzSXQQOg4wCGzs3JjBy+4+9+UiXT8ho5ww4/ya3e9OvOA6NPcWD4RDiQecWDY6WJRrhujWTKlrFWZvruT95iSrfKBAhLikxzwu2gYTrYtqtprmZWZP6QAQHfUrP7d+9sv/p5rq+AGuCDBYWeIy1KS7G1uCFbdGY+jc/CN0dv4afHDgRbCkxyeWK1gD+QIng8TngNnjhtlDjAwRPcHHqER1UcCwqPAExqnycRoDz8qWWGHKKnGwZb7WaLfqOMJ0VxEbPsQpgOPcYfOnl9zbygAOAuKic95fBIOkX0eJQHSoxVwKE/Ru6GvzGJ9DIzoyhgGrBO4VHXGsjMeWmkKcVsIswMd+LNudKcvALTwEYh/poMsQ8qYCSeTkSlCbQYEswH5rwhXzQkF0qMVtC7lEO3PmiEjqiiLfPBhrfAZAIO+gi/yfhg5+7N69fQtWuNlUqWXVzD5sE+vUHukkaMGT3izBK/QsyYIRHmjJEMOPFDcGqMFFahJp/NmrVMAVt8VYfi6zcAX8pvJ5md8i2uA0fjVER7iRQwxfMYwZNMEAItSYRQElYycjIcv9Dhc9xs5r4COThHnNcADb/HD57wyT2eFr87YhNCO4HX+8OR+FTTJpHuUWSbE40xUoZEZgqQY1GBAJ68SxJhV9IoQ7ljKmImlzkdIiDOEGiOErBZckBygpBn9PnQfAh3STaCbCWt97lormhrxL1j68mViuy0rGNENJiTHjWDDtRZb9Db3bl/7+5d0GUyaSTrrg4EQeIym0L2tGsRhv+GMrkanqhc9HG0mUR62nChzQNfRTcXPP7qdJv75Ih5IE9DW0+ysfiEIYMoAbSY7e0d8I4YeuXFF3/+Z788HQ2+950/Pdzf+9L/4j998ydvf/Xrf/Anf/qdvfvHlcagUKmbwAmA5fG8LAthCK1wQAZnpBnI24SXFhx5ZKNZQEMQRIYEdwXkvcTxqway+BGaLNTEwzEUHGZ04cKFn/25L7/11luvv/76/fv3NUa3TFdWVohDrfH0msJPXqpQG30SblkLZ1HGormHEE/in6hmPElHZJwLXDzh01m/4sURgCkBZ6UikHXtmNC1cm3KlR3d3Kj9+g++xwJmBhnY9jTgILK6fnKsdrCZRE3B2SY1lhqgYsfvvv3257/yi6liVUaciSY3CtCSs+Gkn6XJow5pQPGMx/CJioDfXs9E+nABFBYqOx3kT1S3vUaMTSbhNXiSOSvQWjQeKs2e+o4n+JPx5/wUTZS0sSy8BGfyZAVbuVQaljk/VPqV5omrlkv0lCeDHj1p9SM5wM6QZpUR64zQWox9tTFAI0jZ2ZD3fCc4OLgp4WdmRyZtDTdtXh/kwZraNW2p+kX/SQ/T3wLcOUrYeTWATnis4qRoZTTXi/jFvivwBBvxtloiwxzW27DiFIMhvCyUJAlputgXtXEAiQNiyEcio3vFSFqyNui0jnfu3b118wYz5QOOnxxCpck7P3mrfXwE9ZTjQI+FDrySNZX0n43vX5VEglYmaHdnY4YQoqnoCUbyT8vCQ8IP49GwDLcsDw/nSZyAnPs90NOG5Lw+qgNs5JSDyn/26Xkln8lcQrh7IDQQqH4emJLFoOas9cG18IYHqFBERgH7cEcR1E3LpjDhwfXSb+hOKQMckSWlbOcJfm+BwjiuM9ccnBiJlKfhAZklPzwJJ4SW6TFJS/Zg5Y1ZbGrDa00ra3XFDKnqF7uWzOSlCwVNxZoSKqCQGP5w/Bb6ngKonZg6FkwEJngTwbQvp+apURo1yJoIAQF/BZOzjmgeGDwhjie3cB4m3FPpixc2uu1mp6NuBJg3D/d/8L3v3rp1i5MOv/zlL//cL/yVr/7+H/63v/3P3r12g6vZsNkxqAqQ8QSUoAPOP1EjnGDAScU8v/fdHzi2niO6ISQPeHqEgGcIN484CkddcAZvu5O6fKH6y7/8y7/+67/OWb7b29v0yaH2Bx988MMf/pCTF99++22GLJyVDw5UEM4R8x4AGdG5AwEcfoeMB+eBhOAJT8I9Tnj61/AaxzyJJjgm++ZiKpxMo9YRDcIgeDmXZeDbbR9+8MH7YAvTibO4OQldinEwZn5RVotP6cowecj0bm6UHly/fu24eVSopHWyBIxND4OlQcOx7mi3mRdWZiVRnfOfLd1chA/zCr7nJBcpztCW+MvCAygDG1Ebv73qo3u8HtWqopCTeuEThHU2oJfGLEa5WNZmGh14kuaVBX7qFw1YNzkoldMsMueVFkznnuNRUD8aC3e72qjDQkvaKmYMOjqoXw1f6bZHaIAJuQozo4D6jfqioYRWYvKmgmuY4QTwmCYHDMSSx0JyJQKVv171X346bUYGgUsoYPplJoy0t1dCCE1MGnAasZONUQT91BnHyanPQcsBSQ1j+D6ZcKgQhjHO6dzb3z083K+XKyhgxr6d46P3331vd3eXUpmYTeSmzCMnvLzSjR5GHJNpcYTzf0nukjAUamF8wCqjM25Z+JmIjxzADlTlR67O78FDrysCZgiFcOgLBbRi48SJIZD9RpSIlYNfTAZwk1DenOmmEULl6ErUdBbhAVM2yvlOt8cwcJypOD7IDkiGcuJJxQAQRyCYEIKfaDxZfKtIpt5oHkPki80m8lQfEaOtxhram4RipZGkMwN6eWoqTOHQM8JYxLQOBqJ8gZT9Xi8/nA4m+U6dnfOpSpclUfRYCemhRMmO+LQd/Byf3hz3ovHlrNQ6Pnr37dsXtfv+kI4uWIHSjPvfQCybLtfSw0kTtEVGV12xsskUOVWDGeDReNYvZyu5UvHg4N7Xvva7g1GXpVndvoYvYH84ZDdqdq816DTvMG6jYzCFVqMhxsKVeo2dP3RZ6XlCKpkXMQOluA1YbYkK1ooI+ukYu222MkLDxAzUPKl3kGODIKZ36MI+BGBpzzfLu5iF0hk5oC454Apfq8wQ2mP2/bU7Iy52YcfWxtql9954u9ce/N2/+3fv7ty6dvP64c231lZnv/7Ln/vWHzXffevOcJBqVSVDsKTTowF5BvkcDcGIl62npUqNXbnlfHrSPS7kB//5f/4fXdhY2fvNn/+zb3/rq1/7vds3ZKCCZRjc1Vaq+93J6vazO4ctjqRnD/ew166VCpweMF3RLd9sKET5dNqtaqVILjt37lbKuU4n9ekXNv/L/+J//Wt/7Vfv3Lr5vW9+DVPzL/3CX7lwYf0gffxzr238ys/8zdu3P/9Hf/yNq1de3Nh+5v/2D//RfrOfLVZz5fpxp19prLBOpniwT7cAVPeODg9b7fpKA9oeHR+x+9NNu5hLGCah0qh6tHhjnIXyumUWtrWjG3qzEcPwSjnfo/ufnnb7nQuXtpvcn8O2jOGwWNxMDXrrmTY3IVTrG51UsTWrb2Q4d7+XGTaxNw9nuWG2Mk1V89Px+qw1O7hdeuaZr/5//umN997c3tpsDXqTNHf6YpNQI6EuS/UKV7f3+92tjY3j48NJpcpZie3DFlqku3f99ut/8tkv/+XJYTObK9EPZRWNToTgZKdus9ven02bsyn7mGngPlCDm/FIiGGKxDyjtUazESQes3RLu024jDiWG9ZC1Y8154vpvM2GJx7uaAYccZG7sKcMPDJ+ItzhTPhDm/JgY3JUh1qbq5xhJXFI7k88ISOFycX5ssFXjc7C7EED9CZIQ0BK4OiccT0UzV8DwCyLN5nAYukQYoHWz6Ge9EbUCMslM4SlRoPRpFrNdnq7mUK5fXiTimb02x2luFuQLmaql0m3s9V6LZexS+lzBZWIIXKxVFtZ7d057I92bt/br9VLK9XiDAtzlpNVuiybBktaaiZbTGXgXl0aOByM8zV6VNr+hDUGbHwkQF+7W0wfTPpj5v/L5c7xHrfxVgulfk8JjJZD3exCd4qcZRsus3AlPS1h/Ga5AMWjolmuMRt3Mnm1FIoHUSCaTV1JYmej6VgqRDdUQkPRlM17dh2n6oYUzPgyarClZLAudqPdvV2MA1y8O+4PiuXSeNCfzo7bB00UM0KXPYq3rl/bZa3+qJ2vbSJV4NBqo9LDVKDdzFpwoxEEMLWclPqW6QtbjXa2izFAUf0CGEKTLJp/Y1GbnfGugRane06HSKWoA4FcZwfwUDJs0ucCzhXuGZm0xVrmnCEEM3o/YREC/asVOfISC58/LTxisOhz/MPcY+w99esEPBVkLw8YAYcEZLwMhMeJShEnILInOSlK/Ml/DVr0EX/kbGdLMuIcWP9EYDIO/ii5jWjdj2qk/WgEYB04AonEK4zCwY7wJX7antuK3U9FwQBaSmErLbrdwaTVY+M5okrBNiSifWHfwyG7WazCoa+khb3EjNMxYo6xE9ybtxNTyRRUWeNMHCDQkGI2Vj5CCWhCJFXKl/IZVkuNBu322urGL/7iL9kZWJyGJTsj+lVZTNS32Fj9IiO2t978MXEvbG6sVCvdTvu9d6/T0YZJgckEMF0U0US91TQnrTh9EDfK1UbbhDyqU22eTuP406tlQz/nlXz1q1/9jX/zN37wgx+8+sqnuJ788tVnLz1z9fj46Nr19+kof+mnh9PJt374/fsqtAGSWFHvT/uWqaVs3k4DVTUJYRzmBnBeX2/88q/8leefvfyjH/3o+vWbN+7s3L6f2tvtjPOpg707jfoGDXIy7LABatg7rtcKSD/OILlz43qlWrq4vX375vVRf7a5Vlpfa/zGv/bT/9P/8d/5/Odee/utN99/9+3de3cZ7+7euvWf/Mf/s1dffvl7P/jmmz/6cblS+sLnPre1efm4Pfr8Zz8znBTavfH+cYdJYqoX+n/qxZdI1evf3rhwkZ4Qa7ypmtWVdSqJE31n3OXLVB+W6lyeqwObLe2BFs1z7MgaDzpdpmvz5ZJEFYp8fYXpwvuj4c7tu6x+4g5zJObe7j0CUxyIwcq5QZ/ZCe6NY6kAXAKDmY2E6tVCBxgSzTGd1Ec7+9/54RvH7S7jozK6ZDCocaBBt4u5vFRtNLsSSkxwH3YGx50hNxFWKpxZVGr1x/1h871rNz/zxXHtwsVJb6gzQ6bp0bQPBRm50GNIowyMqUKDO9v0rK4+tofY0trRHAbLwueiLXtVMU+zOyE4iRMjCE+YgdbJFjuUBAdxDFCRWFsLxVKxnC9ivSqwHIs+F3MBSmGpeK9xO9JqvVwrIzGYPB6P+7AccQBOX2amDYeYMUbsnJBtrkx3f4jUp9+jKVdTLRr5cqGk3UCsDnCMEnRYSAovI1wKAxEBHHjCPzAPlr8UZ2MqHYKD7OVokbzBfDyJ7PH9E08UssZPcSI0NgpdGr7RoJswHA7KnArAsZqTyfHh0f7hQa+/w3Q5uXOuwd7O/Z1791m2iURFPkMSjmijMDQZHOQkx3Nq5ERZCkt7A8SyBHE4cQHrLg77BP0unQMOOHoBeA2e8CnpoXbDK6XF788QeNYTUcV+opqWxcI6O9biydFdEhohvPpT4ZYRMIBAndhBSXhTiCc6qtpeIZ7RV/AjFVYk7RQ3/SeZxvcYIH4aVW6MISXVpzn0OpPuAKVKINBoZkTQaWss2ipkWwyJ8jXJKXZrsA8fq7UtIOp2fLrX+uymm2E+IGDVNL6NuN2ooZLXynlsVnAwh0F0Oei5S0dy/af/0le4SZ2+J+NKNTqyMe2qVOn+7Td+mM5W6e19+uWXOa7o5gcfHOy3UCpCT5xp25hEUK36qxZlP6CxwfS8Wr4n1PPXh3yKUOLmU9EpAiThnKavf+1rh3fukMXV554tVyuH94/Wt9Y3SkX09qWLV77ylZ/78pd/9rvf/rNvvPt++7h5uLPTPOiwIwaDdJ6tCfkyoovFKmg6DO4rVe4w14XznOpXXS1efebSxlptdbXy6isv3t8//NHb1958/05/Wrh+5whdNeQMsGF/pcpijnatsbF/uA+pr1zc2NvdvX/z2i/+7Ff+xl//1ZdeeG57fa1ULGxvrf349e+89eabX/rC5//tv/2vf/Due/+X/9P/+cev/yCTHbUO9xlxsEipWqnfvPE+NwX/9V/75a/8/K/WV7Zu3t39/T/8xvffeP0N3I9+VF9d2bpyiTVlO/v7FIAzhY47h6yfo8eTL2vSQVOqHDJY0Pkeo36HZTcMgRDKxZUCPNTt9w6OjzDRs9C9czy9tL3Vbh0jlUadXmY03t5aRRKVitntjauf+dTzn3rxcnPv1uG9awhyY9SoZWnMxSWy1GnpYqbbfvblz/7b//46AhDzx87OvUqpAOdglalWStzMwxjl+eefpa9ZW68OOI24WK6tVPKjdGmWa2KZGU5XasWJlhdwP/yoM+LwSfqLaZ1WaH04ax/i2/lu7ylG+KhfoAYokWvwOAbhNXjOwQwIuFAu/A4wfka8DigzpEUNR0lsPUFv1F3PlzFLIGroY+fyXHJU42itfJG9W8DS3QZhjE7XiyFfpjxbWeGmizon8qGZepzLR+PO0P2Cc7SFG2iILFBCNdFvYyKZ7OgKRapGk8daaE2HXzOrNpYnOSN1/N5H1q8ZDU4kspEAAYXjK6KMrWisbvGYZkI2MUquXtk2cQpIHWSgWTlEkRaU8uCMDSKJZBbTN2qC0WTQJVajUUMc3Lt3+4Pr1+/v7rTb3eH4EKELggxs+90O1sFGrb65ts5BXBCD1sPKPzgZGYWYNDQXP0DVVK5+5R5aAQMOAgKfsi8G/bGGSgE7PzmiKlrC8Yk3j+OeqIYScea8noTAKG2smAPkOANBJk5wxkVSwB6TZ3CeBa9zec29OiixI8yO7MMSiKlWdRWRHkUI/nxmhEp1hKwpIvH7HPCoFfNwGkcbaJUsSoujd1GuDMs08OUgJw5xI22+vMGix6JMTN0BZ6dxKC8GPLRcHvuUDTV90ws6kdwYEzNpp04A8QOSag1azZFjlyx40ndmoISY5RQpjCf9LjZytg5rGolUPi5UK8Tel19ZXb9Cu6ivXESvFitH9frW6tohKDJKhkreqQQ4OaanHTycREMPgbWI6hfYzlo+iZxialGRyuCTNbU5oi5+pRRKxUlyxXy72WsUc7dv3/wn/+SfvPbZz9LhgPL1xiodE3hrbesCtkoKsLlx6ZVXP/fz7ebO/bvvvfX2u2/95Pp713d2do+PJYvYkF9rrFzY2sT0Nmgf37nTfv/9d0v5lwfTgzojtlSqhiX42e2rz3HNzJVXPrP/9T/+7u7B0WRwyOmJuSL3nQ/bk8nunZ3+ODXuMFmgcn35K5/53/+Xf/cvfemn/vD3v/7db32DKaeN1RXstK88x/B8q72/e+vaOz/9+c8e7t7f2bsxGrcH3WZmOqgWCgc7O1euvHS0t/eP/qv/OyboT3/2i3/tl/7Kr/3qX337vXf/0X/9j1ix9d4HHyBEV1YbkHvIeFETsQOqlxMWJJtsLMHCJzpQhVqZCYtOrz/pd4gAxRB/HIB689oHa/XGaNi7/d77DK3/nd/8m1cuX2Iy7IWXXnzzzR9hK37m6sXPv/ap7bXqbHC4c3Pgh4XIKErF8d+YjBHXhHMzVrb/1v/gt2q1OvVnJ0L3S0ys5HK7N29gw/vGH/4Rg+mf/cpXuFuaqcpKo9jtchhrkVMccgVs/uPK2naL4ZuMp6yjyXAgKrBpJ6zxHczS1TP1763S6v/Mt6cf4ELA2U+sHgsozzm8Bs8yniaCxwkJ9Sq2WdwMCKevTr9EjVa9WY3hBvRXWhowYgsrsfhKIqAM/cyUHTq7QJUDvP5hDs2ls5xMQ/esn9MEU4rTbMapjI7cwekgH8JUG+p8Y8FSIamTNJrP5YhGwHT6udsQxxDZWrqWBThxlBfDCjV1FSc4wMA2tH59dFmEEBy1DDNQi7sKJhGKJe25VNffKCwU8Mm4xy1JZrWzHUdGLVrnlHDWhcNve/d33vvg/Tfe/DFH39AR4RghiAbjra021je3EERFDs0qsGwN5BgiYGIRKws8Esiy5stZp1Jgf9Z4yQoVVRURFwst4Wr9JGJ7YaHSWbCPHXIOqo8EUybos7BA3aHwyf3Bswy6A4nKLG5T72xZ5AA8pCK+O4hlVObLCQPxKSTBEz7h90+KTXwDAQQqlD6kB9qHCBRJpRPMGRiFk50PUvGQT542VMlgdM5khqXZVGstytiLpMQzw6E1EVYazzY21mgmHfYAdtpsKFV4rsqZhqW81u8AlmMOmSSlw8grdU8WnACIAxQi2OYIWd4147wrXmnPXJvOWBpGpB3pGrhqTR1Cxu20MaMJWDPZxP1I2eLq1uUX6cpW6qvsU1rfuPLCi5/d2z3SKIrzdDBYYWJUj8/u/+GoACWUMsbhB6qK84hOSaTaldZrw6iI4YrlGNyuownnf/gP/+H/8f/wv2POfXd3b/vqJaZ2OBWC3vtgkuqx/bFQvvDMc5vlPMaoL33uC/dv3/rgvWtvv/3ue+/dvL979N71W7aql/34qXo5hW2MTgkT3cP2cX+wyhKXUjF9fNTiCOV6Jf/ZTz3/7PPP/fgn137/G39yuLc/6rNYPfXMBa6Sn/1rX/5Fbp7BSl+rlH7zN3/z2Yvr/+K/+3/+4R/8/qUL268+e+XFF567eukSnfHv/MkfffOb37x39/bW1pXp5JnLly9OZ/1rNzqsZ4KGWDXYH/zrv/a3uOvm2s179/f3X//+cbVRZ6z9n/1n/8vf/eq/wt7+wbUbe3s7VC/CsdsfVKt8xWiCSZCpLC09B06vO+gdH0vt1UrMl7MGh77I5Qv0AdZ7zXa32dw73N+olF557YV/79/4N5+5evlHr/+wm+pV8pOdvfsI4qPDnfSIizK1DFCSUMIZBYlYlBlGNTKd3tw/rFaHrGpu7jcbjTpLfNbXLsHUVG9560q1VF67fJt6v/DCZzhaPF8qbT6zdXBwpNVwmKdXN2/d3mmOUq1Wj8kUsUeORQCYorGfD7CocuedMQzVfjJMfETGeSrRjQkjTnaW9myWhZ+PRBICMQEiXvc/eyEQ9cb0gVoV0iUPIeVYVzIdYIJVT505oGypKGXLHWXjMRZkAxUBonWoyXFGLGe0M9MN7vlcIVdvoFZnU4w9HBKHDSKb4wg/s8nRDbcZ3xIMxrCBYYRsIHZSHuYyFjy2O0JTmOnwVLMgy3SM6pXmFW1UEP1Is0nKCS7dCNt1iVAiLrMcqHvEZvTnsUlLG6+UhTrOKSHZigajg8nchNZg+2hb+fCfv3xm99a9a9evf3Dj+vvvv793sK9eSbUIXSDO2pputyyXdC66kWCMpKQjKQuBAGmmX7jFozVHZOFT8QACAvrx7BdEFClMThELD4KXRuQJF8T++IIiE7TKEavMpIdwL4B7hKeXWr5TzksbgiLiWHIPPAmx94gdYmgkd+csEyITNwonyCInnwEyNUc0G+yK3LSSSXxrjSWjahmOoAvonCoH4vv6Vj6g/8zaedifjvPTfArrJ1vSEP2snelhHB7C0fAryhVLCRWJvYUktK9mp72/v89V0ohaPldqVYY/TIgSB6tyt6eJQ2qdHS+abqnYOYjZLIr56PiQTwBBL/abR+yRrZYv04qkPu1aApq4Leig70a/U9e18kqhGHwzDzjh0PWpMOyOiMCqK93vzSoStgCwBE2Tdxy3QHu3062LVeZaIho6uZye7n/4J2SkoejJT8JBvtXV6qDVQeb/+J1r779/7ad/+kuMKBkSFKpljPV0CmhWWNRLWVbopPaaOwzzG/SH69Xnnnn2C1/4qd29w8Nm9/Ufv/XW2+98//vfvXvnIAWwfAr7MxNRTJ62mkfDfpbtHFpRNRi1usPZMPXSsy9dvnhp2Gsyaizlss9d3v5rv/iX0bL3WylO1UDV7d6/f/PtHzbvfbC5tvbv/e1/nerDVP7OW29+/Z/9NlcX3Ll5a39/jyM4fvj66++8/eav/Y1ffPnV5565+tz1Gx/s7DBrNW41O3TUEbosrNvYusRY5+DoaH//+E//7I8/9cqLzz535etf//pXv/Y1llCxmCaXLd2/f6tYKFNf3ATS6o3pbK02VlZqHLSlQ6ePDne52qper37+tU+z7OsLn3/t3q2bb73+xnTY/5kvfOHCxnqq1/zmV3/E/oxrB7fYH/Xyc1e4Ceve3ZujXmarUa3WKpj4NPDhmEO6aMzs01pZ0zgYbl99AUY6kNvPVWrcFMwOvG6rXatVZrnKjBVk1Q22zGSK9Um2MkzlW0NWsRVZFJaplDiHMpUvkery6oXj9jG1o47alO00jLsY2GkuAxe3UdU+fnvOs0GCI56ul9xBiTySHs9yWfhChFSw2CER8BKNZyhsMhV5IQD8TCcaKUdJMLyjCbd6w2fXP03LpcsL6UzHSoFKBQb7Lx5pC4BbIBv9AS1b2yybYyiIUmOY2Mnl+0z59/ptrgAfDDvMxDKFD8z2zi6bKpiJYkYDGAglWhP9MRTKALPKiDO8x5ymDIZIPySEDzzIi9r0lsonMkQogadFk8Q7ps91vX/7zh2u8yBwzhHf1VUUntgj1Z1yiJWIT4eQ8qgnaA5JCAuSkHXO/PFppdbY3t6s1MqseFhdbbhUBH9Z6vMluJehvbokNp5GIOOABITzXRzH2GB5dMcKUKAKU1McVdCTc4B9IsCWzgFTAPLw0gbPOVmGmMQJyMmzhKZ8SroA2QMdiMPkiSM8xEl6+MQrlackrLRTZ06KlnYCj3tMq1wKI56x3uJJEl413HEHZ8K2xEbYs2ig256xtHEmBcY9NIDVWNZmdxiJYI6mqbB4laRkSitgRkeTcESwiWH/CtvJvpKmATOI4ToU+Rk2Y2OkGcAWOdrQaMB2e5iEVXpaiMGCJBmXWMeq9Ugc/qwOcrvNK2Op7cuX+yyPLpbToww7a1ntwUQS53doPY5OYaWPqnOqyU1lxyRkS5mgCjSBMJIFJifAA4LZwMYIy1vUZEWch3EGQSIbvPOVcqfVW6/kNzc3EQPNo9YzWNS5AZVDhmlWqpo8VmY6NXRH1PZGEI27vQvrm2uVRuPyOPXCK6/89f7g7t3b3/zG17/zzd9/68e7t+7c/uLnP0NHhy5OazxkAMCuoc319f2jzt5h+2jv/qUrz3325Zf279zUvofm4df/xX9fLZcPemlU5l/75V/5yq/+ZYbU5WIJg++bb/zoj77/PWiOtj443F9fWWGy6sr2GlVTq6/cun396LjNWV3V2mp9bf3uvZ1ssbx31MQqjmVQ6+TTE1Qss7bYh99563VqY7NR+9u/8Te21mr//T/9nWsf3KNpMwWWLvWYRSil0isrle3tC5cvX6Xf/8Gt71c2VrY2LzyDu3qVzhaT8v3DnY1K/t/69V9ZbzR279z84J033nrjDfZHcvp3Pz/ut3ZffvXVZ557djxc7bQO0YMwXrvbgs1MkqCC88yqiefHw53dPSri7v17krAI6Wqm0+0z75JhPX0ekZ1ioMunSQah14BJ6KvVuAps/zCVK+4dHe8fHTW7/fX1TViF9iIdou1KuosCRoV54QSx7OLG9zBs8uTj0EKc94LH8wivwfOQeTs0PU21RIrLEgNK+tMUEms8YGRUJ/skaJJ0srFqlKpap6YmxZ/2VWiqiSah5boiHX9aWcyfIsgRxBJrznXnMCw60PA1vap6tdSY9rvIKKlUutIMk6mQ8ejW9RuwP7cJ0cRZY9DnehP1/jMvvPA89RUwxxDFP5b/oYeFrvDmv5y/0U5d9JE9vXPODjm8dXDUapZGHROPenhkQzGqd0/Ck0B3I65PUIMWVyAwcKTiM9qXjsSLL77I7Sa1BtM4BQxRGHvyFdkGWBFNTJZeF7OM++HHYqfFgmQdAAidMGZ71gY5IpNAJ5x9Up5ETQQ/wAtY6EAqL/4DYn8cnx+wChr8reQPRo1oHllFdgbwp9XW2fRE85j+KelPJgesu4XRCOQr+ZFcDARzS8tgkdFmcEI8lQOkJvBYV0idQeJQK9QMEIgJl8xGPYxAWt5gvIi2gmnraw2SEMJEWmqcZtUpBkA4Dg6TfmPhNCMr61uh7PijN4pVqFRGFuo4ai7exl+u5A+PdrmUoVpvEFKpFlZGVVDAuJ3usUiH6eUeOwnR71z2zXJ6FsHs7B/QPSzAuaxWKKBr5UF8d5hsxDaLluXUQJZsaBkxFq+hHeMLAjrrWPJSjQLOZi4PJEGZialY/YrwrH+QsDXaiUg0qUj/6m2xI5VanjmIxi9PTuTZP2hfWNXmMTTfL/zCL3z/h9/v6VSHzHGzjTSS0M/lOI1SQ/VSdZxu00dg7SRyB/lBKsyhbHxh0rJRLl68cuG11175d/723/zDr/6z1KDDRp1KIVsuVuiT9NrcT9EusCWoXOHylerq1tr6xsX19f7xcSWfWauu3r91I1OvYPXfH/X+6X97792XX/nUpz7F8Rrf/tY32QHMNANdJdT/q89eRnRyRBG1hnp7/870s5//7L//P/qfcJ3rcXMvV63sHHZu3Li7tX01V6r22+Mc49pimqsrR5wXP0sd7txi9MMFDoB65YWL/8H/8N/Y3dnn8gZWaO/s7B0fdSDCX/6FX/qZn/lyrdqg23R1+9eRN9QdXAS5RDFNBXP8XvN7f/qn/933/+y9t95aqZU5RbJerTx3dSNfL71348YbHBLJSpVGtdPcw2SCmGJaHTsdPT/QRmyrV4Om5JI7XStXunsnxYAbrTns9TG/M+ZgZzOdITKl1HAaOHd6Xa6r6017cB03wK+sbfC1Ui6jS3qdtkyjQ/ZxcK/DZIbpgoUz07EWSnwinVhxkWBZFv54hXBoPEkObaEF9IBomXyRhX6Qt8E2Mvo68LIYGTWCAZlGheCQzZrmgbVMfe4IWQ1A2SI7Y80i2lTVx2YfLLV039l+xIC3XK6sIlim0zVGwOMJyz/HV//qKtzV7vVRwMwNoIBxlH1n5z6YwFo0HZLaPcOm8l3MJQqsNs4kkVawaKsuFxHSfnos3B4N0ZprBYk7nDrK5iQVsaHZkBFkyALDCxBAHmdfVVr8MpPYwlWeF8qXW6wlhFbpNDv4L1+5SANBqNIFx2YHi8GEgKKPiLjC3s4GSQQthIngYrYTmg9whqDiCAMe9iPvGedl8WD85I47E+vjD4gUMCgGXN0DTfGE8OCH7h5h7hmGm8lwklO9nsSpwNNNDkRT5dHLswhWnegvNIPiuyMynoAJHneEk9zjYHKBDQXWHOMxh8wTitvZAyCsOQAaBMoIBgLVmd1RSxyk28XPXLxy5RJZwS6NYpUxxfffenewVtqs1Vg130uPtra3aX5INJ4vvfTK9qWLQGAGBwMm0tQP8Ydax80m9VwuaCSBCXQwbDP8oycwmfZnqcJ7779142b++eefRyHtH9yDs1mYyvPOB++P+kOs2sNRV5sAp7Oj5n6xUoX/d/Z2Oc9rY2NLm03ZwMx5HFqXmmEPQ7t33CgVEZmYvmurVb6yaDuTGnLYKvZDOtXMa2udJLLaGk+gJDTBfw4vQlXocHRwDIlohMyIspeaOqclSkVzV4S1FCc+T6CZBGF5GjPBA6R/q9XZvLwNbVmoS26wEf1cXVhOdbCfGJKonHy3HXl5LAxZ/rPsDfWArNncqBVz27/1W7/1nW/+4e2b76VrjYP9ZrVYaB11KNyge2dj8yISjiO9sfKvN1YubV84uHu7dXhUYQcIXR4WRfUZ0O796f1b3/2TPwBDCazBsFEtZ6r0mbjnETg03Vm/0x4xNT0rv/Typ3/wozf/+e/+zoVLmxcuX0wXKtuXnymWV2/e2/vsa186anYODo/pr7B+hO7dFz/76j/4B/9ga2vjpZdeuri2lp/WLrFoO/tC63OfQqtVynW2YH1w7dbv/L/+q1qtAbf0rnUYOq++8EJ2nIfZCqXi/tHB97//fboFzHCMR721OiuWuys1pC/Lzw4y+cZKKc/q5T/62u99+ed/7tkrV27fugGtqAsMNTYlgdSeZAopDNqpenW70Ng/2r+0tc40G4ONTiFbtbU56TXuoq3RSMii2Kisr9Xv37vF3s5yefV470BDpdH44trGxuqaNrgjCtEYwwE6mOO0OCf60oWt565eTLMrpqBVCzCA5LRWaVFx1vGyrXrWg6V7qqkTmpjFjOSD0lgjVYJzHYDVtbMjVhhfwhcwOeWlHcE/asUMzenEZVlwpP1Rj+RAzOMLH+HHG2HMq+pEJxzAUST6Qm8aTRjLdOJY5mor2G6RGxSTBer0dFFItNxamVUaGLDYJkSfik3cGLFIrc1j+UqV4Z6gj7tsAKbfJKvaiA3HpWyGrluDTjIHfbIXftgd79072rywygFTLMZsd9rIAbpqTIJRaDa2Yaxm9/DaFh04ZnPYk9Gn/6aNf9nM2z858tEw/S6wxsamXgI7/y1naI+o9S6zlVe2NXqEjbUSeTOqBrv1zY1x8z4mYa5kp4BEICYZQfyXX36ZjFxcWJdechjn8pO6pkt3eHRw9+5d+rhYmBGDn/70p5mVa3A9y0oDucTiRO2vAqI5Gj6ygCtXJ9rkzUbqOixVrzaGXTYvM+jn8vISfWSwIjqU5wk+phxUJbzip7JMa0eq1xCOGIII4MbiNLoAjDt4JS54coADqoC2QIjDIRUOv5VOMe094lvCT/dhTsL59EDnuZyN5jmeDX/ACJgEpFwG9Cy4B4YswyOZxbI4AXgSJU/IE+cLjeiBwoK8ipKnHYG6hsTHuKyCh8VyGSoelVNZX3/vxvvT8dFasbR9YevO1MzL3Xb3/qTd0XoHpCfA9g4PmLghOi2ByQw4VQai/pAQ6puRVnrC3r4xkWFQ+n1EaLdXWi1WZsEdTAd22DQoqTiaHhzsgkAV3VJQi0KhTFj1DGuhrNBQ5dx2HsXPQZhq3oxzADWcaFKaxd01zaxU2TZy2Nxt79+fzgbrG/Vhp8kBeIxdkGVM5flqWV16JvaN/tRPlxMvGn14dSp5CM/FEk40tzRzD0hKv1b2hllKB/2wULwgg4GcMlMiMtdQwb1sY5B1zjLSiQeMINT7RcJDDa4VAKA1DNR0lrbJ2ZH0m4jH8iImwzBeqbOvEmHny9fZJLS+dXzvvna2arE5Vn6OtqeboP48jZ+jE5jmnFV0GTBEEFbe1adPhsU/y4bb6ne//8O17fVafe1Pv/OD1Rs3Pv2Z1158+cXD1uCo3bu3t1cu1ZFTaHHGiUiARim3tVY/2Lm3Wi3WS5lt5ni1NJXjfzorpUqfS59m6csb1ZvvDN5547sMly/Men/c0xwBGgTHFibOirl27X2qWLtNEOgZzjbgGIappg+wVg4H69Vyf9BjsHD/3r0XX3qJeQcGrjLgaa0KtUPJREoRmPF+alrHOJLLcu0MY1Yt4eOKnsysVq2++97byMc//eY3mVQ+2tuBOS9fvry+uqblrNQW6wXGnPPCQRoD5JzgwZnw5bDPkI5d1Tb1IoG40KmRCotTzhE7FfSUX5KiIJnVXLiwfQh3Dv4wNn17apAOJAKR5n9wfNBj1UUvs7FJjZUY3GqAPBnduvnejevXqFPm0uFkdqlhDWPvGTYMqvL4UM9yiWX2zzBGZKdvdVJWk83rsLZsbiWbXcWqpF3g4wH9ucHhbXok9L4Ra7oFIVMwRcZgGrWovzBylEfd3VNOs9HmAE6+mGJopKyOZD7kZ65+GbJsrxThbWQTNjPaCHIMocj0NvO2UING6DSh9avpzNihkacjCD8POF1mNGq2WgeHeyywokOD3eXOnVswHnKP6Z5+t7e6snbUPEZMYjCg+K12F06j+9If91YLJdZosrQFPQ1VQQ+pDKbLaupsOCGnNWVUUv+x+BGDUgQvxakYn4yXpXPAoRgBdYoU/I+BvOhlENxjIlhgRBsTKAthEjkZPvea/ASvMwespwSDUHXncZTQcsFjnKRuDo44VD/9WSQ9MeGbYxbRFCYrz26ynwDbzwGHa3S570epdDTB3h7xmYzkXgGYleSlchXOIZBOFtFooeqI2TkJjAgVAe1aKLzyyiseSC6wo4CZpuEACfbR1Op1cqHnkC+xpYbJO7YdTxlm0dql+NNZFkXT1lhUy66bg15zNO5x6GylXFircETS0ebmaq/fmox6nPmCZMckoPWFuhSMBb/iQvLSMxA9rkqFmvNP4TUOPvmlaBCMCEa0k3B8NJ5hu8eYlv41ggmy0KRFLmQ6PWAIj1mczEVs+q9aw8HwWPP1wTFQZlSHlpqxioRwxhJ0SkpoXLZFEkyniZ4BVjTmYo3I0Wknqyv5y5euvv/WWyhcFsFhgS1j2VLJyXuCMqcPTy8Yyrc5UhsRo9G4GIGhFWqKw5xQRW+++SZDkC//3JeO24f7zYMfv/WTu7uHe3vt23cOXn7lxude+8LzL76ggVea6JlqpfLSiy9e++C9O7fSjDUvX7pIv2rE6ZXVQrlUPTg45oDwZy6/0G01d3fuHh/cv1QrYhNhXTQ4YAS+3zrCUsnUAeu6WdfD5kosjSzwptPFXDXwp4Physbm/YMj5Pb9W3cwaDNKaB03tc84rYGmNxhVBmO4Yb/Z24cgsP1g0kRJQHleWd13vL8z6rU5dOvb3/rGn3579sUvfpH5ue2tFboyyHJ6RpM+mraDzEWM0g1ilxorELR7gP11jDdRBY06yxFP1fQn6cX4UAwbPI5deD3lsW+EJFoAbyeyRYQ910Fh69HS+dOkjpOajYucFAZ/wdwcHwDrcTvkvbs3/uXv/o4meGzGvrGyVqrV0UP3d/fp7Vy5+DlGimR34eIWiz45TqVchb25o5D2MMSGw3AZRPiPeECqjLoHcD4VzMCR9jzhSDo+YVYjMLE2ylWvL/FwlTzXX0YWUdfMzXJiCB5WQWIovnX79o1b+2trGxe3to8ODt5+75211XUk1eb2JUzEaifRBk75TOerGcKrLOHm4DR2KhXK9R6H+GUKl69cZdR79/7O/nGTFdoarU61O0CilX36w/HRceu9a9fYZH/12efYL99nU0C7AxP2OvxGAhaqkqNVzEnVzFVLqCmP5pVoImUuohjDv5KEyrOES8HOJ/6o3qWAwSwg6p6Qe7K0cbQHcGpIu9ADfM+CJwAlTsRq5rUE/tXThpi84g+vxHZ/Mhy/w3FLhVFc5A4xLZXi8MnDYUSpiSjhDLXKps/2zl4qP17ZZolOeWOaPWZRBIMEDEtsldTwrkC3jsWtmW5Xg41s/tlnn8X8gu7BVoxm7bfvEwedRC7ARyCSChxQJzg8OKZ6AMgnhDIaCdMpo7RCKY0JL18qcyYmSY5bh1iG2FpYYT4OMtnAFOTv3L/2rT/5k9/75/8M6b/RqLz26gvvvvGDuzfeP7h/q5RJN6rFarHBMgeNyqS0JJopoDuSx97H+QVzqAkMK0TEyhCQrcj9aQ+7d3+UYtxfW6lxzJNmKJmHlr4DC/BHJ5IUqUFLkHQysaI1Y7qLGx3tA3YzoKHLET5ILvUiZPHTAYgMjqkEMlMtsAbcKo7pTGYEAIZdg24Uh02xEJl9UaQhGnGwpvIKGMaIzDgx2QxEujXAlME0N33hmau5u9PXv/ud55+/uLG20h91rt+6CebAY8n097//+tf+1e+9+uqrzz3/7DPPXGHWoNXuSdYUSnSM7ty5Q7lY70a3Y2NtnVKYeUNHi3zmM6/cvX+Hg6zbPV4LdKHoSlAl7BjGNHyxcpE5YFYG5HPpXruF2oUIo940X2FtzpRVfNjSuR+n2WreuXnnpVdeYY8Q3cBhhpl+oS/aibGZRIQkM5gNUwwDWFGtyIFiVj+TzKsvv8hEcq1SXF9f/Y2/9etYwsGZFbccu0rd0KNEFmvWB0ZhBT0U5cAZM0GUC6yeK64yVjZlsJBXlIcwOeGoD8ldC3M5G6hsrcHyxO+ZeuDi8FjLKrJha5EFBBcS4jdQUbh9PPWgG6cz1pjAhM5mgqaxl3XuMaaNAgdZiiHJIp9trNTYHLixtob5BhXZWKmura+xH+/azRuceFzIbr7w3GXERZ6rHGZtjoOkv00bZROtFBv2ai1DkVDEKkKF5ssNkKQBqT3Dx+zjtdN2yUyV6I6TmdWtxKxxgj+MYqwelYKInFhQxq4Nz7GYYTZjyuzdd9/F1p0rVbZms9v3dr/57e+wcvDClWdXGhzq4tZ4p7DLW7rzqGXMXZyAa2PtdGr/uPXmO+/R6n/lV3750sVKszv4yU9+wgFhL73wfK3RAAg7gQ+Pj1g2SAOn1NxKurF9iT3rTGYfHrd2797rHh+zgprxPiwtYRUtnBXaIAkdoiKeriwLpLT6VVQTSvIkXPTJwAJZwJPQEzGfoDdkOgdzWbh6W2e/JduSf02GzIF++FcHxRMdaTJETO8KOAmECO5Cprx6BPfw5JPH8TqQqLXYBMKpqDe9mrPySW3g4iDNFqhXaQ6/tvIVCgwi2TxaefXl7cJkN9U97BxwgmqtsorphoiYmjlqVcOpPGbnjqwuNMfRiB1GTP5hombwSqPp97BxavtGsajrQSXsxqyOZmKIQBBQR4zhnZQArYtZ0nRBlqtpptXW2KWeLrJahoyODlu0QZbRlIs6C0Gncth+Hiw8m2urLK+9fePdd3+0M+kcv/6db7X3d2j1nHFIlxlZTHyIAIWxZLMYSiU3B/zY+8i/Sut/GsmeJMeyxAshrPLiOAEmhC5cvnDz7k2kgTVSvsgmyuIhXvFzyDTx7RPjBgapUF8nu2NfY2ABIAYRbLXEIrqxsck6Js7ELnFEMUCyKTrLmPVanVYZoZFjpxZbGnIc6Mj4kasO1Vln+Em+0Jctm4wpmTNELPKf/Y7cyyjnE11afAcafClmpttrtVt3DsddJNL+uNupUieslel2irkS0Zt7997stT74yQ8ZnbC356c/+6ka08n5ynDU2z9ug0+tWqGmKRK2ZfYK06lrdztbly58+jOf+bPvfCe/uoWCBA+UNPZuSnkfI8vNW1cubXfag9WVKoMgDL5MDbLSq1qo0McYdfs1mGqmA0pvXr916cozmCJbxVaWtWN2+ww1IVEsactuUQQqRnfYynYfY33maM7hkPENG5MQeS+99EKVgw9TE87Z5uSQXKqez+mmRRkXoRZ6XsJbB3hI5nOE6oyl/hyKieWg1ulPG7JEL3XiCnNkz68/l8Z+ch/I1/MKHocdXoMnhCc8QpUIOPeEXgQw4wJ59FNP4kM3nswJ0ZmucPp3KsfiIpoYG/d1D6mWOU5ffuHFL33xpzDGFjlGm0VP9IbVCGacWI7OXl+rMGF0cHR7NDmuF1bYccT54iyt0H4k+kAsr26PMjBKbVVD4eGYJR7kSHNG+8+ynE7GdZld9qQxpQqq8ANfwQBe5r+2X5xC+eQF+xxCTGIhqwkjGJIVna9+5tMsoUCmUfUc+pcrVtY2t8u1BgtBORvcEiO0XIBY/apd0nulobHWVBOtsEmbs60PjtkvgB2Q5WkkRQ7wdGs5d3vCWpdXVl5+5SJx2GJgNnxN39GoDw4PD3Z2Ea0ght2OljLrMMkmR7nCM+nRN3NEkKg19KAEYZ4kfBV9jE9czn9kzOkIPOQzMkET25hPxZjzBLy9eAnZ+1BZBGjLYhPBHRGUxekMAk3xBH8U0yDGqU+A0EjgMIiuSf8zDiCwFI7BFw7VgEplAYP1+Lhbu1fp9lMbGHeRgH2Wux4OmwwdaDmc34L5ib4vtmKWGmFoLbHJMltgAQFLeXKFEgcCM8LIpIvsjO/3WICsXUYcg4UihtnAD73A4NiWH3PxKmcdSgfgGAy3u4N7O4fkXq1q5hgkWUfAIqFadVouSmP1uroNAoS7/falC5d/5a/+0ptvrHzrj77O5mTO4HnmM5/Nc/NDJsWE8v+Xuf8Otm1JD8O+k8/eZ+99crr53RfnxXlvcgIwA2BIgGAAgxkkUaIFy6UqlSxbxSqXHP50SeWyKZsSJVISqLIlSCQIgiBmEAYEMDPAxDeDefNyvDmdHHc80b+ve591z00PMwi0+p67dq9encOX+uuv02UABEDKCX2KrGty1HV+Y4iT09H8uiiH5H73vI2wj2LGr4R3jsztj0GaO9sL8/X0XL9+9dOf+XjY5AoCXWAQHQAWZW19DT7JJq0WjbJ2QnYXcIuZAQ3YC2aXBL0/cNnO6PhkbXxid+sKkxIg2OBQz9YmwdVwvbV1ODR40B5o7TT2DvdqY1WGFcMEBjSGeWzHoEeZUFNPP/LAfMD70gEjmg5kb90nKYia4Jb3mhs9rkMYGzl/en7z9YXevTZT8pfefv2h809UqpXm4I77FhsbS52E6FqbQ792Y8Hhig2lEWHYvhpsEgZOT445KQKQzU3hJXpbnX1a6yfOnJ2Yn3/n1iJ1WePeOzQ8Nzt9hnro3Mzs5OTvf/l3b1y52LOnmkO7O2FU/6B3Z1B+A30NhuzHyp3dA2o+11eY1Fy1GVEqlduN2GInMjGZKOTRiCbgH3R5SVAtIW+I7gw9qbBmuLG1pe3vXrzwL371X66uLmNHPvbxjzAZNj9eM5R0nY26vjFO4D7/YMmMJRhng5qAIa4lHHKFJLHFA1yaSdLdjlCs4gek+JMJjnmYkP1xT876/uFpwudaSpIrnDy365Nrnp7afTv8uC9oetaY9XyI52NJcog7pJge1KHDI4FHocThkdrkxBSDUGW7D+WR6thYNtxBAdMBnX42o0IAQaKxMjlF+AXf7LY7G3ay3GKyucEea+9YdabcQ4FukGLXAFUP+M72r01oBxeHOkg9ACLJQlQgFh1nUaG8TP3M+Aq5a7lmeZv1hTC3ILwi7x4++3Be7y4qIi4ujdTK7Mnsu8Ng19XYKeOYJcl1B3o4tGRt3FCoZHtzoFwZnZ6ZJ54iMqG0jRyfmERxblOs2tvbMu13Ww2AS3G2n0+cOOEsgTroRqTe0GDJYgTuGPKk3qYhoe2iwBieKDWPV7weGxVpc4hnWtkpZozbHSOX4uQcAqMldJBkcLk1f2rP41U9XsiDwh+4B5zb6Zlzkb7wH8/3B/HnrIoa8IQ/ZeyT3+M5x6fkjnsKvy/8OT4PZzhz58YTNRq0ZiIYE695V/Uk9FXRkUOij1BeHIbRKd/aSK1T7yzdWhweHHOgNTQmelZyDmAbMSCq15wDZ2lyUbyiPWhiQXLylCGuyoQ7LIXISGXgZuabBEUOdHHZW6SLr5jeuLZPJshFij0gdSpCnfu33WXTivoEzw2RUnWNuzXSpkgYNjTpQ1USLzw/Oz/07PP7jfqHn3vqsNU+e2J6+dYNxg4p1GgX9ggPGI0NeW9uQXcu5pdc4e6HH+wn+i39FRnmdPqfMk8s7N3Ybb5x40bS4wCIkk6FuhiUMJcd48ofCz6GwEDFilNH0jW1tP0JtzjAXOLb7223tsdGR0bKlYWFrX1q3tgDV+UdWKrtQ0pCwyUERme3rWjYl9Hp9cUWJrhaHurvwEAwijzTkR0SMwqoYWfRsEdxqmq0jA86TSVmxka2BvZvLa7tNrbYQ1lbvVUuj01UhpduXJmbPxgbnZp6+Iwp1mnXnbJwVme5sfvelRvl0iCr3cH1b26T7KEXkBG04ku1MfyljSyyOFTdcy986M3qe889+6x9NYaxSdLXl1fcwkbR+tnnP7x4a2nvoL/cSy64M+wCHKeOOxh3dEMfwXBw8H3947WxhVuL5gNYjp5CpHDKNbjIO+xVuX/UZjA6B1hDWJJ9UCwzqVgU+Xt/7//xla98udHcVr1f+41ff+W1l//t//XfmZ/5aByTSbMiNov1S2KgrAmkJPrHViPCyBhVRqcGhquELz/YBPnXF6uYvYUnl128Fp7jdYrABHR4Ak/FUyu70OZ4zPf3S2KFwsfOyLniqn+gZqK5Ujxm1B5Bfmy1dJoMyBBF9aPaUU52BswKxwjK5Up9Y3W4NHrqzHSjvlTfNqUbZNtrq6uw0dWrNzbWWjOTZ8Zq4z0HuzjM2JeP7eCYu0gt1DYu0fQOeRrcq0F3ukzb5jD+4y7DHNPGTggw5dNWY4v0pN5oT0/MjoSm4cxcvTVSGbOtRsyWEHOgw7tdMCHMnkPjWNgeR/VqY1OIQaQEIgE9guYA/VjlDCq8N8TOtp+BBQcRd3bdkkm5MA6H6EAg0iv4CRhKYmKDm3cXd/R+70gJicC7++AoQfqNCFqSXMDDOz7+L+KlS8qpqPqpUeE5Xrv7Bh6P8Mf359KLfHLHFa/v4wFPQVPJdTK8mscjKhxh4bTp3uQimMeG3OQwNSnRdEo9zXr7YGOTRE/glRvvtdbWK8390tQpMZ0ClUnKrpeuzcxMGHG184G2oyajDrGNkeAXU4aimVGmFrYtKFQKL7iKUqXThhXwaIOhc0Hsc4hR3nMNgejlssMJE41kyFA7qtVx9bLRaHbayjRxhgaGWWc3a8+cOv3e2++oIXD/7LN0a5559/VXrV6Ls9XBIYVdYpuOIGiAaiY7gte+7X7wjr2d5k6fHI5nAg3oRwtoBxfb0+MckeVtZQUWDAog86MGwQgl9fOgcm/v80SXxhjhWO0u2VsasBvOTrx0dmllvl3faDZr+0NhsKDdaVqr+M5evTk0TAVVAdMj46Pjte0NauTUULunKTBzNu4JWS2+qG5kF804Ki1+M/iyCzV/ambxFkSz+8Sj569cvwTXfvDppzcbHcIMF/ntO1Xi7sX9HWa4ANDqyYdcZcjaQFKOG2i221vbG/XGFhUnH4kNWWIZK1W0x1myj3/q05/6Mz9FXvLm229/+WvfaLF+sLxUHRp+5vHHf/yTn4I3Uf9spuBYh/rpAcSJbrIEVEhnd8/tHFYjxYIrC9cJXcbH4xwF1lbLiHb4Q6xAucwlSKGgCwcQdQLou0yXk7r8T7/wTwDrv/iXf5bJkWvXrn3ow8+7nIKRrxB5gHLWROr1gOyIpRRApBg7fPvYcVskrmwYgT6o1t05/v+LeDOkeuDeqjwovIgpQmp5BCR/8eUP8QSSYI8uaDly4DiNGzP8sM8tt6WxctZON8vS4SLroI88jFYder3CVEel6jrL1bWNxsJifXPh3Pn5s6fnGs31S5fX7TWN1kZI2uxkvf3OW43N/Vplki2+mK729EtlnKRVkJoa+EOGoY5nI/+o9VqRHUlG8OQPcGg4ChOq7TB4iUpntQruba3Ut1u7UCaY5oQuXIq22242KxiNEDTfATpzgaFGoRr9PahOE2mrvkW7anN7C7GAQlATZ/ZQvKOj4/u9O1ubtKS3VdVzr2dhpDquDmaZGUtpzQInyS+z81bF25T2O1acQrpNkFVUID0f0KYI7kZT06MOEZjnxvG0Qrg/LLP3KedP61OIsXIzispFu4NqSA3SI3miF55kYkljUqxuU7P/eJcVjaeL6muAu5gfCrHMYz9vZ7djH4BmE1Gb3MBfIg2jGIA6TnPRZ0F6x4s/VYrpHtACRxXUqyQJ7VIedNNkgCLKJHGeFMuFQrOXRhJHFkdep9AwXSCtPA7GaOeRy1Z6SvUeRyD7+ne2BhbfXP9O/15f67DeM9RXm6oBatcv3hyvTD31wodrA8+uLi+yjMFmX7vpQtY64s7VltVyhWlCFpgxJ3g+Ko+LS2snTpwaYex4CD5ePjxolocno2cCBe+QB/cNNjEssZ/b12kyClEat3vT20z7KT095+bHHeyHRSx1fOC1GztklY6wEngtrzQswv5+8qzRgf2Lz37wya99+avf+oO3vvG1b/6TX/zSres35qYmHj5zAtdXLtd6Dutuch3ugzCgeJo9UQczOzH8MYrkX2lE9KoeNY7Rnzo0vaDmcVM7fY489uHqEquV7gIWTzsA68iIGBTKhO4G8WptZmAdX6zUKrbGr6y1Vtu9EyfPIXhtqLPZRzjv+CMDEDhCOibV/WZMHnMh5gNu2FEku5BxTLIaF1f09bRccNA/NnHm1trG5Jnnd7/9xYHBkaEQje5Up06BfF5wnsOHO/3NtTE4b3Pl3MT4zTd2Bu1y7Zd3BztMfsS6JHVIT1vhzvDGAVMCQ+0PgYJp4T5DRhdZrXLM94rLpa6sbn/06Y8NTC84VdYpzR7sbruyd29jo9lyV27N7QXNziGj1o3Vt8YpMPe0HzoVZ53VertRX96oj2/131y92um8S87mBDCUifcR4b1XX9qqb7N6EGYHoegxF0QNLmxtff21l3dHhvc6zdqIOTGw3lqrDQ+6VqfVV9tzPsr811n2ttubU9WhmxffPPnRD40wCl4epK/S2Ns9ffLU1n7v2GHfVjOEModtsJ4p4r4wA9O3t7O9uHHrnT/7459hdfqt114eqB2OtpfXli7v1vrfvfj1xz7wFBCK2sPuOKGN/mSqaNcFOX3E+zsONW1urFEMGimTJdiQhFbIO/uMikmYYHIYNWFi7Gg+pOkU0gyiySTa1uy01ItnCgiIkQckh/OHJ20RpJkQX1PMeGQIE0MVG33eYsqZpjFp7+dy2pwqf89+/ciT0BUVYsx+AAR5tVGoyET7O8NupO5jQkMcfzs6y2muONt7aJ+DbAxN57oPim6Nxo7Tv86t9u8N79Zt94wfkK2W2ZCJY3JsR/b3jIeu4d7uUOXcVvsWkEYnYLAyxQ466Q5ynJnSWqNVXht8ZPbkreuvj9T26gtvjZ6fWXzvzVcvX3vhoz9afuR8n/PsgbB7KqNjDWoDJZIW94x06OyXgvHsHRlhH3ygvla95Gxe68CuAWSJQ7BkbTe7EVMnpbY7kmZ9EaSN2M+nfNBorg6Uau5BoKsYwuJ9qvI0DdY2FvdGz5497Cwv33hzvPyB6uCJgYPGkC1dyzOwQ0ypbpdrpqaYpIPlgZ4WCV99vX7zvVfth/e3N+3I7dRXRkp7Cwu3dvbCkoHJNTw8wURgtVKbmJ7QvXSfbcQFhzJUXl/Z6O0ts6azubbJpuVev7vgtvYOmVAFBWIsQsU82ORwAf8NW9ovsYiDAAHTk6xR3fBbJpLw7MQzV4F/CVttt+O42XGIJFxXUe8QHwqKyLF7HtAvcogyk6OZEvCyi+BAi274n85PHNmUc1He8VKKwMJTfC2mO0/+2m3GD1bdHLnIrfDclUkRjSc7MXnyU7nZXyTPnhyY21V8iuTxEpdw9dtghPldOphcfbtJQlIaGxydJz8coNLc2G6zvjRSYl6xUR3fbLaWm61bw+57b265RrtUniuVD9Y3rt64cWFzuz49NxqnXfqJQNGz1k4Apr2d1lp9/fCg49YTGcVVsjZJSv3N+ga+5PLFd7G2Tz7z3FitvLu9i6DtGQInnNOH0PSn7dS9Rx55yMbc1uY6OezEeMUmizPBUAjVmF/5xX/6e1/9+ksvvXz1yk0qYQz/Xi8Pv/P64ER14NEzs+dPT9TGxnr3tsOSDio1icBzS+965i7pdkz6dtx/V+T7vopvcmM2jQWm3ExiW4cfd854JlY2rE6mU8FFcgwcvziWjEcOj9cj52Meu7xsLCGkGD96mV+JXkMAiH7fYqCjtD8QEnvhAhH4laN1VOR8V6OUczwk5azEXUS6+33xiHan8PG459XVrQ61YbfN286q151iRNohtjQBfhWonmolByS8KSSQbT9qaAZXblThlTVKy5RjAyQRM9JS5ttaW0Uz8Ye4PC1Atc21ir3zIDTRudQAgjjm5GNajtVGm8txCm7gIADu6PgEZZ2ZmbGAS4d7w2UtZzk17FNyb7z15tnT88Qw128ufP9733PVsZq/+vqbZz/0k63mdnlkLETWkXFAHhngaWgSMcDauz9E1ww6b7Ub7GrVJkJjP1G/0fOx05Kl31EvA55HMGBIcmmRHb38a/jVaepzvKD7hmioOMfHPScJhAwwpAwyieodMA4y9U5nshkvXYHaJxF56+03v/7lL+PnXPD77NPPEvpAUoYtbgvvsF6zE6veUNLC33XXWYj2SU1IFCpD0+Vq5ebSgov5Gs1OdZyMY/5Xf+2L7i1ZXFxafGuhNPbQw49/emx0ZrO5Ux4ZovjOhC0VAcgL9wLNoAf6KwhfF0iaj8QeARYMIlYlmJwHjIBvnLlKMEzIkcY0CN7hEuUs5tKY+LBTEa0OBiAuKbqfC8oZrxEH2eQmF8uQRU7Y0AxhmdNVMTbIpDRVzFN8beugkbs99XN0tAkf+23OMPWGaWsda7lZO1RxkDlNJ97/pF20PLkfPGO1leIHj/9HjhnQUUm5vFzk8WeRbxGYe1N4TlVEKMLv9RyPExP+aI6I2cWK8kp1SMshZlD6dJ8uyAk8c/z8LPI/7gnAZqQLF+Wm/+ZzInZCD4BSCvOq7Z2lhdXJ/tGe9d5ab9UmWGuruXB9+XC+98lHn+7s3mx2rnZ22kMdXFS7f7iW7kXoXd24cnC44iTnzt6izVoH3Le3ri6vvPehZz7h+F4YX2tu4SEmRodRVK4pJKp59dXX/8k/+cWzZ87durXsdOljjz5khoLLSEpIpmeAcedDUmbaVnBtvb41OX2ybC/YBcWOELRMTOeXhi+/9dZ/8w//0fr65rXrt8pDVbOcjhjGZOHaxU69tzx4MFEbGJooh63HAxKskcbdkKTbSbknix7Lr/qzCLmvp5vqKJbXEDCknXgEuGB33Lqv/sknzi+3ttTNKWiwRwS6wYGxwjiXQgIQIEG5eDkqlEdw5JkucYRu4QzIzBofHQ1Mb7zwZBatQ6tYrY2DQ+YogUX43ljKX8cdDt2GQDKUW25IUYqQ4+Gx54rs6m0jHRiswMPJ0MZVIOClm/YATs7PmipSIaID4CV7TwpCUKsnJAtqsB0E1vAgrCFjgFERsZGvOa1tIgBdYRsaJyefsDd1eBBCOUDKPgj9z9QRuScDIwZZEx3rLFns/MOOfX1u+XV+dGB9KxDnfs92sxG7GPYzhlBm2By9iWzbYXlNZ6PoHJ16+dVXT58+eePWwuo6w5ZhGNrULcfhdeXRm6Gh5r5BKoKBgFTMJYbj1cnNjZXD/dL+fru+uXH61Lw9S6iC3hfCkSKg4ci9oZS0kHWmpCiF292eO/yP/5Tv0dSI6pks8UzTJhpwBCILz/HAu/xFZUTOMyIxTAEffIJ6u/GDpEgT2sSMj7mrgqajhgn/hZYlSxT7Oy+++K21jdhtef6Tn2ETjfoIYyl2gmIbZueArVnDSlhNoGTu9PXsOR1WYbh9v2dq4tTy5vrMVPnmytrFC9+fmxl+693XL11ZwO9++SvfevaFz3zuRz+XJP8s0u65onpsfESWqKr6Vn1l8WZjewO3zSglKZ3pF0cLuB5nocgnjEFIK+7rUmPMSi53JoFUmFMNkMsWjMO8u0x/Ixmd49jpr+AitDoafpeLVRwkY/CIlnatOqb5qA3nMmx0m/M6yhqJdUQpBN+NBE8uUHRe92HMJKY8Z61ZR2h31cMUx35364fTOcjtyrkdfwrnhESDfxgnVU5SeH6Y1D903ACOx0sqqns88HiuwvPrXRGOhx+PX/jvjSBEZjk8lxsjRcEu79ulgvJXmUTcI5df78w5Yvuee5vH0HrmOJHOx3ixrBRFekpMSagmOslZ6Lw8/NBj7X1KTAc2UCvu8enfGgqzSoPLa+80WcNyB4i928M95Fq7fcuZzvfeu0BQbLN2feOK/T5i8O16++rV6yO9YaKyvuky6vpQ/26l1Ds1PYMqrlaHFm5e/o0v/PJf+St/bXNtfW150sEQ9WnSTNjfcRuo80rkfC0qOo0te8OtpjsKWYIe7emhcGGTdc9m4d7u5q/88i+tLC5UKq7OnaVntLG66T68zWZDoWDmBstYqxv0ePFlrmawGdjzvhRl6qIM1/7waRrdCDXqv0T/6k0B4AzpOjWlIL4hhkbz17/wqx94/D+ksjQzO24pxmj6T5yddhaKtHloimfkmlwaqsDBkBYOw2FoCr2BYhVDjyUR1+h3RWYcBiKMT01a+YhoGaQWRa6Fv/AUZWWPcB41oz3HVgrDepDxc88998obbyrdHZNAg4siwCkwBykwNkTNJEwgRSqMAp4RzMD6hCA6WAFbuWHKMR37Dg2v5CSXG+YglHSit2iM97MZ5Cod9AgpgH16wA68x/DidsKMXnRZkqd5JUzTd729ttqUopnb7U2CT1BSCLOCB8OHzi6X2DCNkttxV4hLO8bHPvzRj/6D/+L//eEPf/jipcvLq2snTp2+cu3GZz/72ZNnTroyM3gmmmU0bvvT9jOdn06TGoHe/e3f+o03Xn+1ubXhYoxbN66TfIK3Q6URdaamjmC1sDQ5+vloygTvF31y9O7lT86lXozsosQjgWEeOyGFJxeYX3Nk/pQklBK7q/+ohsK7vK9+MNdCcAypBO6SpcENlJymh5Ccp8kGeCD+WImVnbu8KAkyT7u6togLlY6htNg7D7Wp/eGRwd4dViGDV6Y4Fds50FAIafa3NltnTs0tL1373J/9c+feO/kHL36ZpdtnPvgJSLfV7vnxn/ipuUcef++1N90zMlqddugIsYW4os+ws72/uVJfcVVXu2XO0ZPCaxob5t9NQGvLik/7LLkn7n5GxCSvCpmys4DJcBA1Uoa3NBABsfeQfevpubn5UqmSZulx7BsYN+eI/oiz49GzoV85MTX5wedeGJ+wTEyqw+nJGYckt6YmkbPWI5kJepR2pFUgOZgcyh7GBZWSDCTE2Ur0Y2DfoFk7HW05Xu7drbjve6rM7dkoH05zi8g5pHi9rycadDTW/JJ4jZxvZ3PfdD9o4IMyun0M6a6c1ECa/Dz+yTB6zfXL4TnrooDjn4qEkVVuT2pUkTA3LycRJyZJUprh54pw8XNI9hTZJs8dy76IZrADciQnMPaCA5qFZWAa8Y5fgKrovt02q86lybFpxwMowrCFtLWzPTk2+finn5ged5XHaMVBgFoJiUZJiPSm0ybxpRjBUuU6rWfmYOxOhPCkjM2xV3l+f8c6HCNtwubs7SBYl4YoCMdx4eHycN8zzzz+c//u337xm99mQnVrbWmyfGrHIaadpstocbp08pmwtNQJGO2IX79+IfbFe/qIRienpxvbS7/1r37zv//5f0xoRYheLVPidx4xLMxReW47P2i7o90TklKnZNlx3G71NNweG1P/Xld0FE/xNfnv6M/iUzEWYucYOQfh9MJ3kgIFTAEHv/jii7DjpYsXXMMY15CFKhmhAAjDzARa4kidLe1UHZUeODqPV1GQEOvWmf2bN28Qjw27SjdY6uBM8uCyjBKDOdhhK4prMHSX5ok650yKrIoQxQksntE6IbjbwcGltW12CT7zmc9AwFAZMCFPLAI/IVuZAe6RquufLcxUepyYwvUSfXg9dMjbAbNSmC4qVUJGDR/DktGiPnvyjir1lNgHRky1KM/3lUdr2wyc4ZddKnmwT73HVni6DhpwdKMzqgViiK0IWA3tiOMn1lxeXHEDwOCW4x4tymrLq+ul2tRez7aNV90L+DqpjNkygVT8b/wb/8bXv/mNte1tVx7dWGD56ObE2Nh//H/8TzrlgdXN1bC+WnIeKR1eYtD4cJdeN2sca8urX/7d3/rNL36RRacR50RC/h8GdcfGa5jpME2MQ2q7BS+WKjLB5Ay8q65pfUNR0aV/Ei5PjKPpEcvfqMYzTT6eYnCzpygzJykCM9wpInejHdUzOhfISUxhoAU6zIqI3CObKMoIhIOzrDlTV3Bobz7z7JM/8RM/8Yv/7Jcc9x8OUYRZqUdwfPtE99YhmTM2cMCpwKG4dcRVK6avBdtT663vNKvw1MlTHz5/+sSp+d/50q89/tTHTJXnX/h4+dTDm9dvsSRltncam+XqWKfhhE9sEExUx4fOnj9h5yJOOhxcv3JZo9JcVlWwDkwyER/IPoqUHTGTVmhS8KlRv0jpbXx8anR0gtUBvDDkSNp0tNBTBxw9tAIOjd6Pg1FhatYaoeTR3N4kIfCNCjcbb4zJ6086CbXpGuVQh+0kQCNGd1oelPz74/CI9csOlie/HoCzkRZHRf1AvxLKWVSeIkGUlQgOnuyKT+/vOZ6bhO8f+U/ka1cUIK+iAbkSGqAGd1XIq+YWBRdfjycvvhYNSPl0gyXJkeM3zfsELUXpdqIIue94ssspj97iV8ix+PE9Aru17b4mKNmNHD9xeNIkAEA0WWqEmPVgJveN1SYfPf+4MwX7fXv0mAYOB1tb7cXtW72NnhPnx6qlsU5pb5f10gbAhwSGyeM0wFOPf+jWwnJleHJ0ZAYn09nG4gw9fPIcFDQ5P19dZwzngEVflxpubYYGAX2tWzeukdSdPTl3YXLs4jvvtOobPT1zjtC02mxfuY6m1fCvHX8mqLsNMVGWRJDVlEEO6levvP3/+e//4dTY6I986pM2gLccgO+09Ro+JqQ94ehkHqyvb1N0HKqR1kLpiNTokAc5/RLDcDT6snhQzBwnOjLFSDHjjWoZrLO7S2bLwOLByBDbkHPg0dKthcb5U8A84BU0rp7tdyn9PqNNUdMoKOfULRA+y7yj9/Q1xhQOdjji0uWrDOUIhPBi3TrfFOrVIVunK46yEW1sYuLajZth5zuwQuTA5ayPqhxvAr0WTyHxNbUJYfCdF1/8a//mvw2ebm83YZ9aZZwCDnxjsyIEa0MljGwuLuesPnQ4lQ6CbG3VM4NuvBj7M+KcSRhHv5noShJsqYAsIhcaMGgyYnOIQGvIfIB13EHIZg4Z1ofOIAWQ0ne7wWFjlaTY/nR1bBK8G9psRz/QThx0s29D/geuQ3RwqwrYtV2H7mbjiamJf/Df/PzVC+/8zm9/efbkzs/9e/++i6oo9qxtrNkjGB+ffPyxp/r1XDAKFL2blTLT4vvNxtYUCDpWqY1MW53GkZZDuTTizgaWPVz2HPvv6UY8CEZH2qhOo6Wr/8RQb+7bPDTG0AB151z64CWP7HFPTnJ8WHNyzzwLIosgZrq5RQ/LJ6ToeN9oZuheEPhHspCceUWvR5ycNRtzjNyhnoJvi21L5O8HPvD4ifmZGH2nyOAlR70ttjiY5EjOiOWcNi7ifgcFoI3QPA6n04OemTvzi//z/9edK+fPzX/7G7/XbhwsLDUdVLt5c6O8eaFcmYyLEGiHtZqrCxvVoYdB4r6B/SEbHSPTE71jzJ/521pbZmAgVJWjmH7i7jj3d4ztyxUvnuqpOeHAvDQzPaV1gC4O4jrjx2pVKL2b8GEYUr4pbQE+uuMbCkqJKDHb4/yb9prwTfbMQckOIQob2Ac7g9gKhwktFoOCHD8ax7BhYoHYM8GuxOZHkh6ZUUoMAwAIjQdsmRUNudcTte2OU5qMKYZALk8VAYXn3uQ5pIh83ONT9MSfhJPtfbMJaFh8EIkT4pkHLPs9uRzteDYCxeyGJ08RctxzPH/+KCMiR1q/OYOcf8yJ9J4iRMzjmee0xZMnpxJN5YrIOYmsjlKnT16iabHPL1OCaBt1oJjnaG3izOmH+suHW62N1k4dfF9aWH7rzbevDF57dr1VmR1ElrW2yJESl7YbJMvgcG1qfKbdcNR+EgCsb9vGoA7cPGhuzM4PHnhvNs1cR96A74Zb+ZKVc1p/m+sb6yvrJOAVe3HDwyzd9JX6S04BUNTusXuJfd7aK7uLe3CntO/Yy5OPP+YAKBk1+tvhpWWbQGutd995a/EWJY7DMCRbjh3IUrWyvbkx3Lu/xnbINia6OcKoLKaMYi359h/mouuOxvf9494V0yttNltHMiDSJHwzI/7aX/lZlv/398LQZikpp8BHriW20+NChXQ+2bjBHncUleebUQuWKs0rmXNzJ08d9n3PeBlPmF6ecUVODOYhHOkZi7a3130JJgEQJCTPijtyf/CLrKLovZ1SqR9mwhawSArJkGiUBnqb9e3dTmtyeha4YcnZFiCmsBQ8Ty46yBvFeUVqgEGyUh/YFi4EskPxarfFSPWOYdgL2fLkZFUN9xhqoeFpo8BxDKen7ByShKabZFGKgdUSvRjYLRjh2E6ojVTWnWJaX586+dDIGqZhd2x0Ath3gSXNQhdnLd66+fgTjzXq9UtXrjhy4qIjmHVyavZv/51/15QenZh2MXBrs64jWdBkduHhhx91T6Y7mu3CUIu9fnWxVq02t9dPzE+7a4clmtAkbO1Q6rZkWAKZmBwzjQ2iSwJ0v3MBID94bayCjAUCbR6hTv+EnF6SU37mLJM/oBBPHuLCU0Qohr74FB15z9xOWUVVE7UW4CBEN4GJg4VVQGi/xSZAzjie6xsbBpNmhtUKoeA4Z2cnP/8TP/bOzS2XCvQNskjq1IGNrT2HeU6ePHXt+sVYErG/bj6Y7o63I6VIMjrXF2+NTk2/d+2Gjfy4nqpW/uCHf2R2eua73/3u0tKF8w8/9vgHaq3VlfIonVBKx7HwCWFCp5kwK0i1XcrEDDGbR+7VRgnZHXNPUswflmgeMAI6QasDwJqjJpQaxUqD9FxZFMsKRYig1FJQMQ4LPcAlxpIxDmrGkksXYvZOs4HqsAc8MToGJiEwdSlbdWyJd5I9IoRl3H6sxjZokM3tg+3YtIntMYHmFbmWvGTxIJ2VB1SnOxliQI8GK1p6Z+x7J8Cd37tvMskxC899o/3JBt7mgIt8Y5COcFsReNuTZnOuYjT7KGYR8qDW5sj5eTu35Cua7a3IJ3uOx7w3hxRy1PEpqpAUmHTtjujXIhOftA0ZC47Yq4nJ5Oh432B1ZKy1t038Yn5QfnjsoUcfO/WovTZk5QH7MKDr1LSdM9o0hCStHdC5guOZHH1oemoOQh06HJ6bm6lvujS9uTGwRSKIVC4NOYMwbJJpHboZ8rBxe/bMI7OzJ2anl5VV32g0t949mGHKo7dSdSQOPi1RYsBWMW5lla0sryPKqSZub23DMla4TeF2X/vKpcsDxFvgQU9/s1lfa6wwjthuNcqQQyAjf7aNUJeW3J1YruiIH96jFXoru+OpXak3TH0ssWhE8wj+z3/+J7/9rd9zrgieRA47ZcX2rCNnljdhKRZKVmnxx5Ofk2GEAHqBgCOQPwbr4GB8YhpLQctI6eCZFmqir54Qgzf8n751eQuRRk6Vk/O/jxNH/iIAX0h48ALKtLtgU+rUibjZ1FEjNxnAuDvtpqtz7QvcvLW4ur5xYpaZtKqiQQ1qo9tOQCbBq5ljXjmIBXkmPmkPVo669QKx5b0Gk7drJNTuVlDn9SZEGKxSAoph4xdcDOgfG6lmKGjiGVgNJaySPJTpMV4ba+tnHnlStpv1ut01HUR0zCTnyvLC1WtXTp46QX4YcI0cJ+DcXnV89vU334W2n3rqqWdf+AiibXJ6kqzSNFbfQx0bEFlBe2+9+fpj5x9qNxuj1QrOhlns8+fPTU1OvvnORdNyfGJyUg+rhzmXnKNIGq6SMXTRkbhG/n9Nzti9zyjf9yt8mp1f9T16c8geExwNiZDYeo/+D4l6yNV1TgRlB/U2GttOkTmfRmYg8odfeL7Tf7USZ/8cco/oLHYjxaq16b39K+gY8mnzGU6HYliTp0YNITle+OFPfPxDzz6Hjf7ei99mY6UyMUfn+eOf+tyXvvSvvvXid8+ce6SEbti0dJi82HRvxsFBiwadldHjhL/FHYuRNWU205plJDxcyHALYYl7QR4wCHk1xfoiGIdjzUyTRPMsrX7rSCPNGRan84ZLbJekwc1Nz8/IGhmBGma7S1qCNyUSgZkX9CDED6bW1UZOauILGC8q2zHv7zgDcrjX7DR2G3U6Lnaut7dMtKA8UeUqZilB//Q6eFTjeJF/NH+xuiUv5knheZ887ztz3if+H/9TaEHnWqpfrmIGhcezVi2v+Um4lmPmyDmaT1Ll2udoRXKTpggpPJE29lLiFAeJjzvmfTInOnthCEq4CBkQe+UBYXN4/iTzIjxlElMSEStcZCEZ7cXGb/oTnuvsKxp2vDIKXLK7S/YiRWm42js6MXI47PjsbmfVTQlM7+7stzEr5ZFROjAgaJjS2LV7N1irxtrsdYnd6oaNNJO4MjhB33hlaeX82UeA7/JkHFeY6BsEVXE54+Mn3ByyE/pBaMz+HYbknNsvjVkwZ57+EBh5UJthg9qBJaueuHt4dthsHhouU1OdmtgZrtSIBWmhTpw9ge362//Gv/P/+s/+n08//fQrr71ODk6fFgIeHRsv23UZ7FtZWLGkiAr1pDoQikHeaE9On0Tnh2gtfoSEJw1r7hxB+TV0ktIOii4KeX2oLDpLgSkPOBQDl2LKMOfpYsTYFGTmBnu7f/j5z3/O3Sf6CsuOQjAQNraHR8bZ34HM0MTQUh6LfAyJP5V7iCRSVso8tlfTggxUbZ07qb+2ukF1RYQYWZvuQ4NAT8ZJdsSZQHHolm07c1nD76pn0Uye7M+F8ousPTmJU2c3rl3HHf7Mz/zMyy+/xKr09saGmAyt6E+Hi+BgOC86drBvfWOVmslobRxQ1lE6DdWgzg6SgWLiqwMkSsIpw9SHpl7cy+TQsH5DAE6wrGEnQ+FMBTU3VaXsHCkiYpBNyoR0M2uuknELiBHcx6cQFq8uLZ49febd68vbW1vl8al+gDm68JApGAY3TszNu2l1a6vRbO9CDBvray9+53tvvfXW5lbj2ede+NznPre1s+W4ESA87B8CrtXsde8DVgif1W6Nj9ZOzM22m3VD8PD580yIlEeGn/zAU1QWLlx4t1Ie1v9WNOzL/MjczLRLGmmQEXOgDfp7a1PTEz17jWj70VTh0V7P3POFp3j1Vf+kDUwr3X1C3B75jQrkhFaoUeAP8apFdTd7I8tw4ngW2RZ+3c4Pifp05JBbcYOFshgxU7ixVktIxQj2DtsaT/sb1klgp6g/GIW8aq00w7zVwAA0HPyfM6aN+ic/9kn3ecex4V6sZA9xvbWAC2RXJ5SudmKDX7uCtZbfgaOGDIPTVWy9+c57506feuq5D7EjQzMewKPX9xd/9q8tL8RMw/em25z3B0Yckmg0W+thUprcpYGwG+4ZnWLBg6UWt7WopN5jHM5uruOVsXdx1BupS3RMOK2gSAjx4kA1k0pds01ZBIZ3TCPYfnHAA8QoWlaeOUleGvz6IUUwG2N5QrCK0auyMhkYEmpvbdhFsT+ilY40nZw/4ZP1Qlug0ezdObDFRyxXoqTJAFy1Nukc+duX3mttU9QKG6hRYiLwlaiqIQSiwqaT05imJRaAPVUszilEuSZtYJeAaT6FVew0yuF3p5f5g0RmSMFxqUQcO5HsErkgX47miYQxN9L8zPPE611O8hySI+enkCL8rvhFhLvCH/TaJTeKZLmXPaNFXeDY7fpuqccmeq5EkVaE7C8yESIfgboIfRi/okSsYAC0zNjLz38uevOotfetbv7qGdkcOa/h5HFnpxyPcxQ3fp3GI5nF2pIaGyRw/+WXX/n1f/LP/txf+jMjk9PnJ0Z3G9tgGnOEPUMjPQPlnk4jmBM0QCmOcrqWtrndoEtTnZ3uYZcBtTc4tLPGFMzqzMRYZWqspzrkcpygoYMTChOnRMysXBLBLK/UV9ca64ubfb1lWLW1sFyem29st/QQAG6jD5uIowt77Ovtysio6+9sHjkuMjigxMPh8anP/thP/P5vf/VXv/h1puJoHm7Vm0xgkYaxAdJp7YP6s7MlEtQALrHYTeu4ZbZwD+qTIgLP8Tjh706/41Hu8JvcsUM5RJ0DLdWmEUaYdv7cmddf+84wm1wO0dopTC6AWWR3e+zuyOielzyww+Wam3pZpkSrkUdomjvMYSVwENkeHZYcMxco8Q6K6lh778kyAmKyHD15ABYaqlBhm2mYTuetN9782Cc/BVStr6xiLgkuICF6nCz7hLXezg4xg9boGY5iDtSVatK/srKqInSyOABXVhxGEz8UsEXDqbxArgcHCJHGdp0xonqzDQkgLcFBe2E7dN01TP9kldyjnlcEYpUNcewFezAb6+sOu+DXg+Pcw1Y0VWB2eurZp5+Zm7H73nti/vQjDz/GRMzOKNi3w1LgxtbBE08+OzYx46Dvbs/B88+9gLSsbzWW6kuEhMOjA+16/blnnxmtVKnI3bxenpyc1DM4Iyw8yoO5tY9+9MP6B0n3ve99X/X14fmHzk5N1FBaQPhg/6HNY4fdN9fXZ2qSdjtZF93lzyER4wEuRThq+QPiHA8WPw/o8UD+Ijzxr9bi8e8Adc/62qr7VFDb6G8QmGFRYnluu02iHMdkTWykgJPVKLQkf4lzt8hwB7IpSWkp6rk6f2KnvTtU0mYoNmENZFOwA+VTc6f3D5guJxUC/vfZUN2zA9PZM3uMoIscTp86OzM9G4tUbw2NbK2vjVZHekqVKUKSsXEkr/njdpA5CQYPyxUM7/b6ZhvD6bxQ88oFW1uidKG0RoYYwkyJ2h5vauHPk1ZdIC5l6jRkIg2x2/TMHb0eL+KYWskT3acTI4Sad1Quts/t5lLpgHcP2s1SrUpF+9rSLXslNqcBBLBfjxgIdKfNOBdN6/dqq+MSiNmZkzPTp3y/dvnaXpIRalcUlIhOnoC4oWR95I7RT2lsU31kjUJK6CDNge5kS3553HaiZTR0/NNx/+2oD/DJ4QFf/rjBXS1otSnKyH6vOSS/5upGUKIoi9dcfoQfVTHHL+oVYCo+BdqNXjAkqTcLVCt+7uYiqyItT8o4HrnE4slz5GKimPKiFglTifFWeIpPqCdRAxew/0coGpf+rjtOgMav7VSqEyOD1THqzj319saNW5urGzSJSRrrDpiHPKp3fWtzY3MThEWlUrE6MXvijIU0MUkeMzS0s19f6D8sMQ5MC6O/MloeYHCGqS41tGFz+NFPfGZyYu7d965cePutlVUy7HZ5boAYan191TbcQI3YZ/fq5euLN22gHn7kI5+APWOHjoS8MhGE5+HQQ088+3f/7t9dXv1PsDtrG24FDjmnDZm46/OgZ3J66KGzp+lX4lapjRH3MqdwVydEh6SVdm/P3BUz91iKf3tuFN1YeEAp9/kQ4hoCQx0UaWmIGBPHQAptEaKTu+uK8BgF8wAAUWRYePL4jlRq45PTVy7ROR9Ez7LI7aAWsg42E8HMSecaApqEae6t+nEErPLZyVPMnHNMljQxcv4RvssgkktdQn7+rW99i/1IMOL1V185eWLG2V7CflQFN7ATtMQQHSqGwHR3pp17DxhtNvMwOzhhOBhvFVzr3h4sC4Y+98wTa65ZcoglFOIoL4e+jtxIR5wnoWhXU3HiCx2FkqFf6miStdKtbECiWDxCnFZCf5QcPFvtGarOP3Q6+AMo0X1bg8MzdPnGpmDM9bXNk3Onx8dmxsdmAVuc2fzcqYfPn3nuuedPnznHamB1svzMB55FDGCg2XUgwtH8uK91p9PeDsy6sLDAKuHmdsOVOKxpfvZHPgMfOyFN3rO+tvGd73yH0c2f/umfnnH0K9gghwhscu4Dywu3bvzWb31p9cabeiZ3cu7nYgh4hOTA7HkffxHhQR65HR/KItpd4THfj034XBmBTzzxxI/8yPzUZBzyZj7W1naD1eytxuLmBi4/iCQHEup1mBgSIgaAd6fGxyDXamWYPOyhs+egYZsLYX0d/QTpOm3DFqpdkiHlHbhWCGkX858znPQQmU7dPfiDS98BcE6fPD09Ma3+9pt0ikU+Oj5G4CSuk90lGfb2VTodX5fXXifdGhw63Npe3a5vGguTaHn5VrPl1DvanbGnELpQHVMFiwFRGAUedU6UnlzuK8+AlUf4zJcwaneHSxUWHqLH2G7wEYGSs+VHUdD9t1GM746N87AgSwA0tHDl0q2bN3TX4OmTY6PT/f2juhPAlzLh397SztBeKcxQm/yYBMSHVbZ0g9WawEFHmD6GKqp4Z6WEZAwqZloQGbB3Ix+1KZCAoHhNDSyykjYnF5IDI8Yxd9frsS/hTYXe4bkrwh/ntYuAjw/YcX+R9fHAokLFV54i8HjMiJBWXXxNniKJEOg4xifo0dTpCaIFHZecTsl5emZP7qYivMgqJ495lbo19/VRolSxY91tcTFIMzxUgwjEMXmqo+PPPvf87NyJxu4mFELgc/m9C6+99Orbr7zNEtZOf1xP1Gg22V0cro7gmyma4nKAPqGvK/Sw5+yJUy88+9xjD5+wHm7d3MAnsUU1X2LusoQrDMMxPf1rKyvDldGPfPxT5x95mP3Y737ne+++d2nusSfcMlZjT8eFe+q9wzpheXZmnrApjLof9rfryhoerI4eQDsMth3sPvnJj/9X//Af/vzP//yv/+aX9g827f+NDJaeeuY8G0ZzE2Oz0+P4QtozNG1t8vijX6h/cgfmHjvyR/cmf/IcDUGOnIbr/VB1zqp4Rt0TcU1RaQ+NsrbSrDOUjZuw40OFk4QrDgRDQaHtez93fOzy9xjOZLPOxcC4E7OF/TIccCD6OFFrsQebgr+gpmJ73gXhOwkvpkbd0eR7C5RzDgz+EqUD9fXEZc9vvf6GT/Ozs/zqXxoanJieQqXdunWrs38wPTUzFKQUJDs4GkbRwokPsJL8ow/y8tdSIkpwin6KUpxHwgrjJqi8OMkWLuSuccInBt1xjINeR2yxMPRWRFBc7D3mhRComHau6obCNKS7v9Vsbm8QNy7dvFYbHd9hyuCgtb1dd2DaUkq3WPZfeOcCSE7HhwD8pZdeddHNF774GxOTU1GxoFtYuGQNJDbpGVakGOhOYt2hLrVa9fXXX4eNHHi2/3JyYuKdN15+49VX9MYHnnhqbnZWQ+x6zM/OAM0Of5PIpquZ98lRNzdWr9+4avND/sc7PDW4iyyFe81fj3uO4txOFyFHtEfyJJjcDeyCC2+5rAhOrngtPILTx9urIEXuxdmfO3d+bn62GfffNe1zS2JGPT77kCllgkkQ2sfpvGxIVnt6xsZqyJTqCNOP5BXtyxcvdd58a7A66R5QJyNC2Mm4RakMCZuT1RpKK2ybUNlzAhESd2qR2udYqdozsj9Hq2Bzq7PTOn/2vHLa7twcLDlVZ6Kw7MMM36DjA9jkAacMz8T9gwR2O/uIbiuoUh5136Xj98ZLnTk5ON5HRyJ3fO4WTy71d/ZEp+Uxy9LemK4hXxvOXXf0NGMDB4dixJGTEK2X35g93cf1o0twwyMjcPCNixfeeeety5cuUv2zXwNAUgPN08OKR0mwRmIPWIiuMJEcLLBqsnBY946w7Jucbof11VW77Gmre9Q7OcNxVJfub6SIBnWdycF3JwLudo4MZJtzTrHvyClSva+TPH+/13PfdEW0+369NzD2gO+thFxyYPGp8BwvIAfmkPwsoikph6QtiSg3fdLa+PU/ndRAt6HAAuOKzKkMeqpIK5rA4vW4X+C9LkWITHJ35yEUTXgulB9Hw6pGEvWFiUG0v1Lo/fc782nTc7Dn1e9/7wv/8lcvvX3BfvTM+GRfyf5KXPFhu8TWLEzCIhwWwSR1Ey0B5sqtxYuX3nGU4+knHsUoUOJyWhSFWHGzrD0tSopxiHCf4cq3L1wAKV977bWdVhOriC9qLS1udPZPnD7BYGRjfQmiJxZzvVdoKnR2hktEfGFulu6VecwYlo3PxtbS6Q888ekf+QwWGhBxPuTalauf+sTHltlMMMtjr9o5/f0SoQ9VRbU8mmA64Wgu3dFzRShP0eE5Rnw6Sn5HmmMvCHBvMYuMXRpdx6KwC9Bi3M6WCKwYjrhzPgb4WNL386oJB2GQIIyMkP2GfBirhQUkrYJ90ft0lQP7hqHEA7xIQMljE0ZyBWbHXxQmJH/KIegnQj5ZgAWkZzfXNwnYHLyGUPGoao78QjDduLk4NFI5eer0MMkvi+JjLkF0O0JosMtHBJAlwOL+PmXofPRIBHmaY8CN47vMCeNt7DNubrKb0jh7+pSLacgTTXjb5+ahzf99J28dBlKngCUE1wmCBYVKnY3sO7SlbVg2GF27fPk73/wmbL1p533/kDwm7vkA7AgKhsuUGxyFUZmvfuVry0urInzpN//Vhz70oc9+9rMrS4t6QLfZkJscHWsN1x0XGa+M2ffVwwacrnqlWtNFcMnM3PwrL33z6tVrM47OnD5XqYzUqmTX28iR+RN0xWMsdtyd3GmOj7JZ5jTabhijTu54nxcdruj8tfB0Yx/9CBflQV+PYuUVHWMq5vGCitfjnpSqC0aEc0Ko5ceA9A/U97btPoZyfpzjPWQs1le9GPbLnMtxM+/omKlFj98QEyPHUbrS8PpW48p7F82NwSojJexnjzFrTKBgNiWGAlxj6962A47Y7gy5iZOwFKj6Wv0tHDAqjlL9tqP6rYZqoOnnZ+dMFSylyGHzGbu8s0NpqTRS2tjYUlv2qWhi2GMpl/pqo5Mbq3Wrg7wnaFpNMinRcRZZTJ67u0WIyLnhPJL4DyOSf7Du5ysX3RgC3e5KETn7ebgcQSr7//rGn9vDrr779rVrVy5eePfGjesg6tlzp+V2Y+HG+sYKyzk1Ir04dWKBHDTDwkYP3neENuJG3VShckGtz0kBF2/r+VT90PnIBWXk2vULPeJoj4UISx8E3c+lOncjyFzfegYwToJ0KeLbUXH3y+COsNwDggrPHZ//GC/BAXO5KrmA49U6/imXUoTwFCF3VaCIExkmyjU8R/FzZN0RU8a/gPHRFyJEUJpA+TWnygk9s8vJj96Ofo/o45THHQg4ik0dnRNiKJNWy65L8EBG1BFGJ7QJtrcPBl2ot//K66+9/uYbk5Xx0Snm8UskURgZiguh4tuwJWQzLu7cqm/RTjxwxxhFaLSuk0fXry+cPX2+PMoonNu4IN/+bXtrjdbGVnOdfb+V1VPnztSoBTLsenjwzLMfYMHq9772e85Xbq0vzJ2aGZ8er4w4gbq9t71NL5KAp4K/GOqzJeyPkgzgV6qUWttL1957d+7kic9+7nOddtvhzr//n/+9kydP4GUa62vu5nEjO4Vty71nH0NIIeQ2JZv7U3/rtRiZIxedmMYg/Uao9/CnmBE1vv8hTmVLPTvu3nHCCjjD/NGzQPPG1o/BTUjablEyZnWfrIoR9y0XJyQCe/tovAEgTp9SevPV6iUwBT4AHjgG0PcEJgnxCLsIg+/NXYaFy1+LVyuTygzl1cEB58LizKIhICLDoExNjgKO0LxqzM7NlSoJoe60JFGHqAbt9rjAKeR+ayskHyw1EgjHvrQVH83GwQ8Mjg6O4sxxKBiqkC8yv9tsuLDh0DngXXcAOxt8SKXbrMByBSmftsAKUZAKy4cRgyEcKwUllpX2DjZXVy5fujA1Nzcx/7ASR6ZrZA3whh1N9jL7ZoZIkhkw+dKvf4navvpsbG791pd+u1oZffqJD6hkm1XJ+vZSc0m2HbfqsZHv8Dgt7mZjZXlNs1Alaxubk/XGT/7kT/7SL/3S2tqKTpaPsqyISpWt01CO0o2x2GyWOu7ad0gqYK8yV1idi4EQTYTjr9lfjELXc/ecTFMvTUbxYwqKdwRAeXMREZZc8Vp4uiny50gcLgUesG2CYdRkZAewAz47f8/TPxTML+OOdsv9D/KLjZt6YLvJ8QlJNFw/IEX2xnZmZ2ZurF5Az/fjVisVeJzum9FCDhLLIBSDA4yrK2hDGjwd0jdYKR+099YXVwacI9rdgb1QyijN69evxTbSgWPflq/LeMM+805rd2yitLW9RlxCxYLFKiJeBrOGBl11gF1IvErs6SiLmgHoKU6AdD2QW9pt+hEC1iLVQLIajhwhj5L44YrY4bFmRYGTQyszf9E/OoVJBFtguN433njj1sINo0LRIehDQiTG2JjkpBA2GIcSgVYaN3CzOYOEZqNGtweQPzxEnmYKVWcGN5zGpVwqbTZTUbEMwuVyeXKqqHNUKrkc8cgbvzFBun/ikyQFrZHE6aao7ynDmABFzkX+2XPvM+XaDS78hef949/79d6Qu0dLzXLu0YDUfq9FYHiO8igCBdxVoeOfoNiUIksVzJLU090u6PaFbuKkujcrOd/lchyBeUi6A5O6XUWMbQIIWdKXSj72UAR4GqJZerbUStkkKpdtgVlgp0fOHrgixcVz2jvQX50Yc5fB1WvXxmonTsyfGZ2aiIPDbFnZ8oLhBh2qGXZTgt3c5Rs33JbieqPdTl//1OnxPURrucdF7Z3mqnO/2w3baWjYJ598cntjk6rGE48+snTzxtqqu2B7Rmr21ZYdGVxYmnjuuWcmpieJLkmshkbHdrfoRQeQo3uDz4MKHSe1BcVYzeV337aWrYFmq85EsELsIgP7sU9zsBeKmkEV2xYOkJ3Bnu4quqHw5441FMWn7Cki3BV+31f9j5j2ScfrXgss4BMXYthQog4m78j94DnLKjsqKFSi9IlhZReLyjxGhKhOCYq24K1ejv3lkE6Dcsml+txustc8T3JFcpz8lH60NrniUqHhfvyNTHTs3EkbqCGRNsqH7f1KXNc2Sn0UQGE2N56DIToybVxQWKlVAXHCCTSaPME4EAbgFmHY5gLzQMNDtPHWt9z00uOWOohcqsiEDSwHPxh6tMdXrRBVa2PSRgk0HMMScoPY1Ladz9Swuplkg05cAu1Gmkb0yvLph55lH4IcL/Yseijoulnk1vjE1JXrN37v976ytLSiEVNTExgppshVqfK3/m04hTHUddsEm9uyxfuG4fKtLe1ttBvLqytEOOXKyKuvvbG0vHp2dsQ9E7Dy9Nx31tdwbC1aaQ5MP/bwI2Hi2mk45/kOd7DFesMkdBheJx+N3v1XdDEK2XPvUzfeG1iE+Cp/r4UnfypeC0+RJEfOz/jq1GlSL0K/gTyqbUaRfNAeau41qOGavJhEKhTWuQPdqCX9o9CQRVGvaLvdG3Fdd5maK7huXL986fLF2dmzU9On42p69JObxMIaO5gRWkuBYnFffU772AAYLs2fZCprqDxUqbq5vtEzSA2QhcsalWlEPiaYxhZ7LcBUxZ5Xix7lhKmnWIRBo+kszzoBBq0F7TBLowxEST9wDX9b7rcJbi1NneQ39tpiUECGRHYAGXbKqKpQBkzBAYGT69rJkdRs0WNEN9Yaj1Wm1Ycb69+ntvrq90m59Nuoe1CSVofNafPHCXmTbXJyvFwJAYyTRpaGMmNRpL0JecqHfMBkBhzEUTetoAThGUAkXVACftlo0nUShXmUdLohRD3anFCwfOCS5JJGZMDK3NojX5qE4qt5RE5YP09MIYXLWRSv7+PJWYlQeN4n8g/+KUYrWnWEd73y58BcUvGp60k0xfECjlfoeFZ35mMs/cWECAozSkkTIgDNHe54zsf9cs4uB/Lz5OexkAjwX3fL9ihCeApnDk1hNUdKq5ub/HYkHVqylsyCnZ5dKs2jE2HBGKAhmquOjfYdVs6eeeKp556FLOlhrWyuX7t5zfR9/oMfc/hnfGTya5tfbjP/WKdSQz5Np7SfveD1jc2bS8sLjggPl6Znp+dOzC+vLo2PMizXBOBI8CggUCBcWliwut2S5IrZ19946ezZsx/9xMfnTpx1nnSoOrmHNYFEWXqCa6IBccxgp7n20GOPfetrX/vir3/h1Vde/YX/4X9cW1k7OTs1UWFk3ziGcMn0pcdJPZH8p3O0fZL66o6uyH1S9CFPHrKir34QD+5NNGnBDnua+o2xBmyFpZUnfc5fzuqVuLrUlAdnXdRHFKms0jDiCbmCBbSIE5dpcXqVv0UbyG8njrGJrNAMf/Lr8UJytvkpJk92cgA3ZKVriP0VR0T8sCOxDz1UGXEPBGTUCtHcgBsFUfc7tf5gm5zbAFMAZZwTOYqC7H61XFiYTv1iEhIadnUV8x31scGJTC6QXzYAVOjT/QatJhPCzD+O9PWMDiHhiF8o4jZrw+YddwdZFO89PaR2tjAwFmYD2At3Li/c+srvfvX69etx60adudT+mzcWv/LV35+Ymh4eKb/y2iunTp5GCML3G1uatfnGG2/9o3/0j9xfGWL22ZmJ0VpYm1TUIQtlROwVKABv58ZlR5VefPG7AN5gZ8X5LtJN+s9IybNnH5qenr5w4QLwWXbQlWDGmhnsZYXGfCc8gBNUNVqQXB4CXe0t+4uQ46/H/SIff72v/94Mc7R7w4/ndtwvfkCJnriTALSvHwb7ixx3pEiwWbHr3sy+vYF9O1AIWvMiBB5mMAQ8UBkhld7vuBJ47+bq0te+/tVXX7126vSpD33401PT8+ROdBNIO3WLPWBKWSwNDJJNDJQJtRutg5OnT6yur+y7O3O8zIo8c7axZcMiQWzQO9Q+TD04bEztI2fdyEClsbZ/2OjsbNHJcsAQX2zsHevJ9bTYqVrG2oeJIO4HO5OQ9gubujTxUZmdhUXyRvsahiaARkh/gmVUjRwCZ3kpDQf5q3MASe5gfcWKe+T8w0hr09V9wM6BQLphbJKdzVoFZEPt6SudQ+Z86OrCtCdDi1Af5trJ1ie9LcQaCQTvoHCzub/nWs2aYdIoaw45PRhmt2JxClQHTysj+zMCDqCSEUGaXz5l9BI/Cbfkryk8T8Lbs+uuOfngnrv9Jed++/2P7RuIYw9cXiGpeQLyAAjmyfUtPBmBFlUvPLkmES13wJFnN3WQh1608xFXLQQmjmzwoUJb+61kZnDvsNzpbG70DJ6OckMynVjwMM5nrUTN5EzGYtcj3t0qGDOl31qhUBq5mvLkHrb7VYFSrhBn0GJGUgIwvbAiBMlubRtngn53mHLjzmB/05iY+S4x6O2rlfZLrvs7PfHI4O5gJXQR6iwFba2TN26cOjm0vLVGndXJ5fkzk1/6yq8/8dyjH/3wh2ZW5g9tvVUYBhx6/cJbf24fFp5e2Vx57dWbpC7nzpy1dbez1ZqYHKlNDO7uAK/D/Z3W9Fh5Fdpc3oAwektz7j9cqfdWxs5fX97Z+L3XfvLzp+bn5uk0ucxIG/HjlDtoJZh/B737g+OnQLidg6ny8Pzc2OpOfXNot2fBbSqPPNJ2jGVodK9cbrjQNzp4d32n3TMQ+iNZMBOQNnoxhjR+4rc7gGZvvB8etkZsWtIMimlu1JwC3j8M4RcV3X33wMQ4xIl9ixgFrtNdP0T46XivQ8kj/T2vvfrSxvoymnjwcDgsFh9oA1oYWR+yWrule+7W5TTLI0rPcDmJmEhY3UIe8w+CSX+YxHZzcrxy5tTM5vrCzCSdkd2+EnVjx7gY7hnpGT7Y2qmPD82zbzw1N49BY9azVqNQE2cw6aCFwhaIaeXTYorzgSafngGrFKN8N6ePLmytD1SH92wE77XoQl2/du2zP/75selTTEPbCjblGO7Za63vdhqg1aqpgaUNzdTDod3DDWzQ/pIdr539MIHA8sDm9jbxLyH2idlZzKJbrg574vQRLRt3swccb2yD77ZgU32Co397bc1I2Hhu9LpNdUuNKaEyPe54D+6GCM3eoO5e3l5z5zEagwKCo9mdlWsjM5Wll9+8/PbFm2vbo1NnBmrT15c2mNx65+J7D5996AOPPOrA026r+fZb746MTz7x3McfOv9Iqzr4ztvvVk+f+/F/5+cqjpPuEAaOU9gexYIN9E8O9194+aVf/2f/89rixV63w7c7iDgbCTELaOsMHbz89nuzcyOPDJzZ3lnuH6qYGVvbuxPl0c7W7vKbt/pXeq51xpjNJorYbtbBFkaVrE/EAeniTqM1WkFCbQ4NdE7NuXYM+bvT2h9MRiSCfzcjwrgWOUov6xKDdMGcDKLjbTsWkYVgB9z3e9rgNemlmah7m840u5KM8sZwBf3L9uoOQZDxTdhGb++6rAwLKucAPMH72v4FPrDuYc4T/Gk0gJTQKDfZdlv7zZKCyFODsXT3Hxt2+63KgSvLdspMv8NSpd5alTo6tfmNN99885V/8UvUND8yNTQytHeKeHj9ojuRTHX3KzPMTvhjZwEMDdAHrx4etAd2ry/EtM8CUr/AmDFmYSsHJiyYEWGwKGQw23m/dmAgzk0mHCkcOWWGuL8UqRcmpPqrPJ0d9wviOEm+49Ssea4dMqdK4tpgtFGZvW/ampK5XYYdNq3qD/IxgAE1LiecAsZ2nXDOjoLish93f1gbo8AqhpDh0cnx5MmvQmjWb2+via8OUQ2eODDMBdRfXrHl0T8/du7WhVujg9Xencapucpu4+Av/uznGPlloWFjfXU9rAraW2NaICxAkKjJRP6YfkhdG+VFahS8RsLHe4dR5yggzIkeGi+sADjfH+RRKKePqKeV6Vbvzo7LYfsPt2XIRT4yMVNBAf7i+ETkBsVEnlzAu/u5osl3fWSO7K6Qu15zwiL5HUpY3Wqlyhk+rznecU+Oczw8h+RihBcRivAsNMgNiYTJxxP+5HIEXiMWUgaZJARsAFKcbjWKIm73Tuivx2Q14Pl8VDdDYhZbgyHSEDe51KFIRNsx0DK2hqcT9iIcIgj6zgFf8ALEo18T8LTDsACcE6bGUYtkyK6ca7PF0c9OR0WOv/ALv4AJOumahJnppevX7daaH++98krpkWeWl5awoI+cP2enbT+S9F6+fPnkiXlrwOpfY+BqbSkOC/YPrC4tjdY2GxsrD5+ZI+izIVWbnfjeN7/66OOPP/bY42bGAbbIpHOXjo0l4Htw8NbqKhOJropzl/v66vLsRHWn0RgfqzHNg5txHt/NSwxqurzdTgwsFPzIA1wxUvm71+jtNOO6IxP+2xMwvoYznYtJFqs1wNxuKFm4TYdNxFs3F8mjXNa2Wd+ewVAg6ykZJ2eTEmw0v5Rl1I4QcKjhJcVTu1nh5BkOxRQiKTpJAaQCTkmMAgIuaYfSei4jol38FBrpwHRlpDoxMXXQWIrjYpoR22IBAgJ5BHeAa45plcsAigMZe3JxUoLOc9gN2GnuXr10WWXhyKtXr+JH4iizAyFlas9T9e1N3ACWF/EuQ8S76QEZSyg+/gDs4BGH4zdzqKsowWvoiOk7M83FDMktLy+bM1hn8w25pqqC98PkISiblHmCfozL4WnUgfwsKZnleiTtshzaYaPG8/ATj1y6tbh9bal5uBhWY/oPmY2cnZ3+9Cc/1WxsX758MabDwWF1aGiqWpubmNjs23vsiScff+rZT3/wgxOzJ1thz2CQgS04rzrYj+7cvPYeZYNOrTQzO42BI9wh/2Pu2Fywg903OWK1vvv2O7aQ6WozXrHX6EB6tjrajgpsNzt9g+tb64ShcUK972Btc0M6pBL+ZqcF1Q3R598dYHYfZrUfaSMTbaQviRbNrqN5ddhLumDvP/rZ4PfKwbCHeId5TP3mFrwnHnv8sY9+1ARYWFqmtVHfXAvloGFmbiouATPkDi6LzCBTsFIIcnR6uggXKDcibaWynVGu9G7VdYEJZbteCzCgvkJM6XxOqJ3vtPt3Bnown0iZFs3lnfZC78HWxgYxLPfsWG12ZnhPKxjoqG+NTEyzL9bcsSMemI+Liahp0TpNDEJToLG2rvgtAuPpjVAtBUYqKcKf4DCYwBOLJbkUHEvSvozJIwchnmadQBOSFXkejvw7r6TcHBDPHLMGlK8aSbDsYiu0SrA/CVd6BrbLDswxKPKJsTnyeM3nkYqQ7EnFpVGMuIlPMmmEorjr7aPmOGOXjGno2cPDN157NSilvf2TJ+cffezcWK1qdVKwv76wqmh11txYaCxcJr7ZVokFlWslVY6jqsyz6kCvqA+75poJefPLyqkt6v0ul4tBSLqTdhxA41SvNASpqV79HiHc/PFP66nhd2XdRcBRg1yP9N2rQc2x+XlyBB79krPI4XdlVwQWnsgk4VyewHBpRKXKlEV8TSGYrcQqx3lHI6FsayZ9TV7jLgTAFhyTOaVK2JoFdP2H1BOIngWz1ZwLUjGYHtn7jWmteDGBS+9mKmaCfMdoERERGQkKsNdzcOLUyTMPnXv1+zerDImD4Yf712/cWl5dPf/Y48Os4O/tM8h7w5G3GzfoTv9bf/Nv2dW7+u5FM4sR5l/7zd/44E/0odNj3XZa8htn2L5UtlUIWrodhzRKiSAI4I/EZtXy2oVXSWPWbr7nUoWx4eH2+s2B6ui7r3zn/PyUOgbjbj6xRu26nVp7gKZRtYwanBitnDkxf2VkeHp8FBN85sSs1u+167v7u6UBUZ2tAzSYqMUTRBdoffq77UkDHj0Zc+/2tAAHjybo0VTRKSLkOOkZMRJ27MbMCNi8gK0cToZ6z5w8QVxZdysEM++UlJySBmti5Xdd2umMMc61isoll8ZZbazRwL7xuqdrzQp7RNTKgGDDGQYE9gGfAfQ8yM9WWKd0gOsdHBmuHdRXwVPdRRid5kucAzO5MD0YgmAlAcGwwAcBmyYBeSx3e3NuMyhVJ4Z2mUq+Ttfp4YfOf/nLX7aeByrDhowqW7VaYsSRapPxNTVjgoVSPQaO6mwdwgAEDBlC3VcQgljcBrODyxqrJrRhlYe4Fy3wM3WsoWHIQBvJltNGYX+bYSnbkwj//iH8mNmciA/UBz0AskfbYRGIdYOKiLKN7kB5aPbk9NjiwtXFzYXNOnvBgab3D770m1+sho7hrovPKcLsN7bfe/31havXp05jOvZdat1eWKDoPDo8om/iyub9dmN1fXVzffHmpYH+XbqCrZ3G5kZD3SyBEnG5EaBsVKoanZXFZfcmmdF6tjxcJjTvNKlWOMo+arcTu2nnsFQeNr62eEJqQoLChrQ22OGLqRDDLVDnQwEwugEz1BkCxGwNhwwZNJQ6LDhbhwsajVuLS831m61mc/Kp2vz01ImZaeTLR57/4Kc/+uFvffs7VC5sWm+tLTiJgCZjBaYMx0Y5+N34kakmu1sxZW9rYxgx3nS4DdzASRF4jlacOgC+A1KH+WYnZ4iREUA9i1cvuWhPf771ykvf+fY311Z2T54c+cSTTx2sLo6OT5DHNHdJZ1drk9PISQJsF10keoL0NM1yjQ0KMOjBqEasuqiFKZFfIZt41wexGDPoi4CMfnLlRU6rJB6wUaJp4yFc34LM/KHRpxyFp1IMFgc1vfvu27QVKASYP2hZbYehg1gcHZV5Qu6RPHuEgJM5PAo4Fu4ge87Z1zSA3eYUdcueKD65oUMsKBe9YVqGaF0rcoWR1rbXKPZZSXiNvVAGdAxErTRQDVmbQdrmmjg4x0UzE/y3iLRLDcbGKzK0pZJY39AIMXy+slWH5iO1aoYF9214nPUu6rchT8z9n6r1r/OhUbm4wuM1lLC4jFZ5NOm+dSo+8RS53BXZa/5aeMSMkEiRoHZwITEfBUY2KavE7KZOyTjY+1ERaasvTaXUZSoZgyc7KzY5MYVQVJJVAFa7LzEVDcY+PYkQL4QKX2Bii1we4rNGarzdnU1TD0Hlqjh3c1y7eSPwvwN8nbZ7dU6fOfO9F38fywnMtdjhONy28k8//Cg1qI6LcprtsbGJ1dX1K1euGWMXERL6weo47tffeOtWa+/P/JmfsrBe+d53P/aRj5LNLi0uMGVf31q7ce0qwzuhnd/T44AmGFsY/ZMAAQAASURBVIyM3Vq+US2fXF64ZgW6gqZT3xgbGYqZ5kL7w1LYO46tQidNGXts9tRdQDht52W8Wh2rlOem3JXkyoew/YbvWbp53S3qY5PjIziAZjtUimI3R2kPdHeNVI5LZpbGKJ5G4+hoQx7K4FQIesKEVBpGORgCi2T/0G2Iru3rqY1P0P0GlcqVaux/0eSO25fDoaFwIZJk64kKMDzCbcTH1EjgJamSJdQbm8hskTilc9hmN8/RLNaLNYnZ2wOsDqEmFFPBN1JvcRbTFa0zM/ONlUsQrZGHyBIrDEOrs9ENjyfolHCwCYMp2h8mLYxTnXGaBzr0zVaYXdUPPf/Bf3X6NKhptw0do9GBXwljDxuELvhvUIkcOtqSANzKyjK+f9qdEOZYvW5k0y4XmwoBsMAFqN4Q2zmTD8YuSL0Tc6xJxsw93K9WR0xvIlUgmFo0KSncBWKFxDxsHuw4C8dGx4FJhp+lZkCu2+oY/NJs7eTDp591J/SLL797uTU41KIopOeYtxyMS7Si5bGrSGTdaNlnW79x3Ubv+qWrr3zt6ywIygoSUqV6PRSyQuhNUrrXGVMfd2wMMHG1ahScD9bS1vYGfDA4PLDf3hsYqURVevsrw2HM+NAi2N3vkEbsbVMdwtrgP1TDJZQEIKhPY2w5UxAMwcHALlmOc9FCQOSgzAMs6DnUEsIrkJQOVC5FIT0BM1L8dme8tb54bZkYcmt+9cp777z83RfVikmNE7Nzj56an62NTI0MbmyU2q1QbA5HbWLXnWAJKMA8OhMsCHCg55w22KZrRt1Sb5Nmb3bQyWWK6WHBaofNwr7y0Eh5eHC8Mjw6Mnz1zeXvvPrSIkCx56zwfOm0ObG/u75uGxN9gzIzqdY31ztMe1obMQETnNKsmDuBV8Esg870o8Yec134Jo5A4CyeASZjsnIAQPzEa9dpVvaZgRk9+1oEBqCNJZUhZIQHYgsTAzv7W9HPSoCAOZkEVFktIkcy/clljwmZi4jYJj2OgU7GYIjK8ychPNmlKX07K4GREeCLwkvOGoy5TPQfVaCfOMAYOawZDv7sBKVA8OAu1oyA6UmQP8vH5AS9PTn0Z865qCHzL9389XpiDq1Z2rUoM7JA0je5M5vq5N52fWt1e8uGdk571zNn8sd/as37Z1JEyJ4YBt2Un1IWrzkXn+7yGEshRXhO4qnxOcccPz9F03cp/OiRplk3lbBU24COsSMXz4CQCWOHpDMulgmYaQJHVmngY3gMZKLxfAVhJEz7LHFPdFrPkWkwHYF7JcxeLQnpg4u3hiDyFMNcgDmoDJBXBK2OkCcxLpXcJCMliZZzIk4AuW0IviRcdT0728oUKH/qp/7cP/7H//hKuGvT004LT0DMog2Vdt5+49WxysjHP/7x3v2d8dHq9vrKG6+85HywE28EkvvLWKtzpNxUl7O0hL32W8tLp849ZONweW0VQHBc+IUXPrywuBxs+mDc50XTJ09Ktd7Yu4W1dRLAKRTHGPYcbOjve+kPvvvCCy/gN5gYRLy3UMdsU/Q509lh3E6q1KWpr6NvsyeFxhB0XUTi0iROECAPXETjcqTb/vwR1EnmC2gCt1zp3j546NGT5x46//Wv/f7Zc+cnZ+cq1XFrjuxSh4PFnlCRkUjTCp6MiQcmxLgb5MT48phjyQVnYqAtT1es4nuwgGRnEITtKla2WOKzZ+cqNrsHmD2ybacfWEYGVZgHk53aBcy1cx5QDkSMuRUUXPz1JaAZiU0/2m0OTdVJa9u7FF2+9c2v/+jnfnxufmbFrtXBgd1Z99/YVpi2JdxfNnzATdDatppAJvv8yZBHZaQ0NmpvEgpPEIoR8ZJDLSFNDVl0f7+zxXAwptmsA00o16qXfFQO+vesVSqDDbaVhDXhJvnYmAwLSq6cGmahATkC5cOB1Ox7UAuA2vBoeahnaGKmM05R/zqdsR7nxUvJbgLrih3QVs9vt6LfaMMwRr25Otxre76zsrZEv69Uq6AqkHHVns5gtR9VA2KHha5Dgpe4m3OU0cp63SCERVRLMQyrmHJIiZHD6oCzYdvNva3mFrZK9Q5bO+O7G0ABptKJVU0adM7YTTgETKZBzJZQP8azaay94YRvYiUngUdGHL7E3BIjeGbDS+4dsu+hR0+eeuSRx96ZGH711Vcdvr/EMlrHsiMyfw/CI6KUDoqgWQJ5u/w4DZMDBTMwOOCuUxs7O/U2GaX7O5tOcI04D7i/x/wV0K2A8kg6YXjgchF3RIX4+urC5dVbC2tLi4qZm5okS5hG9DnxnPT1cNWwY8dxipBbHMTtHS55rm+V0zHwkAib3THtAifyIKb4TYNoYWpj4gu7fjXks5rQCBEU/nim9ZJ8KUlehp6ZY86pRMu4RziYl8osMgkMBM+Vy2jHpKUYwoAQL4frwVDGWGSQkELijTPts+euJ3irkorLz+zhtw7ELAIzbva63xuctCsccLoCHbJKVDeBUNh7B9boorKOZWZapIAWe+dRdEK03cwt5ExCUYtJAEdBUXf/dbslligSfaAgMynXO/G+TTSGOYsqch8b2rdSibsxihZF/NuTrgj+U/REtZMrPNFr2QnKodqTahbBQgp/ES177vqa0woUP/uzRz/lmIrW3PSpGxIvvKlPgeDbODj1bWyXKF0U5iWONg69xWwSHjwVGOUWMNeJmxEGSYJMUIXEP00Ioi5XZ4aMSSrQKqo3EteXC6S6QEqqdEKSW4sLhGax6t2plywnOKxJmAP17R8Omz6sLkPtcSoF8dzunDlzxuxxxuOdd9/95Mc+eub02Tdfe80x0HKlNtN7+OI3vz4zOY4d2lpfvX79hjV56+b10vB5S3R5eRMuP3X6xEPnzl++cskNrxhbl7OyiN8/Orm+urHW2qK5Wpk8uU02CQQmFYMQ/+mn1F+klBO1gIkQtl4BAmx8vv32VfPblekMJtgMY7XOGWbUfXNrXcL7utT1KUe+Y470yttdSY5918N53DArejcksWhrT8Nt6tshL41UF5eWJ2Zm3WBvJhE7OVAB1wIS4oTMNk2SVESgXoPpo4EyFrF+wqaPt9D2DYwceur2LkyBtFZyVQ56yJWMiHXq7GulOukcD0ylW4JSoVEa4t7gd2Qe8xkej2mZwjwSbk4FHjhSEqaYD3qdvjyEeey5tZtvvf7aj/zoj1ZKw3WKvr1sfE7A0yw9qQcRmcYinqBVxVnPWZ8TcjUWQqKMdPSQmgwea3Jqxj2yALMZiOvLO8FU7NFWy8tL4ucMb95clC2cMdLfTyUq2Emgjui+J2EDvbB7yP6us2btUDFilau33mzZ8idsWF5fXNto2Tp5eq/v7bevNxoBw1n0KNPCDSQXWg6tzh7Tqf6Syo1NWDgL3dHugWsZOe21EcjuSF9s+OpRhG9/H6meTtts7NGI3qzbTAW+rUcoQRIinDbhNFEmO/v4TcvCUACCI51QcqpN9Jx/dB6sXlhc2tpiX8QAxhgEIxXXtgcYN5JqF7hYdsnFuB85IkarGqVCXGoB22OaP3Hqk5/69CceOfGVr3zlpZe+Z5q4KvGRh867iHNtdXnu5CREqKcdD3KpYnvx+obxGB489/jHaNE7RzZaLY8fDjc6Q5sNwpQdcqbZiTHEx+oyURktdLdIhGWb3i2KGUsWbGNzB66YqA6cmJ6cOTOPKmGurN9eeMP9jz3k8lDKgN4qDaJ4KCLoUduVBDXViSn8vRs0Mn41AXVt4F7vae3kJsa0NwRHrkBg+jz49IR+PfGGhf8oYaSKFZIOGvhqvGLI0vwOhWj1M8WjOEUYMZFDAhITPwFUUWI3JtgarbiNqkU+qo4ot1FDEchjUXKKisFSCdxQvB4SoMSH5FSpcFaosLS7TBIZmhyBVmNVRgO0enJ8jB13q8xGz9b2JiLTEQIZwpd4CRRSLP6A+QB4AgdRcvDMnFIGbGR3D1wEKuE0k2OGLspJ7U/MWmz5hTgmGLs/RacCf2juRRyeO2SU+YOnhsklP/Nr/iQke3IZx1/vilB8Mkp5VFOE8Oslyb0Wo81vlPQWzpT8y2u4xAlZqrvOsx85wuWAsJGDgYyObvfuxIG4A+cF4k7q2AtIznUwKRGJZTSFglI+0HZQ7nflC3v56F/HAnyyiNa3Nshvmbii6rKeLOPaoXOmPKoz0M8eTXV8h6XDZE0l5g3ga3NidXnl5Zdf/YnPfm5mfu6tN9+w5Tk4OWhfamNl+Xe+9JtMZLCNgdw215wAvnb5ivMaJoEN4LFxmzEhe+BWWz1D48PXL90cq411+stKefZjP0Y2zWZCMIBYvZAIB5jyFL93Y8UBqpe+93KrvYecq7fxQf07Bz0XrlzDDYeCroPDpRpwwDBt71Bmf/Nw3fGUVR4FfRj1OHJEy3Ql0ieDlJBxnlKKTuDDYk64kHZn5JA6OThIGXhdX9+0QW4g4BIi2jj9ZzZJmkZLi8iLReNSbulB8iBp1MK1oHEzaKyhWGzx59g/uaiTwHvUhQMmBDgLQpoZvYT1PeL6xqozskkTIJTsYAhQLPA3tjXUO01bKSNXmQcpE1mnTWDgUTDQ0Kg3k8itf3117dEPPAUugX10lUl2oNsw49tzuLK2Wi1Pmo7qGuy7W9dbbR488PTklDkvJsoMBwaCEmgHL9vuOGo5eGLAbkVsAwffFyOqYWgInLGG6ysMnOrZGD6kqecW5/ag/REHn9JuqakNHvVr087+IYMZsa3aM0AdAfHvwJf91lMnZ56YmPvkpyvrGw2EAbugNIaMEHMijrg0W7vL65uuMJRuf6XeOOwQZ4f5aRqI+/u14REKwQg7t7jSmg/pbMVqguBtsxGi9IzVYiNnqxHUUrBvuquvZ2o0lOGCdxxkNgLHnQx925Nr7FqNs3MzZ84S4JNh9paGNtzCzki1TOzeR8+liWO567GYCseczknAIAbL/gWK2CjF+did3cXFJdc6PTU79olP/Sgtom9+8+uNK9en2UZ2U8VQSbfb7hkbmSrNTGk49WrKifbjr77zCtNgllPYxrHpbm9i/8CR+YWL61dPzs7Nn5gux4boxcuX3vzON+i9z7gVtH/g3MTo+ENjZOsEzvYe+p3B3eswQTnIRFYoaQemIuGE7IdHZgiiqJA3HGYlJ9lxUBtJ2glAl2hLjU0wMHoPLWn1aO49DbfPEgisCL/L47UISV0UPchThMfcTg5VUERISUAOkSOmpVrET2XF2qWuxM+JlOLnN/KVpG+cEhQJvVE2S5HNwFxOPIVUnZg/ct0scoZMx5nwwdZyLhxJS5G8xBQNnYnhWpWYn/bACEkedNvajoWQcwqwkJrpFbg37VQgZJ9BooQs2tfQ5IqeRuGgHPW6XS6IN3ZVTHaiKcTQzi7jOagz+8HNibQDWNRQ/OxPjTgW/Kfm1ZajEsMTvVn0+/FvAvPrvR5JfDoennMswo97cvtA1RwHOZMBcHSYTJDOgLg6WI7pL0gjuecnORUNkaRlkEk8yA+GNUIqbUB4KVJ6oeQDpAL3rts0lSHpuJGSGbnYias5SAfGuSwaZBw9Re1uZrxaGSsPs2NAjXNtc3Nq9uSLf/CiC+7HJyZ6DhnF2jYzKOYSd5bK1Ru3FivJLHtrbc0ylhXmxnFzCPj1V1+LnaS4wnAEqDUnJkZHf+wzn/mlX/rnqxBtlcFCSUMN4OLFtXPnzuHXKey4yc7kCPZocpK2weUbN1CCi5eue/38pz81MjZFwxf0I+oLuAaUQyWJsNNVG42d7/3By1/4lS+ubdRHBntZ8qUp6Eim+0RfefPCVmvn2Scfr43NuCYY7AeSiqG5y5MH1/Mu15UTpLGwDuI1M+Bp0DMbGmsvFmysE7Oe0I8BWINCkrmwvHz5yjU2O5kp1lfkh4CVgyQkQQQJIaZIEyEKDcSXkKzR7l5BkFBx7AAGspe9v/247aenVh2kThRKaYfUca1G26Iks/t96T5y+7Oka+5Fa+/g/mzsBikC0AYpESU6zJ9bGfaoolBlyxs5cEhyOEzOgI5YXa/Xppi+wlM2nnjicUNpgGxyoclsEJJ0OdJNVXVrvS0O8suQwT2WtMHlj67o6+Mn9jfnfELRWyMXLlwkLzFDQEYHpMmtDYSYOcTrxsZm2i+uymoTvqR2QK6758QwZc6wvSCyyb9TbxLEjo5PVmcn5p0kPXWaUbVTp04dVnrdd2RPfKu5P1By0fs8CsMthEx2jqar400e/PLi+tatpTWb9BtXF997752VpVtOi928cYkdLOyvHoehaCfYd1HFequ9tt482HGqqmfqxPTzz32QfP76pWv1jU3srUFCl3z6059+79J7715+D1TrZ/h0OJmcYOt4eByExLtcusBcTAz6xFiV5HVrfQNAAS4hU+ARfCdsjFVvXhnvgEpdUKh/DJAJb9LHnbGmz3AZJ+iyxZXV9a3HH1b0sx/88Kuvv33t+pX+obYdn9LI6Plzp/bq263NVawqJOvivVGGYg8ONxMdv7u7vdUhJ4cC+wEUVAb1xle/8bU3hgax0bTq3n3ttY333j1dGn5obiroNnOMYj9ZNZtrcUK3f4Qeugra33F4wuaCw/us8pDRQ5yHKKf+9UaDgpm0JumWw1F33WmPlg1WuEtz6Ji8HnVQ9phUqeFpeaSg1CcBnIvIOYJXnyw3s4Lzyg8wCuRMxeP55LQ5SfAawR5HcvGhM/0wbG8joYRIfOSRQyXMz8W4ZBcgN5ZjkI7COXmamdnjKcPb4xffI0L87LtPxtBquRfK/XogmjxWY9vEeTxqO2YcK2zDUCYXSVK5UVhyKpD28IPlFaiFcvZFuJhE6CFgiFpiiCNna9urGRjsmJLj8579AlYLB3q7G9JRxv9fXa68KoQtklwTreL0afaIkT2eRVVzoNcisPDECKXwnLXw7MFgCIdrUybxzNIYiFQSMJqemqS4QztM9GrWm2GNSOFqYp8jhmA/rjSPyO42cPoyQTpxQjzV11er1ARibwFKEJGJIltRBhKaFFMchgWwINCxyQqZDM/M1sCacskF2c7Gs34+e+KEUwgu9zp17qwVjiIrnTgl+dWLS+dOnVjdbMU5yMNDxgee/eBz9Bnqm9uUPlgCoifpKNN777zlahSZK2ZtY4Mo66/+1b/67rvvEWf95E/8GSC72dcGggFx9DUtA7V987XXp+em4WMwBTqZHK1Mjo/3nDixurw2Uam6LhhjNDYON/e6eYk6KS4j6d44/Lf2z37l19989fvr9d2J2dOlvsPa6ATmzA4H2Tiy4Oqt9Z7ei6fnpipD/SAEC7T9rvIOZzjSL0/gThRuQXHHAonAWJsxsjCLEUHQ2N1kxgrfTxmiNlojmUNiUhAbrpbWVlq1EcYbR25ubrmGkSEh99uuLNdnZ8bMKO2iIg6GVkqldMnQXu9gbNUYgaxjpT+9qoaVAhyqQCsukAi+R/JYPIE4IVqge49+05tvvUIH+tHzs/XNLRfWvvve61AgVayxynQsN9pUffv15pableOK1137rGW427EFTAmGyxGMmEsuz2PwHkZSHlEYvq93oN7Y4e8Z6K2OsvlXp+XlqxoYJnVQnZuL1J7HKv1lA1qtjV5dvVVzLtvWlWdpeKxvXFtw3CgwchEa4Oae8MnpKVsApB08SPNgyUMfIbYAwZvtbcc+yxSqxibGTVGaB7WxUSFw9ubu3mYr9mDHT52enhwP1J64B5m4+XjuxIkpsv1KFYnjbI9Ksl1gn3RosDo4UbIdu7a8Fpay3NE7NNTabTvYDN3ZIahOjj46N+OQYukjg5/v+xk0Ci2t5vbqxury6uL1rfUVCJt56u9/73sX3ruEWLWvgbQyY+tOCXf2NjZXR0bH3LM0PzN79cIll279X/8v/+eeidHf/Ke/8Av/9H+ALi9cvqCnDarbN0O609PT3rEjy3Z32U4B2qLT7rFZrTl7uwyDlA1fo7FmHCxpgAb2CIk3jTAjSU8nzihUiTRo9duNpRyLBjEtrYIvf/1bW+1Y3RvNnaHq+I//2Z95/oPPPXz2VO94tWd1+b/5v/+nCIIK9UnjvBuHiJxOCKVfm5AxycnzQ//DgkEH9FEgaPVcXV26mmbdw+OjNguGu5IzRBIxvOkHnwWDFRgb+mZnlloGKo883kQZAgF2UfkOHIZpqoESK5RMnoxUx51DCiIvrbWgoGRldQFN9BiSjrFpg1YzvAbRuHvKL7mYn4U7WrZFQNcTk4lgOUuEU94JTodURoygmrlY6F1BI3gsVoQEsxjyw/gWkWUUkfOTJyclTgx/cj6Rb4Q3IkbCcJFV9sUzi83Cl0oOT3I20bQ3AZbIwbqPJ/nK9qYBDSK1j23tvNaCdwq8ELeoyRsECvwMPqTxYf49to9BDPg2tSLQrqXqqVTO/AvSLTg0C47hPHDbRCejCpI3GZrDE7v6M1xK5ZnfIn32HYXf/tCNcedPjnZnWH67fz5F/t1IR8UF5S6o+OyVv3gej328yBzB18KTYxavhUc4tuleByBiF2IAglwhR9jFU37g8SdOPPOh0JUHf+0XssBikjBwXypRYkonPgcAFLmZr5w4gzZvQ9wPBqu6JXLILpJs07HasO2XjKEbewA56DcuwCq1Ulex4jHBWRV05dxAyf1i2qOs5nZbWe4kYdivPDKB8qKltbq85Lgkc0G4MHOAhvOrr3z/b/2Nv+mumI996EMMWm3u77ls2D2BrO9+/vN/9u///b//a7/2a//B/+4/olJ78cIlJmXPnjkzMTXuTPD161dRrq5VQXY0ttYmh0eq5J17++enq62lqxdWbgKvV15/ecAtobFf1aaDfXNxiW0jQOjW0vp2C1p0v2ZwlGFMCStxuLeBxGeW7rDv2vLG4srqSN/hqdmJs6dPsM+su/TNXUMQVneSy1/TrA2EY+8tdHD1uyRdgp33EAdDVOSQnZkDKJ47M014gGObqA02605hkMjVZYVYoQ7267/xxes3b1RHayAp1Et8GsyvPIOZjg0g61AnWmIcuRzgAHyjTnS13g2qPna/sEm7PZ3tVnuzUiFlnSXKwEnKwL002FOG9JrtBujK2lRlt72wsnDl5rXzcd0KLW3TxuIjhgJpYl/TrNBGPJCcaR3EAo0uB+9HGNvuHbAJPLTfcnqhhWL3gb0ns4iF6enp2dXVZUwta792nJxAdSsHvthZIIKKEYfCIhsKU9DDoWkzNjrBeNDWZvSGI2o3by0RSerJYKWp2O7urq1ukKaO1sbFJ8E1KZnQSmdhgvjYZRt6oH96/uzzzz//2COP0DqAtKrVGkkyD9Rklods291Quzu6TBfLx/JxSsuWAy0EpWxh9zbWXVyBT1MxJ1TcnYOhI2Te6jQQJW6TGyr1TVTn5k/NHOye32s2Lrz1OmuptLRXby5OVMp/7nOfZZeNaP3N9qHTvbal2Tb9/S9/RZ/MzlNKc3NA5/f/+S8+/eQjf+8/+7/19e8vry1cuPj27/zOb7/1+vUEN3dIjmOptggEHQnqn5qAevepw8TFVvs9201yWmZT7TEzsmhQekionHRn68bt87bGl5dXrQIXQpQrw0gmUi0nuGJcxia+/d3vLa4so/D+8l/+Wb3Uamx+97U3htBnZ07+pf/VX//Kr3+xtb42MlIm4kf3hzFQU6qnlzq1CUZpwWp3CRA8b8oBTWYA2GCGOCPo87DJgeVNEqC8ZJI0JjOwCbskcKbCGbPRoY+Tk0i4ftUt942gioIxsPEeAhZjEw5hEghT+rExaL4tSBzwzdOENGNTWbdxXi76X8eT0aL7ugRm7/flCAHf9e0eCJO/i50xUihBc9FrIWCIO0fiy91wSc9ErKPcrHYuEgZu7eKp+yS7E8ukBJFI6T9UnxblRuJj7kHhx6L8Eb1dBFykDvCUkJ+Q3PKi7MKTP+VXcd4//Ohz6tbYDJQ6/ptzIBfSKm1aHDg5MDox8cLzz888+SzKBSRSjcQKo4LjlIs16WnbSSqfJMtz10ipgCAg1aeOu3ubiGAnCrAysUMW6F2RIf4JkZGtAJIi28oDAYLjGtSYDoTZ/cPtzc1ytbK9sf2V3/md3/vK79ktGD1zwkID0QhB3L4AfKADXKBlX3NifHR2cqI8OMAE5WuvvDw3O00teXJstH90/He/8rXHHntsevbkK6+/8S9/5VfBbhywtoxUR8gMVdvSB0IZpARS56r9zzzz2Mbq2i/90i87/f9KdXR72/UPJSIAp0QcvcBBXrx67dLVq85LAOiLO+PNrc1BW1IlO8MdQnKVg10g1FAIJrPboq7TcAqRBsrk1F69tV4MJU/hcGzGruhGLGEsDVxPkhuStsGXERAfIDPqbWh62tiHM1PjLh7A0DPRODLc26jvjtUGyPFoV4FoP/qZTwFztL61N2cetLHtwnQwI0pEBuEMkN4BEEOMbDSNoAhBADtyRBwdSPMQGRUswm5neXWZhtSJk3NGGfO0ubmOXzz/8KNUVwlXh+3/In/GRuWS7p2pUr3FndhKghjxYohmOxV52oQWUbAB0TIYC3KIiUCtHHPkFppKb9XplGbz5q1bdAgePv/oG2+/oTI2tpVLzcrATc+GxRU8a/R52gxWSQ7HZgaiBeW/ub0ljumKURupjapGCwJLIhyB6lsb31lZJ2wOp+1MA/qqcziHiBGFq5evvoHRPHfuE5/4hLnUpnAVO7TQAmQaM98ZKN1LoFcdrm3VGwPDDuiOb2w3V5cX6frV3Fk7MGjrmW1eK2LbPl8cE7Wf0TM5NRUb6zA43s0pIksj1s3eM888QzV6e3WVFHeqNFRfvHFpY/XZp55++vyTlLCc4nX55u9+qbm6ujk7PXPlyiXo8PXXX/7G1//VyRPTt25ceurpxz7+sQ999JnH2/tjlMuu3rh048Y1ImLGTJYW11CRzhkkYa1V3OfMlTYeuvhnoMedJ9q+Tc9ru7G6wZIJVcchFxDpbTZAbNYYKbIBNA2bjab3yTOnn3zmacKDJ59+ujJa+ye//M+vXL509eK750+d/OhzT/70j39W7K3m9eF+V7/vWJ44Xg23xIFyLJWx1w3WF3X3sFtu4gX97Uj5IHgXJ//NFw6WBfcTro0FFpM4QpLN5RipDMisJsgWjo3FErYn7RHDZ6GcgjyNg9qxxJCfgRFMQw5lRipmepgwtdEQ84qcQOhdmKJ4fQDCK1byD+opkFFUJgPh5Mm4P3nveORodwR5SQD87sB4D6B+HweOZRRsousZlHERNQBOyEeLlNFSXZF6IyTJqfsjz0RDdaNJlCPkwqSJHNNHnW0o1NBiilQx2il1qnaUltM84FlkW3hyxOK18Dwggz9KcGiLmiU5aUys1BTPDBqEcHfVQ0iOVnjuSn48vNsJ3V7OHRXdElaozHCLIzYtCPmbzq3iCEPWCj/ZHEvEo5iYBi7y9D+VpPTYIvJuTVB4teMXSC3YX8g1x6IOY3tOPpY6nVBySIrveCtoNw4fkVb3YiJxkkB0kKv2XFdXV2b65hv1ulvHL7578eMf+eDU5Myt1XWbOkSiZCYLN2/E0opzn7sw0MMPPfQH33mRDZdvf+sbp06dgLTglHJtcnF1/fuv/UtiZNpb3/7Od//CX/gLpZFKeXcPYCI3Jw//yIdfUNZLL23TW/700w/Zbvn+0uLqtavUxJwoRAygEpiSPugb7Bwe2E2Na0Ibh80OLmhjv/IECFEu0QiJDVIdaNbhbVjkZ9YD4ioP9TsH7EgFymFjq97XaeTR8TzqvwggqM2dr/+zA9CxBGE1HneH6KCqEqAh4JHR8xQfkg0Wpu3ATO9TT5x95qmnP/Lx504ySVAuo5BANHL1/+q//C/c2f7BDz6LibQjYN8zFi36NdiAPufkZRugKOkx8kOU5lOcscfyhgG5AD0hgOKMc1/v6sqaXUATIhCd20Sbuzbi6fba6nVNoVuk8B6N5s71hUW6vlvuTWK6wf0ADEftEnKYD8inkBdCkPKGg4mfwUZYDOrdZYRjpAS7rG1soXVsY9y4dfiL//yXf+Yv/exP//RPv/XuOxp+Yv6k6WQvXybOiTEAlaxKxBWn+ioOyfT26c/a2LiZCWfoimBuXMjRtsES1dZp8hkbc01WdXIKKz8B/qZ+DsU6LdcfuYdX29v6TSCB9vTM3OTcibJLf4nPkia5EmknSCiCRRG62Xv2tXvCoMzemh3cserw22+8/uI3fu9HP/nhkyem5mamGb3X+S4eoXBJBL1F8OsaRAesSX20nD1C1kz6elcWFjZuLbz16vfZmpoa7t+4euHG5sbB6o3+hcXp2flTp840e4cfP3viG9evHexXN7a2Xnr5ez/2uc/+g//yP7c9D125lO7Ke28RGFZnH6MQAMU+/ZQb7z++sb7lNsOVZXdE1xcXVq0alomsIQIM1geRELeutpnx1yIC6KHyYKUWN2roEA0yW6jIUuFwplMFoGSddubkGfP8ey+9/N3vfdfJQJa23Gr32NNP7zXr7125ik51QgrxgWomPSavaneWErIEz2O20FhmGXRgaJfmWtjCJe82a1naAFnwvoO7IThODuUesk9dB64A3VZB1qo1n8SNOPEwIoHXTTiXOpO49VJaHmIUV2viJOOACyQtymiOOeamoFXzY41+xg6a9byTEeVsSzKmQWTZdSn7rv94+NH3B//eHRuQTZuAKUUXkid/N+Kd5T443z/yl4RmU+pAo0U2hQ+u1Np4pj/14QrUK35OJDD8ARnC5TgxPIGMIgT2jedRvikH2DkWfiiyAPLxfKDL+fucPNFR3ZCjDO+McPT13vy6OOruD0Xyuz50EbC5bv0XkXi8FlGLcCHHoxURcuTj0fKn45kUkbMH+ACksKT09QMRu2eXyYOtei2wQKoMaMkAUrpdK50lj3GCrwNQx8kJuhFRmZ29NouuZrrSQ+iY9O2As5yJ5RHrIKHGZJbR5XiNuFKW4mbQTkhX20uWycCVd9758u99LWR2/f3vvPW2yjS2m9ev3nBxJZnSXnv31q0bVhF9SosHUw6kUtuzyef6PRBna2OV9qPj3m23J4zUllbfZCcImL509SYNkQ995GOLt24sLU1OTIxjPmx7IA9OnZj7+Ec/2tOq/4tf/c2333irb7B64uSZZOQIw9g/Nn/eflrIxMbGxsH03teWaQDZag1DUM7+azAVAwI127x9u/19cHMn9J7dLAw32fxC79OwabGpq8O7A3RsDDp0vs3Ojo6iDhOIJCiVvj7nT/WviPoTv5spfeFh3CcZkQE9Hn907qf/7E/9xGd/ojpSnZkpq7Pt5/PY/DNn4B47C2gO523oXjm+q7tCUaI/9DwNUG/YHeo6SFgpeQFRxAgpU+wFJns3dG+o1w6XmpuHjfaunfHtxg6LB26XojMZxg/cGbC7P3gwNDY+g1K5dO3Wa6+/c/3WFu4mrisNy2hBBpN9h/bLQeyJ0kB1mClEjChxpzcJHBju22kRBjSh7sMDKNtR7pFR3T/4/VdetW/pkK/tUHbjmboy4jjarW3qZmC7O1ujukmpWV/1U3d3ZY1ZvboWMal2UVPX7Y8+/jTu2Vkj1qEhEk+vws0lzzxL87AEvUElb4xIvGWei2Yab+JqGzv8lakJX8WUxGLRmSgMEP9Lv/8l7PsHnnz2Ix/5mL4kjFm4+k6lb/fX/9n/+OwHHn7+mScVWrI1UqkQDGg78xoxGaJkMqj2XnN3azUEGjQDRylh7+3MjAxNDDsW3J4YHWwvXV7e3Op9+NGztSEX3PzUj378lT/4dru1OTpa+cKvf+E/+t//B5/41CdXlm9SCiO+aNU3ZsamDyuVS5fevXrt8uTU2OOPPwprlsulM2dqzz7jDK3j4mNT0xNIxqXl67cWrq6sLnRasYguXboEN2vT+uaWZjL4poJPPPbIM888R9ytV5eXVxZuXtexv/e7v+1Zb7dXbX/MTD355BOSuwH+zNzsbnP7xe/9Ac7exQXV4eBojWB/Xyko7DDJ7KbE9sp6vYnyHxianpp1csiuSsx9FotBCSCarASGTi4kNUm6llkI+9Op/70FsoZU4xFLKECl3rRWUFqmeNz+W9LQGospLMG1286g1dmORVEFDnZzVL1u8EwJ8xgvDp7FkEaG2RWe+74exXoAO4eDL2J0PfdHPFF5LhjG+7kjxunub2Gs+/7uQR/wVxmV5CwDI2aowpOaGl+haemRPBaov1SC/k/yy6B4zHEqezw5K4va8IiWCw18ES703GIQ80KJ7A1WUADZ/SE4WKQ0xBG58OSUxWvhyeF//GcgYMMvXy6akJx8hXu9q4AcJwfmyPyF5/7hqTOOejz3VzylMv9s9YNE7NRbB2gWhQI6kCKMCyKLwxpdxrhEd16jjxNCldbmlndaFbgCKDvqlo575swhDAnhEEcgQoYcl/vZdcACxyrDfu27wItNoSQEB6+ZnXWl2sra1VhLO3u12pii6HSxwovt6+/YtWrcajSBXpWEcoJlcS9hGApuY0TYJKQtQtlrp7+83WiePH1OBZvbdapbv/wvfuXpp5++eXPBorX2nFmMS6lXV2/euvnSS3+wvXnw0juLpZH5D376KTJvC9K5dBuSdK+GqjW1J38vX716c7+yt7RAuFlq2fFzVLXdRx+zuWUzD0Ky8KHkSplOI4EXTRGHiPfAlFrZKfiY8Zz+KZ7Zk3s77EulTzpTOEaHUUZ+/WDWixMfWSYqDy8sutuu59FHZ3/u537uRz/16TOnzgKabkhz5TFVh/LwuMvev/CFL5yYn8MOsDQCpRtcVoVgYgsD3DQkdhEUlxaJSZHng2IdzsCYBuOdSIiYilCmfYTFa+1yeQyVQ7mC/D9MOtDxcWqose8IbMPtsxdvYp0uXbtBbZwJs6fPuaclbkvAJwYZktaf4uQGq2kVuAlzu4HP/XlQHZG4+KAtDSfnwvHHDq084/KravVX/6f/iaKTHmBuBbHo4DVG3GVZqgGP4lPhY6jRJzloKK4OCFZzCMMrAgLHNjl5UtEcKkdWgTeTlr5PqW9D1CS5VF5NKrZA/GHXgUY9okR/CqICFoAFqg/T+7hg924NH1YPzj1+7lf/5Re/8s3f/4uL137yx3/ixOzYWzv191797rnpGsPWe4vXl9cXQtmVPJpea2//5KMf0M8kpRh00yyOGVF9aLZeeOLxGxffba+vTFeGJqkZNVYrJRrRrbHe3puvfvNwa+nkw4898cwLTz925t2rN+hyXb566evf/tbP/vW//t//t//wwuWbe53tqTE62ux5hX4GFUlMpYM6G6srrqtAl7qouFwZ11gETS3Ms/bBzTNz0+MTpywKbaeQ8e47F37nd34Hg2h/92/+zb+5ubHhPC5NCxGMFKd7p0dHENlTU9MWIj1kt2TQvBuv1RZXFqsDfZcuLbsvkBlCRq1rg/0SVl3/YP4OWCU0z8OOk/NcTjgtbrVZcC65+Ng+vPPUZGIxW3rGLBlTPjkUIfYRiDA/aYMlVi2wrumE9M2YC8GPDraXYWrZVwkdqx3Hw9hAjsm5bjdia8vBB7tXaHoQ6WA4thjS0Af3zHXL6uKSjHvU+Mhzf/RpydzfQVTpQxe/3o70gHzsvt2O8wP4ulu598TUinvCUgAp0xECTgq5KV487AtFBL6MIgMHh6Ah3O3cki+6C1pIQEmSkC0fFQfqp2JioXOJKiLGC9YBROlCmGh77pB7uiUljmocZXjcH4HHmlXE4Sn8Rxkc/ebqHL0Vvw+KH4xjdqLy5CdPakyR/LYnxyn6qPDkGMVr4ZFpfIoc4ydFy6UEcCd78yVgPaVCnBsIDB4dY8d9slOjc+FjU9J8xX2GsycZ6pNhoseg2eiBl2RiUxf446xVV7QaNheFYBpDzmkQaE3Xat5jMTvQwhCBerGL23G0Nw7uA4vWCQC3ubpMBXSsUplwtrfPiVaXglXQA0naFOrZslI6G3tTkxPLy4tApMs8SMM6w5RU22SMFj8JNqsUl69e+W//u59vNRuVMohMbWfYVT00e9fWV5YXF5Z35/b6J8fmzozOzdHknj1xGg4uV2ub7Q7Fsw5ebXBgvnb68eGZKQYH1jcnbr61ubrEYuVeq7/Th0N2+hAtAqIOpUt62BDeZbRyqDcQMFqd2C13v34+7vIACUm9mWTCSZ+QZR+B8Io2t3fswIasGD6HTeUzMVEFGbG/xogZMAAFuIM/9NtIpbS8sAhd2E3U+ZBZ8HD6ncoNmjawC0vo1obRN3DdtaEaCfuGRANBY6uOBESGYJxWKbE8Mv7ccx+y0Q556jomONFm1qMhnnSwjM374ZH502enTpx74SOfhjWHSFrDznYcjgJR7XCmVmdTIbF7CgkhLEiMzQEMyhTjifY/2i3KVgtLS/ZT6Rc/8tjjX/7ylx2TcH6UKtD5hx4599CZzMWOjIXesgmgkuabakjOc+r0WSPO76tG6YGofLncdP1gAAcqKKbPAM3lkWpN9WBifaJuooHd4sSWLoMVnZ7J+UkRzFBVh8uUsHBzQelUrdIyCelOtCSIiZ6P/sjHn/3oBy+9e/mq41/XL/7Ihz9c2vnR5bde+sCZ2dMwXc9uY3k5Dkq5rcfFA7397qKmOjZSG6+NT41CgWOT8+fO7p8+5aaES2++vlffOPnI2bnxkd3N1tBBc2a8tHV4sNjaXr30BmqPEtxHnvvA6vb2qgM/O61f/KV//vyHXnji6edeYs+1ViMubu/sN5pLkMfEWG1kJG7U0iGGUgdAq3MDJdJyPXP1uu6v18ZdilzvH5yCkKxKh4nQczQNnzn5zMc+9rFvfOMbELAuNb1RPK7yJN1B6VYpVfUNNrZDzt/XLrGE1Wk1ltrbaLRQ2y8N7m43q6POUxEDWadDWX8hqJlSBWk1OjbcR/YzVFqwu9xXItm382WSkpqYMNxB6A/gIg1I6K0Tr1OXj71iFy0BP7gue5OE0WZ1cvY0jL9dsJ5+404pGgPe2O3f2GzcanYob5pZjkaGxW/jjJKnBAfOyFw/cCZSngM5tz+5J2B5DNncxr7HAqMwWCqUNH8IFxP2vu4BH9wWF6UE+oxkJnz8wr6GCCCI+kANSUoRH8xqncMTLkEnr8VK0VeJHsqfc5QMTALbpiYX9McRYRFtzxne1fbbmRz3peGI+HeNS/FaeI6n+iP7uwj4fdIrL/fI8YKPB97ur1TpIvLx8HvztzKjWw+6+szQHhFeKiIGkif5u2Qgv9yOZ5i+347ptRikHBMcHOg4khToJMHJOI5mqTfW1rEYncYmLOXYvLIgiV2n/g56x8fG5ueDX1k/c/q173/v+qUr9KQGa8GdWDMhUx0cJn6G4cUBQIGV+va2cQdn33rjNQpW1Jsnz590m4OCwPerly/T3/FKv5RCJLxL6H5ifoZdaIzQ2OiIa3CaYx+7ePEiC9MnKrM4iP7K+OrW1rnTJ5jTPRwestnYWxmZqs09NXmq3m7eWl458e7g9WuX1xeut7YGGwxO7jRNWdvS6JKQ3rjjnd2pfjcyoEHaays77UMm+jIj2+2iouuAG1jEMyLA5KqIDArOEy4f2XNbfIOp+rAsQWOns7PtpMwHPvCB0NYh4U9jBIC3t+LiMLz+WjL491f+ys+ykg0sEktE5gAVPKKLnZVkQbFa3iUBDM2X7vzuEq6Z1Ua8x8VEEoYaC6fo06fPzM/OXbtypVIeDun99Bw6xCdc7OT0rA3Ctc363OnTTDWFXmsYTKbDHJKP4Fpi5J0pssccl1Hu2X8wZez+xu1JDEnXavjsTlTVJWqlkZGZufm5U/2UdfGv1Gs/9omPU1k3sx575FHD/dD5s6yY2e1Ik4280QQgHQl7jjywNWCK8RZTuycmJkWD+fLdWSKonBB9pVHimFF5nmuLcH7jKA4LPko3351loqobpfT0TE9Mm04yCTmB0+EiA1qAWG/v2sYyqy2PP/WY6g1Qje40zpya/7m/82/+s//uv67tzI1NT44N9df6KoNDZUkJ993njmlcW99whVfvcAWpx7B1tGinbRv4sfPnR0cGt9eWKr37rfr64EG50T58aO5kp3/o8vLN3/71Xz3xxDPsotCDW1haHJ+YWl3b+Kk//+dffPHFyxcvbIyP0f866HdNZGlu5mFq45hDOPXa1ZsOENSqKNUZauTqnNtroB0GJkcXgr/XLUIcf2L2+6tf/apMHNFG15rGCFyWgU3L8dpou7Fx8uy5zWvX3HZzMNBiHfahh4L0Ybh/e3nJJNlYbiIHt0x9BJAMR6rwH216rBu1bDsvTIkRVY5PzdonbjMQVQ89x1g40CPyr7WVOjlMJsL9JYQnYs6RtcBngdXUPuTPoTUYDgqJabaHqY5BBNVaO45Quwaqic6DmjU2aXOZf3G6DuGimQYd9o1qORFEEB3wJNbpn4qL1ZZd4ckIKQJ1RffjD/gTrbyve9AHhQaCteDNQD8BjcKje6I+iTHrZqgqJgN3vAAdyOWQ7LszQhE5cuNCkpp9x5+pEx4gbj8er+svSrzr24PC74r2g79S3YtDe4SzSDuEipSJlDBneKG93CORYSz/EI1FX6QuuMMTMe4fnimR3CeSdDvHBNernQPKoqXVxkZpouoAzGpza+JgkOTMwb++fnpLoXzoNC/aFzeQhKkqEZtvuDULmdio1xmPuESWHImWzZ5bBWlUO8KCr2UL6nDnoGWbLzSuYyW0d1sb60sX3n3LlegUJKcnJsvjk6uN/aHxkwPjc7WnHmPplW7z+OrN9f3hlWb7u29eurYehifn5mZ6ekGGbevEH0s3YJAMsfBWEY/ra2zyAhpPjpybd3HdeO0mSLC+dHZu7IUPPvnomVknOVoknLudNVcmtJoOi1CbqS9vTBzeOj+wU3e17K0L/XvN4cP2icGB+sWXQWqsp/vn20w0NTYPGnXaRyd2925d+k0opULJ9mC3QhoX9pVjONQh9k/3WtJZ0gm+92PN9vZnAFdmKnr3WgNxVh2P6egW8aNLKLKGEu4Mmmq7lBF1GfhWNLyqqwZYvW35xn5NUCrt3fbP/NiPnJ+Zqa8tkcT29O1utzcGhkap0CDpgwjo37l4cxF8GSyPyMZeKKRiIM0cO/Hc4Y4bEcJuMvClzpxso+L5eScVFYSPCIc9t+qHfRPnQ1GYvh290+o5T3wriBVSkdro0jbepAp6wkgsIzoEG3/JsURL9MwbqVOGkWd6VSzCoHd0GgXhZMpSkDCDpifFMVUdGCvL++wTT4sWp0ZKQxdurR32uO1R3QNSZ6raNCS3CBhaobgebngoxiIsPMp2oOJsWF44AB1PIOJ0PUMG4GKGeCc13wxH/8SeYrJ3TzNKR8HoKddeu5WqLX4kUf8jz1j/02ikiem5W+uL1fGJTTLwwbGzP/54/3de+qdf/62P957/yMMPDWxs7q4uPHPq/Mba0uFEpMw2Ww8o7W0FrSOEQYaPnWS1SBEhFurYPCtPbtntHhtcbW6LMM3AdF9j5+1vPT/UOn9m8BsrcdrgW1/8hU988oVnP/zCSxevXFzafOqZ558e3swIZms1Dhky7tkb+ygHtZH+7c2b9PXck7KyestK6axtLd26hbUtjY1RSNtCraw36JjViJcG+1r2eRnODAYRlRnnwlEtxLnl0ergNuHzZHN7BQM9PjC6t7y9s7m5sL/q6rqbq+vzs4/gO68u7+CbkXIHjYWQOe2ZeTuj6O/91UZrZ2RkfqBaWaf32XTWeGDosNLPzKRjUT37Cz2osqCWYuLCx8npcKSA0VMNJ4C92uflN8lRc7t9IW87iLloCoRchIZI7lU9aXAlTLLsSHk4NLveQr8OsNLedunzQKmzV7cpbgomvB6jLAWAFlx1HNg94uSOPqTfLqxO/jsew4RSpod1g/aNp92dWDXaAcwbX1Wxj+vJz9HB8+SVRLV4IklS5I7iJTxKwi9JJ+mUpC8/0KOXlC7jePMWv5U6hFTaNh9K24ZArX9ohAo6fYz2XslouKgYOqL7E3OR2ZaQJBEyONtlEqpd2vpVT9VKFaBHovLahPT2OUTwTpTHjWQR1wnzXpydv05gmzCCrfBwKbdcnfSund0cU34xbtnds6eeoycwchTn9q9xv/1yP99RuUoJ160NX/6Qxil9OXrcN1xgjll4cvTitfAcZfPA3yKfmNzpdLuoRaB8oiePHuGJYei65O/WJAel7/GwNnBCMgy4nxRN5c85lYsqx2K5VddrNcR8NVuMkH614pjPOMGne3ZtPjlJaVBsv7piaG15EurCCppMktH2JDMjLO1h5banDKxSYOaAnmtXL1M7dUuARWmriyU+nNjqytJ777zdbtapzahMB0bd3Iz7CgNnXKUqQgkIfI4LTQeGiOnsJCHq6aC5r8TuU5yDiNUUxxNHBlaiecnpJa3jeDTXE2NHP1rDt+tdTHwY6iVxCQ8y3r1J6mxq7QSrqVvYQNIc1h9dPJ5WnSmPfUxHtEAxVuFAFtlKjoJ54bnHnA3Vz/oWNFQFkZUnhF80hDy/+njq2/wUftwxB3j8VZzspJKJZwzSUVqfuqN/1N6cVjguJ4V1H0W4URDUzTRNJJ+8qpvwnLlqC8n+OAScnAhKL2BuESen6hYTdni6eDwnF26OiZwzkQPHnz2+2l30VFzOIXs8daAnlxPmZ7ymQA/558x98goVHX2545fx0RiM3bY5vLfTZt+qXl/ptGp/7a/81ZlK79KF1x0EenhiQrffWF6uIKrsgEZdUia4QjA69uLjPeBkFIW2iMomPaOAyLlFdC18w9yzYTRc7f3sZz755tWb77zx+sqF9376z3z+rfcuvvrupbWVhcXhulUQEzs5OnHTc7PjU5PGSy+ZzagKr+IoguFVQiZ752zAhbXIMEUSKngmk61izeJnYt180+36OXYNbGTQwRsZOXXmNOV22brVUQhKVT4gyLUb11m+I8Dwqu0ntAUXwcY7vEjnne11vbW5Odzr/qYYJeMe2MgqIykPg9XRTHUTn+PnDIEF65nDeXIDVcwVI/nVJ6+SeJVtTpjTFv54DWWiMJboVBKLYPxGQFdjHwIFWIWBd/3F3QQpfh6q43nc7c8xc6jsuIRE01Pux6L7KmvicHK7yD3qE1WNSkh1O6H6BbkQPROxggqIOhWzMwJ/CKdz7oqtl4QIzy4VHlGO997Rp260HD8i/QBO2sj+jtbfTpa+3n4tfD9seJHwB/Tcm3+IoHPiokfy64PCi8g5glRFTJ+K18ITnRCueHaLS4ExU8WMyLBN4ibTFOyOs5ytYvOTJ8EEICnNh0gc4MnXEAklF2UkJ9AyyBIeeQpXilUn3LLZcJwXucpixUE/a/lbjd3Baqd/dGZ0brQ2MToxOdrY2txaWVpfXkByj470zpfCjKUrBsCn2JIbsvaSUvW+C+bCRbmxv0HeRLPLceFbVw4760s3rUb7ykTr77z+fbRecMptCrSsLPTjFdmtVNkAVSFojGWIkAsbXskIBul8YIJoYGh4hG4zkjAUwqyELm8X5VoSsXiiu4Lb77i3L/Yg5WxjKxgoxvpbHYJg+jAs7yMMwkrRYW/TVV8jo6jBYLlCdr0fPGPYljeCoRRNcgbYhYGNZBBHMwf7Dn7kR34EByNcKPRMLKeflaVjPQ0iOChmDkks+O2Vo4bZ6XxxFJFd9ksrTxF4hBeZCAk6ODn+XEqOhl/iyX7xs5NcyH3jB1hPVT1eW5GRaUd1CXaniCNazlygzHPOEXJEs4qQmy+QVFv+4tAp9cjxu0mKCSteqlt+isOT43ge80T9c5yiIV4xk8fipASpsU7c+CXzp8Fneux2yt/51mu/9ivf/8/+0//TyfG/8Ru/9D8uvvPm3viEe5zqC0tMsu7vb8hNyZE5lGsBRaOsEed20hecRBHm6I5zcdH8OC4MY+CNaICNDAyVa2XaxK3L13/zX/7zf+s//I9/5ic/d/Xq5Zdff6U8gwYs2bHGgGaiwawwJ3Vy7iIec8N6VBh6bnnIJjdRT2zS28qhOWfyi2+7h7KYHvak80gkgySlCFnqKeODRbD9oReofJhMbufUP/KcPHHCZjPCO6yfbsax7DhjRVvTSYH9pnI0WjUcxR0vjfU44YCsCVZxD/8atk1t1rIdl+ZzfkZPHU0qHsmjSmkC56xcRSoTC1lMDVGH3N6cKnVod0KKL0+7RlaIRe1Et8uQ2VSNe7uDgwtIlVyeMekZ4C6/5k8ZaUZQllam0G5gjoF55IGAuzg4IVchuT5WU8QOfe8Y9AjnhYwDfvmY4UnECII8UkV4TI/bcPmO4iKLP8xpeI5SeLzmzsydLFzf+C/wuBMtfeo+7spEaA75QZ7HIxf+5MndEHn8YOE/SGn3j3Nn/t04gYC1ufhW+B8ULl0Rp/DkzIrXwhPh3W7KneV5u9fSxO7ODCNhZpvBqVyBEY2flNACCU+avgHiuFRefItzFcHI5rCciXyssez4ffLMCNja4AZLtCf3KdP6hPts7K3tXr70aG1qp7TZGuxrbmz07rWnxyqjD512FWhjo85IZr+dTDQygAVxJ1O34EXIDoPghL+iKkEqRCUH1pfaG0sQW7QFN9FOlg7o5VLtRjbgcpXLyKJVDB2ubFxLx6Acf4x8cLoypPY1Uh7RQGRy8Kq0iqQhuCUH36VKEx0QPZL6RHN5ADWwVKvtepJblqqSHzqXaG/KCtKDnb0WQTT9TPikc9AHmDkbFBfbCKQyGlqitrp6htVswHX3IdKJJll4hmZ37+TclA1gPUkwUK6UoeFSmSmPuBdIfTgd4qtC0+B073ONXkngrAiP8TwCZD6lpN3mgGvZCSwa2GXHUrYyKfLJwF3Mo0RdSg6YznUuYub6ZASfMogKFAXBDkf+TDoEklFlOyM54V3P2M9LlZFKk/lzE7yKWRRaeIxIziE3KkcTUhAoOVWO74n8KEK8ZickiyJ4cnHZ4+kShXJppMOSqk3Evv7ZqUnGOL/6u7/zG7/81E//hZ/8yIc+9KVLF9++fLHy2JOd/oGlTrMnlILTojNdjWxs33ilHReO30s8E/w1wWPakfolpzKaiIckKNRFI/09n3j+2Vdff+nS9198/rkPjRzuDu02VlbDKqeNcRY/KHsFctUDfuJoVrCq3lmicOMQAzVsjEw8Ppa1x0Py74CukQkO2JkpIxCLAmalyaSrJY+FTKuvx13IzcxnyzgEUUSaaN9GozY66iR6RtWY7zMPndt/bwtOQvVSfkdCWGuyam3tzdq+GmH+ruKIeIeiVBhNC+SaTr/n5gbYyePl6ZNnHo7UD9FHPMrl8qecTEwheW7kmDlVdJnx1X9ZurDv3qgduz+h2BVIUN8GNAkkiOVI/Cf0CGBEWHLRzjwu4UkwMkt347XrApXzJkQb8OmIB8wI2wDKJJ5pmSqOGngMaso3noqOV880D4MHVpR/8ep/f0/IhO91uaX3hkdhqVVFhOwx2XiyX6rsT3Mw1mb2CNRpOc/jMe9TyoODcs65iOJ5ryflHxW9u6CY8+HuDs+h9zyPot/94d7kOSRE0MW3IlFein9S4UW2d3mK/Hl0tInL6QN/QXInIZwkMVQmBkAXW3YQagyP8EgTE9d4dWGiAC4WRNbwkm9aVJ5CPMWkS0X269guBUiQotx/CBttrK186Yv/wt1BwLr10N5cbm+vD0C6nf3Z8VHF4c8TfqU9kHxubGiHjWWKkVF8HOwAjqNWDPyk0pKtgwGXCzumG4BDxRDknnkRJ5hGGtZhZlgqOxWZGRlAGcc63a1vLCuXHm+cZA0VIksBbu5pHTrp4QsXS0I+YZ3n8GArnRZ1QzyAZWcLxEBzhBx7uMomBxsMugCPPsDArvh9g9t0UVgxDCi8NxK3ObgpPBTGhzHi0f2BUAOI0OpMvCndK2wN9sKFa0x6hXkN/HV/YGKgkIsGJiCV2Jcu1NASzidV5WSr/4WIaUTy01chZInRpiNXxEdz5ITFM3+KuiUnOZd7WICaiMAjMMfMTxnnQOEpXdQhSkPdpJgCDaRsIQlJzIQifs48JwS/c3JP0STJrwVCzeE+ydszZuiRn+cPdXclz5lIpVfvm7bR3KKATjDDCtx2vTE0NX5iduahEyd+4wtf+NAHzp6cP+Gawu997cL1qZmJSm37sG8sTnqF0xxrIurHyRoZl+oJCURIChPM3GZCMTsEJBorhBi5zQ5buWSBjJX658dGVq5cOP/CBz/46JmdzZWt+oa05oyJoSeVwulA3K2hCQScTigwU8PDMVdtipNF59bpLTIq6PmRx57Q5ExmMdrjCgWHiM9VXDtUlaFeitnJUWBIDrm4OLx44fIlxuzGxsedGbPkHPYbO0Cj7A8Nu08MTosdBKedNV0twkBP2K6yz5MGvVyirsB0pprIv+iB/JoLFa4tuR+yHz0rRENEyPNHuFfJebhuu/KLOWbHJ8n7aGQxOiQ/WDLJIUzXoxkbx1mjGuGSSDzjyxwgmvEKnOjfUf7dT87fxFiGC+ybntmfIqdAqDV9SrE8Ehi5HT+KQhd7RsSYExEh14b8A7w8SvhD/B7vhyJZDtRpXO7eos+zJ/dZji9C9hRxinzu60mZ651u/+c491ajCHlQhPcPv2/R7xNYFFd4uiLoPF1yyuzPMe4NL3IvPhWe48n5u+HdKZI7Ig1rkUXM84xGw1M4CcOZAcGEJUAW39KqwEDhiY72aYTCRp6GR4rssR44heRXT6/AvRDLjzqVVevONpYTyKbsuQLxjuXanN3FFVKzGeyvDPTOTtSGHNmzl1uvy0EeiRaMSRDqjEnRSSgGMi08gNhRBZKkMDWrJp6WF4VpSgQiKB0o4UlZRVfweHJOyObq7SZcGiWllkJI5mUcTdQTSk9GyuWwEws4qiGmV2k9OehWi3io4zoCA4nY0xSttVtnsYFweWTYBX49zT31dyVM6HwQfcsI1xon8tS/b0jVCfxsvenOXARQoqJO9nzwgx9UolONcWtYAjFIAiwLRRQ1VFWBnFRe9bOKeVWfIjDW2dFKE40/x+TnCgRTpOIJf4oWZhI4I5ec+OB7Shf9ENEQErx0XI4QcAGbpMgxi/g5JGcV/Z3qGZ0oRqLXfWJEJcePxkE9qV1CfBIxt67IUIjd9PxV9OzpvibYlYsonj7l0nIdujHVM2UuUEh2hb/on6Mv3V+6im6Uc3PhaGV0sDrivBblhpHhYTZtvvn7X3vhmcfjWoVy5cKthXNnhmojtep+WJhSggqES+gkUSGpvaExl6VMUTVlJFXy0A0TJ067AeTWkxld35yqlBauXS4f9K3duNRz/fJP/+gnL73+8tj0Q6ailrLAYibBuy5egOcQKDhdeC9Kt5Mch917Vzc2IGpLQ6epC3xqjGMm73TeeOMNSwD14KvlI60Rx782OiGKj/5PNZdbxG+12Gl1+8Xi8pLbMBnLxNGqPAPp5cOerXa7cjiIF0Ruopj1pCP6Czdvju8PjB7GjgzpgkaZzM4/o8pSr3QJxDxkslJQt8fTj/D8KcvJJclfRVO37HKc3I3Z76mgRPSheyOW0oPdDIyYpk0XKtwuKpJYpCrQ/RQrM3kjZXhseXQ/xYcI9ZsiJV/XH+MmbkrCk/0ppnUTGRz9RXn+BKZZ2G110lsWmr8lz52PXM87w9LbUX5eijg8usUXT7DCuOuJnFZPCswuJ8n+oodzNE/hhf/9PYrLTjSeInLyRybF1/zpeHgAlSNXpC08R19u/x6LfjuQ73iSws/TRcBi5PYc/5YzuCu8CMwxfS2S5EzuDu/WKDfDs9uenFC3ZlekKmoSBYGMjGG588uARROiLBITwTm5VyjY/xQ3XnJuZnYkT80WYgFzeQlZ1cZ7ZXmJHCxwsIV50Le5ulIbm6Bsbf2RNofaYK8rWJiJtzIp9Xi3bRJH13CqFMeTCZCAAsH1xlzCIoel2VhR+4RjYQaZE+6pGvC0PGyaColaBaTtMlJM/5Jly7g/Xf3rKxgEHtnoAg1SC9JaVZ1g63t2BtfE0SiNTchXlXVIz/h4eXVlU4T5+TEHouzAATQAnwsKL118Z3VpsTIcdjlAK5i+f6jaBgecqmLfMUqMJoBPoQhczqg05oMS4WKnQeZmpljOUyVgqTQUXG9cpmTHEfZNuqCpK1KbE4OYqxfjkYZGz+dvPGwYqb/MhVh72QmXVdGunDCnDUAA9hvldIxfh+Q+CTXJ5OQTtT9ajeoWCfN0SYH5qyrxSJFf8zNKpCifEKomcEXN+XN8SRSRyxISMzHBQ2lzbrm2mekpsi2a0GVlVMk/w2MbIX1T/9wPktzhuQ1NBd92Cb/Hay4ip/LKnnC73mZdUTfUpsZ3tuOmarTYcO/ut7/2jZ72dq02PDk//+67FyDeyclpIh99biZGq4O6is1XIanTzOOw/elfvvlXW4MCI59Jk62X6W0g/OCAAtV2fbOGKVd2q7Nyre+Vb3z1uY989Mz02NU9tiDDnBxnBnI58zAKvbQEHdOogE3VX7iNfEKnmNlH9DElC0TCuXLpzTfeZm2bWhabJ2Jm49vms9Niul3D1V8q/42Uyn/rxW+fP3/e3VMqLLLD3Kb62YfO7V18jxoXtUJrMrhJepMMvAz13VrrDFW2nHoQGMqVzp2nzs09nAc9l5KnRB7r/NWTy0Psax4Rr9GleU1a8knkJlrxNSUKKGLhABcJYhDoDeypXHzzCPIm6cLluBlfJjY0fU641tAk6rgb5fhPZEOZyzNlGHv6CTJGHKXGT3i67oigUGhw/UkzS8VkDvWarzmPo9hdsH30+sP/xgRL7rhH55gnxpf62l1fixLE4c+p+O/2FPEe4CmKKzLJEYvwwvNHC39AsQ8MLorLnph12eX33NqjsPi9b7jAol+OJ3lQ+PEMsz9lG71ZuBwO0KZJEsPvU3om+i8gXyIYA+XGmATHk7bucw45/l255cVjVXB58FrtpltsCLKckHcXgwzwgc4aOvboCGboKBEm7VA9CVv97Em13CMT2C7PD1g0ZF8Dg7GSka7hBpmFpNkbRJxSGJoWphrWf7poIDAlwBv6nymcKotoea0GBGlEfHOeX5LIIc3IontztwQXmhToBw6DA4hrqcN1kZAAHMbWamt8tOfP/9mf+PN//s8TF6MwALgrCwvf+P3DV512PdyvMToRFw/0D1ZGWFtyexpLFA3GwtAgup0WCvO81a6Wsu5XJaWHxumpU2oFAVfZmGaustnUa6ldIVEPBgLwSEo0OTCg9pFTt1TVWO9R8USR5EaJor05PI9UTp5DpIq0jvkcrUD1yV0nsvZ6iilJzi2/Wsz5NT+lLVwOyYXKipMEhSeCT54OaBW1k4+Y2YmoptkvPBKF6KXrpPWqPrkgryLIPFc1MG5yYmfPXa85W88cniuTX/OnHC7DnH9+yi17WP031mq+trLe1+qhdn/+4YcePn9u6dq7LCDS9Rs9OfvkU0/fcsEve1Ckx+4Hc/OE8QvmFrSNco0+Ks6/ROtkXawoNhWBbjTYNkCoF7gtkHntOKQ2PT62tLI4Oz6+f7jBmMV3v/YVlyd+5qMf+s9/5beoRzETBs9JrgmmjXlIJmz/ghKfDuQQiEg6T4Ya0UyaAzebGxZAtrzxxBNPrKytWhG+6slAvSUHt2uGwWtuu3nILrnchEtuorJbqTgK1b5cuHQxrJQPUw6Pe2EpSlF60jb61246G6wH580iVxi0s0JpeJBmEXmlazpz29VHQeomQi6UJw9EngP85Gc5Wv6Uo0nCEz2YqLTsEYGH4kXAit4g/DvMDzhP1+Mq0YgSEjR9HrHyoHhJjG98TZLxOJskjpnkCaXm+JEshaQlFtRh3gf2m9jf+I3Y6Tfnn7053LgHCx6JE/ZVVhSfAsDY8IaTbTeH/P5He+ZOkDZ5Yhj1Xu7AnKFwgXdlLiSH3/frXZGP5XMbONw3jsBUjft8/GHD75PF+wYV+YeoUMzoiTTVilRmWBHOc1fLRc5ZFJ4i4V0ecCKFRG5p8JMnwWvX9g2FoZ+QSjHMa8UC5bJ1d0noRe7SJySYClrSaXicm2j4K7JSoxaZJaGSY4YWp9dcn7xa5KNdeZFYw4rg98laZbKJYV2yKJel2GOSOTQ8Wh3Zbdd3tneySVim0xnSUx8MX3Mv5I8OzY9Ph01gWBKoZbXaulQNhZowLYfXdh2fUCZhXRhDZsJQlZaWmqRetZomhMV8H/VFXj1CdEwIig8dcEOrEoe7Pi7QW8CnQRYYOvB6bgVIof6qqkpDvavdOAkTiCB67rqnHxk3HC9/+2vvvfaSQJALMGr3hs352YlKKZYgzDGgSuTQfYP9AXJCA9MBjL0+hzucUxoiNQyOEFmxsbEKojXa9dX1dYZEdHK7ERYqRkrDAY0Tuy+ywfJUQxHUkD+jRv48LrkJ1phsDURsZqeZphUCPbOLfjxaZjyccE86t3oyN58AE6VCxUzrFARy2FtIcWMZU9uRRMwc4pkTyjkTQT5lJ1zMHD+2Do5crqqnAJsHoh196f4Kcc2sJxeou9MFsl5jZI67wFJhdAJV45nbIpdcAR4VlqrIn180T2rzAvNrkUqI/Iv4MsmVFEJTeKWxxrhV3LzrxCzGyg1dQUshKzuw25NPPl2dnBgcG//+G2+4cnFmoI9mAIoz4d/90bDNxvgoTnTIbEAmQrEws6rpoyTdjNPlKFHx3erAMlTU2mHe3V1XpxARTY6OblpKfX3f/4Pvnjl79rFHnzD0NnNu3HhXJfGvMTSDgw4ThbLggSuDSnOzJzTWPNlY33AUlyFoZkEReQ7cw2eNrW20oxZUyiN4WbZQLDQolsyWwXPXjCI4KHCZAJycoV7z4fHHH4+rSvb3XY1lxqqDS43ffvfdx8cmDwaGCK5pfpToPzKCRp88xL49LLePTrRcdmKnm/KDnVmsuUPIMtF7+lwmZqyqyp8/hxejqfOjaenQQR7HPF45ra9cHimenEoEgMlSUb4TGG4WPe/c+GgZMxDEoGJD4Bc6mOJHcRQkDUiSUrCVqd9FMk1iGzvuFyGPCAvJAQpDVyTmj0P/cuDCn5ysuLwIePLEU1qulWMXCblGDX1iOYdLucbcjlwU0/0Nf4GQ4+WYU9Sxt2PeWNfxKn8PnqKvOnG8G1SM02WeCs+10tsF6NAQCXN4LsJrDvQqPOV9rLhjXhmaqKJJYqoYd1Pu2Pc7vEX1uqG50j98e9Uu55Brm593lHTnyx3sQq5rfhbRchapfkVYdGIOKTy3v/1gvpzt8bhC8hTPgZFzzMg0USLIkojetnjMSHs5OZoxyE78I2/85vyPP3OFja5yDHBYBOwb3GXQrq/PJZ1bq228IRUsEmEXlQUeDGkq6TB7dbt1wtlN9vdsbHVwybBjvdGMMVKZJK9RTZPJJLRd6rl7GILoydkKW1fyYkHJaRG1UoeYFqmlEtoK63cwMpwvhg5SstoDBw/tHDijhCW24ijkRme40Wawf7Y8mukJAFwlQXL5R/JESMn5kDpsPdgC1EP/YXsEu97XU4aGic/jK5RldcbBJ7oyNr5d+B77Uge7ruWTDUCTuzFXUiebUCauGewTYsQYIeItpSgLokqiNoG+poZE6zLYEsjvqTLyyXGCgDpahJE+OSXmQM/oiGMu4E/E99DMQGnam3oxDpJGaHLCwSuVTF+76fnT95g2qUoZ33sLJ5IMQGaeHCKyhokk5xyYi8hPcXiAyKPId/zK33tO5Vl4ArKH2mD0TapPZCKm+DwJLJqruTOjE4x8MXuPFnNE03s5/24HHBURG5tkFbAbkcy+zX4SG8ZO+rGha9cb77719msvv/aJv/qXf+Sv/Y3qV77yG//qty81tgiBx2dmWvZHSwxUDmxvbbAbGXflxhkq/KBDaAC6IuWMFvMTYueYPCi1hLrCGy7mfGyvhBnYA1hTsvrWDk7XqVkCZwzu3MwMGbSWO/EDBMqETjOAq41mhWhO/0Ko8Ov01JTNDoQdhEr/zflsh2rZSYR43AoM4ypUwsnxCWhyezMk7YgSWDE0DV1oaIP5aJtJX+lqPSDb9sIq0sGui+5BdpvAyO5y+bBaoRTNtGXbNc+WcwhCDg8365uV8qR+VlaezHmkDFYeu9TqLgJQqEAxC5cT5qeYwqOPkssez5HRCYSCizbHp+c+9ukf++m/+JfsIrM5olM0RNf52mTo3HHnmCY97Y2F0Ox0ZH8XExy6nsQVzQ7DMHEwMgYgZh0elmA7iouTDMmpnm0riyKySVnFmo1l2MVaInPuxtRdnDZHVsY35kG06SiZEJsQOVc53NbK7gb9YT9FP6Q8u7HVLsrozvzwRYVTl6pVvN/TeznE8w8rML4fj3bcn9PeG/JHC8+p7n3+4PmHnC032FNGxWuRaRFehPDkJMc9x78e9we6DFc8u91XVJEnd6neN9FtRMJi5haX0sUYmYIxPxB6aZycBGLECRqOP5vCKTCyORpFabO/eKY6RLUZ+AsmgbF47G8IdLTaaqx0NoDeXgossZumuLilbNCVOXaydtsHbVO52erfgbJaEDBkFEgs7ZbIM4tJAVml7O61fvRHP/P5z3+ehA1Ah7qsK1cy/PzP/7xZbsVmbJRrGJjzgPwtSGkLC33ul6kENdATVnd7MITS7k63LnhI0SoDfQwz4er7ew4G4xYKLjBqXzmQHBjkE36FLrfLjdzI7lrcuLBhgElWfeJQZywvIkWIPchmwmcdzhdPRAKqP6qmLF0nN10P0mGFwb5gOChOA4vySCJocXZw6kkF2thpmvqLIKaGS94dxDSv+MUxtmqch6N46sMiJj+X2hXEb4gPU3Ih8i/SZk8RU3Llyr/gFHOqHCEXlP2e+ZPKc3nUBObw/Kl45oTHn8bw+GvhL+rfDUnT3JxlnFxVY3yP2P0cQVtyBfKzyMf4yEpgEcIvJGMgHk5Ls/OJgWNoxYzBC2BwD/c7rjuyY//udy9XqxWW1371V77wBy+/8vwnPvn4c8/+x/+Hv/v1//a/ZstzaMSlu1U5EXdESU4hxUUmbujFbYXdapMDPAeVSV6UaL4gpVRL3JgZPGZn7A6EYW27vg430R9wE9RodWZzPY6/A+Vu3pyamNRwM0EIpq3Taje2XfobexYmqCy3NzZZsIIpYWvCZ70L40pCdE0xmmkbc4lwY6RUVqjOga1df2RNuZGzOloTGGr/rTinZB6SxFgAaysrDgXHrQ+12m6pchgnfSCjXXI2LZXEgWmRqStuj25Xx8dVJo1RDJNpIAInhIt6pnmbP0VXHLniqzg5SfEUUrhiXuUQVoAwzZMzM/bTf/NLv/ON771CSe3UmXNuiUKskwI8/sxHCCUoSBOkaZr7qY11Y3t7bW3VZUruCmNQr7G9OcyKlEEJOQbhhJ4xAFHCdmtdVTOlGQR9SNPiTSa+BprVNFNFM1OFVuss8qYpoE8o2eECEsEF4nRdEnQnWCxA1B8aAR9lFL+5iwpPMc2Lrit6NeqZ+v94BwrMr8fzSfl3w5P/Pg/xc6FBYxyVmjxHlEWqW075vuH3yfyuoDvzv/3xvuEBBUSJ8TiqVvF6PPC+/uOBt8u50/cgBGwq54ip3OgFU8Ris2HTM8zqnVmBto6KBYbMW7ABiYixQlIRghdoI1QFiplyxzIQJzv5c5FPAuuYMRADmK63mqQvkBFBDjo+baoesJgYRhPpTPYN7gcaDgY8iP8+Zi9hU/u9LD+zPdTGjMYucOq6mOjJKWKoNH5ibobtRtahXeeAxg92ub5t8xRSEyFd+pfU7ol/oRgWcKJmcZwRUsNAAwKq0EM7Cnhz5hKjDNYx5dN7gGrYaYd2NcAoaYgI4yAUppk0OdEsdMSYoY/jTyGn1QP7TkyG8QhYU05BcKI5gv8NUlf9YxT4g7ZJvJQ+VZ28XOEzMSBgcA0s8Lo3GCxyRIHVE2LIOE9B8omVnxCwOHpYJlyBNaMyoEA6NyKa5JJweXR8jX5ILofnp69c9udOzvmIKBMhPEI4HmUpl1+SFBaB/J7ZHc9ZiFdNKwKjpGNOuLf8zB5+RRT+IgKPyhSvRSohmpzrE21PLn/F5+VX1SgmT1Qp6h7O11xnfjkggIoQdYA/cv1drE1FPM6JM3bGTLPdxYHh2dOn3ns58JhT3etrK1/78ut/8PLrH/nMZz766U9/6BOf/OpXvnz55uLp+Zn29mbPXufUzFSj2XI8JnWmjExDUyKB5WAMrTCLDkkXay0mUNTnkIIDGzKOjKNCCU2RegQjVPbwwtAbFAIhw6zUwdScc3EGkTUywjPWNtqOBebDns3NLawq/ArvwgthPCY5mBsNp+FwLWXJWOzk/Ay4Npqycr2iZWXJ1EZrnVIHVjNL9YnRd0RYV8fZYaaka7WJmbm4YWq7b7euEU4khSRZT+qtZtOdRVteqaQZwKAeSe+TpKeYPBHz6K4qfk315KIX0rxCvObX4pk9eez4C5fHa2Zqkg1PO82of0KCl998e31ztx+FnUw8mtRawdbYybn506dPT09OnT45jpQ/cWLu7CNPuw9G+ctLCzevXX/3nbcsZ/sn1kACGvoGLOutjZxUoh7WLustCc5jIoml8oaPTp0v4gB+fAOV0WhIjLglFMICZvxgdftWcgk0HJYPIn6GFSIn/w/6SL3UXUdFmiKT3EvH+yr3cF4y+ZlTFUm8pjxj8YbnKNMi8CigO0ZFwuy56zVncpSkm2d+7cZPQP54tCKHo1R3/BZf7/Ucz7bI8A5ocjwnnVJkUYQLLFIWgTw5/HhI4deJyZ+exjlB/Jwk5++Zx1RMi8oC4IkhseCDuOtC2BxJneBgH2MyJQdcFGVlTw6XSeGERIbZoX+HzXL2cTdp3s1MTZl6hzvs5YSExiw07+IsHdKendQeNmKJpoleQerB0Vqlr5f9iaE9ZwiBKGRBLL+oaJwBjlJ61uubX/nyb//ub/1mIvNDzmapQ94Oa9qJVpTVQiQXMiN0q0LpcyXI5oBx5BnSpEMFEyoGwU+a6O6WA1cIU9bYdw8fETIhE0KA0169ASSAW0SIcCEn0FoMubOVlCypWlcWG5ydMJVahkUPwFQve4lgyNcyw/GwQsk6YUi2Er2cUAb2F0i0h5JWc5wSgbqJ6q1eHLdO1q+5z7WVJ9eBx6ei54tXQCHHLzAWj5CcT15+OUI356AawllqgQBCMgqI7ikL5ZEPgZC/BiiJI55xeNfsyGMhlay6Li3XqFKakAKVFQq/yQRFKiGVIn1yOYQ3t6IbamgSC+WrkJwgxxQtAiM4wnN8HrKWHCEHRm7p3bZfDs81KZ4EBDmt2uUuEs1X2CjnkJ/i5BJ7d+NGaNRU0GKDA7TaAfPS6Bgjqa3GFtLt1PzJyYmdxc2NF7/+7VdfeeM/+ff/zgeef+FbX/v95fWNyiA7Km4C24ckjQsAnhylXCfLjUgSb5fN7Bhi4FjVkYIGlQJDky1f07G/r7Xf03Sm1Qphra1v8OTMqYmxSZWEONUZw0ofSlXxc5BubpHoNJNhZWtkapLt9fHRWs3wRYuMHdywuzcxOg6dCjGZmyVyFtbXXY+0IRoNLxdry42zjCDjkbMhoYnM+3qx3WdPn9G95q0daPcN73WGrWFHGg5abJ4TMumnQyKAwf6GbCUMg1kwk8sRbLLY5k5zQ9G5CF2tUARodMLRyArkvzdEYBHOoz9TQDxykkqpb2ujZZuoOj41NzHpjsiN7UsEEXENVqwrhPcBsfPilVsvvfhSJBskKYjdFtN/bmb6obPnJibGbAUs3LqBBYAmkS7UMipl8vuguWsTw8ZRV6BOUGBhaY8lsaRw7nJV4xXYNPB83DWGHNmO4QxcPMBqqsuT3Y0+jA82JZpk3UY7oFMSE3ZxXZ6+UbMf1EXlj7quSCMkuTzlAshzvuanzj/ewzlVzieHC8keMVOSKCKFdDMpohUxcyZFVnd57no9ntyn4rXw5PgPehbRCs+D8g+55fFIR83o5nz89bjf5+K18HTT3PWT4dQRFFVa/n680BwChloP3FGfmhWhdYzIiykCXwVuCsBD/myp3ptDfD1yMimcMEWop9EmpnTnOr4HpQuIzARhCHaHPdjApjJ2HgiCCrbQFUl44wZVCExAmI8Oq5GsMbtTxeEcHLAkZGZdriXvx5Qq41Sftl2kWh2ZnZ6EruyxseNYG7afFWeDTW62qSQiwIv1OVCydacYSCMEwpoF+R04/ARDpj9cGjOB+D2Y045cWtVWmPpmAAFqZCZLB/FEnvCZZcWOrnt1+l3DHrKqyDY2hNTaEnRPFGYp7OAwu6GtdEEPHIcIdkajAvRE4xLVhGvRdRoiR3thIBEEPXhAGsDodXc0dXVeSaKJgL0zmlzuc1nl1+w5PmHycMW4JPl8HinRiiED/YxFhMAHIY9Qq1AMgDnhZKS6zov+A7nd9tcfVwmJw3nNpXtyMRmiQ3VvmgyWvLaSPOx01bjUQbSYJjFTuqAzqtdFiOHlQj6bqsefPcWTp/Dnr14LZYX4FuKK+OX0cDcHA5E6Wh2FExHkcPUhV/YUKMTGrWe0IgkSskcI4WxvXD5/2IE3hzFwDefkRsbHNxvNmcihz9VfOJ2JkbGaC4AHBr7wpS/9b/83/x7q861XX56fnVCftY2tiktN8mCbDCEUUiJPvAQTBPQiy4QnGGf5RVf29VNBI1jebLvfoO1Sgz7LqjzS3wyazASA2OxlqrH6ez196hREAkPoXG23zE0Vg37oFhC3BydVJqQbxtpX/yFs+7oiSDLucu5RuyP7G6tro2NxyGp3kAS9urqxzrS7+XnuoYcsJnekZGwK6w82BhVB3N3eG+5Bmjq5bJq4syitAnWAoUYqLHLv1rc2J2Zm4CNlEVkf7oS+Ve7koGcTRZuHwFPCPAqeXtPo3EYbQrgcJ3v4U1j3IVV9fZkdvtFKzXR25+n68pohL40MsAsU6hEAjNUVTAWaPpiCXXDHVUyW8+7O9aWNxZVN4rdQ6YzZFBonCLzyMDSrC2MDaG8ImBqiv0a9Di2CmXZPFHAX2nDUJKleu64mFpVD/4xa7fbbp9A/ccGMM4f7rV0kCMZ6D4Vvk8t8DLrXH4iS2qL9x1v0A/pzd4nMU/hTR0V3FS7nluMU0QQajuKTyMUnnuipqJJHgYNz3HhG6J3r9Hj106cYoBynSHY83Oy/MzzeRFCNIvy453hWhb/w5JjFK0+wLLlJOcfitci0CC9CeHKS457jX+/wd1ucmxE9dcfXbuOjMXrZdLcsefJCFag+gZGAcj8hJQmQlJ4+dkFjEchTuJSw+8glqjNnJikKRHB77m6jtb3d6N1tTZezQC8Ai8KxwfuZG006D1jP2A1jFosEVuHQ8m6DQSnLIqAUnlV/mNQhqyawqk6Mjgz07pL+DbqkuGeXPUsGAtstBSHCd3DYiFOCI1Wxxg72xw/36c14PbTWotjUBgqb0cJ+l3j0jfRWFdW2TUthud9pXTedx86i5pEmKfrArQr61XmRtEEDiDvsG0i3Dx8fLCR0an0nZjK2b+WrUXEbUggZzGhQFqeNo5FlIIYMd/SWD/pKvwXCCATsKuW4dYqNkkDALhlkQTqhBOBVtAC7zSaYGGA0QU+tEMErl/z3mbtKFMczj1R+zX6jpaXc8QgKMkOEqKfI4I4I4geVkOazCEI8OeE5Z/4iZvpy+5M4csuRi/g8XBEzvxpuHhkWLr/CLN0I6cfX/EqScTy8yC2zNcczyaVDMzmt12h2GmU5YP7yq4Znl1/ri86w9nd2241QMN9zA9Uw46NDA2MT46WdVr4ZDDB1q3G9s7u1tvHK5q1L164//tTTzr6bjaNjY5sriy7KJVRB5UVVgd90R46zMgiW/d52QsjRvWZsINTAECxC9zcPDpGwjc7eVqPjbqSewU519PBgfdO4mzPwn95GD3lClo4hmRWwBOerwOzvOyjreUliBpgDxCrJIgfcacLIhJAD0rRD7N6kw1OnJSS5rbuqeWRkbWPd8WLnhnXI+MTE9ubm8toqZDM1M43/Fk14eexUbDW3MJ17prXpbUprik4Wc2O17SbEElXwimrsqedOI7YeFe0ZnZHWQp66eey6w3QMBOcQkQsnRFvyaxE/v46OlHDncUnn/t7IyNj8zPRW/VoTgbATazSWaawPkykvW8iZJTLIGgCKo48I5VKlTNxQ39yAGnEonR0EBnDVMtONXIPdXKKQkOzgLSh5xi3hbgJ1QKvmvtVx981MOB792Ac+cPLM6TBd0miGHQIdT02cbtvKysbSrcbWxv5um0QMAEArBhhIYFMT2JMvmvmDeHRgWoJ3xBWYQa0VyX+vE1svZMcfE+NoQd2R0e2lfVdwvN6V5EGvf8zw+xScgopsC0+OWbxmT5cD1lTvnvdml+Pd9amIXHjuTfj+ISnbO4oToqPNy+N5RrQUnipg7O/YO1REHhvPu5yEx11RmbyuEN2gAM3mtfX13p3m+NxU9EAas26qoPlDWDMEK5qBsCsMtN8J/olAyJ1ZwDWpGyEPPsMmcKwc1OvherNhvpfjSjv0aZCzrU6z2Uj3w8tDJ4epg4Sqg0fr33EvBDRIhAc0YOEMQUi2wx69OttX02ZX6mFV6YQHMhp2FW6cjgCJAhDbUeP6XN/WTE2LlS8CusCrDLEqYb8TatU6NQ5qIQiAYAmiepZWAjSx4YM2MA2obRmFWGaxpPvjOJYSgEi1AonsP+8SCbBuD5z19RYIWHxdB2LCgkrmyT0pXIYSykE4v+fxoeHPcaIh98zAfAQih0srpoZzEJgiZOuTGsoz6uaWxmPHgUQOyJ6cJGKKJjC7HC6f468ClZKf4mcn1ZE3Bp3LaYtnDsn5iJk9+dmfzMjcG0EpRczbmfMlGlDOvLncXIp2SZLbiDvUUv7IpFnqKQ2t7TY3ezp9lf6t7dWe3XK9yb6jU2ENpwwrpuPAEOyMMZqfmd3srPze137/b/+tv372/EOvfPdbZ2ZDVVjmZlaYuDFf05/yo9yC/Agpib+oacwgaoxDw+62ReeZTj1uUEDDhTZ0z8jgUMeOCTYZQYdntlu8026Ebe209dvZcbM0IiMPB41nug2QdB5HzUFc+lQeZrSNJErAAcmzQ02uS6JUNT8/j40VnyVL4fRFsqaVJMtLS9ayzeBAM2NjOlxa2abjZMFS6sxDLGKypGdqm4m2qGW11W5Vx1dHOiOmjxaX+muZ+ozFlfh1WeUQnmj/neObX6Or0ldPzmsOj9hH8XOgu8BD/js8SE1sd6cZ9MD+IaWOUtmeDtGy6IE4Y5PHimE/IFtkQ1kjkBDwbn1CAkeeQUjESrKkoWsJUlnseSnIGu5ph8k+N5/194IMvReuLZO9B1HO2F9laP7MKTpfVAlOPv6ovj19Yn5uagrE2V7fXLi1vL6ycPbUCUAvTfbY+FJUJs/0a27jH/OpktzxlRSDrdqJ8fAptSYeKWJ07/u4lNX7fH/gpwcl/GHDH1TAH5oP28D59AgQgwyKTo6xNT3zlMoZR3fwpUmWnl50TfHkkcTzPi6sih933WhxvIYMxF3vicHFXpUqPc3OWmt1Y+jE6Z16g+YPfT9ixWAJQw4SWDD+q0yW0WgcpGcPj1ZVoIq+0C+Iy+N3rHy26EAc0hWnGWIYEZGEjQdsKaMfy2OjfeXDvQlXpLRaFEbqW/WegerWIasUPZV+F7syH9/o62+MDewtH4ztINMGO432/vjB4LDL/lp1KHiop7cyOAx3Yj7tmwRBt3fIBv1YH0qy6ZqCYdo9cbNxyzIm27HZ2+kdshp03E4yiaXHiAhH+pdZtzepcQQwSRwJjiGIq8KNQG8NWOzr2GWDSFACpMp7JYas0MiSxNW0/qjfELgOOuBr0IJm3XWSBDZPfGG9t3qw12YjdoiCNvmCy5L7DwbLh624D3jf3hjTX5CXiilPjWxBHbBJaasXkTFgRA7HJmvbza2d/Wa/rbk91+uRvg8Ao/Xt7bH+cZgAIsSOwGTAmZ7mJ4RXVR6QC60T3Z+kGphjJ4mgfvOJHqoFrmOCm6JH7WJ2jHkPTRyVV1DgaeFhWaxcZnnbFRr6LlBsuaxI9zmDRHbvGPBXAZ2mnyWBEvSI2Yw7iPWcZqYCQLOY4pnaSDMnvVF2D3Apmqf4vNSAsl+QAJ+SeKEbrTRcE5KmfxxtiQuqAj0fOgbD6KNh2t6qQ3gOoekBGZZLAUGEe/XUFVnNyEjlciXPzis3ktZfKqLP1Xzrq5s3l1bWN+pXbt3aau9sNB1XJ0PsGSyXJmfnJqdnpk/NsNh2uFc9PCjtrjRWr29//Z1vblx9u754eKI2aaNgq7nBuFlpZDjsSfbsD+/MXL6w/uaF9bmHn99578Z3rt04MzV54OLint1hRrKM8cGO897NoZ1dB9QHewdXDV8oLlAkiJ0K10NjSM293dYIs5E9B5u047ebJp8x3aMANTSLLGq0O6US/ax+O81aV7aLU98Y3B5k85lxq6aN66zZ1NlisM1ERZ/qGd1kfODg3eZWcMn7B+MTlcmJskF3LfbmejMIBZpV7gwtD0PAjgNOleawrcJL1Yo+wSfGgd9OOxS7RkacjNrZ3yQad5y8o+kHVWjEmJq9wEZtZHByZHOr0zM6NFitjm416832rostdIKVBnAYMrukVrZEgBWEFgqIsSzNNlOJyMA061J4EX7kYv4lJjv2PNIk1DprlCuNT5FxYHf7h6ivD4tQGamRxO21gyALtYZYzHrYLSp43gNaz5GXzkH8H8N96MlcmtoALlA13C1kOPQmOQgZbIgZlWa1bezgooFPk3a/tXP97UtXDy6Jd/j730PxhxLn/kGt3FMt98xNj/7YZz460tfoOdgB1KImIegNuJ3a3gXgub35GQU+wFllqpA/dhsiTSCUcOaGsbNyGVoQh8feGqBhucT2CXaH7jpOQ+dn2kQCfASRX+rexKzcLjjlGJ2gySLGaIWCKosIsdkvXIQArl13e7wiAF2ZXYwj/51fu9/u85N7ID/jc6bQ7xPx/kEx749/uSu73FMiFJ7jkd8n/K5of+hrroZS9FqeMTFxgkuwsFPpNkKOuVzPYwFpcuQpkp73RsiRc0GeBiaHpGKwpMkVaeMtBtJsUCUOcrDUJIHzQarwGEvYPyYDajQgqpUKEAkP4O7gUPRtkDVAVjoKEUm6OMBXiw+Rn4jyYHvwxmHuBj2iFCVHi9JEjekbjGzUyEZXSAiAcjEUnVys7GRUBOaKWCZflu2Is3HgQMiui+adD4XbxZTW1TRrbkOi2BXU9x4kpJs5hQKHISAn92NjiXQzJQEha9VR21fr6xiG0GUl0hTftHaey6ph9VKZIaJOFx5bO+nAdGYu5dH9S0sxKIwoLLnsV09OpYUJKQJVNft57BLretFyHCFFDjnOXa+RS3KaXGQrwGsRnj6l7k2J8ydeHk0WrUh4lye9Rl2QdCIrARrQG8JjA3LQ2TOX38WZqM3NdTMNJUHgKU8RoqkuqB8dzb1gapHb57OzWNtbl644ZnPt6o2bN12U2yBdRHkxWFiu1uzWx3bgcJnxhoFSeWNrm1x34StfXV5evXr1+tKthZ3tDfdOVg871cPmYyfGTk/M1UaqbefccFZsvUHRpD69e9euXl38//H2X82WJcme2He0FimrsrJ0d7W4qu8dPQAMBMwwY+QYzfg2TyS/DQ1v5ANpNOMDh/wC88YXChAghiDBAXD1bS1Kp848Wiv+/u57r9x5sqq7uufeidy5TqxYHh4eHh7uoePJ5/fevvX973/3X//lf7f/5OHvv/+uIVGjLptL2XZrGY+2nYkOky3hQ2xWrYRNttMT0uT1L2e6lZSyUQdHJyYxl65m9o4eWQCodHb2tk1Mbt7YEOn51gs8wqKZ+XOdV9nEXzbSxGQXsa/hdkk16TU2lKSqsNQXNADomrgwu+oVP1loEGyzMWSf3rz/lmFnjDVYDUBXWHHg+fF+7Q92hmtJZyjW9rfoslLMgqx5rag52Kxlc6yGeSKf2iEAQgUU8as+CdEQGPeqUFXASMzELYivftTXFvLBLAW+VX7GqQi4YalSelhiU7SkAQyS07S1Rek0mlf8Pg0UCpxwsknVp9LA3ATKOx5TIeM6l/BG3mhfIu8IRUN7YZ6E+Y1+8JXxVwC/IZLOhZjtmUTVIZOYhbySRr28Hvh6yOuxvknI1+H55uExwO0m4xA7gSjw7PAJz4gw4Q3Tnsno34T0IW6n4gkDp0aRePVa4sjQZmfk/Gm7NcgiygCHTiZw5F6W0Dhk9LfT8mwnLXhUTp/5U8essapeWqZ6NcOjXmISfcr4cIF55K1GYtFD+xhwq4lVXQNLlE3iOKfpynqtTLvamtnnwU3rLccsnmmWZ9XJlebcXBYQSVnerk4dt8t0G67LGqzcSZNaYohv3lVF6dMaD+7WWYixr7SUlAewBFQtlq8ZyyfiUsGsRYq+aN3hSgb9DwcOaUumaaCH4YaGqYVlfTfzRJe674a/ddpYXZrPIjV9SLSB1IhwvdIbd9+kKF88eWhB76VZ8GIUlZflpJrsl5c4aTTPkmts9ZQ0c87jB7iW0uaJRhHCnqI5S95GJRY7hsQwrKdScTzbT7O6zXCdjMjsDHOQhvCoOCr7GclXLAEfO2l0eVUWXqrCLvrQ85ob4PsLGCHdPhs+DR5caX/JQ+xPxUqLu1vxjEds5sxMhvhWV29O3SY3+WnD1z3K+spGAv7Nv/lv9M+sIeLMVubMshqJnat2RSYPXKc3r4fkdqzLk7Orpy+2lLADkzzdtuHoKCugqE9bZUt7Z117SY9RnGNi9eDB8UdvrbG3mv8riywiQ3Lqwsmd/e2FuYtHDz794Nv333jz5re+/d4vf/ijJ8+e3raG9sKM/sySecNZq8kzVRJ266VUFlMltACUGQblEJh8ta52yWqKo6upvSzktSp6aXUF66QFwgJca4OKXc4dWzKUbI5WCBtp3BvZjx492VzWSpHTyFXVtSz6LeajWetDn1twZJsfKo2+hmfOGVqrybAOJ/XsDEcTRbzFSTCQUCaeEOYQmuVlrLSvXF8xTRriaImxO7ZLdrySYpvYLi4PJaJVnKRLWryOkk5Jp2rEhSdd9FGSeRlJQj62Ey6wvwoZgZWRSxWpZgzepkuX6NrdafPFBVXxOxUsbkDVfq9cwxb4iBL+jAcWMQCkzvVrN2LY4A6BkocC8TWUxaXKD2/xFZ5++tQRh8BJD/9v6wZsIibtcgLbvY5NuMDXv3Z4I6ivL+kc4K/F6ij9VeKvp/UNQ17F8zLSbxU+6gWKgwue7eEfSh3i/tQpVImNWNbAwnlepv/b+CYj8tOiWnbEPeMn9ZqZKbJJf9XxbPxcpwg4/vFreV8qYl8nyW6ihnzJnW6cQB5I2AsqM1GqiGVIsQhHSRY2MbCjKpBWv2VLUfqzTGt0U6ZXTdWcu3w3c7xS1W3AvsyKpVbZUuSAdGmw2T4RFaPH3aQFOn1srquqgVpvjXEZUfmixXlHre90ssfCt5M+bjKNJZX1ztnV4oK8Vz82CWX4Omo/xu4U8W3w6WqbmE1BWdll4NyxtE4DcFxw0ZfmDso1vvX5wwqELkytbDjR7yZ19q1vfWtve/3Fk0cvnuuhPTX+duPOHacIHR7sMZaON1lZzlpWA1wybVEl45l5A+vKM2ssN6e5spGIyUjGQpGXkm2nXOS2AqtMq1WEBpAMMJj+CsBa1tDmg7HSFoAUnoIaFTdDJI2BMzCLBYPnhCsqUvdeKqn+2vD87Rleh7iGVfkr6UBhc8Fku+rqak6TwGxtSKNoxkh3Pvvsyy8+sX5KL01nlaHlcI/NELnzTgI5SPr1RS1iqqJP6esBH/dZJNbkp8VGTWYXWiRI303X8HzfUhs3QxIZEyMZ3DV6f2FV61QO+jbxcnyy5pBTU42WIR3tW0pw8+byj3/853/wx9/dvPH2f/qf/acPP/3EbfSXc6sMjoXNJnLcDCQXy1NL8zNXB/hsCDqD7TnHsTpREeaM+mgzzsyZlHFK1fL+qXYoAZB19wnK3fr6qoGRrb09pswYiSEBxsTpNyzB8bHZ1n2dTvB7u85tvbDSSDgOJIly3rNP4TBLsbTbsEiLRJqa5+F79tPPG2RW8CwuQ/vLn/38nfffM/zA4i4tLvXuL2DPtrfSnrBFzbzJ3IIJ5NPDo5PqDLLCutIsurJb2d83gk3uze5UgaTbPXZKO65k6KVBRUZTMkiX5IZAniG8AxsYnlEKac3IbtU1EiXjLbZMMzYYclBRCHytckDSgLDYM9qWNgQOnqE/MoTwcNLFGTkqAkJn6ozFn633gjRc9dr4Q/z1WhM8Q6Ze9+Tza05KDekpCc92yrBhk/CEG399GTSE8AzEi8XfnxoDvzgd9jLyuJYNIQ3m9ZrnFSIG6K/3XIs+IMS1jvQ6wFeGR7u1axT8Ddevg789A7OADdkeAq8B/8bXIS0YOK9kovsBqipxgEF4l1QDe45ccVAZWKwxBHWReLZnHD7KUVA1wlrIow0uJAKnGuiD1deOkjTRM84AmLjqh8t3qg3jm8LORCxnXZZz/CgVw6iGuCSiktnxDpj2YndZ0Nzc12tcmOSsZ0oTG6ieCY/knB3pLfswOtz4XnotIJOECogqhWPbZbuiN4FN7dZuFlLrlIKkrHRrsytQX2RaT8oxz44vObOTw/oO82x057aVlzpKJ2G4rocx6oWZ7MVamZ9z7gEapOVHw5pNf/z46Tv3butzbKx8cO/Nu0YUnz7forZ2Dw7fu3vj5Pjq8IDWsy8xLE1P7zR7SzLbxJ3nBkVBxk1pB8MG+CILpnfS6S9nfLFNLOhwdVy1ClI1i9EdsaUMcMLDmSivJCF2lhTEVYc7SfRr9Ek0Z72mXVWlWnlr1jHfifWqU9qvBkQeRiGFAEAnkSmFuGwT+tM//XOX6D1+xMQ6jXjHKOjDhw81S3CYQyr1p0TgkT9DCE28jHMFwuJYUKURJmMj/WRY1Kwp4+OUjUMLGuw401jRfMqygRPFs5ZT0rPswFQdYup3QdTMqJsNXF5ZX527XF2cNrF7rlLNzt5/69bT588ePjh4/uLx9/7wu9b+LK6vkrQYPKsorPizF7yWCZJWSg5tGQCOLYoM4rIgBsI0PcZrt2KATFlDfXJk0fLRk70c2JKdRdZAzJ4jmMt2NUk4DMuKf8eD5ISN7CG3dtAlXRwM0BT5+CzZK8PUVixkNtCh3NnhHYfRuTTBSADelUXR5TXSwBIz5w6K2pud/eCDDyD8yU9+sru/5ywLQi4LjqexT9ZGZesDjT+nfZ0GxGXGV65igJcP9tc0Pq1ttKCsetvwl/il6CH0mnJOtzkhwVAqiD++DizBngwRPiDp8OCRto5FWsVdrUtlBLJmYVLyCal6r00fGznIMyTtEJlUX3MKAnC7/gieBzwGTqLSakHMqNoEafI1uAExVD7kOcpvvvTrpCehX+UmIo6QNFSHTyYnpF0D9KcB2CfUehXegZ4dmJAKA9CaYxJghLTonwxvv69D4O/gGaIPnmtof2N4lJ0MNNwAPYRAN/jbg14e4YAHz+9AeotCIxnwIMZ8mArsoCXpDPTwNJ0DJDI6MLI5Fo4OqYDAdxJNW0f0FCiEFtAD9hqFGJuZMcPAVAOcN2hLWbsTwghYUq+U0hHXHzDfqUdRtl+R65fYCHCSSVzN9oi0dqZOHxxRP5yNt7qiGVw/nzoJw4V1FTrJ/gsspWRisdviMqU1IqinkbMOovyi/0L5pgODiv+e7SqDM2+8mWODOMl6CuwcORLI8DOttQTp1cXOcWZ2U3cd2mCpl7HpkwsLny4tKqPOczNaTFpZJbxKnwYeuuzh47ON5cU7N9bfee/d997/0BbMzz5/8OjpEzrXEhmKOib16oIqhLtMJuvoB1mWV2Q5y4V1vHT3YHSRn83JnQtwEuocDJXCpyoVCCCvMsm4vFMzE6nsMgWf2UERFSVwnhYAAPjWfvG9erZnCMz7OLEGGJ5gGjgwE67DhwDwxWczC3P/+l//63/1r/5PJiI2ndS0stYLdHV/FbRtJpzG1eVRVvxyGGuwh91lZSSlbA062w3nWH7+NFZq7EfWjo/dUh11XM+sWDOFkemEFPH02SG+TS3ruC0sX+oHs41nxxenF/tnp18+eXHv5urtzZtXZ3tEbHV93WkwhrXZqrdXlv76r3747e9938zDOx988Msf/Xh1MYdHmgFeToas93NNbvbtzOlmZ4Vtpg/ouWau0pBxRtMoipHTpfnZjZXFU3uhjvYtY715+xa7KI8Hhw6My1K7HJxuQ955Nia9cXvTlhijJVtbL8wHT99a1yYjs8s27vUyOpmbyh29gIVgFCSeKfC6FgnTTJS0H6nWdbUZZrNbDJgWfHMipsbtB9/9CKNSxeYNwC8aHdpngzOsTcpT/bVZa7lV9QXrwFeYEQ+Dp+LuIkZMSepolDhjO2MF1TLTkINgDJ7+OryGGJJYwki2NW5M37gOI40T9buZnwY8CCgzfTUZt4mRa586vPEPqQzy3wDDV1GwUS4ACOTxlMEoNmKUFuqoJgof4U/6ScXT18E/0NMhDTAEXvOMat1EqFgV8aV697Hp5Bl/fRlhCKlYIzL6c3/q8CFuv0IYT5E9Ait9PgA3fJeEwPD7t3Gv4UnkJFSpNKYBZvC8Hj4ywEP+QbS/44yyUYEjLJWxAWaIeC2NTuk3PodUGg/haAOs12ayzetLiscC11E8W9QGgGS9nPD+1K+eDTOQ6pWEMRWeaqDGORB+Lq3+SKfurn5TrHKjEkUZZQFHpqeNXZmRNOOr35VOHs1fW4Oc1XxO+xUmBilmDmoAkMBEL6j0qkDPa5atjxqTVmhTJRBWZ/Kiyj6pTtonCFP/y6G4PUNmyzNtFUzvwZVa2evUNE35O7c2Xzw7Oj2c2lieWl3ODKy2zfzC7KkllpmolvhleE3BZnYzLQMhGkAaHJmDq74jVWQcD/4vj/fpTb29lfV1VyR99/u///STH3cU/ZLDvX2tGfUczcdnhzkcj+uhO1iNbGc8LTPE4WaGGGqgXFdAQTPh7nIMi6OgkKIVpL/e2aeq0nvu7i+ba1A721VlRwlEF6O5rAJMcXiSpMsBE4KBzbHh2V+jewq4ueopfPC3ZwiJ5+XXV98W0tM9ONSFTXLsLnbFDpUCFYJIjsorrYcTu1XKi6s31iEqQ3tsmcCs016oXRnSFst6v7nVxXnFAbnGH0kgndpHmdjIUqT5ezftCZg/nV7YP5vdNZScY0otJ7JS6+LBi/2bD59d3l2fPTsm5a78OrRC4ehk1lb1zbUf/+QXi//X//Kj73zn9lvvPrVYeW8/kpOO4dTG3LzrP6qDdmVmwWLU7JDAVT+zGwpI6bgR0jpdSvzibG0hR0tYN/z02QGDWIKc5VGWPTCgeJhJjfML5ym+/dZ9R1AZNI7pvby6d/fN/eOdzvuBIWVDx4ZRVEPuKpuJ6yaRFAcGesZm1urfvGbYo6SkarFerPF/4crAgvkf/OAHJk2YZPd82xmRSSbE16Y+3DOog8l5X3Aq3qwX8sXGq9w5NrrSCg3lmp5xYRdtwsWuXrBwqEag4z8BBlJy1V87xNPmRPJd8urlcmlhQbvInNSzF1smF7pLTAOlDoxc1otxIThFELJLM4WS+jL5HINN9FhG/Cx6WgKFtIsZznD3KxjgHCf96/4OuR48XwfdAJ4QD8CDp7IwSlHgED5g68DJp09NpEA86caEwH717PZNh+R17MQaXl/3jKF+u79fh+cbhr8cr+gsDc+O7zlktT1N3RD+2xH7a6Hh5NTb1lCRcQLHFWdxFwH19hJLvk7w9+WH8r3+CQbOR8+2EwQxRy+qC1SbT2NPwtsYZwl8PlVZW0sZZaBn6gS/TERaaeSSUUc28xh5Y5udcGtwi3ZhqYtmueCyoUZudL6d9abaV49WKleL677CLwRh/ALRRknxqzCdqXSAigLXJraHMurq5Bnd4lirc5fizbz//ntMI+1jPgzOLz/91Y/+5i8+/sXPjGzOz7n8B1v1tmn2hXOdAlt5GcGaT0374Hzqxpr5spzPosNmHnF3d9/v5Gj/4uRwY3XRGSNSp+wMU7gCz5TeP/pH/4ilMd1pIQyPT4hPs6auyOVv+sc0s7G4kExFd44KFP3RJhmmrBLnT1mkneTvqLwSmFiaNGW2F0o2+NOXiQoVDjh2uEoooxjxJzwGnydJxNUrL3y+JomAJLmSgUq9sQmcJMBrF1OQRG0Fa+LDVIuDbt10Q19G9ZWOvGMpIYKqi7JLk6LvhAgMAAU9JI1zB8527nW/1VlhKo7GFyDqQ2oZNgGWGC2v5g6D2ZMXN2/dWb/91uWiawEvtxy3cXI8f3V2b3Ppwc/+6ovn+8Z2l65OXAV9eXx1sL97a+n23uHe9O7ZwcHMf/n/+u+f7Z1+9NG3v//H/+jf/jf/Jk2fowNTBleLS/rCqwaW3VzigrDalSMLhjGISlhmyOfUADhGZkDcQUyby/MHC9P2Ii8tbsg+YObTyLnjJpeXVt66d986PurXnLbxeYdPpbLM55IG1YE/c+Y1EWMtFOFUD7L3pAZLcGDqJMwkVGG7mlVlhode06rDlOlp51mqw/gpXYNbmmiiWGh9qCOu7WfilzTMXujsWqPlKIxFAway0Q2avvBDQ3DqQulAqxA5SdS2wQiG10hMYFJJRS2R8fZbOuMJpRnU3mkVbW5mbXVZOmqQNRkZbahBn8EA9y6+FqGxYkyK6PGU8ZalSSIEgr8WIi4nvIcWSmRHIPzlRqLeaCej819DOHz9uvCvBPiNwB1rEuyaf/L1daqufZ2kIZ+iSK67r4tyHe43vX8dnt8YPkf6AY3LYFRykbAJxSf1fuVpjF6/DvU1Ur8ObMAAoOp2+ovqGPkgKLoODlVXGciZfafu7GYyQDawbqd2MgyET0hXFfpTCCcEBtg8VcghL0LAe4XWJ8rLpObWixfOdj+9iMrQgn7y8IGe1q2bm+6FoHYwRwu1drVm67EG9bnBMCt/51R83RSLOQzBTbu1VO1EztHZqRE7Zkm4JGTHTUKe/CqSQ1kphVagyUlVkoPLKCB0uh1TRs5CcgbQ+EGiyisLK7udWd3w0lB6FT6yB1mrrKwg1skw6Pv73//u/+R//M8Bu2bVLUxbTx8c7u2sLy/pXbK0M1OnBputSZ1aWjtLhydJ12B5ZMC459HpiVE//dyWWEQ+ePjwjbu3eVyVtro051YWh/G6g2bx7Hxh8ezhw1P6zmWu7733Hs0LTAnaVGMGVC9HOcKPz7LPw9EwYJCXFk4sYdKlS2VG9HBJ77xmDlsVtk3EoZzmtJjbo+lP/Srct91FLCHpk2VUX2HZoDxS5VaCx3KEdRnnwKSYfq2PcmQl1hgFtWpFmG8cPIPrEJ+E9JMnXe4KCYLAe4tHSZiM3N8/NXZjtMDQJtokfeikogLwKn3FGk99SiyuZNhQYPzl6GcTCe3PBET5xPM3k6617IuZNKrhlNSHz3cXn+8tPz9cunlv9da997/1Hbfq3ru9OX26+9M7N372Z//tD3/1ZGNxas3pWG4TmZ89njuemVtyvMrKxvL0+cnPf/Fo//jq2x++d+fdb/3ih3/+xe72H334/u2VBUeau6bxrbt3iG6aGe2KXjnGzWWGEzmodzmYNe/H++tzV+/e3ni6c56LkA1onJ0YQcEBUZU7UUQ/W6kiW3rsNKcYMidyLMyp4Gkcyd351fH0ucsxVJNc2m0D+/Go/mYbkXlqI0+HuT4S0qF0MvTicuIcG2D7cbQH2YNNocODReqvG4FOjw7XHJo8k035d+69RUpTr82Bzs4c710+c4TW2jpqF69y5ra45IpHWkoPTp/kgriplfw+8XAC+dspVlR59msDAB4VdGEDgfzG4UCby0xIza0sL7lxbPfwyNyBrdL0hJMh4bGfPPxPBXmZ4vAKgPMqISkSLW5EykRrsknCn4ESIdA2VcLdZb7sWtQsTKFTalauJFbOISekUhnQDjVoCPn1nqQSBZXygsdz7EY1bvw6+ttpgR+iwC9Q1q5BCgcj7w3Zr4or2czQWGgWpRMFJm1livPCBydu+0FOeJLU6HUM2iFD+Dj4a/825gF/5+t16KyLQ3HDNfYhDmghw6dvEv56Al8XMpmTST9GE46B3bjWHOwMoIGnlteGQSJOxu20OqS+jJjY4eJyHctTrlWzzrtnwwzPRgJembVYY1Ku79XbBUwordTOzCwcGOgKX8uVwsxbm6uicOq8UW7yLbqJ0oy5cek1m8CK63ydTC8KGeqDdL0SNomgLgTLLzOh6V99ak1ywRKaqwsZeYK1TNr62pImwp/96X/7w7/5M0OgKONubaycOVd96lwlX5h19M+MYxvcQXzqMDwLoc31OV2hHCaUo1YzL6QBgn1KAuVaJLaR5kyjTJdpnLi7Ys4coZVnh4dpKFBDcqrz4enV9KfOhw4xephVyo4qhBydLn6vDOaR3EWRJYpzG2pVTrWoYlXTLhRFxFS01NyxFgiLFEqWCdS88FgS2MbaJN2FKALH3x7PZlQn2p987deO0s8GmwwZ4gZ+8sOrfhFlKP/HiXYRD68DJUIKeATJ/5Keyma/0qYGHDsR4/rmM+Du5hd/DpnJcOTU6eXM0dbe8dbx1K8eTM39cMpFfQtz3/vw/taDj/e29+xkcnjG3mkGVx3f+dnOE0gWV9yHvTxj/HXRcW6Oj37yT/7BD+j9x5/9/ONnjw/2t79157aemR27M46X5LRFXf2c4QaCmfGWRZf2puHIiJAMh6ekq6z7urA3dZotb3PLK6tOJNYw1QayMyjVNhJs4Z0j1yAxGKtIyFRsGwEIl3IAi0UJWSymDafoNcLSYyzF3WBqYeip0tTOAaBZRs4NJGgICnf3tnXmRJEQ+uQQLrXv7q3bNttlvnp2xumbINNNv5o+3d6lwwmWxdgw3L//ztnBLmKkBad0axguzQgSjjbAXXC0klpLhrsoQ/xYVQIOfWPXxeetBaPsgvzEsJH9anlEoRl69yPaSbSMjaYhVJp8I/UcIQkyeIRgDkiuXqO+suzE3m0rPMeuU+ynT2OKQslAJGivXEcaPGMcla/KnZDXvw5gv94j6V8P8E2+NpJrqDpTk9ETMhrqmgz+Rv5ryIc4v234EPE3el4a4E5jYPHwes3TjAR2LfxrUxoX7esAjaGfvnbSpLxdixcrwAPGs2HiMQ4JujD71G4ynQ6ZTLGBK9KIchLMdWUjjo18EmdHlyqYjBpbV7y8zIwQVdtW19c3xeKMpAayaovqov5DggAmhOE5r01VaHOZDbiqXBkrboGU7OmF1dN5BRO1lJ5aehiwLpjnVCdz8V+MnETk3bEBlI6TeJstISDMSXt2c33ZZJyUz4/PHNm6vpzDCnJ8yGyO13AwEeVxChlNcnF0Me0452y/FZli1JZI4s4ZZJsdjxmbWgb4/PzLLx++9+7bel70I7WldDyNTvpuEs5JHEuXF4cnx+4wQ5jF3lSmSbi7K2/SWTjgyYlCwVmve779QjKlN3PEtFxYMk3tyF1W1pwt6hV5pZLql2GMziMbDBqYnIYRWfZlqlhrNxPZycuomRyASdd885QcxzPpQHr1HD7Bw9/hPg3hDTY0oCvKBKbC0Oj7k2dpvUgvF5IjybIgRZ9GfVyfGkv/1ZDL5xobaOPb4QwgMGJKHEyux5KpG7r4gLMUwWBIobHk7dAVlJd/8T98OXVyYI5hxond5keUljMI56Zur6wbYNEtdz7k9KEJlOPPnrxY/OUnnz968gff//Cd7//R6Ys3XXpgpZLjwg1KzJ6mFags0GmdAn2h+0k83KknRJOUKFoSsaAv6y7t2Zlba3M7OxdH1hTNzWtG2rV8Yn/ezKwJEfxIU1IN6rX6Ydb01nYOvVpdy0lq7NvxoQtEDBuf3755M8MWaQJenR7H+MEfya+BMOFo0KmpPOehOeJUaUhQ6Ig0vVvczlbs5RVrsxSEqzyPTvazpfjq0nZk6xgWV5bn3MOYdV5T2pHb27vvvOMktdGc4lDu0gl+rgoQJXmtOWAw7HSSf1VOhAMX2IU7FLEQSLyGlyo6+jEh/gv6xS2n6ZGT5Vy54rPGNm6PRhEGbJDjYcnSIDnB2CGoHSgfPOKqiWB4gDWMr/wDDE/pnlE1aRI7Sj1TFoOH/3d2jXOIPrwOnuETzxDI066/NtmTIUOsBP5OBjjxyg2ea6/fMHyg5Jt4YoCHMugEPIUMT1j6dUTNxOtk+DdJbBIGfm4ypP0El+ki5f21oEb0NICQzK+WFDYBk3gafpDOyU+A23WguqqB3LUrSqGIASCVRqIUA68iVAudFfam+mSdcYyE8Wd0xPhxdueGvBmLi4GHe/Dfu/eGpjdzQR188qsvfI8FMWwHZR3Bo/K2MW5OdL8AMSB1dKJuMplatbFKBHMcf9dkyyNWgaQYdDIEbqw6btelbPNG1xGAWh3QldUFfV1dyvNcyNbbnLKkNvNrU7RN1iqjSSosmT4nhIakk9M0KkKwXStGAt1VJldMqRzr0tp5Sotb3Hu2pikwBcBvcTFzeVgqC8bzcz7g2gbCkO30iV6XtH+44xWeUmRJrgpCoWawwdJgJ/kLbCcX+ttG9ccB0VdVChjPITgzvUbSHcOU99Z61beOtu8OsmfFGkUu9kKYCEk2Jd6vMA/OJ4HXXoUo20DnU2mH0Vu1oXxNhuJEhAE9LYpJqDTv8LWHlPt18gkwr5MqJF3FJJdiKw1+nnUL8OdnmDWHeJugYZVjDNJhdCrM1OkRKw/egIcQE6npOs+tbMcCACLJGbtR0jkf5Pjyv/7/ffJka/f3vvfBhh1pOydPzo7fvnnj7dt3LdaTF+uw0mawwguioDUTnJ6oVlJkp1bOZxnh7OXtjTXrr3JHlfVgxiTOla/VAlZfO+tYvuQBy+FEWkwRw4xL+3sHnhJaXFq4c+cN4ygxtcVDTbeydsqQQU4LgHQZeJIYBwvTAkZlOdjdc/OAbUhEziUNkAtc3twkeCqgSlHnQtsmffHw8SOymz4uCVfNc3Db5ePHT27c+OLNzbVog7QTul+u/ZFUkpYhHS2LuvBDrOokx/pKyJNDfz+HEJ52HQ6nbFYISNBMrLZnRMX1CbYLssCWQ8Qq1ncZNxM+YAY2+FUW/knXAmYCTOBAySRAUzIg6dckNbjEu57cJAb+jn4t8Ne8jkR3HLGjV9JBxTXOl54Kngwc/DLY/qZXlKFy8TflPHFfb4Dz8SvdOLwAwoSGHKCHiIOn0QyvY/ghxlcmcz0wAtSkD+ggGuEa0zREGjI5hLRnEsO1T1/3OqRyDSDWrIagGb4hOZ4hW0XbiDLhQxngGFSNdkBewEkBZDt1QBS1SuVUUb1eg2kkYLIrMXN+6YSZcdPbYOiyFUIb2lGl+Rj1H3WnOlZ9QuWL3al//s//3j/7Z//Z/fv307RftsPg5MWz5//5f/6/klCa4hk+jUlxqqBarYq1JvK1KNTrrRGw+VwXQ2n2yBKqkvC0LUP2RFKmMq6VneyjyWznRbo7mcqxFPfF8S4GGQGUN00D5xBavKqmM6rStUzXPhanS0uR6ozGj/molgT9fdnbmcI0bTPtINrNeUPLrq5zRnYmtl3DcDHnKH6nD15Nbe9ML6+sZSpVb8DJTazCxZUxQZ6sZHGcLOOg/728+saixSYzh0fbjLEVW5Zr4YwEjQfKaVo2I0cJxVExqBoVUDIafc3WloqoAh1XkpAacaHKsGMkAw0vCidOFOiEqHe4JzDhDVP8Hz3aHkidA9Au8LVFpOCh9d41P60hME1beaJVU9qaNdDH8jB8mQ/LbwIyLxPOcDGHpvSLGiwVIaVJ9UcCc0ZLBgMiJVzS4M4MghhDqJFdZX06M68AskPuuMYkyaejygnb3vE20XXB5YUjzmu9z0zu4pz+wz/53vnV0c8+27JveOHsaOH06Onz008fHtx/523T8jlkxZTqCqLIa8wWM5EZiaNDoxzay0UqCVjcuFw4soSwZthdGbiwvDo1u6if6MAqVCJILzn3j2Q7nzejug7gKtO76PT0pczaLGe1BNkguc51LzGQaZ1+YzdnJjlkuvOuqFIQJcQIIKjzU1d6vW2DiZnSxTTpHhxnURh4U0XrSzfX9w/3Dg9csERsJeQGVGvVz/amPv3k86UPUm0N6xCYtB3Two4GCPHVXxQuUcGwSVS45+DyaewGf8sYsjsEyRUymt+voYw02mG2AIJu0ERPNcpQ9EVWpH2Vg60lFc52LeEvkx9Ldcfur9IF3BH5uchjOWDt8ZxMMPDjinbt0yTYr/HD0F8Hz68B/rpP4qLW10kklYPkaDKWV8XzSh4mP/9a/4Bq8DT48Dp4fn34r03klY81z1GsHzBKYzIZTO/X9gx5vRb+CtZv9jKJtmPASeZUOU/iKTCklMQQfzLXIdGyXsavADp6exKh3GQgP+ScWD560f7VKheuFIX0V88hRNpeI7hG41L0Mbpa5dH1M3O2/ldnSFxqxfdAhKTZ89u3by2vLD589OXPf/LTTz/9+PHDR1aF3Ly1SVOQIltuZVGK1B+Xqq9DwqXPCYPGfc8/nS/kOlhdVEaQTcpR8Lk7kBkUHKtJBeQKQboQVbRNzm2/PF2yCCjm+XJ1WfD0rmm8qStn3es+VI/I9o3Y8jQZUFNdd2iqEZAOZXODxsncm/aNSb+5XAB399a6fo6xwNXZrIRaqg4r/bWze3HjZqwsFKysRak8i0uzehhBkgm/XNyOx7JmHdWNxRv0rGVZeudUJDNMufFQdoarFQpPCkhGa2yGJITBVPHV6ex5dJ+1t3XgWJcUzkRI5FRXuKaKU6DJT6nLQjUKSemMnXAOjKmDDvMKyeCkO8ALHGDSihnJEnhJJpbUwI9x529HaUq8Qj587STkukPGuEdRltzHwdmrBWttRwlvS9TQo1IQQj+MYqm4p8+f9DWCyZwTNAzKl4xoMmJIj8tk+sAtWlNzh2eXM2sOxVKs0wfaN0WD/MjGl//2r5fWl9bXFjaW5t5YX7m3dksT1c6kv/74qaVL66srpoRXllzzpThzsfvx0Z5J1fml9eVlZ/hr3QUbt+LYajtuldH0laV/azc2d49On269SOEoF42JnHvKRa1gQTFKj15g5oOIVqYqTnKrByfLdkS38cBDssCghj3VPU0FLsda2vLEa4DZBDArbtE1MePf39qpxFKChPPi4mj51vKdN+7Cw9zj7ZPFFxfbJ8YOlpczGEPUtQidfC66dFVZYokk1l3+pKyxiLCSbhmKMEw6nyZf0dxOIOBkKf3ptH1T6QhPVTwV0od0gmfnThV8dl2nVawHrJlCRDPWJu1UWpESQngTnvdqRBcAJJSFJ/z5MuHBsaZWIOc1NLzqIgdjl69juS1sSYpnQpAFfBOXiJOuUQyIhleeuMoj+H5LwIS/A4UgcwAYAgdIjflo6a9yjfArvkxkbIAJ5gnQ4W3w9MfhlWegfyLer/NmgS4542RpQNey4rXzOelpgl4P/7pEBrTXAAair4WjRyUMVbXaQpVGDGDPdhCyv8qHDEWMxiU1Jn/8Pi65Jl6swXXSnWtfJaFmD7VUCACu2cEOSCWBusTWkeo3G3tilWusaEovI50fJ15VVby6fOPNjf/P//f//X/7v/8XZ8eZ911dZSFNg+VEGynOY7O6XnYOPaHNS3k6UUk5Z8GKJcczzNL10LLWdoZArzXuUKD5hJfRwJeywbZXTF8a+TMvDE6vnu7IYNmRi3OmZpcsFrZiq24l1F2TZLbaxqIxMTofUsheC4o7beqoCR5DYRonTh6mwZcWlp3o9OF7by/kZtAL3RMwqFVGEB86ktjamTqayNRv77DM4Vs1x7lklDPnRliDarLbv/OFHEY0427X2zdvIdLwoCsHWgjT0nAWaW1iSWFd5dZY6kgBAZCiiMoqgcVtT042lJVPXPEwxdeFmLxOQAIQ11e4K2oe1Duwdkl0rJs6otfGBqA9Q7gv0PcwCU8LaoNho7RAJkoM9viZCuUTrAYh4oZ0efpVnruKsf885rc95c8WYRBGYFNIJIF/fo4JYRdZEd1mO4RypbqxffviXJdkfFNMOlvhuqfd6l+DxvrjGGbuwXwwK27MAiEOr9QRvKD5V7YPzx88fPGrqxfrU1O3l2d1Nt+8f3Nh/mhpYV8xsjxmqHXS5qctpnMj0cLaqttp3XwdNllDp7JqQW+472htZUvTb2fbMZnudDI4vHnnFhZhVPYT6QWTtFwAf7G2sWY4hBjg1cbqmo4+PpA6TUNVUnlpO1TE8AdXdVd5hnJsYRAChvn0PNzdo0CMbKsF5kG2js9urtxkQcXVtjs82veV32Eg+thWmOkxb73YJWZvvHH37p03T/eeS5e8pT+tDmRhVJyEoolUnDKWRZJkM28ylJ3Xwc8jVj9bZiQqxLNfC7IEg5m9dJmZA/KMOPnoxOpUGYonBV+tjcbcCPkhkS4PJ1Acrl+h/UoHrJ2vosPO40mQGj7Rx0Yr3jH9/fV3fkq0U+AZcA7+wTOJvwMnQzqiPHbgkFOQ7RpgiCIwNe13colbfCjPSxTD6+Dpb8Pr4HkZ5xv4RgdxKJJmuiiNaCiDa54h1a8M/wYpvgSRECTX6CbZ7VLXx7LVHs92KYiqEnkdt5gGwhrmZTKv+jpFMLLM8TBjsDVUZ4o/iFPlo6nVRn1yiswd9KKoxsxwbLaGe5QkPZvBT1nx9fDKNqe5O3duUWxmnnb39oyo3b93m+JgfQFAQxEV1iy4cKdhJ80WRn9BJXcUaDZsROCotLSJiZOf5VTVBWGnVbs0kWmFKIYCSIuZPbSySoN9an1jav3W0vaBRV51tUGZA/VuaZlqXjjcyakgwUFWsygmAuABmAeFKuel5TNZ9ePQoi36yNoQHTOdHIQKFxuwdbB+LGNxyYKptOTqmXOtfbIIGnLdCOGW8jIABixhoN0kYamqOT9+rFpYeJ5xx0uLZrMMZ2E+Edc3N4SENQZdmWE90DJskuaEc7EzVWQV1mU3eg7hHctru44r0AQAv0Dk8Uw+C9tIMIZYDewVU8UgfRUrSrBhGhU8/doIx/4SKUF1NdA4MJBC+ul+TD6ctSmH9a0FZsia0dkNAGtk64oL90xHEIvL6ZPjI6uXjS2sLFgpaF52fnbKQMHZzvNTTIHVyHQu+GA+Mv8wM+WkMj53JUxpT1LACzjuQLTZhaWD/SOlmqWGmuNnp1tHMFw8+PkTArq8NLW+PGMj+Pra8saKtGbPDk+3dvbOTuwbdnDVleM12M4ll2QtrC8uLd/YWN/e2beS2XUNi+sbbpzVqWt+eqbM3MJ1dHh8cvizTx4RA0uiTNm89cabiHNutoisIfuLLVqvwMRS7dhUUdsw64lGRGv3mqfMR7AMw4C5uiJOOrLuD77c2sUeEtWp5HJkh76WEdX+A28wRkLahyvLa/bXrbx5U1U92M/50hLNKVxQO+P61BWpnEhpbNIGwn3rch+Kj0esLkpPzivXADxYPgA3QMNk4KtsPYSZ5kAuSDseLlMLhujBMKqq/sYF+zhFr9WpDuKOMnhCcznhwCD3yTMsmwC+5q+PeYg6IBwCv6GnyWsk3zDK62CNZEDVAF7bDa88Ql6P/tuGfB2S3zb8N6arA6ApHB1fk1m4nBKlVnSTRMb0ViTliZeKb9df8nw1wy/DCy4TPW4FX109ObI48ch2N8UvxJ3bhh/NXpr2IPF6R5GeaVfjre7tbl+cpSra5kDGSTSzZNOJZ85/5DJ+S9HkmtLu07Fn0YampgzA8eQG6UxYyg3jUrtIjVcRcWO02flqRcbx4cHb79z/i7/8UxXSaXdWrUhgdnXpxPl55xeLiytpKetwXiyuzq9bNHp1djm/drQ0dz4/e2BDj3OZgq9Op682ZZoyVjrdFp4j3o1RXSzMHN1YRc/Z3NFj85wIwxx5983JRBnLNg1ca1hxkSXQwaTGZy71MFZcyaRHSGteZeFzzudJXvDCWbVO0OnVnGn1BpNhaqMC53vGrjeUT258mZ06PJ49u5h/fniYmxKknmMPHE5ke6ZTmGVrZf/kMJf+mpDDLcu4lnHzbGp+aXfH0QVTVJJBS2cD7+4Y9Fv90U++fOfeG/fuLD3dylkHW/vnC6u39o+P16/Onz56/P3vffdXv/j5+vzU0k0j7ZcrF2snhyfWjdtvuni14MyG5fmFp8+er9xfkSTnuPz9k3MHLq2tbx7q2124N3ztne+8ZbXu9rNnC3s7Ok97u1tmsJ2eOT99m+HWt84ZUVfTB1vPqWL3O5ios6f56NTCbE2jBZtJ9fNP9crrhAccKr2Z0ZRls+MLC1Wg2VhFABQ67VlHFR/w+GSMn4rHRgXklIYStmyhMXRATxHOsv1Oa8wQqPKKuMbuJzum2adPrtymE9s2R2trj7iOIpx13RQz6RfSS/dlQHl6+o0bb9rf1TZAipJoMpwFiVTkiVSSXsvpWavdHMySupll0obYro6uHDlpBR0Cz1xs7YA1JHGX045zXly4mWkOmDOgRC4NyEwda/0tbif1dmfOB5+KEcqNXedH5lQ47UPi41boHIUltcupfRCJfTm9dTQ7c4Q2qehcrqw4cfNNp45L4GTn5OxZGk/Ly4+wURd3amF1YWX2WPU72rN66407txyaJding6P9yvshRMszl9/77nfM2or74lluYNTaur2+yMJfuO0JazNlkkNbcpY0mVyYtsve1FEUVFplylOXnidjCgwL46LBTKGoatp4N5fM8h7Nnh2szK/vvXj68PHTSmvq0ePnlJKWzltvv22R4r/9tz/cvLWWLr9q7vSxFTvqFpUznZKCw3NoZfP4UMOYk6hMaXk4GjREVvNLiaEzxca2VYPDJ9VT+6nwoNLp8EqM8LhuiXSlGW1yRj0mcs4QdX0GTYZ/Zqasn6Atp9fWw97a3yyD5ITaIHmzi2mLWAWZtMjy2K6fHDuMrNqjZV/Jkr6BKkCM8IpawCJXwKg1Idv2ivMDnLUKj7ZZXXeZ95K91ig6zZ1aJqouyX0mQACTi3D5TvjcrnKaboDW6Fla2MrXU+EQ9+SMm3F0j3ZkLrXLspe0CKXrMDIHGqgc88cu05qdPZyZO3Kg26wD3PbBF3oFqtEWfvJgsHkAouWIQK+qfDHbFNd57uVO2zLjeXQbwSD4zksP2emgEBrt1VwPQ5qvZrJIbeQKuZheFVMCa9mmEISPYOpAw3ypkInw8ff6W7mOb/B8Hfy1cBr2Ffd6AkIGpK+A1svXfR3ClX3HIgow6U4pGAs3SnoululQ2/ANzWY3Ttb7NLAUO1GBsp6yxJ9i0UCDT/k6Dox/zKaG6ZDBH+jKi9KF3JPKU5faT8ImIUfS00HN1nRDJaa0JCR21FDrAWjJmqoWj/LPJqnohUzaknnD1VmIk7gFQrcT7GCBD1tUVem0vhbhyvVLbKrKSY50hKJlyJTX/FTBaJkQkW1/gWF8L6dWVteQl1pd/U6fvKqtZ4Tu6EzrR3OauoGNBrCYdVd76AQMXoRx/pdm0TWKv13yO/abGzs93N15vnbz1oaOhS0cskE1sBPWs1hioxwZEdVj58Xzz7/8goJerZsEWbss1dVegkv/1+SwNoWjO9PBy2Q/equChf+KwwSelo3pw5t1lezO3nNTd4fZnXKMRoiK95ljdjejiN2NH3M0ohI+1EA65sDpyQnhmi2Dv0JqIG7Ic+W8o4TgRFe2o/HkjPlf1dleVZBqdFXexIFKZYdmwIQMLkJTrnGOMTtibBdnok7ca1txQNGzWdOtCWWBk00phKH2s/ER1VHcIYEkGydc6jz9rLDIFYT4iWOhrXpOJNZ2nY7S8B3Xs2N5NrWeHego54bp+gEQVb4+ffoMSZDD6ZWkyYtXq6RJlMkN0c5IKPnPnRznDx8/t57IvLWDLqwkczmCbesrq0s/+M5HDKEkcm7Ls23I0ZyalfXO6aZRG50vqSQtB3NOO09Ta15lSFmz6KIsLDKe8mt6embqOOv/LSzAT3Lo6+bmTbTpB3fba2lm9uQ0Y9EGwbWBFMFbb61DTq0LxAR+ogLYGrBOPUvcalnA0OxCs9Sn7aQuDnjlACdQ/a+y60DPDqynYiJSSiT2IfxmtDQr3ZHsZMy5udqF1Q2+i/2D3akMZKRQsiCzR+y0L2oYoNPzlYYw1iRpTmcCNp9Qy6bGJJUIaTGaZwcoYUKMJ5aaZMU8g58MqDB2qs1Wa8dIXWYwYIZbmgFezNCWd9u7lCzk1nVSZ+IzcsLNRwRclQhYVBwmlo/1DaVIkXGNKfNQ6qwFIaAR4HouqkkPQ6/iKnNokdV2AGTccxwQKe0QnsGllVQww7NgXor0APkbPRL6SpivC/9KYIFfB/96eAyw0Ndz1ajr0yiVa2DD6+CZiDLiGrRLhobOciearyltk5RLy6SZSyPR1XjntrXkZh5f94+c0/RSWMFAToZ80gbm5xKzwvlT/NXc68DhOQE4hMUjHElVr/NKZdBThEmIPoLPCS06M/vaKqeKPE3Y5CnFX7Ywr51KSXRamWJDlR5QrIJzoiOY2deT43/m2dVYs9Q6rVCxozFhO7G0qyp86rzmoSiiXRh9DrH+azqGmKxpjWi+2DpsrReexKnqNMXF3sFO0NeP0EZ5zeaoDle42457fuQ43BgLecpFdaZvF9GTkw5Y/7OLA/IfQjEzu5LiwoyqQD3IgNSd3Z39XQu6Fx2bx+jeuXdvbWOdadK/2tnfd/7Ri+3dbPdM696ph4cW7tBgF5d7qxubehVKy86UqAqLqDUyctiImWL1Mfc4dXYUjlVYLq7R8FfDJbq+nzszZmd3p6f36SqdObmx69jIIbbR0K2VcH7UenOMIoq1RNJjNTlunD8/i4SwiYbGl5zonYquXaNXE/2Tsi5ui8sPWubD+2oLC5A0XvuU666iOX2lrzIyEaU27h+0BFW5REJCWwp55BoD3npvRZ9CqnlNjPK1DbCvsqPHr4CR6xXUy7hVOiOME8LciQ5PaDlgnRx/p4Wgdg0Z8kvOPRt48iuYDDyN9ANV/jKu5qbpZj9hfnS5sV8ViqSfZMtTVh7oyRuamM9Qtw7SCVNpIPliykXXZG2FuV5dXVtY2TQhzQoeu7drccV4HBeR0q8pV+9pOZUnE8IIYt7KwhEkxznrkDpf/Ri4xobTo2REvdZqkSlGF1VadXJhphmHZVdc/OfIgaTXN4ntjW0Xil1OrRtcKJ77Wq0+p4oaH4yNR0Cq3mwd1l3Iw7q2ymWwvUqlWc0Tgqush8LiKWYyhqVQ2N7skSP+uRhRYTJvB0dndk+vrjsWO92DPfdclX5QpSkv0dGDfvNhsFWWR4cZSD3OCTkFFm/tlBJlbjbDfiaiyvLXIk5LH8DRRdGItrCpAroIGjOa1m7TuG/M5eJMyz1bsFXxDJBGJ1wt9i2uyVykK4nVwJ5BB1/1Bjx6BiWZn5pacRObY0GpetKo5imvtAMujXvJub6Jn43qutxmvDQPGXYYkpH0ogl/1GPqWiojEZUw6Qmf21Neb14zowc4dCblwQXD2E34k6FXXCFJYFf84VuF523wDJ++0jOADZ4GG14Hj/A0q8PCiafQQCRLVUYVewDozInSYIOnoEbwDdwAntpOGj2ECVNddWfX/tGp7QSrTBH1nM2plw5CMgA2dXV8buyjozdVigJ9ImaZUJHUaKNAy7WK7NT72WA+8pR/RGq/dk3up1rK9TCgr10gPOKSA46AM635pfAVbeaxioC0cImI4s/0sK6ZIXL9JJo/UWIsI7Y1jChWDZEAKP5IhjR2+3Jq6qCO1qvm6ALrbR+J6mC9TFIxKJ1mI33hFr/MyyahEu/0datdIvsMsP2ZvvoBBlOFg6oQdjqvrpLv3MHgqjs6cmltwX1s9nc61zraBKWskAzaSyvRQUM3Nz2DLptGUx8uLw4OTz755HNHGbyzs/PeB+8fGQqeX3DuwsrmxtPHDzZebOv/KOszY/05hOjs8sgxWIuX67Ux9PjIkCEbfHF6kqbypd4NTjvOV2bDcAlhbNkKDQNtlpl799/FaXcLsa8+0afUqCK7PDhcOMsBn6UuM4ag1MK0tOgVi9HOsh1pkmND+Mh280fp1atSRYrRAK3xKn3dgJS9UG0e2iUmVLGr9dFS4WzNPyuGFFIUEBVS9ikyw2RIZ8IRYBow4juuLA2Azv4EtqzAmb5aTRqUanHgM5zVDEubqM5ak4wsj9BPpAIPgCFN+LnGj1dJuKp2c6ZfRzdkVMQO6egi8oDkWrR4hET11Sd54RFFYEWMfu/AlNzFlRFCZlSbz4ouRDHJTKOrfhaZ4JmpGxtrWZo6ldHACOqZqRMHn0795d/8vA0e5GSM1cHJGL0aww3DVSUinjihcM5axjoJxO6p6HvV/NQ0cbpxvkIFg0pNAj2Rt394KK+GZyDRKhPIk4OXYzqZbdYr59VI/eGjh28oaAXP9GfMM+l6ZjCYlFbe+z5jxESbRSBM92Q6Q7peMxijD5q16ggPtaN1ysW01KNYLHY01Sz4o2WLudMzTnhdWdu4dXvjxs0N7QADJC+29mTa3j106qCIbbQO/UvWhS8tZUAoA1pSzao0aSVtzhaz7ijnwI2YcCnISDwR3HBJn5bJQ59PGjrIIvHTM/amLSwtr9994+333v+eESsn5JwcZtLbgOXpsdPfY/hPr/YkkpHdckQ1vQmG1vJ9LvUh4pHWU5n4s91MhSTLJdZp0EoWbSVY1ivMX1mycIGg2csTNhg0bFxSqdaMJ38krFnVzCyYZLyUsyoeIsYu8jlCMw6S2fI2EuU8DsCQfBklSwWU0pb+GOUI0ShiIF9D/TIRuH4D/BC9PS+HoL3LzvB5wNkhyeqEK+CEDJ7+OLwOHkVISyojUu5KMBp5cWmF5jdMm4uzLfgwkLWkibq0tbP9dHufuHXcTpFfhvKIL64T9d6lkkneAuivLwHG7yI2qhKYFCTZIEkCSbPqSqd3LCHtvCqHEYJKeMidChphoJrsAGoaQhuRz6BZ5p8UbQxh6obljETRVHX6oYup5GOkafG00/GJEF1pxOee4OPaaaOu0BYAKBi1GrXILolMLzZaPcNKSUKxUCUaxNVA96n7vpF/iEWZW96QGEFXEcuQGBFyFamlzJblGIELFmdWsM9IHRFYVryzXM/YSjf1aL+umPFzZd7x6c7u4e6PfvTo8dPbawt6D+6DXV5dN0J84C7hc2vATm/f3NTbYGBQjsM3LNRi9s6sKGFjTdbo3ZrRruEztTGjZqVBLmo4ZO4inSYtAkveLlcYTmuMqAbZWVs/Nm1svM6ApZ605ddYTLlwemApTT2tZrOCy9UPCiO/lL4+JZnVHOpXRRjNGsuPn0ObxlyXdLnKO/WCy7E5mEO7IF7ljJzY6gx/gsOv1Ohqq1Rfuf2MwlwSeNWBFaBMuU4FArFZDa+aLbR5YGq5A0/gZOE3ORQ1SOMMheVgGBwAJSK4k/AU0mAYCAyTcEMIT8fS7huit0cUGdBVid6sXn66NjEoorC7aWenyag5fXq+l4UNOUFDG4OoWoeo0jGPklZYO3tkb09yhTlWTSFasiALy05mDFtTkxozqRVuXYHeaA7iqNqHN1pRAGJIqomuzsw6lcuYqRZ9+qzZ1QaA34o/pY8DFhhs3NhErdFvF0fqHzs0RDhNpV5Zna16SStELi1aBZWh6AwpK9CiVI01Ql46iYEW1GwUpXnL04HCJ51ANS/N8dK0hCmyQ5xmLZlcsajLgPkbb95DjxUQGG9R2E9++nMmMGNOe3s8R4cm79KJX1+XRBjO0KMz1OKy+aasVQ8/ZVkgTvGzkmdqS6ZIUvQIYIMBKGybLvDX8krKKiv45txzdvvNe+9pRlRj2Jr6UxvMIoTZUH3x7OAzXlXbFnCNAJzxCtKOiWS5WEFAoohyh83l+lJudcscsNA0S9LgJs/pSBjWzBidnuuZ7ZbEyeJBt3Chv0veMygqM/LjVQ7712nxw6PFr6rHwnvmYF3FgfjqOzX3Mw7ZsCFiskSu+YM2kEnfY/har3kbPMOnr/QMYIOnwYbXwfO1Q9AguiDH9XqcdjVwmpRwaoKm4XXw+Kq01bb94yMLXeRt88atf/BP/oO1jRtrZmWWFpdXV1IRawXjn//lnz/Z2j/Y+1KBNbmNvNkg0CD0ZLiQBE4Q0F87b57tGQI7hLhI0JN0tgGGpCGFcAPxHUh0StBHyFR0Ra1u2CWFSm8+J8qMExjTCs4Ei0fMotU2aQTqHV9NzV9eZY+EdLVRI8hj506ZgJyRm8w/kWlNE1O3nR7N3OQ1jaQCD6hN6l+tcjqVLKBZeuCRUfRHgjkZ9zyIdWMTnUjgwnSdyFqlYBHH0nqWSmdgyOm8Z1cuK6/o1wU0ePyybOr8xMKpHPWQocWoqanHj5/uPDOS/5h+sKTu7p3bjh6xW1SZssGa8DUtOO98QY1p+FVXB/WwYSynoTK+GgA5Vcm6z5G2Room1GhNoM4mFl0rqtn2KhVZY+3mrVU2eHVtgxk2SRZVYNvoqVGHnMivyaIA5D2CoXBoHTyv9pYy8Ql32M+wS/VOQ7cau/irNGdN+NWUh309hgLKzoa9qiJ6oKVCsr4vwzEZ367Bt9Ik+j0xG93qD/ODO/wfC09I6jLN17GfNuSUu1wLHEQRTNKtjAgkLB1riNiego9qG74OAOLCIJyTRPJbTm/RX2DX0BKkIW7yMbbNGX6Rh3LIaI+QleXq8dSnRq6MYJ6b3zQl0iHGEpQ0Fuv32ZWkvmDK/IybiMAGWIoW6WlsJbPR0Wdug1p2KeHC3IYVliRTHUouLHUy2JxxbcMtlY9WuviC03hlnYORmFqMBFk6ftlfhtrOqWoFTktRp7ONGTmVS6IBklUD4Ct7PGf/HpfxfwYsfU5DGxnvypXOGt7N1Wr0l23AqKKnq17kqtk+hHc2B96y9PIqMIJuYDDDbBoTaTpvPX/84OFnDx99TrZR7oKTd+7f/Y//w/8IbSqXPjGHeH4Efvrpp2pTSK1mbsbUs7lAaz8EdHIW3ETs1F+V4MRFKRjuU+jFHOY7xq00jJa4upjqEhlnhp1p58w7PfkM8dMz8pWqdHl56/0NNJNYNZpL06Tujdh+8SJMynKxLFuv8Eyf0+2ipglPnEuka3WfVq0qVsoNU7PPQw0yAp5JOOQlylgty0ALnsCQPlF9kjkVt3+JlOzGJJvEyTcurJhwkw2j/lQcSn0cQ5XF74iFcPgwqr/gOqFxhFf+RleM3QA2ePrL8MpT7aPKMKK9D9mTzeFVtK/0TwZOwkyGs0t+lt5oLRGR5fWb3/ro+x98+zuu/jB2a0pQwyx3kMzN7x+df/bo2WZMVKoNSkLMmDFwthM49qaQOkv9FD68NszwOgB0lMh9dsXE9ackVlrPczJWmmwjx9yKTYuna8t+EGAaRv0MsZYQRMTUebKDQtYC7ejXwjNye3VykHtauJolIcwxxhHlWpXX1VXn0VoKqdeSpZARYnJ/TE3shLAZy4nR3PCe/Ia4OztII/e6m4eHBzyARd9HkLppWo6ym8/PsnNryI/OVMhsyszAq0P9kaXLVZ2GIrs5iZjUHbnQMTk12Jdzj1SVjpjxVfXt7PD8J7/47PnW1u9956PP1lffe+etN+7cZoX39w9dAXtneZml5NhddlgTmzlEnvbLhYaQWYjjU7dCWMnJqLcpki9qMXUgB+XPnTivOr0EJxOfIp2WZBnvvPnm8sH+yuEa+9vOSoOL4yN22CmgKcHL5I2q8DPJHANTXWF1PNVd7R8NNkWcsM5TiXgqGQRgaZBoQaa9U8uCUtmjgRNaFSWd+FjnOFovrf6aky4RAmGwsb6VWPKJ51M7BdelFmQhIFOqneSVGYIsNSj7XzKpXAo4GLpgKsqo4dgYPNszSlJTY2xEO+l+DoGSAC/jHUveE7+cT5WdylIt/pIPylKbA3w7Vy0gqWW4UxQVbc6I7nzJoLZdpogzSkmCFxlEkKrMoWs8cpNHsmIUWtHoeqbEDSfab3DmxuLTQyv4Zy2tYlNjTF30aQKTHbCQH1nEIMmNWOqtdhaZXIhKD9+MtCfmjDOrj2W55omc376+f3Tce44JhapjHAW8wXCCZ3W0IksfqvrWkBNvc7s8xrNPz4+UCDANa1njNBDkNEXFEleixbxYC2xpPndIk5qE0F8jHB1FiE9EiDlE7Q3D9LoHTtJxn8Xx4fMXM59+9vP7995WjW/eWL9z+wacwVB6AxMYZt1i2WGV2eZedPaLT7+EU35VzDQa0v7UYT1jjBGWNoQ/MpiZ9OoJpC19lv7uqF06Uk1aPIoqKiwmOY2gmrGNKS/FqbxWl1eRE9n1ePudDyFWr1GlPmpzhwBr6588s/QKTIw73Mb28DF3emjw5S9daohJEWQaz3G/OQe3kJawpM6ly2yJmbqpfkQjDR6QiVq1zFI/mqwaywPXRyI2fo/MRKv8WiftfIeLp2MU/Ch8LHW/Fkc+fh38tfDfPATduEjMZJKwXAvpr6+Ha1uuLK1q1JoNcm3f7t7R8xdbKzd3Dk5OX+zuPn+xbbXn3Tft+rtt8GNt8/bl7h4k3GRyk/7hU0GNICcDB0oaAN39tV99HSgn0JzXa25ITngNZxKdDqudvxDpB2VbUI66zXhVepVZ3Q8os2ATLjp6OsY+Bq5ccS4iozqoCraC+Ko3aFCNoWUdVS36iPgKR15GkfJtZHSvTs50FhmCDMCeZbIMJNf5Cimpn0z4TA71W16+tbls8fLu9o51RVraqrpkHerj1jMTY5n9unCKxjFCnETozjrkDdl/6Zm+2t55sTQ3bU9zqZcrp29gAHSsDoLl5enzvampX0no/XfeeP+dd/74B3+o622g73bNO6iTlJenS3hQaFnaheqPVAtRKdxiiuZHmgrGSI2Hn2eNCQVQV+RcmNYzXa2loDhoFfFMiqofdgnfXnBl3mmroSRxvCNic0NC7TBwCORpf8OwriptdAht2BonPQMlWz3dUgYp/DZm+KvCR4Qyk5wKWjY2jW6thULcyNP0iuS9lD2xJl3sTQ1Ee5b6jgCwbwY48DPKOhoAURGP6MBxBcxrXDBzGVAs16+87YnklOuvrev5hcLA8fcTfMMMGIS3XyydoHaQdWA/W+REZIo8BQImjUurS3guSnhO58Y2abS4ISS7wuazIjqdP1liRtGisaEHdXB2AEn6SHa6s8fuRT4+82fpjJDGArOhCMDk+Rz7HMFVR7TToihTXhc3Ntcd8UWcjNLS/gbwHd+ZVlfOrlqy3l6ilpy7sFAf1BgvbG5XefLsha29GAHs7t03n29vWV4NkgErjsWO2mPdddBksHqu4sqyEHm01cXqphRUmdLmQ1fzUFtOIAcbB9CSNMGw1ugqr2Ff/Xu3Pz0zu8s5d0xffP3G5gcf5GLvucslscBB2Dgv6xZOVWl1cW5t6dbbb97xVRKdrvs+WUF3eDt9b8uqSPd5p898+Ktf/UrjwerF6ByLqlSu7k/MWriuFxDF45CfOS0QZcJKlr7SaGaXK2WKkqTZXzfKlz8Rf1mvdhWPKlLj+SMJHGX5W6n4rPLhsenkXDl1dHyQ7Vz7e84ssEb+/GouJ6tmWH5OM6RuXC+Wvaq34e/QzqzXDlH18lPTUFPzwaPI3lND2/neI9Jh1Ddxjfx1yK8Lfx2yQ74OfgiPFhheMFP2hhx+JdKI+xhmiAtDx3o9iu7W7t724vwy1mv1qJGHVl5NzTx9tvW//z/8H3/0859+9N3v/Mt/+S/f+eDDjMLWCkOio3YRUiOK2+wxITUeqRtdTb9IP61P5ZVQdroIaPlGQInLaKghlNKNNSYz0IYSNVAdIBYOxDGYI66v8DNIh44XODtf37jRNsNBAlkJ6pe6l44idW0AyXLLMkVpZQrSw1cnJWSjIuSPHj0ljkg1fLq+vgK/WuFptF1GSKSatryWMx3PDbfWiF+Tjd4uDvYm/rQ4L2wicpxtM1l3s6lFcOda7U4uYyPmtvbO795c/hf/4l/84Ac/0FM06fX86MV//V/9P//Nf/Vf7G1dbG6oIStGIvaPLhbWFk3YOcPKbao5XSsLlwwvO9peTY7cpk1caOEvXU1vhhifJl0NIFXTY3rq2fMdsT7+7Mknnz75+ONP7r15R9tcPf/Whx8ozdPTA+30m7dvLMwvOWnBPQ2O4NBspx4cwmB1HpXFBusrSxfxhNGn9ZUNfoESxU98bkVDlSkvgQ4XFOIkB6cPCnn+8KHyhbYxR6XVQndljcNYx3Ioen7Mx8CFxfTkhNDyvnL8ptMsFUIG0VHwFsFkA0XXlCjOGh7PGujMUpuajBW8ULiKLJOsBu3gSS9/jFNCgwva2nHUafEnF5RIKeiokRbsqovwi0hMJNEqCLTALnSUd9HAMLhGKBb8nv3aX4UYpuXvr175k99qhgrkIB9e4Tfwy8Q1KsBNG0vILE7ioRZITHhhazstzrnqFv7ZDC1K0AkdhlnwUQbttum4DrC+nFnhT3bSr8mY7PGh/b85G5LC75aNqrS87GDpNWZ3+eLKKnh7CmpZYpa+45/GpK6228C09Pm/+Oxj53NBaqaD8ISWuCmtNCpGT5cpePDo4eXFc/SQsbX1dbdZf/HFF1qldRpJCOqGqPEU/UdljgkM9ux6D9STupGqJF1QS0K5c62jklg5/PQX09oFrbYIg1cXKTq6RHTcZkCsVXOD5/sfvHv/7bcJM3sI2JoJmMQNDCJYKVwxGe626avoB4XFgUz+bNm9mnP/+PT0yv17t6a/977CLzua5gGxf/zoySeffPbFFw+ePVX/Xjh/zPXVGiTqt23Cpty3t5/Oz2rjfvfhg4+dSQaz04SUqAEMQ9vKUopGpjG8ZHZs3dJwukqFLVeU5JGevSbYonn96eWNqxux1lUQ2U98sbuztbe9dW5t1+Xs8dn00saNw719Q2TOAJQdl89VA2vW1mgszSLqc3tWs8ieR/0k2iZTdGIcTKRzbMGfKq0Bz1TosKh6yTPpKYd7rQRQZQWSZ+SzpALJQOTLqGL7q3IITGYqdtg7OJDtH38dvow8kF8P+rXvqQntYBwjHwfV3wofpTr5YQj/SlKGr1EVal0cr1qmuDNUcHB8rmRM6T19vv3nf/lXLMAvfvnxk6fP11WpmttfUG7lmhlNQec/KVYPYJKeb+IXEd9B8nCwETJPXENcggpLPGP9pXeVTtlZroGxW8qgFmE8n53ShquCzCBbD0xO197/bLJREzTn5uXOikz6aEF9vXV7E3zXN+jIBO1MUBbW1gtPKrDXs7p5DZG7u1ZvlqCnf5YfJ2Sp9Fn8mSGVASSHTZKCQQ/jvffv/+CPf//evTt//ud//tOf/vTZ7oPtrRcm7Dbf1s426eVArqmNzSVcMGekglpkpHz8SiRho1D4Y3UxIUKc4jNkFK/V1O1Ex7+MCUm+glKyfCEnJf3Y4Mbertr+4x/99D/5H/2H//Sf/GOnJBlk/eyLR/SLHSfm3WKeVdSpy8P9A0ssjw+yXpYt0/AwDWhoRKZQpYyqNJK22siJ1QXET017RlnU6QjWreCD8wVpHE4rij3moSDEwnYIlYLomOZV4fJXcWjVUbOZCNNB66YGNsDMAchTJdfiyOB1mlpps0d5RjGFkhBVhVFyhVpwxZvf7tFZE6dSTqPht4v/KnTnVFiIK03d371yKJ98bTDhgwevR69jOAwUi2u2VLMjSLpQVF5+laRaDGFJh0cucLfGOZtHESB9SN2tBMIQ+x0rzofDKYQIoalha4DNLxydbEUe9k43Nx3FsK1aslgMp1HPzz/7RCfLcRLvv/3mP/2Hf/Luu+8vzGQoaPPmrR0b8EJDhCSUV7sWHoY2TbQjA0hTbhORF9L4/JNPb65m0RZIzxigGn8i+/odLYT0dvOk2dJCBaH2AbniUa9h04uVKWLDdWCH3759V55k0Ct4T2s3hcA2v5iD0EtFhL2ccEd/9lcAHNuR8tBwrOa+hBoMsSjhtACrCReZrOJVfbCRXbr61vv3v/fR+8jRD3ZK9ueff+H5cPfFz37y088+e+ECrc0b0+srCwd7z376oz9LF8V24Cwpd710xht0HuRleeMezEnX/zoLwSv3/OmL/GGu3Uca5ZZ+RUJWkRF5YjFxVd20Js/Wjdt37y8srpjcttzDtLiFZaYkDYcoJvnlKunRdJtAjGonnAMgsxBmGDtNvjSDKWeqtynDRmsRouwtQHOQQAx59MmpOzfC/5FDZBKrnfcd1K+eXovwIeBvwfM6wpEB9kF6ncJr/lHCQ3hjGeCv0TUJ1lkiNlXzyIFRW2yjpWfM4f3H//F/8r0/+IHq9+a9exsbm2ZodECbrZGkLubURTogjB0SCgG/yQA3kUMUHiEcCSAfPEJ4StzTX1EQ+TyOkDTLGTxn5FLiiNaFClTGTWy8AUtbGEFSS0kcbGheXblFUJaXM8YFgMfKRqwlK90lAuOOVE1lT9rKOLy0SBhnhIv4URZ+DnmCwK+LhTLi8QPcXC32SKRrYO4murG5trR4/PGvfvm/+9/+r2VzZ4fUTi2tTTnSZ3NVBZpzHod+uAqyvrG6ve/wm2g72kaOSCpB1jRNKzvczS/YZZmZzQR3DG3lSZSeXAngSGiab4aSatwHa/ZPMqlpbdajpz+l2p49e/GtD9+Hbf74bHWNAZ4zWD119USh53ZbZ3Pp9Rwe2FisNZQFIC7bcfaGM7QMNWQHhS6i7lHaQtqqmMDhGITFz5cre73PO7JwZRVFAHSd6VlmGLy4CNMIyNqDsmqiuzQ5dpL407exEREUi7S1A5Q0bRt9i+9XVlmZnAv/U+5mpMILmzHnrV8jnBGLVNrwrTxgf5cKLDuSiGZJNzuuUyysv91jTNIrRQSzJCDqrwNGqQjpQH4w/TqZeocMMAS+lGG6Yo3Hp76/tin3CpMUG0meYV4c2xrnIJolNxiliivPUJAwTn2k/7NQvsLSHFJs1vdYuzN9eKzVlJ7b9NbGzTuK+JPPt+02pdtXl7bMGzv5Umvu9NgR1DuOVI/yqdIPdXMaW2kY//2///dz99L62v7eISFmb+7de2vPSoVjt4OcZ+dNnYqajMhnnRrhD+QcNWVwzkC4kDfvvkE+qZEs3641h147+8nH2GFRO83BzpHXIK6RXX6xrGAioqTc5rgSxzJglXTHhQxmTxh4BPazQ/rVILFXQuQ1lbM21OGpg72qN+yU91PthPffv/fhh/fBuEfFRPinn338V3/1F3/+Z3/65WfPbm0ufO/Dt3de7Phqch2FyZ854bKsC8v7QjBQljHDgxcZmw6jV2x0e6qPY8nTruVO6qAPuRMLmFEEP5Ud1MpyVMuUHk5qncTc1bJsn6PCIlC0rjV3Iqr4Vg+lv6FVl+PLDZWrqcYAclmbA7vUXwOU5RgYUhNxJHWYiYCmNknP28O8rF3XTIOQi9yV4y++BQ1PVfQW1pd2p8J/90cnIf7g4c8iLH/GqeYbfwV+TfirprrJGTA2nkYypOSVk0iF5L4OHQiLAj767ve+lbQvCTORfvL4gcLULcayjjJg69fG0GjZdHwstAK+2vkKwzUkapeQRighdUYZG4jJSRET6LpU4FVhRIk+LIVoLjaFNj3jbJ2gjlxp9aVQs+DbgX2HlmhdOiSfaIqoS+vMCst1ARwe7otBjBiWNM2yViL3K7SDwG8pI3jxCK9uhO7VKN8RSm6qm5YhNulnKpE0T99c39i8sSFFw7wINB1291bOuV1kX6wqsqBUI7NO9MxIoN39IpHcOBZYXzgmjl5MCuX8QTMXQNWa5FbXdkRuerkZpR7Ek7TGHmc6J85phNis9a7h+9NfPXrw+P/ynW99+NG3P7QM3uDu2vqK+kHHuQDYamK1RUEcHO4YeTO0gJyQphafmJwOM1Kze87K5cZ2NaftsBKQtJzqxijWdjYlu3tyqETaMPCsb26ubWzglXLUAMIffs8eZWFgLXbiIFFenvGn9xZhYBLCo6gxPEAjPz5VxUWib6XmJGotox2iEpWiVpS4zTwQY3b9Fn9TJjhZXYyONi6T3wIJGoaiLHoSl4c1lM1riIRzQ6D0k6kKSZlW8nlGYvwrVy9pFKnNBdDB+iJMw6hZSDzipEsrptmEiQ3mmb6t/Xnz4SelUAAqg/ZfKIGznOgpndAWNT0143ZpEmaZ1eKU4csf/eyXBMkX80RZjrjglJi95dkrbdGz44MHn3+xeuue6JpukpY4U6wxTf0Y+DHx8ff+4T8gWS+2txgTYKyokU2Ku8VDKzkpVj8vR79VhahOnmqfshZlVKnHGZYX2azcpYlB0tpV6sn7hhNmyjWYyiebAtI6jJNZ7YNgFujdKomBEeUJZ0YlkwIJLxCJAdUozlQnJySV1T/AaV5atJFV0JVyhhc0KCOu7OLFxbv37n7ng3f+6T/445/9R//kpz/5kSWcOy+eGEOqNlJugjHGoKmLJjhPnx6ijfpOFXPoST0E0IfhVJa+xSrnSxldy8i7lZ4NCXodlcnM1yzkBFOm1azTrKln6iJ95FMNeAXtn+kDnjT7zY3p7miXsJnKPmMRyWv/UqhVVbV6ZC+jeVHANhRnQDFWvGfr03/DatkNCfjDhRtIqfJqsOJwMSmyFgbgX3yvuUT9Kvc1wS9Bh4jteWUIGkldeC/Bi0qvXxnegQNGYPxDYHs8BVZO9CLVhJGAdqckO2HSPHV+ssXSVlGku+M9nUULKMLS5LPFsQ1QYyN4wCbpvObviJOBHZEa7yREJygas54mWV8he0xzCo2+n5k2HE6e7ty67exMEWOtqzEPpwLUqaLSLeP3aXZqVYcPAhWPzs8ykWkWd2pzc2pvP+So/+o1lUKD+MricmBK72chvjaxsRQVTwWvYxMkMtHRpDwm1u+kgZhTr4n34d7WscYzu6c1akjMugc1Z/o8a2FoNRcUZb/H1On+8dX+7sHF/BJZzWgaZY+RJD0/zG8FGKp8M4tU73QKya/qrnaTfvbBym+DAeAqvGbJCdBIuVszaS+Cb7omYtiE8qOf/PLzBw/X1jfe/9XHH3744Xvv3GN0nQvM/srv1Mbq4dGRvJ+6VJHQX56bxpMFm5Ulow7aytUqptR4ij58KXlDZ6u5yIkuGG2U0YQeWMYto3yzt+6sqvDgRVRp2WNKlmf/2fNxHc4AtRrreDYEdCAegI8Ms//hgckow9cscxFQxljiTh1eW90gSDDExqWXaeDERvGvrqVwfp2rPFXXpXT66LVU6tdF+fXhzSIwnffXEXZIIxlqQQP3q0/t8WwnhObtKJgvsGHwn91gWDGrv2bisoSlX30nag0vIkd0dddyUFJ6MQ5+H6VlLa2q1a3T9IXTHoms4bARVGhXllcVyH6WUF0try0d7x8vzmWF7b4VWIvTt2ZXlLsS1/hFKoYSbOUSS3VlGvPieO/ks88+u//Oe8axNzdv0ANEyAql7tVZvogDldl0AnlSssax04TgUsbgEeDQ6M5Os1E4Ivm7knagZ2c/uajBMBkXktdqWwCg8TyzSrKcrxHXy3O2rON6ijUgnMQ/APBYT93V03PkavAqndlyEkU/4lm4k1M795ZP9vYPXes9c/X23bvrS39ix5OOw+72i5BdKRbZ1tQh2FB2KpFFEY4UY9LxKKlczTwwXKFVkoGAdrY1pzqsbtysbGYwP0OFjHRIyUy40+B0f82CzZrazZn4WaWZw/IqUXF5xOWkwM+FpHI87WrR+kv1aJqsw9NcQC5KU1IZ/TqxD9N08lwuNYGvEBuJizpVCmarOiR1vSuLzJLWgY0V7d/xgbbGMHhe2Yb06rfIH7ih7ncczBh56utAUEGGU5OeMcKyo11SWEJElWON2xA7asvqCmv/mI01iyB6ydzhYYY9FnLqGzIYDksQJykZt8N/C/ZAlSIcSzw/vhMWZkDPtGsCgnmUO2B+SfuewFqcCkosNFvIae1GEGavqaEdxZyGVUZxD3eOz6c+ePeNjY01HHJssi320vzrv/nLuflnqqdFMJ7yUhsvpxx/hTFeCQyw3hOpluJKGeMYgxh5lKfH4I6EmHBJ46OHmQ/PqakTexhIlUFZWwB2tnfYaL9oP/BzLlV1DboqzVS4Tk5dWdx1aRKMJi4de4lutStzM1nCWsohgidr0mj60i/P7KekE8ClXAiwlclFf/hVIGojn07+ohVnRpJN5VqQurJgherj54d+DsR/8PDRk4++/d79N22uuGfT0q2NqhvWlB7n/pfjw63nz0wVnx4fKiCjmLY/XelTc2nNWxTlIB/U64+Gaal1Fm26uylrvGNHEdfqkk41zIZUpRx21cq+xQWrPhfPVqJK7qysGm1Mmaai+ssdnx5VN3HUwY3STx+OcdEUyAhOuhJ0uaec0gq1VyXVVUIVFk/5iyvhzDd1VZoKLopYnCjK8aDxN0UxhmtU18gouvIYQ5UsFdH9Sax2DZO+ONCqhvmL4+RHrkfxAqvKxKXbZjIv/GkHHhKB6XkkKAHVb5S55I7TIPfMN53BGqxOfhm2uvgo/Z6xrSpw6Zo3yprmvaNjZbqyvgH++PjA9LFLPcBmJmsu0x90rhEgp6YB0GODm0cJRsKvppdX1zTCmGfLMBHpU5YLHB9vrI0MapNt8JbWCJ2J1NnJrG19DcMOD/ZG9HcuikrhUs/ncsltZVusHMkZIQkvvCZu9SUW57JNiBPROu0RTEYOwv5CU42AiphYYw//4AJctc/HlxGTiG2EdQh/sUVcSafzOje3/3zH8LtLLVkgE0Y7z14wVPjhTG2NbzLdCSEiXemp6aW5rK3LoL42TVsnBlWOAum0gZO9k62dLaowo1icKytpeiVlol06yysmwzLoSH2pacbi2HWzzSbPsrzqeP5sPoeGSqE51rkYZMDr4DrXXnk8tdL8ogvKJYQJLxUtRR16fK0v0eH4rO3HY+Z7eiYjZ51EG+YgrLpc4V9dhZstndbk86uKZfJ7/ENcnjSv/JFSQ/1G/yRARxkwDJ8Gj08we+UCpszaltTWOm1Ugevrq5pKDpRTwBkNrR5Es6OlPMFtEwpbJwegcXr99a6zFgrKeR1C4Gf+OR4Onv7a/n5tDbi/e2y50tFBWkluDsEt0FUhS+CrZqpPsXmLcx999O7/4n/5P/+TP/kTxemGHPXaROSXDz4noJqd2hUUPT1ACuFYr5thK30UpCDYGEmr2E77yMy3xMKYkV7LuSZCsmHd8i/Dt+JcIMYVhPbdiWjyeGMtuwos+H/2bOqtm+xWhv80LV2ekz6rFt/s4rSlh5keo5Ziq4Rnm6VjWR2hb5uwo+G09yWj4DJzk96zTbW5ykS1K+0bYhkKESVPgr2S23SLS5ZshTIqa4Ph0YGNRhZb0ttGNCS0d3Txq08+39ne+sX62rtvv/l73/vo/lt3meJM3Z2cLiwu2Df15PFDlzGwxzXjayHqMQ1ixpY0KXptd0+1CAWyTH75lZQS1oRWuzPK2NqtBA9XTy8c5RgDjGvpEnFAZmfuvvFGpvtqAJDppZENKsLGvNMxsJb0sccxw3CGUSM50R5JWy3Mv8pyXEiCtYShU4cHeb+VKwzRHZw8dqacFsf/O+C5FgXyaPQyij41ToFcBecxGcXrZLoDDA/t2ZD8jQFk8CTHxYOqIJFOHM/oHx3tEzU9+nX0OsO/C5EBzl0RRh6pR11SXTTTCtCWUg0lnDaWmViSee5Mc5sRssUoqyPXl23VPTUA6iiY6fND7WOr75ayMK/mHcr6it4WRUfZqhwEmK9JmRYrpLi+efPkaFe4jABuE5LXS0dCLglNoP+V06bfKtwGFtghEMps1e7gEdgsqnij0YKG6fDIGaaQrnQQq5Wgf2IXRBnI3eNtYO06VqfS/G/8HQImHqNdlWI/E7HO/dCqJFF2o/TuXbJK7O0NunPjtl0nv/jJT/f39wzDr60v31xf0wywdYUm7qxp8fLEJYv7oTZ1X29BBZq3EhFhziFN+9tKCiVOg2UmIfScHzoCbfp0f2b3xdP0fv3i3EO6mdb8xYlsXx7tX2yuGu84OzpwaEJlU1TcRoAk8fD6b1wUWboimaYuBCdeBLKUA/UV5mvKO9WE2hO4ve2IsbRyZF+bw3/j3mqZ6WEF1APUMUMqdzn4/nYd2hphe16ObwgVJNHX0/u68G8IKfo40eJUpaBx2uM5VFvYceyghjMjrrZulPIZyZ/CoIQIvBnWUn1JsxCOHq/T0CEvkxxDDCGdR6886onkitV5gM2z3tvvMglTFW7N8i3TJlmKkHFyNI8SAl/TVOkZGESzm2927mBn/y/+9M/++q//8onO3sMHWpZ37tywR139mLM4etHwXRZuGH6fP45u4uy9SycgDbMoHZZLdazJoHS89HS7hptFYcYknZ0RRpyiuzThcuqE/ihmumjq2VNctP6LJZ463M+4rqWGnlmIdTmzd3phc+D07GpZTjgoxAicwQEsyZKLuctFXcrzaSc3Uqe0v+HrXNogmZRV0h2cccFMAoZlCiiU8XmsrC7vH+zTbG6JXZpZYp+ELi/OHJ6kwlBWL57vP3267/Sfna1nmxsr/8E//kdpwVo+unhLGkcHexSinczmgqSlC6xyqK9JINUkDQG/ZpzAdJVcZlGUKVk1yqfmmPKFVjNfIFRt0nwCJtyVtvxgJLe2tkIs3aADZmd3y5OOouk0mECCB5al3rI6Wq7LO2KG/hPIThEwh039OvDqm3iaqqaZpqCBxKK0vkncbwLTtFUZpaQkJ1YnGma+mpBXn9rVx1aO8cIzGdJI5NfI+5CEiGWuwgdyVXKmAZVVAim+UqL6WzKrYnELFhwtLALmjBjCKQk6258OxA/lbDyCBl9Y0vqZMgTNBFrCeXKwUwMfM5pQZ4fb7gMlJj2CLRbp7nLPTE8WNFzoAZsqAiyc0EesLTdwMFa690k3SXdXV05nsmWxw0kTgn3t176EoAoq+qTzDgDBALgR+8aq1WCZQPSDbA4Yl0Kc+eskGbRmOqu/XUksL600knAgzYgoBxELMsiTRrkuugxBJ1FJ+ASbYFkyVL4WDNlUlbG6km0NnPOffPazg8M9u3LVPgc2MM+k3jYtq1fUNKl0a2JIyAaoduFXCvbcJBdqRg2RMn59SGeb6xrBSn3X9hFS2aezzr/89BMWMTurz8+eLs5urK8ZLTMmkRN/ai1b94u6u+wpJTRwPCnQcvFUG1fn+cJpgglPX0K+UhD2Jmga11SjHrBzERxN8s4770ECQIkzwIq1h6afPXsiuiZ4meTYb2A4SQ03e/92n40cTv2zg64AJBO75ZKCUYKEzueC6zLGvtAQHZ/y5UYiGF96SiOB6/Dxc2r7fGnaFfD0Ed6cH9zb3Dx68Dcvsv48B6p5ZsNmbQbFfcrvcs0ddbtb24/uvOn8fQtqOhXMzJwTtuBrUiS9ZnyMIGRDXhZXYxj657So3f2hZxeSegcv8JCcvIlB9t25UrcRKwajIkxqyuMiZ1DQGKYuDndO9k+PjKDqas1dbGYO1f7Rw30bIxaWL5fm3VJ8vLwRrHjhp06HaRcO/p06WLza3f7l//lf/W+MURpttsaTnG04DXr/yfrUuYWT81fHBqxNejgmznjUyswaUUCA6m4pJV1AJmzRsZ7Exqdzs5e13EPWZAcPl2ZeWKWWDGVc1AxrLha1E8dxqoJ0AvVr1+2hNeNl2tTio8Xs5oo0WkFYiiy75+yprvs7nT51Ma9jLKGpIxtrDLquLN15697v33/7b37yk6dbuycG1HHhavEsZ1nPrK7f3HBm2c62jfTzl46QZJRTQ9PSdOdJr2hKskg6WXWMFTpPDunBKsap4xP3iWZK19qq5t6TvakXP3uiFfRwd/67nx2+8cadN++oJO8cnk//6V9+8Sd/8o9/8hf/D3XY+ngLx04PI6vGGZaX5ra3HuPbjVs3Ncyev3hux/POzpZzOabt4zpJ/8lu4xyc6fxxubaQeS4HcbGvTjPc3trGECbZnX97y2vYaCboNAeWUVqLpq1cbj+78RZhUBa6AqQ0o9OU39n57WJdTPdSRNCaMNI/u+C4zcONVR24c3N2ZF35Uv+uEs7QqoJjAvAknZFknDJRI8irJ0GlffHDW1CNVXObc80q0amDRPsqF+aXA+ZvPzvQc/D0J6+ZbYsgjfRLhiyiqUo5Jn1+XcwkGqzTLg+kF+IdOcEZHbgwplwpewZylBICMjIZ7dhJp2JonloSUXcGJKV8qF8SglpjzzrXUop6VOW0ATc2bmB+6UTrac7s10GSjbwL50400y7UGsvJ0rPZL2tc6WRlefZkDy8vKBPjG66jdg+PbRcn8hBSsTqNZh1Bfsr96dPnno8fPvSzlwkX3JRtr+tRNETzhAznbJbEdV5OVTc5HXEmf9MjMCMlkBJIrPoxpHktG0z7tyr3tZ1RGqgQYttChHB51as2njrdK4upXtoAA9Mn1vSdrUFp7e7qhlJ+TQmYNsbQUpICm7azs2fyRb81GDwhr3a9C6EjtF7tvtDysBVYE2TdxuGVmRtr676KpRK42OJy1tGEVXlFblfqlNctvp60TNrt5TISfXWlknr6JMUO71dH3YUtZVzGPIg8OaESskhYndG7e5T2Ll5tP3nkK0gyICOaZS2fyMvOLsetZHwubLBD4uTibO80N7JYurrgEKO1RUPslrjqIU3PLVgNYJQkG/3nbxzaDjW9Prs0u3UWOvU1phbdWzG9fHPGfmcyfOvDKOFISPWNGSO1ngQe7z7QyEgNTFtBZacj9I4vTOqpl+kS0L8Gd1LoeZ5fLKG/wJIL/IgwRHNnzL5STvpASD0uhpsFl1j9Ib7XnE8D2OTH3xguVrsWAqX+wx/+8Msvv9za2kqeiumhbtyuUWP7dSCGp+j9agIQM0AOhL0e0mAwp/C56sUBQ1uvORTG74FOLhYMZmsFTrPUIA09c6ROUM7qPhPDV1b1q5Y4WKIINAcHGI8kL9OsbK0FVjGcHR/MTLijGtKUj+hW14hOciKNXv+hgTfibqD3auqYZp+as3TLZfV1Vo4RJbIbnZnZK/UZL9K3ihpMW9T6lfQEr2aPzICiovJHZ4CXbNIrl/neiiVbBC3N6LRGwwz0tINN9XLKwbesS7l7d+ZnP8OQ5ZWF0+oHnx4e2X6hyA5dqzAzs7q8Yo+8xRzMiBSKddIjbDr0kk0hjvCmgEZe4eKqsQgt+lJ2pEDmfvazn1EK5iPu3rr90Ucf2c5roZZDc5brFA5tY7bTXl4D4yqFfSJeqa3DA+ci5WwNHVCoLI7bPz9EpKp76YCdJOzBpclvRfrJSZpxaPOKEpCXc2nppy5lRE29xCkqNARTAQw5+5FWfOQzVXRuf+vqxDiAdrOhNYUxpc0nXTfrIc+ghm52kM9BmI6dWKPMv/oHTQLy7MQURBVW+FMUllobMXbMv1dRTLw1tomAYOtXHg6A5yTAv6O/sL5MArbGP6QiRW5IRYl0iCdmcv21w4EJEVeJ4AAP20Dn2sKn7yKE68a6grqWDcCS0T4iG+m+OE7HYdNLqXHKWWdOEmCIjSSUiLTM9bq5mjQ8ePTok88+I9haYzH+dEF1m8E3SQPNPgrs8MksgOuMJEu1rEQWgEmow1EOVUUdRffqEzq9n89nKgqFwHga2PoNABUlvJqMzsg3WvSUecoeMNJbiYelmzc2+SFnSzzFBemJHkaF1tWI8YnRItucmuFrpzI8ZaFj+dROKu2hspu28ZfkZfgqsP1FVUpfpoYQnsEvvDGI3gSjlufWrTeQpxw1QDmF7okekwVoVus5hYVjTSGlyL+0uqeLnzVDRIi5NKN5doApUjEAZ9ZPxVZj4V/bvNEpYpJX/szupQmVEce52cXVlWWlgE5fpXt59m2E2ZXGIptJPK7roZxZYKla6XF6NWKmPDEiOep7juPN/+Ja5lDAEVNczFRdDAGXQd0RdxJz7JA05naC6jV4Bk8DDq+D5yvDSziQkiEXjMBNPRW/FlBxxapeDZmLnTk4okAr56mQo2ZdZSePdsCKd5HsxtDh8jSE8Pcnz2ZoxzJiWpESKIRTfmJB6CEw1GT8EBP8tG8IaBQz3D76pFDVaMcroYJlDVRmTxGvlThjC4Rjiw0Zc+lzZjwkW09NXUk4LTet9Fgfqz99osHrFMl0X08upp0BZOPe5cyiLblHWlqpO1kamt57xtSS4vKZDrnjYjPhoaGQAyHc7RerliYlvT3qvqTPlWSyurJyRwTyiRnXoZLd0Ju2QElJuq0Bw3xzsTc2NnVVZV/nAgIlhw9v3HnbUfhbz54jR/XXgDDZarBBKs3TlpE26S2NI0EaVd7wsPpDQKp0YmQyqpFkr6a++OIzqD5fXjEpZWnMi61n2hx/+L23dvdzsqwTyRbqFEN8VuP8cl4gxmYiKsfvqSecOtOeFKtKlP2jaW60+XS/mtls5QieQuYzdy5Rd0OlQVrkKv/ixGWNhJlBz/b0SEJVSKf/MfYHR1rHxpxt77dDwtli+ita30QieSxO1iBYJPQVB4+vndDwoV5TWDzUSudC6xp5DZm20oiVQ6SXHgj7ZfA0qn52xGvRh9fB8xLdN/OFieUGDO0RjgyuRQ5Ih2P4AC+ktScPJxywKDyKrwM9+TOenJ3fuTGwYWiwVmsDGKnjN37tSLSGXHfRgo5yzqWM0ZUW5LB5Ig97oeVhktVlBomHTu8UPQeawfdrUyiK1yGDjZPl5OHAeEqFh5PugGpAEoC6rLNs7siSQWiHOeM/wJcEjhCmoVxkIDUANRhfIbGpnLRSEYqBULGywGSZ/KdHS+5rWeujR4/AyLu4GpRtwxL3NCxSOiC5kF495sEPGyewn1QSDF49B0+/enIFPq7w0e0jf4ePkBR/Bniezh2ETaFXWehCEYhC+WrDrA1BP4gis9zNjXVMWDxY3lveM+hlvixq+mrGcYsxdSzq/IIJLL/o8ykzX0/QAHknIRlIWtUXA8JJk1zJBoOE3/OuC5paWp3auHU3pRcFYxLu/OhgPwMv5qjcCHNoA7mWnlXWlpUlW6YJue4T95oe6WBZkEcd0abpPctaCpUTp7nJM/k6+AdPgw2vg+frwqPzpYcbxTHnWmiNASYf6OD421H9ACGkQfFaIBOQ3JTzDafaP47xihwUJQnhErccz9Bg5ofEJXy+8GcrUUIs9DfLa+WOMRdDkSb3dGrIORRBZRbWkxihJfTFkWNHMEgrDRuJNZWdoo4rI8mpmMXzAiMuNRYxHokZS7OWu6W7jLPPaRz5xR7awHJqyqmya9WX0qzBDrPEAOQg54HQ+VRK939PsuIyuQZWzEaptyAEGokqUweNBVOkiw6TBemGdBxK2WgckBjqyXwn8sJJ312zzWOcykTVH//RH64uL//ob3744sljp7qSQnO0FpZJqdOLR6rFlZclF4nKr13IellLvSUAnVoafFbg7JwdfPb5J0+ePvr8i09/8YtfnP/P/hmOUyVuY5qZHc0LGnIzLJBqdnK+ruGrOazNxJqmXws9ctKu52LfjRLJ/5RmluKLXLHBDiCwkitC2IahdF6YyKWuZN+C8VcnsHSxRkG4fmPm0lrspdWLVVNpp1b57O8dOOUjRuLxs+fO3UW/IbIa7CcEaWgle686CDvw9U8AW8jTJqny9xrir+N4iXFAcs3TZPdzgB5ghPg0hP8OnqEyXovbBLc89KemIcUw4YANb01VlVFidDjlyy5WO8lCvJRCAwDmHxLlV+CqNI2tTewkB5qVKEe3udry7LSuHbyiHJ0BrZisFYKWuiRQjPrtu3ccRA/GqRq+ugPBOGzjD+ZxAwiyyEONi9LdUu/sAEgbuxzy2nkDgHLwgxsINurF/jEZVmuDh011FyX6J2WuAuEMWU5+SxmU3ixKKCeQlYoDvLJkH1WV+5A6sigZZE4b1FdOD9JYQlsvIzRGFOSa2CPMV2zhaZyiIKaz7OkVzQP97RGioeDZeexnv6IKDD/Xnn4y7l8T/hK+Y0kUQvibHoFCOlAIdnW+EMYzpOIwS/jV65PDq9M5w912OaWfRMuxurSEZX1EhP6W95ydUgqi9JubWHEwbX8OTzwbLRo6aZj3Q37Wa3Yupp0LFHBnnt/CBrnWRy6XoReEPf3yS88YZhOJdqSQYSOFF3V+ODxxsTLZ/wSnlkf+jBk3UDAZ0h99GgJRObwOHoFfGR7NXkqkmmJhehez7vxkcvyo7FLkkRkhKBN3wMwjuvASwbIwY1FomAGyMQ+BTXmY5SZtaEudRaWWfux2Ij7KVbhRQ001OttyFpq7JeAECw0cI8mUSclAxhZ8klySSEybVdOwkZYaxCagIZv7qsPXqjjYFECcZxYax+51ohCNhWBmAU75jXKpehkr5XfoCKa0ZzQC5IaKT0chf30NN8rcqpRlcb06PqD9lHj1pxVHLLvLSLyq/JZkSEUuYnajOMy36fbNHh64Q9LNcfrr6Wi/ff+e/UJEx3WDe/Ozx3hx7vitQ1evFtF0Rdo6ciXL/tMevDgtpPhd4akqFHECOdwuk53viOz2txcrWJ1e9fz51oMHD/Z2n+sNv/feO3U7232dc7TvbO87SprHCSfUio6/TDkqVoaUJuEp9AhIQTjtH4XSVbZEQH8ga7qrExzSaq5L2TF0SiT8twQ1IwSj7gIPbLHxabDMWCEt9tzS6tzS8qJh501TlYeUu+OErL8LS0tEteDCj+JG+APthBvBvBLyCjDo/F6LOBHjFe+Af/C88vm1lybsteDfImDA8JUp+sr51B4FzaDCLiQlMu7oCMHYDgfJ0/CeLIen0kz/ZjHmamQtAMVF2BnEDE1FgRlhtq8upUb37aunJw7EOSPDC7fv6jCZ+RJOMDjl6AAs6wwcHv69733vg29/y6uIGq86TI19eA65Iz8Cox/GLvKA4HHJ+tSxOoPtF70xtMdTKoZaM/Vf8p9nmhARYOInrnzRDxUiLZU9Xws4qfOntlMc2YplyGU0go1LQW5fe45ePTRg69k0C6frVCIwbeaZXnhE9wnTPaVbSSeJ9gsEM+kC3LJdodcAfBI8PNtTgOr4gCYw/WoMYAitQPgChzkDZh7E5EN54CQtmi+TpXB6uAcmPMkEmkqHJ+hQkSliy3zPjlx0uKUbeGkcOU2flSyoVHYjZ7U5plo9k6vKo4VVPN2IsKboWVjd4Mmaz+jYoFZiikuK8XFzZjymF6o7C+D+vXd9ipzV+s02zhL/5JNPkpd0LUfO/kwhNaxRnOj0BqZ4DTVVPIPn2us3CXdnZSmvVrVBUOzKAJ3MeIeEq+SigrNapSTJJzyKiSqH1oqSFwDJTDHIs11wlRu9jokfB4/QSit49CKt6zU1q2W0tm78Cscoz2YwGLEkUeV5bN+4OYW8VLq4XwR7j5aMU2G8pbqwLeO+Fxse0xpcgnOUi/QCMZNzOIhEe6YWDSO7slD0pFj5qqSSD9W1HfKybd01bjl7rSmpE1/GHVwTYJy0VClJJlUpZauAjNSnLN/I0uq8ZMzHCG3YbmmKComXWRR0mTNmlRcLrJdwcjQ1b0mBm0pf7G5urP/i5z9hdZ8/fWRNkbWKxnDdyn5hZ1IVBJzNW+zlUsmLjVo1PjVjPS3xTPLhVv6IwjFXOo7wRJ+m05pPyLZQ78/+6kebm5//5Be/evvevT/6oz9yyRJdrBsjcyura2SZ9dXMtNTRcmvTvPMrMa7hdFHFjHYSOqyoUjeMQlv4Jk0cVVMWVlr2SBRAsTQRNMPTTPSuOMzUh07J6De7sxmNWW8dCrMUd22RGYZ2ffP2862D5bUVm0mNCJInZr45HNCxS2ar+JA3duNv9VdBCwcUro0hO1YFXH8AHoIGf3uG1wGgPb8G2zXI3+p1MlFJ4LboPO36FYxXT68Nj3XA+DmfhHsKIT+shUAMEejZjpVSGC3gZIQNjvhUY5bWLbSkmHgZZNTNmH/uGq+t7YPDIz2/zRs3tQPY3bfeessxWBbuWWogxKQM5IBJftPTJI0qY1VJ4UVjjFxT6BXYAM8/0O8T4ieR8LezapKNOK6JD/CMoqFiT3nWeG5jnCWk7UhsHZVXeRxxBodIh3YnkVbJpIVXhmE4Rvdo/wBJKIdAw4XJidUZL2VSX2qktOdHgrAWY4Ry5EElLlZorOCkkKaiPf1Ua3hed41hCIfqWohPk4G+em349jQ8CvsVAeogNgrnBHLyhUKOv+Ou2T+c7YLGhTs8Otxcm4ldI/s5Hc2NW8dHW09Pd7ZfQKjHQ/XhiXJftS8512otS8UyzBIqVV6ZZp2MtCRx6SSvMeVIyo7MmqoHEWVWKro6RCMZPjvcBQ/QWQuLBgwjz9lLevuNt2XNVcnHB9l9rqRoLaz+dYuwYJfzIXmedr9VOKOfwy/SztP1Dk8hQUqzlD/8MtZSh7ViobNT4Od8SoaTgXAG8zv1RBm79g/hg8f3prxDhlxIBQMzwug+hSz5jZ69cXPDRjFtZNU/Q8+1yyfNYetxYgFjEeAhj+NCR06ks8KyvKm+JxdGvezwlUoI0DqX8JUl2bEGRprJyVl6yPMxd7WeziK9+bPVQJUSEWtCkuCJzeakpPmhWmmdZMVWulb5NT086XhFLwmUpXCs+BZWL2ZQKxhqFl5wdVhLAwpLm7HyBaTBVNrHjx9rAaytLOtL5BbXVEWrxY5+9cufYhmimGdHuk5trljGv5vFB8ly4eGN85qCHpneSt3nclWvm/iQFYJZ3SnbGLIWPV8zI0VikilCbuOgW1uePtv7/IsH7u14+vTZO2/ft5gAsntvvCmy+qDzospcLRlVc/aCVbU0mEFclGWdIXYQPqOLdJaFF0aEXPnO3vsKfm65ug74q9ayCtn/pxRY35G8Ybu0lIuvHJnJ1cSSOHdTEMKhtw3j6tatOzrliOlskiIzEVkhWeLagcNTpgf/4OlAC62FaBf2GEwzNkX7mxy2N0h7hleBMA+vPK+nPnz9TYm88n2I1Z7JpyTwqpOeTI6/3YCoGn8vKYdElsGwhZ5tDHjAD8mN4yqXtJmTjDWty8unV4e8OitXTnPUK6GDZ2f2dvaWVpbvvGG/tztP71rSxcPuwt/dayVLq5s7jJUq7dT4Jcc1MTwDDeABeG3XADLbr0NcHp+49vg6xJIpzmvHGgC8ctKSBHg1SGOYp8nw5KCBk0fzwpOlLD2eBRA4yd1Y3xCRGyLC1k4GpdVO3MaT2ciyZ+Cl7gnA18E/jpG/wrnJkIYXMngazGtDomQSfojeqffrEAiSKvZJdjy9NuUA0OMVWp7BeXWkWaJbIWuptaMKQmNscLQKTWjljTacYWgdqIMgnFtZMqh8uD+1HSzZ6ekJz61btx1S3YtLyIbp5NVa6vXcNocMc+CXtqBr72xQqdrYG5qzJ83KvfkcdVQD+DMXSyjnUJ5ijnRhdXosi7MueVpdX799p3gIZeiRtj+Trj6MAvlf+x7YCr8e8SvDjSHBUG2KNLvwSFyvVFVjRgCuFrczPh/2lYOtPuXBJywJj93k69f5x7Cjv4VVyzRC1tsJeeaW5vWorKwzTjUamwaeGeAscWKHyhIgvK1JbcHoTqeJ4rQtUqgZG2ZdIzQpK0FyNOf2csdHmafMwREGSRcdQ6XJa2WVzTiIAeaI1WWbl5RP9QhlUe22IxG84syEdSr+SOawG/POL/aTHw07O/5snEkDJqJEDNTZfMErDBv1oORhZIADVEKcpJ1r79ygmmeyWFKixeMkpGx+/tOfaiG9/dabVmO5X53+2rhxK43ro9zoR0tpRGvLufdNT/l4v8/EQLl8ZAQpFioneVXLMbr0ZYEiTx337CLLU2VJe2LaJJ3oImml+V5sT2c0e3mwx3Gep2e/+NXHGgdvOBH01s0vPv3sH/z9PzGw1tfOaICQKGTDSfQlkUrQLaFiID8ANUIz1McSAxWDuq4evDSzMwevwr2RbZ5VXrXUXN9K/Bj1ywNbspaMH5CiGrP3KQPb0yd2P9VKXHlR7C3nDrrtnKLnGzqSlNZqaE7/W3R+7hviGcA6okQnQyZpgHPy6+Snb+K/hrZfPdEMc0RtrM0bmw5Hf22V6gmSI+fCBzckDYCBLC2R7Ouy+KS/4uwIwhBdUGJTuQiLoHKvkT1IRl+nV5cs3XA50tbungUCLO67777rTDrWNxOBJZlqnwGQqqrTOkQSCgYH8/U4ZNWjJgZt7RkoF9IZ9CRR7W/9W/QEnGn07HzxCG/X2EQBz/lEaBlOBHCCAcApHJ2iQIEwgfF3voPi4mA3Y8011LwvAH9kzbPa4aphxjo9xWJ3oQIsOZAchE0Sv/W9HQKyKAoMSjpRYIMTzs9iDSGveEZMGoWNeWZRcMTsdYeZUhyS5qkM9miWxEdSJBAxnTQ/vnUsWevisIgjTewSZmqj1G9qcRrfPQsGl8ngrKjKDLethT5H4YbE1OJMGztD/sVzljhnUmuLOVI8SwRW8XNx84bmHBqw0R8wNGvsuupJ1YTRpy5xksGWpRxSXBpZgwA2J+5JVAhAhCjBkF1dZq9oSN645CAdkQiKJ5iOxn/NAb4W0q8dcfg0gJWQJ3iQtsGfhDvpCf0iabm1cEBlIzTsDA1rt4AtcV0S6G7aSNiAKoElXpXJrA8sha4TMsoRSGlBbjH5+uoaxpjOzGBDOS1jM3mfffyJqvDu2/f3draOTk7v3r61t71jEiYjDdHnhiMc65gayp66r9gwV8aK7XD0Ht5KZE6fLW6qttNldaKpUk89XmvnrtSaTCdwYzZaPCBacy/YyqkLogpU3srGLx2zvE+vVBcr/joQIHXG/qWxCFYDIChCrgZFqAu7AlWNa3LAyXQ97cqI8IHVOMZeLLW4N9w4PLQQWptQlm2tE39lyddVmoLpdbsrzcEvotYLbPw0jtWYsiXzhFqIFLluxTcM6+IyxHbJQnKWN3SOu478zuhOIB4JNFRiTNyreD7tOt1jf9+EioN8f/zjH3/no299/3vfMaJ4Y30dOjrI5TZlry02zGQbYNpH1wcH0IAtJvu3tp+natnQnY1JhqZHNRypwKQlN2qcJi8i1CYVQgdDXEnMzGWOubWHy8/gVL8wmLrzsdtDDAcDbOxa6/NwPwqOgzYZLo9n++t1pKFCm8Lq5nY+JBwDPb+6yvlQrAtVY8UESbuOCMDX4dnwTcNk+ECMuPztBuQNfy3WiLDKTuMXiwdD2jPCUn+EMxWicDhM0jiBHPhI5Eg9RRI6nAZEjHAIBGrzCeehCD1HLDOAajOBzX65AWD27Mhs/MnB4fHq0qK1R9blLK+t/t4f/gkFwgYbcKZSQHZd6FSaVPikLySDk3X8rSSk3imieQBu+kPA2Fmf3WACUevJNc3CO66QxsCDYMMbXe86oleNUEM4fuBxhqaDgTQ4XM0RlaoCahSkwlFDbSHRzqAbCbYtNndu3hJFpsyjcX2MZTyjc9kyPAuYI/NSVE+FYC8/GJBZKFRHAktdFnpoFKOSk7Fr+j0RNjwnPQ2AaWK0v59arWNWjf52eOmKhgx7S34DMHCs8XT6ADo7HQiMR1oykk5JKfZ8qnZRKcawUSzUQGj/DcjQZlQMExV01maPi7Wql2/MKa3r1lm9IQeTNJ3zK0ssdFhtn9PahpFrpjmXZFuxq6By7JLxsEhmBCYXw0ffSsQ0e/XOIgNcZ54Hw00OeG38o2rgBYohtL9NPr/u69eFT8b9Sr+IXxmOBjkhJa3Bdex6dhNwZSRGpR1ImRm/BZsQjie8KNev/WwAUki21Kka4HV3goxHZRj60EA2hZIz1WrHFCR2lDlFKtH9VBNVhdmuCmOAQ19WqaZhk3W2SgAN50c2x464HVWuTHxil9TwNMsCFecTMCQvTO2HMP+qa1gpRW6UsxwTlnEWREjWljIrO8pppDUuyz6T5ba4EgmvmjNT53OLUtfUz2RmSPGAHmGZn0YDa6IlI39kFGGkQ9MkPbzcv6eRIcfafdiu75uFwMTTGL41S+Ghi4m0PTQFSH0ukrBYtAi5uHIQRlJKgplPMiWiw5yMX4y2XXV+8iVm2EO2ZCxtpknX2QecahJY3sDv5pLBA0uffvKTn9y5c+u9d9/9vd/7vQ8//MAqLYQbyHUEmLK+ecMiqeMHX35pkEO5i5v2KwZYUlHWDokGRNh3tMXiZj2XSqsHkMZEFWCKs+py0tVlyxi0wkB5hBFMCjoNooxKGbQIeysftTRuMjN/l/4mpVMYM+1vM71rOCXXKfZzSKkKdCSYonjtT5jvlfMasRg7qk0gsAHP+Es0WsN3iIg8mfwpfee1VljUosmrKXsbN9ZWPnjfhQK3j472NKzevHv32x99eOvtdxU9G2wCopVy46Q6mrB+CmxH0V8LES4EMUPWmn5P4b4B8AlyT5WI7uIaplGF7ELi1ZZjMNVBzWhNxxURPYyfFqETqPCK6DqTVaD63iuZGV3wEoEEfLUpR71DVoO6tOUq+JcypSUJDg38YnE8k2SDFCjEIJa0AMs4GA0djnkW0jCenE/DswJGgR0+hCAM2n4dfRpP5QiUnGeoGcMMkIDb9afhGdDXgKUioidXI/Em+Eqv0UhIrXz5BAbRqCkNgwO8kCVf+TJC649/RjhF9Smj1pGs+ONOciTn1T6lNzv7TP9YjyVqeSb6xE3Jy6t6yqyzIrMLWaKz88sYLWLolmEO6yzLz1Rl2NhuBJDz98sBHktIYg7+hhNn0jMR5SvC++s3eUpoAOMfXtFMaXKLSzkmRg+ywcLu8dgIGIGjZo66UdEBNKn9lZ+O7ECE8kSgtVrmZrobikEaUALhwdA33nrji8+OCTrMRlezwAgf2TZ/WPqaHmZFqVVnTB24C02Tydy/tVE2w7JwRFYLKA2sOK3VEoBo+7z6U1/wEmHQNmGL8+QebSl0YSDzNVObSdb/CEgwyGLNG9WcV4N5Qpqv6S6NDoCTd4Xd1c8nAy4VkqaCV8jlV0KmLfjDz4vj86szmcww+LDNH0BGm9mibIaL2JmQm3PM07TJjjPr/eeohszaXjh35vLKeILf2YK1Vxn7ygoImc0veWfzvMLgPbmqTIUPwqv7iBXMYX3kK4hUnLhEqr/1IQSGFVMWYOdwS6sZzB18/Oknv/z4k08/+8zS1n/4D/4+3UTb7u+n1a9YNRtSipVZCXHNHGyJP7vwM56veLEopZD+h7Fv3GoaQrpqZDV1smDkQ7lkCjl11VV1qfFeJrpW8MOc9vA4F52Hv7tnl2zjR+SY8r+FBL8SlcCuYsPXTtSTdHnK/vAcqBLSfrEGB1JgP/trl85kj6eBfeVpGM8xNgIVU0oDish4WJG+uLSiHfbBh++/e/+t22+/x5x03xcYmEYi+kBDY2sa5MsnTmC7Ad6r8H7t7HtKHk6fhHfjvmPpPzSkZ7uGsdakFQ6Cm1cNb1Ocad256YxYSoVBdBATzC+ebkukk3BQpPmyzqkRO5CcJi2lJAkwINVOgfz92sg94fRVhfXsXDTaw7MskqVsEayyWB+Eh1LxOhm3MSRiuhlx/B71G8pC8Mh2+FoAtN+oQYNCKQ7P/tp4CjYyk/jjIuYZ/MK5AQwkfwPQZUFb+xZKyTZ5ge0oMAIYoug0BFdSKch4FN9Yq8YM+4WSfi7WbtnS3ee1dojuj7V5+nCL8mR62V22l58zDLFy+80o2PFarSprelRvDdrGya4U7cWNl8Pr9TlfhrwJ6Wx0oNfEK7jXwzv6Vz1fivLk18IQZIURjDyP0LcAqYELFgiX0HRyMsP5KpATKM8aFqJ59VQuvhbO5ILzysVXlZy/IcEIVe/Dtsts0jIUacpz6/nzF8+eSYV2JcEMD9vKLAZ7Ok4zhvvVOdrZjmsdJqWp9pli6EaC4svaq3aJMxJHybFPnrGxaQfEe0K+AAEAAElEQVSUFQxpM6sLIwOM8LH9pdwZqgyKQJC2QiYfpRqZs2MZmvSmI+tcnkjsfFm1hW+qmcEwhIl0PG0INEyWXZaFtISFc3NuALOdRk5lc3omW+VCVilQ3LAwE2Oy1im5CX8IzXnOF8n6IM3wXOdZR1+gPnXd4Zbzqn0d6umC7cvMxoHE5MplF0ryA61nspO6O1JnQrKgqeRegnxdsf1ptskx2rwWE0XMij6Kw65cqHQcnjx77niXv/nJT//iz//8Bz/4wR//8R8bdXR2VulbO/UuHJvBEtPFYVe1USqz8yfnfdJZV49wLGmneo+afXkN8xRKhZ/X3J6hYnxMs9lToyAjXZHO6lfJTvIASZD9e3KdZieGmMnXf0cKmvOTSAbkxahIoK+e7QbIAUzIqMRfgyQ6Dd9xReEZMEx+4vfJKsDAxAx40zqs2j09ZfbBDVpGRO7dvXvvvbe+/a337t9/i9yur6x65kLKGq9QSo0nqMb2oENUTYF0ikenKyGuwcitwA7x5MfkIKnqptKxYTq+LH3EcnGRGeu4JWxVRSqnRtqSSiUBtw8AaBI2Lx4j1OmMWixrD2BOF5+5mLV8J1mYy9VtkEvrxLGsJyeELSe7UhwUU5QHoR3xuVDlIaGmFsEwtwEWcaDNIi4YUC4txEtUzVV3Bg605+VrGeDKwcvHANNpde4EBqKOVwqAUguF3S5N8Y1j8YBTmQLulEdI4ik3+JtmYajlByB3SU6MsCA4/JqARB1jgMj3hq/wV/YxV0kmuOyuWK1gKFwhkUwNiEEnRDumYyF85ozJzwx67lkG0HRKxSHXRCVD0/pwNfuuoyyAfFobgnIusmRiGS66NEmXa9KHnA+BnZFOYARagtiQ18IHgN/o6eRej46t6MYyHkhKHNPzENJOFK6/Csd1/pZj5QBGGABxxzES0sUg85SitxyhzKDmoEcqE3zazlb0WB5pXaEBnVgwh3XMzenoSsDMLsFj9lg4AqJ8shpRry2sJznVCs80f64M8sY1xVHPcTE8ISlriuNqvCoeJ7ckD36JRPIcrVFeaZBJMyjVvpVQ7HAMc+ZmxlkeNabEPD7KUH2BOf2qPNAJkT09NSfNGllmfdNeS9Pb6jP7fXFsdv7IiCpBMIYKcxYC1u4FFPrRWsSOMNEIGeyzQmjRUSDztk3JUdg+m3kptdcIVl7d++boGR6jwDlkHhvxqcoFlZG4kQGWBc1pYYBTTcdLNJVWATYXGd7AVKn5i6uj8IXZTAw7+DmFI9wEtuGHo+OTTz5//mzrb/76Rx988MF3vvOdD7/1vg7x4uLC1tZOGePUbaWnMDFfvo4soaitR2OJiuQoYV+DtlzST3Bep87r6uiMWVlPp4iMVBCC9OBFwVhPiiHRGYsRsYn7d+pa4ENeE1k19G8xxcbcCCfTGpIrPuXhaz99ashJMiZDgPk0ZnvicgNAv3p29C4OT7VNSAWnsgAo25M5QtL9ztvvff8Pft8VW2+/bafRm0g4S0eOMvEMYcC5/iMtrgkonEnryqKeMeUNMHwCSVY5IZAoa3iQ0oWuCtAhBN7oC0updnR0sQAID/FXVzdWN/gBe8p9TGvaxCHLlMqhq3kd+XSeKwtJkiuxN9duiS7R7qRWopEu+DMVVgzsXPRkOnXf6U4+gXGQDJnl4cAUwqTeWrdzwR579VWsAU/h0AIelUh/QifXnyafEHY4BgsvqIB1OJxSn4TvtAI5ruCTX/nR2TADKoFC9B5CZYawSllVtIxnjfE3dQEs35gqUctF4jqbnYvRazROOaWLc40tzyjhOAvbQkmGzUZKGHqZdqGakr48PUy/KhnPKUuZzyvjS7NmC1SWRGc5gsBICTz17BS72iSNgdbBAwJdw+vgGcX8yj/jzv21j+JKAjI/f/Nj1GoWUyH5JLfxlNyIy+8pMJQVyxqh8NSJchknLtefeH3lvCaZqioEl+HBFqn17LKoaq9lXwzwrVs3WZPnT5/tbG83AdLLyqZYay0f/VF/Mjyp0RqaEea/Z9JSVGZNLT5KnWS92G9Viz8klCCy//LttX4ymAHPotbuIkKjSezJ0quxLLDuHWvKjGUdn4os40e171btRTbrZpNqsc4Sryqayim0uYaijLrDnHEADZw2V+pt9EZTEJGpBmeUU/q41mKUSKDALyuEUcBgaUfUKE3aE6LIxvz0fI3jW7qvuqb5b1dwXHXBz6YsVNaqPtjPWTBw4b+kkeCk5LALhTr0dglNOOHjOpKCq1fmt4aMRp8ApAfTrjiQITWirNMhV8rxxvK6JvzPn3/8+Sef//iHP3z//ff/8A//8Nvf/pBaPF88nVld04sQQdwWj9kZa8qyYkvTW6KNBGPDqJRpBI+nqzlWz154JT2KSME78RnfzQrnjFU2GGDkLBGZ4VjiMbF/h3+7Okh08PxdJAY/tJ3ENfzJ8Ngpjv4KUljDe2rreLYbYAAQy0lsjaZLv8OF8Lx89jiEcilPwUtIH+4YKtcq/L2/9/du3d5g1Mi6jqSBkkRXDkRa4Si9/M2+tVG6RVOTxNv7+OOpROFvAYhoj51PxEM4R/CkC14tMH5LAj3bgBVto0fDN5hYusicegGlumz9oli9k4hiAdxO1k0AB7KO4iobyRSFk5G3YnUlgJ4oH5h9GpxM+drZ7MDOlKd0fQWvfweV0pGLjs7PQgAYIvKI3q8UW3uGrz5JpQEGsCFp6Q80tKeBQTZwewS2a/vbn4YkeHwd0p2MWJsEE1WhpqYWmV61xjtW6+emUcgkhxBZqUcFlqnpbHZO8xROV9ClGeFKsycuglQMzGd6LHpXEw+FUWu3NnP5DbmGnKuZX2tGzvf3dhiJ4xqbdiNKowIXbsLDx/N1/kmASZhr4YXg9cdLxr36bUhxYK5SDDEcOSYBRCFNm/1DEZPzMsDCATQqHnOVnjkUeBye7Cc7yZRY5U+V6zxHztJaUi4BiCvWPnz4UPd3dc3s0S0rpSWgEJlmYl1AOolZqtQTAFWZSwcoIXxPzZ53uBS+r9Y4Q+qPa3fS10y7LACZgu3zG6RrhDvLO7Mo/XIZ/aqSn0V4jjC9cBuJbUJGuNP3Ns/szBsVpmEu92YMbeFSWg9jNsiJGpicajZXRfJIdRKQjbzpisXpA+OiZw20pG8raXHTSmslcm4gOdqESwsDyWli+loNU7mHwMaG7GmeYYNJ2MLVpTW/fmeLqbSyUNNJLh+dP9VKnJ1z/IzSNERfSLNmVbIteKatI6M5HjIDHj6kWENpuRBIsj1bVFCaRWFd9ODpoxy/nsqTJkY6Fudnz3aO7a2/e/eGHO3tHvzVX/6N9dKG1P7hP8zc8Pvvv6eZtbwyWoUornKyIkbcpKKQX5IU9oSPNcpX/fCknK2GZXRRwtSogFFUtNhRVpAmj+XS4ihhbmr/PTwlO2bUUKH+1pKt8uhSSDnC28/XExhoGGCKH688Olbj7A+TsfrrEGF4BSOwZZaUdPjwJOX8BvqsA3Bc/+HRLml02MHGcqxXS5fCHOB95e90feVagWh/8nfqACIAVZV8Lah8Et7AnowVJKp7Q2puEgOQrNqgcIZEeQzzggegUhweZR/R3t6OWJub6+CtzF9YyGIxJAOASkubGmxUYjHtEmWVM/VbBli6nbjqwG8VrlR8AsahVmDTLByezlo/fRIivFeoOWYZ5s6L1BvGV55mlKcoQtoJH3vzt+GHwI4ihQGmP00CdJSO65koVdk7/Bp84xlo4EF8dlCKMm6NVVOrTGIt+4Au21jiMrToj6zQaTBjUSImhEqL7RQopJPOiGdep3YODnmAUYzNmR4DoAcrmWDOWiD6kqaisR0LHKZWrvUngznrtjbdNyyG3apSPnN1Dh2fJK43Z5omWNs1EeO3l38r/HodePn5G/texxMWGHcvA5wWXzUWmlmA20HfnBKuIeqVh/O16fcs3lbhtK8+CWVXQCK9eoyRSKkQ8cePn7157/HbC29rb7Yo428kuJYKw5sf9YzvzqJ0DNPKWjb9V/8sdm/aoiYGeGazBpmrqGLuEo+K1rHNkVRKUg83iadHXUJzeMz2sMN1N5LjLtjgzCjbxAJCNbK92Nla2cjWBvegGqZQICKt8FT8NMb1RCvTKfyQE+lM4TqCopiWaIxgLCkj7XtoizUrSB3xjIKp0ViRxrkO5cyFYym6t8COMqElVSVRzmttO22l2YV97an5xf/0Qd2dfHJiyDrNapykuIy3GZ2zQYiimVsNl9pJHodRIa6cVzVo8W6qkiH86+L2DN8i9ynxkF2neknRcJ6sWjc2t7C4lsbQlXE8OIFhkXQPDz/RwHKepbO0fv/3f8+2JTa4SxnlpTFTvK1cQkwdCZL8MrxG280GdI2KClMOmlQpR0PXqb0WwdS2q8IjduQwxJZKas+/zyfO/HtIrtkroWvJNaM6cOADMMU0UAUGqz05FqXB+lXEEteIBCfQ6xCYTxEQGXyZSV85grS0PL9p1fuNGyZJa/1dGtMney/xK7xUnIpqdEos+FEFawxdjaVFT9PQ5drTpDZYJRUKmzyBzKy4YFT5scAnMkvmK3jAHVcUfuFqgWdE9CrGVaNB01CrMdGctDZ2aIPz4NzuvgSqQVIxfqN7CqHRaoFwSt1fUftVG7k9A4WduriI4e9XXzv7ffCF+g6D1sDTp0/t2OYB38D97Ix7drgkGg9P+yeBvy6kwyUNuEu2kQzPpBL1mBJpN3zyys8hgL8Lzmsstny1RFTPRCCGKOY2kGlWd/PZuB0ksdip0cEVjTzKSJC0Ayyw1I4AR7qACTPJWDW60+l1ckupXbHCVnGDKYI6fXwimdZaWTYb9AretgkJR+/KOZ1tCUv35VxUu5p0k3zlkFIsPyRFd3Wt2YIExkGfZMiuf9UdGbcEUJhP+pfF3FET7JYugz5OnbciuqMQSZX6B9Jh2RE5+izzOHS9/vrF9Nx7dpDYlfrgy6cfffid9YW59Tk7s87nT/fdXGnM0rpbXbnLuVUEulzrjXu5RN0ATg7dXrxwTqgWafa41divtRFW9hMv3NGeXJLQ6UndODY7v7hi7/D+ydmesavFOxezaz/+2eNbdz6cX3HvytLp0dTGytppXWSkVbPmfkAHCZwfLFwduIxWb9OSI2OQOe0pzLBAzn1HludoEMiC2p0ZV0Kgn2qQp85vpqhdcT+rsWTishiScr24WscEtai4MRf7y2kktbwpBCoiPdRwVUorliVXbe/XzNKWq02EYKpdz1CV82U2BQ9JwOgcfdIsSckVpKca3dhycrhzMMvCn+h4m7qamdqzeXVpIXek4KJYpN2Rqoao0qkvhUhUqhbQU64DPncvhWKkwaykPLk8tzjaNUkzG0tHTtJyTPKZXu/F3IJrReYpYRuhrS/RcnFKHlOfFqxDya4c6rjITlPHx+aR5wweLChAmRhlhfxx1XxHlRphiJHWJDb2IeRW2cueNpsyhl4A2BeJzWVNZvUup1bm5/7y57/46edffPCjHxuUNj3sZAYN/w2HPSg/+zDJz1UOys/48uWZkos8m+LWMrHTFMvKZOS+qhqXPrs6ujxzosqcrYFuTcdPW39dXGWDuEuZLdKYmXJa2OzO7B5BpVVqyHQ8YaHyYRvIk7O1jfW9sz2bOMMo9WBc0br8FVCy4//XuAF++D4UXDhWrj/x8iBk9Fr8KdWVAJLYcpKa/ZIGDa3EQkZTwt8y4HUAawDpCkw7rlwHenIw1tBMmOi1BV4lAG8MI0qghK1Z3F/5tZBgwlhfedQpIdOnI/qjISk0Lb+qINMri0c22rnO1nVD8zl5X4M3tcNJk+VCR7RTCES5pKG19Ehy/CFSEs4PONyX0NrKumeD+To4IRx8BLWpItyGTdBBXoDVcE5aiBsbm2pHD805M5KTkCRePN+GWU/35t0burtCqAefXPgV+23EyCW2tSMI1yVkuody8VdmbHZ2Od9Vbkl3vl6OppFH9hIS9ACGOQfVM8k58k8+0yyu4bqUoDx6CogpqGa2FM9m7z599OjjH/9yd/vFF198enJ8/gf377lF1wAB5GFLNJA2smfqY+4fjatSoPrh656cg1cLfz87OX6l7JkIyAnjjRYEjywT62im2nmhMDsKcwA+sgloJKIJSEbKAciEXPVDBLjsSNT6ErNZaCIaEWE/wMlzytpfjoJN6IQbvWpFl0OglMOmkpBV/nRcizx/28rSRJHZsnfVmqxISe9sZmOUUkUPUHreF0SZGig1XHSCza6W3GJbcyElkclyeFJZjXGNHgeUkHjiVqr91fmZONHjwrCPQP1LpS9WuF3Ovti8zi3qj2GMjOt9g8rxDkiPqUqGcywvE3p2uXPwQt7mNtbUC0tsNpx7v7zsp+5RjqxUsMYAZPix1xzC0PxkidPHqhEZ9ZaHhKGZvCKrae7kcA8N2iXOIhPrzt1b5NhhoS7tWnz7viskJQKDWMmJZplKFhM4tbQ4r8gpJZ075gBTauwLZ8OflJVFObJDWCMp8qXHzfRemgxVXl7t9YZWhUqv1wXgh08QbyW1VGAIRo/pKWecRmuT/dL7spCUdDYTaPMLb0A5njTKSvOVbAsIS0UB6eCOAJSU4GE7r+3RcLEpy2ZyIVy1tYPfV5gRJi7n1bNZh43tB6AWacEByyS1sTJdD534LBC/2D9Ig70iZlaZPFv/Bf/cbIaO8VamFRYkoJzHavlU48ciSVAoyIDBJESRn1yEpDoAGz6x0qUPU7ou0uyR2wbjuebk1Cc4v/jiC5PEv/rVr9w37IKH+3dvOnKEJfZJom7AW1lc8EpjlrgSG9nU4LtcdIyo2lGlANjCCnRKRS7YTiMiqG0nWFrVtrxGxd/mK7ZfQzcZgoDhtXnSr6WkXsYT2F8929PfRsApuBHAyzjF5AH5ZPjr/gE/ke8onUr4NxbgjtWvYBqseTvACBRC3ghiA/h0Lf8dkK90mLIuDaQaNLwUCZ4CggQxbUHhHL42GXqEQy58gkcUT6uUFbRuq2fH1axPZ3F/jydHHlWVMXRiRQm0lcQxawrYtdZiwWYkyWwISrLjKO3ZNEfSjGTYtMmrGauO8IAh7PC0a5o1nKNXqgJKTl8kDdDSnwM/QcIprY6ig2AElJaFNbcDUeA5A3md+kSSI4n/4sf/ncGhTz7+pdtxdrefu/eMwlnb2HDxbawjXmosxkwqL8lTZUGCYE4qHHraI3xwvrbfwTftB5M4Y+crb8Pw9Ne8tvXpD1/1nIzlu4iJVKiGT0JQ1bEH2trT8B1lEl4pi4JpnK8po7rSsVH1p87pgFn0xjB4AIs1JNThneLwFF34AEM9p4WSZTLyUL/2Y3mCy7VkB4C6qVtKRvTpHswvgFRQ5AxSgtdUgixwXZCcR63ZFMoSmgE8bU47QmhaZ0jtHUdeIc/qqMXlhYtlBm5tZVXo1s6u+kC10Yei0uCzuRwKGTkK1Ym+2rmdk5Y/SQAj+vS7EK6JwVMekspRvhnDYZz0vZ1qFJUaO6HB+OzZ+c9//vPleccDx+n2NQZxYWBNq9JoSeQmQkq324aOdlJYJaIC3B2oI3+pP2kGx21o1lMT/TMXBuiGVR+fYBAN/Y22G9pBMoRFiOcUe5BJaiGqhqe72O1P3msoOO14lJfD3OrtZzGIcfDKdx5BNzezqqtYLq9jBw8vbrghBMKjg5hPq8Ggv5Oj+qJNqjQjxCCHdoyIXVgA2t8GePZSdGsNuIvz+Vxd7GI4K6OXj06OXBl4nq6GAQLDHLMrujJMnS54nbmjmOfTv1HPqcckXWtStH3CKF+qARLEbfmsWrCLe1SXWXbT6ml+BfV4aBrNaEvghMerVMiGiS7jbJ9//rmrlpzI/90P3jMi/fY7912tWFP+aVUsnRnMThMk9bg6KAKJAI5ZBI4HUOXUuswqaEqartPsyxYs0J1u4v77dZ3u8AxLU1PihsD4x5W6vrx8AG73CjAe+vc1Rvpl5G/gg6Qd2MHTfk9JD35f+ZuTQy4EpiiiVWMOq506ilVkR2PSIsCUgpaZA+jSVFLoJT9gCJDiA6Ag6QeoOiGfxC0BS02R9OAAcA355Zdfknm2lkYSvdZMnRtJXl1egYHVZM8iIjm6Jwg//vhXhIOHSLepXqmbEWrpRnVpqv0KP4JFlCiP4bphdRitkwYxC5YBRHokHV6KUxTLEs5BGqeprmQ/0yeju2u0QAi95k17OBeGzpyurW4YccIBE25HZ2fPGN1PPnn06MkPf/ZQq2L/YNfGw7T4a1qneDhiQ5qhIxFKZUNqcI9rVhg0Lq8OHEWrPyDRBLyL17Ng8w2TGrICAzN6zSzBV7gq+oQnawXdz069w4cnT/TFWPKHsgas5dPhobtcvxbWEWYh/cqjBAc/zxClAz0nAcBLS2BT21/b3+GN2ZNrVAZ10wsZ0JU/FSArc8bO1/byMFRixoKcZ8WN8KEyKDwL6OQcGEFUnNz5pb5sxlVrfZEMjSgWEvswt7Rk/G5lxZWcJmx0Ru/d+x6Vd+LK88P907Pz/UMdqenjs4yuVLrJIUU5d2GMN1trDCGplUKkK6I+mGvSPaFCg/lBqasDVRMY/XmWMJ1ny8FXVg357btSXU5s3ndm9dTU8ye7n69/9tbdN8KQaoKowKqgyiKb6jIJzUBhGhoyiW9uncSN0dYj+d3RGdS1TSPA8LepL02Q8CGmDFNTomGtBVvppU5PO0kFbeVSCjylMVI8gZ9QCB0i1xhCF4xVxpRmtVgyxCjjUjDkysmRTjG63GgriZdKGYWLCw6K2rh964YVejTS2toSpt1/84YTwbRRJiuhuJLzrExEKDt1JLkxWKAQXqnHO+dig7mVFQc9ns0vHi8dLx3WCtUiA6x7HOYoI1rscHFR5bftS+vbuvzq8DeS5DFcJz+nWR6lEKUoc3k6od7sdD6rty2l5JAnHGsiESOW18FDCUIiFgDywGO0w6kIe8+f37r16Tvv3M/9dG/edTWWttH++fH6tD6xApCQSfwen1Ls0ZpwNm1KwSLbEhAHg9Tdn6MhzVAeAq530hL+uzmJlhwlS5MYpDLxJZ/6tbPfrwNMPlWt7K8DDDD+4bV4m0QCP05t+NrA+fxVTpR24HmAePK3wDQS+Nv5qjJ6dnjDD1j7tUWLX5RAZvXkuPEpWhqpyik2qSqmuRRXbKt+R6pANgCVAm0kOglkoMWg9Qm0XNCMpWV/f08kJcuJDJigMoTjtr79iq55jYOTUCEJKlFAWk6VgwRevHBLJnFSHwEsLTu9Jng6X6qVjLCdXmWFiy4hlnpC5YgMbMJLh1iin1m3nCjrTNsMq5V+v5ruXrgKwWCH+IxzxrlRAJrspTQ9hOuMrvoyO7O+uWlsydHunz/40sE1H3/88YMHj3Z29s6nM9pnYvnGJsk/NS8EETCj6ipZ9FWmTdMJLlZHKeKYhPpZaeYhg83DyWd/bUh55JGWQDCdxwbowPZXabb3lefEWq5ReA3toqNeU9fivJSAv2qA0zqXmTy1m+OvPhWaUxRFWGadon4VfQRSoI6T1hEx6PRahDuJBkh6rbEr3QbrOQgylapTG1s8fapndFpTWjSE9FEPWHBlJBgDMqE5hIzzGE8OFOzZjhRBaM0YIZsvhQykMHDp8Zhw9UXI9nFMo7bjsqvabjoaYZ25pQHfff8DA303b9xe39zQxyWpAuX28CCTGQf7e1989unh/q41heuLK9jiPotMFxfOoj4jWvCLx6MI+bmQMzGCio8CoW0AHNcA1lZl2mdOz47Ozrd39w5P3ab+VN6Wl4jR1JdfPrTwiXz3IQ8m4SFJ0bHB9gFbOAFFzG+MAB9xR7Cmqrj4u+Na4RKBZqSB0hr9z7ZjlGBFuViSJmnm4qhmsqJEKkdGs3E0cgQeTCxuvIXUnMdSLs/CK09Y8iyzVO3uiF1VlVAXP7t4NhoMCKoJLuGcwoDWELSLfn1SHOyuLWw9CqfiSUW43AFLMlV5qLmKFZYiuPZFZ3GCQAFpBcVqTbuXVGnRYVNLWcmulYDk6BSzxDVRoYspOcf27uZursO06E4zNaoBIpbOcFid0cJKpWynjVj62dGMJrc08CqrJWZthpuB0fuIET54+PvVszMSSrTUjo9/cXi8ZFT6iy/ef/+x3Ur05s0bm6Rxe3ffSEzuGNYW8F/Lma2Y1fySw9hgqMKHkloeq2o6pJLuGhMCBP5duMbsOZmCpL0XAaFNuv1sAhq0C7FDJmH4B1yDpwJTskNI45x8bVSvPxvGs6P3U00CKZAbUA34O7xh0Dl4hIPhBHqSL8qtJgOEd8UPVv9FYX00mRmkLCHQND7PSU+giJISV+4EgOwFOUVMrjJDSNOOi8zEbSUnrXYKX4iRM0m3JoFQWr5W4c/75AAQRzQT1/XVVVuhNjbWdSrBEBvjzBJtLQGDIxo6m0mljEXnq8dZBPZkOfJK+HNzaU4DMPCbtZw13iSns1dmi+kFNIQhVdAR1BJIZ4GZCRJo6NAKA20AvHj8xK3Iz52h/vNfMr0P9g8OQ//yyrJli8ynnfxUmYbyfI6/JenG0MNkvPE5zdvwXcoLr/VQ4QmGamEMxLRHeH/tLHemgo4eq32DPCDDinEu6m9BvPqQOwGNZ3gmevHwVdiXbwPyIYkGF95uIC/yUPIjvD1CuDHgSPaGV2Xa/peYx8k2BuECJv3j7y/lH0wOlZZieC1d/YnyJ6fFeCajBLsQlYjuHWSomdeD9HePVgEr9SRWx+G6OKG7U47KvPHO9x1PSqc7DP3Grdv6WHq86oA5UU+tMyyQd/1GLddLp0nUDEqGmw0JOv50ytIDK/8Wz/djSOSBTM+chkcoM+LK9ip7ZeibX3vS+O2aVhMkBFMNoG5V0DkpXV26H9Qq44Ojk+29fQ2dF9tbjtHRS7XtaGdnamXhhSbl0kLNC9bqTUnYq3d85fB+c/hJ8TiLizEhRnd4hi25/SmlRWI4WVQDK49haH0aVRt+zhh8MU2c8FWICVlVKRh0Jml9Y0bVxy3waS+FNvjBCGR/WbgwpJaYCBn7AeBPetoDPUMs0+fEixhtbqzevXOTKvHKIBlWAN+qSqCIsPnUFYw/VNa4GbAIKFXHdROlMkgtaJgpJepCHdc1QJiIRonjS5PARJKz58/Rt7Ka9oSDoxmwE5NfCiMT3BkKy4i4Sx1rS6LU21VS2hnVvGOCtdOjG8K7pJC8h4f9mkQnnIQ6O3LHYQUmnB1m38jJ6ZPdvYPPv/zCFYfvOsfhzTfvv5UlWnVDmT1LQe7Yf39yYkmtDpOEhiZBM2inKABrWVIrx0fWFtXIEIiiaoKEv0Nv5b05kFSuvXbCAz39dQDjKWKTTZ4hbnkiToMbMAwh1zwDnmvhLFALIZydBP7zq46eHPiO20kAJl0N6RO/EA5cVFbyh7kV0Uo5hrZGJiIkbM/0hUZtlpdMT9EoBjAylJTx2awRcwyAa+HQE5xZQZA+UBnhpK9hCwddwVyT/8gMu2tL285OtfxydZIGGaHZs/Dz4uLp8+ekyAzxB++9B74yUjcr+GaV2al1JskvkdC1Ih5W2nnRPKjAZnjVoNSheJoPiV3aXyhQnOpfVaSA0AwKuTnTwPz4Y4mJYcibt1elpc2rI/H5lw+3tnd/9JOfPnry7MsHD3f2D4At1iqzRK/64qnHbN7MQp5kAZ9x0Gc1sAdNwu9w28cm0sd876Cxv/6OyjGgRZJnslwWrnM9vHb0hvScxNyofvOzGwTSEr2gG4m2VcftFKXOxT8eiuCNPJV+A1lfsjiA84oY8qnEhU8S6RUeACJOhndaHe4JrEN42vXrkNwAk3F/LrjS74r5jdGN0/Lyyyffxx7BGQqWFTKdWLp4Wfkzc//++0srJu+t/stWgJt3DGTeJpcHU1mcotHZ9Q3xhlIcL7W8fhuLVJs6ksi8qmuGgnB1acqNrU4XtHTQ4Rh13qMOp7ZBac+aoteDYpi7kzcaVKplCyGsTIVnWFt1mAhqoqaPNTWFsPXFpfO9i62dbSPHp2ZXzBZn1GFmaXH21o2MI50e2xW37KS3FBi5VGNqQY1yOcm4EQFlwt16EfHHKi0zv8wIlkKZXrkNPoLta5eCKcPRsFLWatXXFGoaO1qCK+n3c+nP1Ugnfy6XbAlwqijq1LZiDvgszC0XRNVK8sdSVQavw7sLLO/9qp8uFge8WDKy3GlYZVTG4u1Yxgv81iM8OrI/h3bA4VBjcXSGvrrJxfKMBmeCTs8hwkzTYXpqjmZNwqvKakPDQ35IV1h6arphBoMI98L81PnCucWaB3ZGa0XiG9YXX5YydEYxGlE4dddD5N+SrcypjShPiZTtVxbdRkl6WabctbCVVwqJAznpwQ2vONDhcArB2KmlNEQgNhxneviLzx8YoHvjzt3vf/+75kTevn/f2Sw0rIj4HBG8TDcoA3s5kSBzDfIIj4UtwuGEudNNWb30j8N+17/XspP6V+kI71Q8B49E2v96agOer/vUAJXfRjhkZ8TSIcXXMQwhDdOoJuFfD+koTW2X7yQSwuNVLJ9CTZTsqOMo3Gs//U3tSQs6G3BdJM1s0icUmHhtawGnHlWh8/Nw0AqEv12wZd4qJ7tJWuEKT6JXV/q1NufQSC7EpFLAAIbh29/+tlJvuaqIJyZyxb1956Ymh3WlkJBn/4lpGgdzGVEzrwunKsiVNs1KDyEiyiEA8F7hFwQ5F3LLhRWimhOrfTVeuQHAytKMNdndfnnx8PHTTz/9/Gc//+UDvhcvjnWSrL1fXFheXaOvrFDR4KbimeqaPcQHV7cs8Esawpav4kmpKgp3TG2R/VLwvCLNs12yNnY5iV0spVbTRjzJNrVQQw4KLmWX6h9P/oy27Y7jj/+2JIzfAhngCRnoT5Lu1PspsAIiP+3B+Y7VGCaRJMulakDyyJGQxtNg/AOeqI5KvT9Npt4JdcTBD9UA3586oh6wMe6Y3rwnXX/CSjTEAEujXstTr3M55XJ1Y12P9s7dnN24efNWerq3bi4tpxOg+4tKDTEu2HJiX04lqZUrxky1AxntWQMjPlYfL7YZf0k1uT/Y39o/2GMY9Ui41bJP+s07Z1kWi3vQAtYJRZLesIFhxGKdyjXYKXlov0q0s6N7uwUVNeoIsOPdPcntHexT737YYsx/aX7uD77/PXaauTV2pN3w8MEDixdxyhalLonTiyN7ZsquRhxvbSyPmyBRyzJCjpDk5EpZTcblLnJY7fVswE1gGjXVuIy/4F1zRXE7skOICkgE+JHGLAep1kHsS3E+wptJjJGjK0rQfYzgRGL0nuPAZ8i01I2YzTHPVAQTOqJdTh+dHGLI8vpKcf5EfbDEbXXlBtWifCEBjw5fgUMOJ1fpvJR72Yuj+tSvmN6uGNl+TudUC3LOMLTtFpYWW5kF58FhyvFk7USPQqvI9FZKUwNmdo3SMZGqpA4tnbfiKfcrWTASlnemOiPgedI6GhvUphBtzZCQNHaTIfSjV5ky9I05kkPAwmyGBHGQUZVjGvPpk+fbW7ufffH5O/ff/u53v/utb33gJncSmPF5dyEfHPYcihaFAuaaRSGo6iflGYSjypmC+7tzlVDpyVFyXT6jkK9Mt2nzBHoNoD8NgeOyvs5VYNzr0YeI7QEzhPB3CYqVyCNGxWyUah8AI5teGnmDdRTsDXA+JXpUFp9XRrYssq9pCNINivnixH4w29TAzy2uCeC63D0FtnmDYXBDotYtpbFYE4FolhalJPqf/un/4Ale6buCk9GCCoDj3pS38KI2QzxUmxpBmWgNCEd1hotLXMGw6iW7pUiqDyPp5KgAfNJjF9JCRUqt4IydhSXzMhl0BhxFUms74KdRqpXuDMqQdKyfcHzmZPQvHj74OJd2fvHo8VP9D2bZzsvV5XksO7Ugx8Isd4yurlvMZZxNhyeZrFEBmaIBXDsa9UVGItdRHOmNRYN4LU89m3hkNK+G1wGmOePZrsOBKQJPgQpuKFwhlUj+XnMqvZBGIkp7hAwI2x9SQ12a2vkT5x3Hxp4yZxRV5WSUmq+hxKlF+geFE0nRM1o5YTWXBH3R7atR+RzbUDQEnGcgY2zoE8Dfr4Ao44IPHTD5EhqMZJxeGPLM4lm9WiulmheoN05IwJaWbDvKAIvu7Bt37+lBvvnu+4RvOQNukULFGaGRGOM0PbV7MnV6cKzHyoBY7k7Dv7GyDhtJIhz6wYgosi7pOEq2Sj1jgBw8kCxO7YMh9PpjomhyfjlzecNJ1vJYRzf7ijUuDVHklPXi0ubByam6yFaZR/Hj1whlPrfLATY8eP/eW2jQt15ZWFR5DYnXeGfOtzBBs7qypAOn03Z0cMwPTNK0synA4zrHGHnIX1tdVtmRnUYGvhtrDT/Tz1MpIgvTbHlCVDT0RjtgUBvISxe5W3+kXZ6Z0Vj/OvRxdjmtqiqhHM9shDmWjJ2VP20frmqpSggmKZRqSBolu0GOFcqyNiQYXuPIJ8gSIMRWS4VwlWHGPXE9dfqx1LyYV6Y3mH2yh9LVticZXg+fayYJTqhQhbeQu08t4kHLWd2m3ZDB/rg6rEJJBxVHDUV4q6bU+Vl2HLtT93x1ZTlxjdkuW683bTpef4I8qobmWmdMFs9auuIcjyx1cZLl+uYdCWlISTQL5abrvofqoxARcRUr00hlSBQnCQnKAXPNsQTWJ5Cdd+h4vFa4YlI3MhCJNY6KEyg7uzunvzr59PmLbQvj7Rh2sjTnwAQjdUbrkQeGVV5ftxFl/+GDx2ch84p2Ptg/dKeqvSf377/zy19+bBBe8sVbiFs5pCARb1mEPAojDKS/SiZFjDZu9Gfsbwze5GtQduUfNdKDvBICyYEJksLWT4EJyarU8kx8Et6Bg0eU5pKBJ4HcJKoBoVjXImr9Nbxw5PVXT9hazvm5Lp1G+zpmIQMYD7BUhBLg7AGN690t5NxaX21Eidap5yXTWQBa25cN7dg6JCK5lSF+F+DyK3okNElFTJRjtwroByV7bF/u4WGWWtWNCCDv3r5DxjjYZKTzktV5YVSTpN4WGzPiBX2u9NYc6NsIgMXqXZwurtyQd4DJdRQ0DGnj2gfcDO9nZT+qlY617EuGeUTUPlb+EV0n81WDcnGZ0GUlNmX44sX2/ulUX5X9yaef2yLFZOCd66GorOijaP3opLAw51HE0stUZ19YZ7BrN8ZTGmXrk98QHHMcZcV5bdd+5EGCQm7yq4iVlzwSrZyIXj29eQZ76UCvdBkOd1XtcJBwJn458M2iSiq9mcYjbsxo4e/HKFwiKRfthxhnHRNoAlzPhmmcQlrGfOXhhEvFs10T2SGAmzzE+Oq1XcMIEc7P03Q2hoaBXyD8PHO7+6YT9HW0epZX1pcNIVsYxWK9914WSZm6ZX2X7A6qxfSez2scFrqji+lDx5dmNNpgpbNebdZlqrOlMiubORP85jysBc4x5Tko1QYQC/f1cOhNmy+R0SR2v7ZJ3Np9zjK5TFuv3EAfUVVAxyeHkkY0E60poSurEYowaW1t74soadpQng2qWIVomEgtkluBIqJFrfMKEjqeFABpNCckJD23mqdMSWN9F0N4lFZKYsSl0gDA95wiqSAdixgsAgOWOZqgXajtRQEoxtuqojMaA3nlmAtjQ9n75398ZQCml2s7KUpK7Iw1wxOGVm8YDmPLqErKRUmOHSjpEQqQYZYWETPq5BWv2Cag/MDwENUNL2TSBUvhkVJ7KsCjBLaadfyiSAfPGEXfsJEnVBkcrqpFpzRaeAKcWh1nBEyW0x4KJRH/dOU1P1WuDKpgoFNmsq4p9xsumJdIo9LwoV635W+GAZSZQtw5SvlCouZUXvDa8HQ0ha8t/QSMJ6RfXa2tjlY5dk0QEWM9h9dxNgPMr9fUcwGJXr8KTMeKGiJLBh6fPH1uEM8ClgxKv/POrds3eEgfbtB9WqXE7MmTJzo921s7VLaSzO6zszMwL/ZHQ+JDur+bRxbSxKs8yriq7b3aECnfzsuAuTnWr9c+dbXvwMln0H+VA9Ng/RGY135+Ffh1Sr4SRmAj+bqvvya8k06NmwAKZ6rKsMvBHJNmyCnzlxvra6k/1afEt6wPLrEnP1BhoCYr+SAeWnOK7NmjR2SJg8dHLfVu3mW0dqw0RYRQFDAtMqmgAgVTBi+Jq7FiMCWxBBiYiGKNlEphaNKFSTQLI2ovA2zeuCikEmAAllYlQWtQYgQtXe4D/ResKTXI9+jR4+dbL/76R7+wnGJ7J8db5rggF5qlSkZHJE6NFxB4NMqB/3Q01aFlnACsKKuDDxowyaUsylM0CZcHiw3wmitqU9PxpPUSfzuZamD0C0nWiwPCvQLmBHLNTz1xbOf31RNwe4ANiXZ4x4qGKwdsAODxtV8brP1gouIL5xACQHBnXOAQsWBDG88APOkR3q4Dh7hDIE9HnyS+gYX3p7nf/6N/rHf79v13NfNJG5OpvShCir084Gw3Ojq9dDmWJbGnS2lt5VvcghkFf7yaWYg0z+UmnP2D46fPtllZ/tPT3YGPBTB3c311an11b+8geDQUKXjKOE1CtE2v37yh77t9tHtsG9LRvsqh82PRvl4aiYQzjVJ7ADISm029B4eR08ScntZotamcA8Akt1VW05KF8Wa7MS9SS5J0xlkMLGCzv1p8FvLOXdrkNLJnpfM1+nLyQ+4SzlzNqaWMERGnc0BANoMzvai49boQIgy0ADItuVCa71c5xEPjG5xXVHUPWEMjJVH9ErF8SmmXbWWlgkf0MqiQ+3IyunUnRcDGV5opZ68hSTubVYJg5MKYZk5DCm6PRIW3E9KvEMTiV0KNgF8g5B3oObgRQG4NjoOB8xViPzpHRH0DmjFpwtsFoXUZ853VZAunF6fzcyfzC9kw7dSV2Vz6YIBM5c8cnn6hUeLzHCoJsdJXSSWEGHw0czLnDkQui2kyXiRp2UlfvdxA5/DanuGJWn6UqeUdVwj2N18MC1m9Y+395eGhYRi3DlspTaLMBZp2sYH4jUy/3Lp37837776jZ0waDo+z4BZOlci1tFqcoXPqb8cAF29HZaltjXZyUfijpLjOToGFD4PrrPUrPwbyA+Pa089rYA3vibeDv2H62dGHT4NHOIABOf/w2jANwD94hrjf0KMORsJGpUZTpydRElgzL0zIpQUHzB2BmNXiJzAcmKaqPWyMyskvg/wKS0uLAtFIxtX0H2pYpfUAsFYjgIestdSNclG1KkLP1IU65GUldpyjazPGQm7RmMsPKlDs7IsphDH2L8tlbFGIdAZ4TFFRqhajpNU15dRcY810yvrqujEoIzF2tRuA+eTzz2xtZ4Y/f/QCw2Ez1RfiZ1VD9NR6ReIT+Uaf0aaRkCALPFLpYl0CDV8gkqaXUepLFGSxunoCBmBfGsJkbuxE4ZWuHOE2DwezlozYAEsMUm4VS0jIVHbARKzA6jJqs2f9tVhBLjw0aNdTDeWwLHMNFZ7hnDENwJKRcjyg2i/1+gWXELJZz8k6khA0p0hKk3TEJgyRRVvC+IuqEeZKDXkAuqSHcOmHYFR03IEwSAZs5bma+xf/039JX5C2FrWAVuyMdYxHD3LZW81MEk1L+CPO5ZAIy9mptX5nDx88FQaJ8mvna4YoTdf2WpUI+rGOjkyKRZd148XMYB2Gny5DZG7/qWrw/OmTp08escFugjd9SBfHdM3OsqxW/B8e59g2lMCztWXiNnN7kut0EekmMpni4SSnh8Qy4R4YXTDAnHzG2rVkmDGyHNLTbGQsfbqvmIUe86qkk3/B7tVlZ1m6+NZw9IKuf5IDB3Ikb4mymYZ1MDeLRsdglAU1wkzaJU2W8IlfmeZEy1AjCQgJS6Lzy2/wILfSSGg5raHms6xpuhadic0SZQ4hkwHBl/qfwh4LX6TnugtcOR8KOC/poRcwzB0h3dWS3Xw2qpZ9/bF83lCSmdAyihHesbJGj8F2ryn8cdKdxGVWr7EeYoUHLLHofKq31VddPTQ4sAhTraV5480V5dvjgTmKIFQpfI2qwjA3ez6XFljXf0mQEFRhTmgr+QwZtY3Y13bJyDhHOa4AheMvNbOQOi+jopMiYzuNYffA4A01va+/e/fup/feekPL9a233vzu7s5HH33khCNmWM+YfIqIAFKnOowQf+M/XRYhaUxToqbjkQKVp9R4FqjMvP63cfrOS8AmXDNkssiHjwN8pzWR4mSSI/BrwEIH+AHhNc8QpcP7dTItIf2aT1+R5jV8r7yyWt6LOWIyeKl0sGnDei/lE8ngISg+bayvT8YH6aMnEdJ8f/TooScycNLgnzIk30rNV/IjXCGeWLx0dgaAZeA6Oc8MfUdDjGwn6xuTMerbmpATW/Z4QkjKLEp59oQqSjiZypIxTqUSQLtJVxVWxSQn3FCW5LRNF9NJkaFZA4ur62uoQ5DWnvmRT7/43MDMF19++fz5loygdmX1ZucxFKZbGfGWrsCSbczC/NTxdtQvG5v2gWZCWVDAFIup9EC27jNSyszUGR2+jqO+tCVCWt6QLaEG4BncEGXwdCl4RWSDwZxsRg3GDZ/aA38HFsdEGsUawvtrYlb7D1in5bU9/RRx8pW/Q4KxXIfwdl46v6NvE3EngQeSxO3kOtZk0k1VJz2Jbe4kJ/1a+UdVhjIsEHnOVKNrhGvwlixiSxPkaZ8IhcZWdojVUqREkAHhyFPOfsrCP/6mxoJ/kJLXUUqPbU6rwKnDVzsvHmWGpFbiMbRUG9VJgHZePAVcs5OnOcfYNp2TA3O6vf0Gu08sUy5HTLlbmze8kV2uyi9tVe0JvRbhmaRhTo3qLGQGRdaMM8WMXGZxHrm7GG8TUHOkqvlAw+n/AsZWlcO0o78YsLQwv7G2qu0pd8UToKEUZMSmh46np5ccylE1TVqcb2xJMlRTfepBl1aPWAvU8Knc5JFGQdxItsi8l8KvBThSHA7qEtL5DT9LuuChLaQBHmAlEcvHCWnP5LNLxJNrGGBNWL9KQgg3fBUy1DGQKfSSUZkLZvW8foUSGWxzCK5GRr4LF53k8MPdsaI+iznMsH6H5SyaHcdzOsM5GDkLUJSdgwVO7d0MKhJC35EfbYAaHQgnqmUW2mRFKhZ/IY9gCPGZkwsF4VNnBwGDJ5RVC1/UhNf4fldQ0YFlBq3UHzzQkgeLbo6PnxFXo9Mbm2tffnlra3db/9g1D0bdgRGwmZkcPw48+P+OnXxNptBZ83w99WSnSqHhGxLBXtt/DVWDdWDHbcivBGvg158D8JDENc/rUX59yAQlMu5NneFR8FldKK7x1RorLbGsMTPaQHO/m0SWhSgstgokm2rMzziG6oxOIs2ZcvO0CsR0MR7Cr66ZZ01nq34JLKZFxZFo5jNdWRzPTJMix2N1IXRmcjotjGZCVabUAkknlk9wRab8vCczBNU6FmpQOEwII6CSc67cMru4lNX4O3v7TO/PfvmLp0+ff/b5l0+ePT0+qmmg3AK/FMUm6SrrdBWnMxgAbaoDftUMruYcGsI6htPJ9daIzV0AUwnUM01wIQYbk5XKBrCYYrBmnrtHLOhVJwkBnVPPoj+vXRg+BVPjm5DYhvRJvjwhUc2LJ76kNPOnPAIBcP0Kvp1CCnXJy+jZVFdrqEis6AWQV13y9vezsfXTVziFe5VcuwFsErIBhufgCf7iqicHQ8dqzJ5eJ52Q2KbF2qdLHOU+klAUOx2K5+jkeP9wT+HB5SlkemElACVhzAvNyIToBx1uvaByCevCzNSyY5Kdkc9Z5T+rZ3Ny7EzUI+sMGNp9J5DaYmSfEQHN0VWM8NmJnZjAiL7jGTQFSYMbizM/SNYLKK3ddApnNzBaf8lAsRODz06X51bEatqCq3pLMqLKAUgOx4PJXkH6VGzp2gt7kWn1ZJZNlhzo71ulHXmSZS+qU1ZU6VCbcnaKeoiMes0cM/zKq7idBzE1gqMURW8byVOFmopHrMGIjpIOl/bSVe5s4AR6TSHFLEUtpi1QjtUnGKJ4y6DC2Mmeqt9WLfU04+PJQhoYNUpTSFIxOkbR6qWkJOYq9Qp8jBuHXYo4i5jKPgooelRLIEkH3vyrYY8adUCSez2UexIaiVfku5CB65zBJ/uJriiNoI2yqbuidC6V9ezpfI6JLp4kLQJlKiiXG1CFF07fmFpeWQLneUwic+Nb6l0ot+MzxBchKckri8TEgo2lVD7UrsJSFkKKxsp7+RAZmos5/Sm9YRKecrjKds+qh+e1qhlHklxoS9dWj9xEya45363nOw6N+eILJtmhBz5BJa2VOuDM/qsR5m/8p6kCXtSNo9UoHKqUK0JaUoxP+jyIzRj0ZQaHkPaIDjk59OQETj6HVx6QHWUysEMmPw0wk57GORkyROxPTYbAEapX8vl6vK8IGZKYoD+L2w04q/Uq6fTlmVYt3cHo3HzzHqP76cefeOKVr+bsLclUlAgQYkTnOOewzrkkfWFtfX83YO3grzLPQ4gnOWvKGwCGdkQdy+hHOgBAGargpyfTLi4Tq+jiK+mqD/nS2ZMQyem0JAG5ELUgVC1veGpnKrgX2zuWGljb/MXDR599+oWBwAO72NOyNzCZA61E1PJsOtGuZgQ/iU4Xgr+3QUpUvY8w+Fi6Noe7MrCamvq+mTJzu2q6xOPlEbS9SFEHFmmP9N5AeXsGziTXJVo8aFZF+/X15yQPfW0MqOJ8msTD36/AuIZpdlG3HTj5HIBf90TLjSW/ozRM4/SEtgH4fULJJNiAsMMn4wIbvnbEzkVj7k+eg2v4uQ8++g42Kf4nL563+qg7KY3AEN/0ZWFhOLTFHJqIR+dn4Y6Bei6HpJ1cnbqSa/oqB66lgE4uXSHkFEYm98iZo2f7x9uelKbVD6cnLsg50umlV3PPbQZ+Iwe1ksj615xBoYNHbFxIKInTMz1xtkfZ2Mxn8ZP+qiYc4Ykd0OGAee5qHvJ4spwikzeeXA9FdiZDa7nkxTJXJZjxqjC7mJaGhRpruDnXLTls0nAPIhBXBYAJ8KgFq46fdp27pYllhsUVXm4kLvzOD+kKgFdcc194NgpXZ1ctlmp/hd8l8WAAGBaQkOZ0NzEBZFStBAKdikNIhIMBHi9EFAv7hPPIn+fIpdE8BqvJ6Q7vhPo5yBkPdnnC4Nm6I6lX3j3Bc3INwLPDeeDkD3NC2mDm4SjXvJEFOCsXKVg3DdVRprCGJKMv1drQQBfHuBc8C3OWimZDWm+MYEelSwzxP9MOmQNLH1mhI4GMGF0upYGA4JyvBgrC2okLkWdlIg8w7W9Pjfxk6jfkRHeMYMyFiVUD+RUkF0ikWnUZskiEsrs4PMhR+87SfPLkMZN8++4d4y4rK0tVaVokRrUX8n9Hh6zwHn2ZbYlfERB7VbExd6Y8Q2ZlY3gOScugwujXymyyLIqQLkGeDumvXvvrADzE7fB+nXxeCx/wgGnMk57JiL+bv5PzZK1IvApqe4a5e+JrNMtByJ9++qny0s199913u6eryIgES4xRIhJgssTRGFiaJn9EMX+QRAZaffVrEwksslHMSXiqRVx9jakLVd2Ok+cyupFAPn96jUJeug6MONyYU5H0WWviTLzgmZk90HU5tMB5Rzvvl7/65PMHD110nR4exclCW4Rh68F0Fl2TCkcOQoUqXYvgpCu0fNP6H4XQniXto9QNYMr4wf4uzba5vtLnRUOEV5kGQl2sbvfKzE5Nq0hNamgbi8cQkpxXeLJXlrj1RHE6VDWkZ31sYWxYIh0JoUj6HQx42DyFhIHlhq/tabnlL5wJa4/n4BqyX+EawtvTUYhB47/2HBAKvxbRa9PG42u4PzmX36lWcr4OeCaRCJ9zKKguQs8By0zgYgnOWE+yIISxAadH4chusjg/syowJ0WEVfDqbWgVne2eHJneO7LE2e9wX19XuVp0OLM8OoktK/Izvm/cN+0vU7TaVPSZstBIEi6DEjIabTuQijTjXjeFn41zLHpuxwtthCdiELmUed0bUXiIC0dwlRNVGIGrWUBfReR4AKDZcUv8CsGzWZocl/MXqhjfOjcj/lRAbkSbagktAbFSw7JgrOxRpmHYgGe+LnsXt9CHn+0knfSocVLNHhd5PMu16EnesMmrHoqOLyikeAWfKOXoGD61LJircgqGtuxaFqBZH6E4ZFaKIaBGxSmT4HnNiQhMME+7RAnmhPD7JOfNOq8dIkpAavaXB2T6Ya1yEo63VS62hKlPGZALSnQXmWmhx2ym+nnKGAHqjM6uzy/ZKGxkQXFnOXTWq2buzYV9lGP1ODN6YS9uPc+ntqcAMHVp1+fUjigYWZcEUom04g6m6gpj6vp6FBNiPCc97Y88lBiUZiCeuePIJytXlfiQ92TKto2cwmGQ3JiN5KK/AD9//t/feeOuDgrN3u1TMGj4W1yE1Zqj2d5lhzBd7s4T9tNUQvpT53TIb/JcbjKkIT051I5BXvkL3td++sDfz0k8r0R49eUaWKN6FeS3exsomcSs7BS0qjWSWGJge6FiOD+3tjRSVNcfecomp6NsyRVeQaJMxUIEmRFCiLx2ow21CalaNrmYC0BnxCcHKTC5kAipwDBoRGTNv8IsPJJTCTnMsTLchdkCGZmMYolC19CPopBEznO2Dn/Hic07z5+9ePjksU29Xzqf4NzhMUulm7MmVO/UfKAGfM1cO1gpOpDgq5Sw0QFV9aje5DGaNpa3F2GlJqpZR4eH29u2pZysr1bvRX/r6lL9yXxc+KFPAGVdgD0hJ83/4Qmg6A9LeYoZeehTdAhUXtsPIAxp7VAC5lNjUDrC+xN/CBhjG+IG79gpnAGgPZ4cYCA8Y8CRv7TqCKC/5mW8LxmwiFykpFLnb4B+em20k+FD1jo6SJ4JkRgxpMMnsfHP3dLWk2qpm9il3PSX5bX4aexWCeucQEctk0bnZ1ydfnF2mEUxxtw8qR5Sgnc8kuTx5NDHZfT31AzJZV1oEBklALEuclUtDiIhSR8K2LrFmeWVDH27iqE40LKo2bWonRfqAh2NQ0TgztvFiXuDiuHkLhXDRllDttWS0knuRFUkKbDfOmQp6vPcQaR3PVp2j2BqVB0wwisLs06Ecber6jztgOgZV9QmqSvq1qLddHClYltWclEJD0+e2ZkMQo4dpR4I//FG5TCWxL6FP5oI1W6Qk3TnVWCjsWpLJnOTUavL28OfxgkSIrJyHYFOUyD7mDKmjeDQrGmdUu9xa9mtH1InekhNVZMt9W5DeB05HfTqo/drAWcJG+Se+noKI8T4YWYNp4OcX87MBddsGZJA/OBqdQE2KBWTTKF/qFcwt5O1HGyxkBYI9ecgMh2ac8d4rKw6H3N/TgvDAfTWF+CSLU3zi5s3HCZge5tNF8rO8j6jj86WPsnuIf3C7CFZXHMcV5ZlHR+ebO0eCWER43SWqmKjdqbadsqHxISfuWWY0tDQpMEURG1xj67gavBlfukk2+YqQLeimlP1cvX8mW7EmrKVToFHKy8YJTLBUfCRwJJimtl9FEd2ui8v7J8caVpdnhDdqEu99cIWRmPU/5+0P2myM1nSBD0APg+YAhFxh7yVQ1V1FasoUiJNabIpveCCXFEo3DR35A/oJf8hhUIuKcJNVzU762bemzfvGANmB+DucDj4vPqeYziByCxWsg0OO2pqqmpqarN99tlXWPnPPCyrqlTooRGrvM3rSlNiLSmPEWaDcTBpWaw9nl/9abbiSwxoGaVyzxvh8I1agCo5uwNJZBUoDFGCZipTWyOQk06WXOME/S5fKRdfP7zjXC3RCoZy7L8ZEXX7tky6d5s5UJYFeVfQOQ18ZI155G7yn/yZyuVSVwOFaaxvn+byvcMTL0EmLf30jKN4GTrK6OJEjFa1EZkQdz/Yg3O9gdnVG6eu9I4arAsL8hzKaKdHyXRcVfG1tI8eiByeHWeRnNLz/sRsxqg386nNmB3LjOiaEOHc6d1ceyDL1qx242yMeXZsED26f5y2m8dyd95eX714+c4jXm8T/fe//IPNc9/vMkwSeHD0xcGJiazMZrab7lRfMW9e6CDumDncfe7Dwceu+L17mO0ndZVJbCje8Zwnrw6P5Y7uP/7617/6ndz//ctMDNxe4P6wj3vvdb97t6/cC3Hz+tWeK5O8PWwQ9Sjl1vMOnxT1OdqHbDjFOvmp55KDXAAgj/kEPZwCCZmf0zMADJunO97awZJvY4ftbD61FtllDhXqp3EhsLhXK7IlcXjkIX8WYBNNThr5ze3RVJEmkRTH+YWehpE2vPkLo4bRBYnIDKVcVXJFKRitPGAwbriyMdmc9y1F6eJGk6wiFJ/LHCABem8AJ3WVkk4BgLOJAo8kqg5BfWIVJQfY9+JojijNGSdsrGTc4bsxwsu4F+9eW2dk91gZzED74vu/TubHMVC1J1cjATeY5LcufdQEpYdsi04zg6+rWjBRaO5irRz+6BlFS7Pl2PzCIxAAFFVKPt2w1BW5VSAiWRMy0GiFTLfcLGDs/NfSbr4RYDv0jg8Yes6S5jKHsfGCpYixfhWOJpu+uep8UixjVBfBE4MRS4pvvigVRs16uyoVZPQil57V1nsIMJzYsM80n84cjGC5lmL0hWmwXOTA8EfMD7zFW/XKNYrPilb0VtSAEetejopYFqie1R9ZY4sEz/x+U1i1QHNRdgLRSLHVQFDXEa6DLGpHmIsx9V+xnnmiS0MV3OXlqeQscy4uXh0adGdzQg01QcTrPPb9r86teMrlJML1vVzfobjrS2R0yEtuSUsHfHRi5ayxzhjMXK2rP6hptcDyCVnwjwHFtRs/wXb76d/RK405y2j6mJtAYLLHuLU2TJtw09AaSKAq9+O0FqZy0ADqGE2gYhdvpPwj7WspgABcv/IJXLHFNJjdg6FE8J8Q+wOWbSveaDmMrZ9NpZUHC6CTj8W+ACWYEbg0uu9NmZqIpWNdDsEu/OPg7XXOZ1hBpkXlzTc9VXqSUvK5sb8CSM30tG0ot9MaLTLGzA2XIZ3uHPsyYI5Sk5jx1xTTDOFUizbAeJ/SXEK3//z5s7/7ra3mX/lakcNi371OdyQJE4jpo1I9ZG6VPMkLlqC9Q31APmB44+r7vNSQ93wPDl9fGNhSiG4wfPjoa4elDcDfPb/4eHezM0Rh8usMwGq+/GkRTf3OnitBsmCBUQqIWyhUUamITdFsrVQAkmYdkEpD5szbU0zw2T50HKw1Ji0Nf+zMmV5krOVm/5zmZHZUJ9OkrU9+B5nUAoSaGzGT9DYcoesvc5wdRxrNFyJyxsHUrAqrsdDtqXaJSyZqIXEtCZtcT1zt0yh+WuK4/etM9O5c6m08T5urhXL374dbWy5s7TF/RyMMM1588JU5Aokof4uBr7+DrEL84tHgKhIMKFy/mHJNzKfYqsjniFq8gktCWRq1CJYc6i4CsctppRqYILEAPkpBVYpT0cHOo+XOr58k70+efCVr9qxc0P/VV1/qtdPUZT/1Lf1gfT+yB14KNEVBAL9pNbnSICbKPKqUJRNVzVeuAcuhIQcNXq5FkvY/asOgFFv6ZtPtm8iGPLotUc31Shq+CgBEEchHD1mWSqio+lUD3ISQocHYmlDJ5UVQB3mTKWlcBZasPgw8WEGAm8o8sbcM8RL14ZVPHVxe7TtLMN8odOiGdrqy09NsYDhHbxvm5asXOhFjsBSzbJpaqyYrQYopUCeoAW1LtD3SRY3TFdzcZrVk1jfLSpY0CK4xeJTeluYmsPND7Z3QD4h3Y5A1mxTb5Lo/IdLs4mFGI2X2qLGgxS85AIbdsG/rOS6YpUNTEZRQi6Ypbh4NThLblPPLIIt3F1hyKrw+goXfBSJoVFjIihJcjLvCwRutpvKAS1mFP6MUxX2GXMG0yu1mADJBikRgNt/iUI56m/6xoiJxx0Va1ris4WMhxmCnkQmxBzA2FJunPJSMufAZNdCoRebCqeqJmrXwNIRsbY0L5Urd0vzg+OzcvQn2e4+y3nVc5vLCLVkvX138/k9//LW7yH/zW88yfBMwV2wd3ae2bsf6oJN+eSFQ8UfbuNSOQnw1fMZHH3DJSySWcmq7rwLbTvDh0UePHz958qVB+dZCP+eeN5x0n/4vnuSShJnE9QdLfAI9Bba1IKGDW1e2pb3cOZxTbyohCTJnIO16cbJp4U+Tljjb0TatKEagtFVxqq4kOvY1ts0sFNk0zDZ5ZPjh5biPQcdmggFcxqPhnE/t1cCxbd0ksQ1sFagasEkogU0FgEmwSrSqbGsXZKUA1J7CLcrSs0BrVKN2famI4jbJbcdjJVj1GlUyjPvffft3KpAeynLBQWUTf3EspEfz5FXZ2ZXbHMuK5VKzm15VQcwRSqfCu7FodMh8SGXcqPqCxZer0ob+06paWhUOX/l4YZYc8GdiV1SF87mmVRg3ABk/NWlbDGAYKaqCPcnltU5WOz9/AClWD85XHasVf/FWcsXm0pBxu8gNJcVtjNgPm2qfqjYPXBdlM1vfKFI5y2/Gmzq4XGLljmJcKeGLaa6LrExRFQIJXvQLLqY+9qayiwRDcouGQagq9TZd6nFqFBv+g2JnkAl3hJjwfzIIfeYPm6ph7MkrZLceS6ldkfk+Jzy93/PuyLNhH4/Q833wmEwHmXu43XJ7oHQOr595Fc45v9yJQQfdljr89t2b05OzPLkNZqMhvGrvVeP0bpOFe/esHtKHks+uzePaU90EN2bbhHZ/KM7B1K+VCg8SPuQwseDW6egoJJS42SAtsEU0xFwBeNty2+D9rCQ+oX7YTGRKlAKNPztS28Q3if6DQjYqfSLZJNTkmovdFH+sxsrULtlnMBrFUl56cg2yyUqieJSQ20H2MzGfjIBY9bOvbXKNiHVhMIJXiyAKsiIADfK5OQjPzoYHfQvjqqNTL21tZbNxXjq6dcCe2GxB3fWILlUwM/Ks28byZDpy6TtLk2gWizLV1O+f560NT1IcLrS/6CpWj1BMKZ1q/vvf/+GXf/MrQ6/1cerkcd432Ts62xg291ynngjar5TE4C32P1U5yQ1+BjHNKKdAMkb6+errnzz50pW+5+479ElQ37sxRNt5fjsbZxnx4pJEhnC7MSaquZk+R2eMhXOS2reQvdp+zF7dbZMjbhg78Bex8UnEPoO8Co9qW+3dVDyFbgToOqYSUIfG+nYY+XMqy7OIrJGNwSYJ4swBJuORppcYk7SOhr28CMQ2OMLBmymgYhC13KJnN/DCTxKb4IJbixSNHJZyhG/qHowgOcg4QYyLF9dKq1x8NPt/+zf/wU84toseuwvKxhkpTdVdkKqOzQcTo5JZHpdfcFsJooEEYCq0BEQCNuUziVWb0ix4sWDncImS6Gf4ljQC+KYLRlkM5HLFVD4uQP1iNJyU0hTONK55KjfP5uC1II3l1HuDh0eP7j8w6F68eTWf6FTRctbMreVeHmb/JkcmN/Kn/kTupwokKojx6bxUDc9wid3Vtvj68I3aTUiUdteoXeJmsGmB60pg1dc6UWmijJT8dnOQNSbiJiS45JQFhjNcNV1+JRMCyCXv04GWeGpLDkBhWfRgnU658pxzm5aEqirKRV+yKsl3I4kqIXUXblwf5M5UtVFV9V1IJxCUyFX2qH2mlBIuKLv3F+c/f/ki32f1ioZX3ZSgmnn/7Dz1KrkmMo/5pWu1QA646tGZm9jMw7qdWZvs+oh3gz+GV+4AtcnkqOCGvFFQEnKQjArqdaySnnPjdIlT2RMUteAC1BixOfP2A9GLfeiWDmjK4jUDJIJ1C0Y5CW3SF1vMVt7nv4seZeM29NtnwJ8z/CPhz9RAVQxp3BK+yP4RMZsdJmRMqrYYoCzxlLKVbIqslXaZ1wBr32/rmCbWmRrrfIDaqn1rJSoMnGquVoslmUvP4fR+zngpNd9cyvaoLimaK8e80ePcBSvoghnT9M7DjrmUaiz85tIDQWcMrXmuPRbxpQSr3ufPXxp6bTo6umr1c3aea/bpPc9VZrwx1GQmkM4kFX1eqwOPMtts5MzjnbNjA/ytFz9dGXy0f+Qeki8ef+kbsA8f+XiMMxPvCPBC5clJjrCg6SYLs+Fti86g5rJ9B2JsPMEZvnN9kGHTvpGrrWd3ynbpnK5iGS4lFTNPN6o+ppfaVKRbV1BPX0f42CkFxEKNzhi8dc2GhfvKDwZCHSAKuSLypghRonWDaRD503i29DFO2rWkd2pOYrfKAJkuMsc1ZSBGbskpsMGkYW6i0ItaXD8gGzwMYjqQWbg0fHVywZXQ4P7T7/+kh+L0Pb3Hl/KqF0Ey7YDWXR/4Mf3ILkvc8dFDnIkdtepX1i5cXeFluLHLD+cYqDR0rUKQaFqiseMMtM0JeMpvk/9S1l9iPwPI/IxsJVdKsbtmAkuCowAC9rI84ntLcChzyGkY8yXOfyTpqFf2JtEU65dlwUtCRyZckoasj52pSxOdtg4mxypmzFjCARwkLvHg5qXsxjvcouDDPa5RMMXzd13JSkNUkwOg5+A5CitZwlwRgMCsmUMDqe9QnZzRQwxTB24S8zJ3ZXzyRVUagbCIySQEr8chTGQuaALsNExeELeo9a0kxSBrdz66CDdn6mwFWiLYUt4/efzwzPUpbinl3PHy3nWpM7JKxTSTTxf6Wx8Hc53+2rMXEiSKkg5iJS027vOGuclII5dP7c/g2mohAaEZsmYQRopOjJGYh24VMOJ5099uuCnMqLhW2rWdaCZeSe/ET1ob7vyIQhZ/BkhWnvQ2tQXBRvtPKeAJowlbYreKg7epbPTtT/Xh613DNm5LmcAu3Nj6VZ61o9tGZGbYigABDPzU2cwzAN2i35VQuDWn9Ni17cVeQBQHFsu13RVDMgfJFz9/eVrpEklLL+tahwft4vpT09QO5ebBWpvCDIx5lilBua9AyvQgnnNWPWlFcrZtrq+fv36lY/HuuPeKHK1yidXvf//HZ89fqglaztmDB67BQPzWK5hGnZzPspRnjLzsMMpn4CcnpoiqMRHfSj2+juvG08R5C+D0+MwFDw/OHz964FzEzYe3Vm6OMqnh5FrMG6nao5WReEOA2/o862EFBzL7kgrD0ES7VAO9XqC9UIC2XGpI5jRW2fc8aSZna08UqMaec4mv9sYiYg0n2m3G0/m8Cl5cEhjLRxF0fEFOAilWheL1xUxIwREburSY0k32B6V7m0LJSI+AvzFOFAu1/zBjujA0lfqtbwtTSn53XOAxLhp5LyZSti65s6Kd+X3hxkDSWScDyUHyIQFD7+D7/EHGEjOzszS0V5cs5twhhkneocR9rz3k1hV54ApUaIOFtyrl1yEsyXA1HII6mJIJiuKTkIqWYoorASApbZf8Re7yIiCnfgXWJ2nxrliMaVfjduSL76In20zqgrsm8kD8/aV25X0WoiiACctyxSzJlcm3ZVB4UTaIEob0CcavQWaLhuGnQfcBSexq+2hToQOPZfhgBTRyUgsBMEs+oDSQoopn0FIuMrFcg8hKuYDPYinZ2kZyKREwiNExQ6BectJCo0drjkQRrp2jh+cERTUWmWBT4RPLFahYsVjwFn/n5rL1UywJEiHhwFfH9++duIn86MA06foqsyUH+V1Z+urdtW3n48ePzs9OX1+cWmQ4Kf3uzZW3g6ZNGn9WcqlqTvqTLK05e7eZQ0SnKXHKaPbxty6T73/ENVOfRUIWs/3dxMtFkwg+E4KQyXelC0y7q0gPvzYT2XRU25JFLxqmEuEFV+yCAZsk56fpyjJXGlbF1dLZpSxc9iUEJSeqPkBUYwvI1IqqhM+CRS6/9E0dTAh6waXeogSI+kFmduIStalNESKGkK2FE1ySdYUqmNgqXOIqyTcj8tgUQBcHhYZSSZmRexqaprdvt2XerZfczBqzoo2onHu12NVtukjwjkdXUTbL41yy8eplrk5TG9/cfvCpmL/927/1vUtbNRTzstrx2RmbZsx2+8KNb8Q5dexZmIqcb8hU+XluJaHcaTX2gU+UjI6/9T7cnp6cPnrw5NH9xw5k5fIBe3ce17z3zbdz75s6YPj20gWGeax2fnzP15Nm6rBNZebrkrFLnhP+fdrt/LnBmzLub3d3znZSLkn5bkkVrsFZCQ0HIEWvmg5ubghBoHjopCGrgHiHOE0+NlT6eeQTnP8ZrjPCc/dytW1mm5kNiJJrkWyzUhw5KQg0Ydi2XzDdIm1btZaqkFz1bKyoxi6AhiWI5iNTkCs9n0NMTlmol7MpUTqIpiA3fXQ95EM/MeLt2h2h1v87ciBXOpvsOh9aROdRJZexYdKjimnYVIltTR+dmtQamAWraPF6aUqwb7VpLL9u9IsJxOKSwnqOgqVck8imVZeen8xN8X/miyKZrzEAyju0GxvNc4TAEuU3UenSX1CKi12XbQPz8tJHI1CVvXwZbGoHKohrQnzu4O6sVBDuuCo5xZZ+IfqIHQWsF4FFlGMjZ2dLHwtXPE3Au5QUEIzpti7yt46qBUXuAvRvsKKWTw64ll9IAHo+FnLQcB2APc2iQA89LS59nPUlPWEowOeqdtWov2u6bAnu5WmuKLRWFGKNjTdZ5ga0k0KChwC+IX7v5vbkaN83lOwyXu5fX+1fHR96cHDkTeLDtwbjHLayVn708Nz13V6jtLXnfNZI8Qa7sW0vc/B5f0DItravLPoQk1EcI+XlzkG82keOt0B+q/kupnCNs0uAcgepwH/AxCAIOOWGjG78ANuGkJXTOMrol92zL1bBMwJBYLwAVD+QuxMoAUSSGWK+54+kkgnmJ8XZMavYHe4NWMYGENcJAopEQMIiWwACcP1d+nItH42kOUBdg8XwSWArqha/7VeXgA0gNsunTuLla+ZwEbJlYADBuXg+O1uxwMx3W76zkLWGzU4qE0nUTF26hi5aEasacxKb27jbs2RI8kU3xLNUU5Ec/8s3aZj25cvXGcmdWnh37T0iR5p9LMFntX7z7Z/IIRmXW9tUbFqZ9/l2gisKbg17riY8ORVhMobSFmTUVlvmZDXh2aSZh7WT8+lMdozhfvKTo9MzH7Y7sSh1uZH8O49298GD+wrt7RtD/r3jg+P9PX93313mznkvWakNZEw6ybIHwO648c02qY3lsw3ADlmOzXqcGsn1OHi/NKzx+ZzYOrKqf9bEmvi4jDB5AzSuvN1exOhjM3l0PhWdBG01Qxf7HjCUk9O6Cccuk3TfACShCUmFKyyWlfjLUVIUP5bccViq7eJNoluHUB9URjiUfMFmVuyWME2sjKIAXGOJheH0kDBF4oLnkHkNKYsYudbPeUszCbhyymVPc2Wg+mr6Qo2UYw4UpjZU46YtWKADkihuN4EZeUMCXw2qxCiZrArWiNGImzd0l6IVlYq4HecqfwnBEenbnnGXsXgJFag/E8lQ64UWV8p7PjDqIhgCTXbJSdd2+7GXuVdbUUyZGVxqzoYdJZWWM3veTU4sB4O3TrC8RLVJt0OUhCAaAGliyxj+kVBAD9y0ViroGwVfoPkaHfd88bf0yJqiJBDog3a5YBBwynGXDB57fbEAXBXY5IoENxfVX0bIh9QMTGIAGBsFX5aKAkuOM+CBO42TBDXQk6Me6olK45UFb/E5KuWr0da1xlKwLRZvbno54vrg/ZHPKe0dvj82BhuWM+13bvKLR8rw/rNnz42vLmKTIVpxPowoCW9mS0XSWogpF9jGtcsJ6NnOjiYsWT3R6/oERclUcyRKXpqdZf9i+BKqNfpanJJsubhGVabCOzwAvy7NfPTwixKY4NPBQzvLpqs8b0OY6V5jEdeq5hpVYPkFqP0ZRjBcqerOmOX2CMbhNG410nwFQZ18raxlbr5tXJmKRI2UJuVJ22YzZ4WJZwhPGMmkG0eavHOIWWxL/OkXgYRKDNsgDLhCoseMzaK2Wd60909StpBUEOfZh9va52PhKhUTSxqvOkRdZ94Nrca5947TK0RRnX9sJ4hu9kNsvMkOq+3fuUV5Hp2OfQzoR/nyGx2ndK5uP+bDa2cPTg+d09/3LeHb7576QNH3yctH3+h9Z4f5l3/7a6Pv69cXMdo8+LCpLREbH+lcs+unZHVI0/1mwbh5c9x+rH1wOqiDM+gkq3rI2UyNelLxw4BWtjKrDj/I12Dc2SsD0r+1wPIVmVwJc6BxvUXgxZab/Y/XV7n6Q3kxgvmGnsPQ5vm3RXeuQtoeFJBnjcwyzb6eWSt30Po6hduiIYGGmlaUk+mc/U6noVqpLb7/rVFfWnPn7XyVRMMxNBpHFL2qEWPjy0+AW3vgPnTGMX/Sy+qPsfMuMgJCZ8qSKN1nYnTjYwT26YqxE2afDxj6TZfIUHXOfgMwNpbmxSfFrYOhvii+p/WljE5TRSHbuOCLLL4wa2AvjEBC6LniBesWDXNsVkLyu7HDNDFVpYUrY5FnDT17dyaJZc7PtAq+ePaVXpWWHkz1G6qNQqWH51IXdzJQjRHgnfgkU2SDS2aFLFFSXGQrUQD5K7ibEOOXvn7lDKVK06qQmjMmS27Ya5ey6uFSBsVXeH3EptJTAyifvzqUrKhReGwZrtnGFJxjIgyFcnYuMl9NtzUKwA9turyYYoIbgX4GE4NzUmd/mMKCg45nAIaPYjPUtfhLKS0OcYVW4JJcLrFcKWEKFFNRpYeXFoLlrDhhSlm/BK2IuCA5QKt9YewA0gDwUfvu+xxzUe3yx7zKJtxm0FqcQcYw7Knwu8P9Ll69Z5nHnC4sMVv0+Q/9Rsa8j//8L/7CQtjhLC97GGER67Cck3aTZHVWzsZIiXfnQ58rGVZFSfPuB3qUfHn1ujrXpFV4KS8Y5bZFJdh3keXIiC/GU0QXxEnq0MUyU4iidIuUka7R+p2L0q+vvQDq7ZRVplYzXt9kEMvzTTYzV4sAk4/qX7+m28X8GG7DwszJBRZA/cFF/xVsuy4GskAzSDL6yi8jmH7I2imXWEsRlNDSZHEh2MUjgGk92cVDcov9HwRSi9w3MUc3chf0jPfhyggVJx7jygIYplF8wUnEod/sSM0zXeNDhg2OMhxyt9OjlBVq5uC81erpQ4yiLt5e3zqZdPPh1cWFY81/+Mb33J7+7nd/mI8l5NWSPVf3kLI3d79vkksD4cjksuEKv/kJzGUQ23WfnoCk4PAaJYyAqhCn/ph7uiD93v7JnYPcOZi7fQy/7gAqo9Hx2j6ROnYXQLABM11THnm1K7AquOM4Rd73cdZs3r8SoUWaH+RzOvO+VXWu3cjnFHyDVTbPUmZuef3WlNfTZWeMMiMcQ2aFU+U3rT2BpM6tBz7Wg+kxTSQMrB/nNVeFqStIXTV/lObUfPbc9m+VLxhgrDo9Z/aqM4gxZCRsXMnqb3GbagDJUTU/w0KxwFvXYLmQAcRE+3RovKTSDEJMERFFQuLgR3YsgcuNMcnGiNgzJGYuxhZuTxEd0UmYbQBT7ZBHStOuj4BTm7dJhgUNB7BpuEsG+Q+SoRwd2CkDMwdT1yCuJQdQgl2yEn8WtUtWONub4xZlAeW5pFXDlLCtpw8ZwEQ1FkYQS+rc1glyLYl+VCosyBgwdo5LlzQNhpzS51jFtruBAbfXQCxYHSJnawrIbYKbXzTUiOSZp/M59LuuMptipZWmvHxRo+BmSghGBl81+JUPgCS5vmCdU9BFFg+unk0RzUoaQfSGYJnYRz2NP15W/FUvrR2SQRB5jdPe42zCMIQ/3cIkYPGQJ23IslVz5D6QY0Omwen2o3fn7l37nMO+a0ftt+oU/ej37j08PzcYnJ9fvHr9xja1OTv65q66mZLP9ULJoyWFzhQBxZoLWatlChS5i6FMs8BfTrdonm8rkl0ZwCFPHXck5DBqPhQhTJT5ikeDVMqHntJhuV3/yHcPEaQE9+75hg+yqDIFhLcGz6PvnfpJcoSv1rStM7HntmgaC1NXgexcOYsXwFmEFygxGP0k8qmFitqhCUHHtmpY4qZSslq7MPxir+alX/WhQf4iK+NnfnnRYLTatbKTI6bznAHrFEHGG0lPgVrW0Dl4LBx2Jp0Jd0ZlO3/IOkBXAcPcPICznkyJGPKMdrmwxV4td+M1zht7FY5WOdVsq9m1ke6xevHi5fWHG6tT5aheS88xqGpesWBAa/LGoNPlfsrddsGwGUHTr/jvHFYuJ7C1Y4mZGjI9sOyYkw4JRPIVjIOGTtG+1zremvH7HqLV8AczVJP+sMXX4limx1eUiG5LwPq5JlBv/dMnpSL6POvozOck0RJsdpLkVIYF2DEmmIZ89DHV7IgIloVfORHHbesSWiFG81QqOTrc7L/OpMlT1k1Hdz2H/gjDR29A9Ml9junZRl6mYARsEhqdk9COKyWCKtkcQQKywh5X9vrFlECOBOE57DjgV1DUIElKqxHFQS7fA4AEuHSEOdKRUphPQmXQnqxnvidyCvYHxsJVVfirW5dMkQWameRkXHNYVfjLNTYCN9xBVD6/QDEVRU5FLcmVQGCBdiiFf0Azx6A2ZGOp0FjRbg8rJa3Wiel4K46fv63wZHvkVD726Dpdie66mRqpKUIlxG+PqSIOYTqdlYVtLf/B8FYhn1IcU+OSL35jSRDkC+oOlkAEXMUWbrB2WGQ0xCgKnqtMwQpslGDJrAPgF2W5+GO2ZYaIguSmx0mNBEOiqK8aLYwk4EM9qTShqvEJbs2c+mf8ynHIxs0jOnAm6nMrp07E8QXX5VHVQHZ5tXdwfeOM9L2rO95KuXj9Zu/g8Ex3ZcPw/IG+0pd9Z5mrRSuWdhARLgcmafpZmrCu0tNlUN6N1PQ8O8/7zXUrs5ODFArVuJUdAAsMJnVKHTgZBQCW70Z36+OLFy8yb0gj01bv3p91MCUNwBhF2VN952rOq6yiGHcSTSp1hgVAk66PbGkiqsosTDrT7OP9oPtbZBW1K40mJCzXSoWMxficqAJ8jJa7DdY4RRazdNhFtj5Xfsn4ghJC/2M9F81nADn2J7QCgH6MemYraFIlpkTtSQII5CtQZGKZIbGWFRlwLPB8DTBlnfcNbdRvFs3qBp1ColYcHztscGbo7fPR710S+e6dJ/TffPfdt99+/4c//ZF79vzF81cvDw+ODz2N3fcMZa7n8DBTmU592LVw1Jhm2BxpILtODUowl1CkyCbKW8d7hl5N7NiD3k2WNeSMBEf78mhXRPbuuhzD20iXtqzvOkqR+eSdu4cqtweP877lHDgeiWp9EhmDZ8vZt4c9/TUOSDELMW8jmGf4Gs49l6e2sERxYJF4W46AhSlsBxzGmTLPbppEzpbNKF6C0KtC8wNjgrTwQoH99/q6fs9ExZLYRgekbjU8eSZcTfhmByTpEOFHSDQnOgIiOdZDriARLK6J2rBM5UlMsox3FBMM/z/kRJHWmJJVeBXY5diNBSOofJ9p22zBoY64aJjOmCAXu2TyMQ+AKd8MyE1l7UonrhV6yV00xWwyM8WDkexWbmR1S9r2EPHYDvOYUmzsMQ4GC5APvwgqYQVbIQR3HRqz3HIt9gJUInMJLFfoZ6s5ZmCLKUpRbJA9jm3BTPloH4YAHPNYY6LIDNFoSx+dQq3UXkysoL4Az64ry8IIgifRqaWTcbycKPhKKFeJSy9Kjiy+GhzjbUZZvIIIaFI8f/EuUQtDwqLHMolvvMVI2i796qAXLwZwTjNutz1Lzxf1WS4gCUQ/874Yn31pUbzRy2M9/YrlBRp2J+Lu4VG28Oc6s6uD99YzhmEWJtyXhXWgOeOSi7CMt/f0X0rWR3G++86604CdHgRGr0pWRGaXS0VVZJlN5x2U2YrU26KUu2ZH6p+5ZYfqb/REoNLpKPXduvCOu1bhCGLVeVLuzJqhjgJWS0xhuHV2zLIYkDdZt2mwCCvSZ4tIUypMWl2D8LtBMDzrZVN+jmlmZ37VaRVsLBnzDhkgkSmvzZSRqooeNvWG5jepV6H6zJ8JImL4+iWoPosYUFf9Y4etK1dDW6rNb5JLPv4BR45C4ZSObhYlCSmy3F2VNtKqXrGTkenisG37a0LhHWSeqpi3gNLGbfilahiKXGthx+JEIfYZ8LOnL7y4+8vf/sHmxG9//zvjrmHY1zLn0wh3Do/OTAfuHXpl7oMiJDyTqpOT63e57nTTk8yeZDKTU7KxZNz2t6E0gFhYMeOLQ21wffDggYmiDNrOVW18g0uVlsTt+wvk7tC44zISaTvubHP49vrs3Mo1X2pKhg3IrXubNNKxSAfeM3KnKnxh9tB3hFWQWm/q35wGu718n9cyY5FxuZZKcuMqTNAomQKdsQ/tJJV+Zhgln7bTUlbv+kfAdHJ5ysSJjRu9IjalmaNtyf+8iaaMOOXVkbTK6E+Tu2kRYkmkeMwXATEcr1d/SCKStl1okpwGwi++NYQKwiNg4y2yaDfTDkkvJAyZ4RpXmgar1WgRNQSL2e9nCaC45C+jbk7Nyaa9iVAphNl2ZueJ3wwqYRjlmnwT29W1sEa+qw2WBlUaBCNjk8PKsQUNv4G3RhEcW8eYZWnSgvLPrytXYfQAmF0XTM6cJUWyFiA4tcXsMFyceVO3jzXIIWvxVdWoAV/JQ85QqMbFSHEREpenX35g0LS3jJVbcdK6zFhRUgYu+ysotfex5zDNxA00lSHz2JTJOMQcSk7XA2aNlqvUS6PfryZoEEC2shoGBJdVcVUUApRNgkwwdmSf1TNBJch/Ox0KXiyCfI4QPQNk1SiyfrWqWLEl4C9NqiG/+vQ1tnB5HpWH59ETPd0yut5M7Z/NfCRJYj9zGjFOtqCp0wMa+fSN7+569kVVq6VsOTKCdYJxzrBqiHX3HsWwkBLNkla2KNnS0Ny1nX1F+/010crXEG8qbTUvhm9ZNj2Fy71nmZsldXbhrq4/Hh/t+yC8V1CkmI7UCbF37569fyGWYtZrS4ga0pCK0RzV1NLyURRklKwrzBe12BeAhs4bQ7UubuNaIqK44hCD1a8Gy7hSoXDxi75BuQMg40fWuIUpDVyB0vAJr2RqFChN5Szk4voxsOpPiGdaWU1630Zz1/ICE14MMxLVFNHDe6t8Ek316P5/6pJvHWZHxGL6jnfeXr18+vT75zaZ//CHP/zyd9++fv3qxauXqUK3aZuOQptGqbeSmCcPdwmyOpOq4Vn300xJlxnA+oDUsE82icFn2J1cbjuWOR8ioATtijrA/JZalPRwVR3zS5RCsQOQNet02ZTRP87nKz6YK/i8AImZzX20AeMGkTlzO4mMNwNkWk3ml9eX1wHnhJCB2UAc/+7H128uqLpJOsm3e4wA+I0TIGwGbxW5SDILMEtdWXYZwR4LqDtT9RQSMFqRbfan15Rjefcpm6wS8+W3zeEsi56U0naRk4wrZfWKaQ1mcbE26aKkvpuouGKS+tZFwuQoY+Gnokk0O9dF6rgGG6UTakI7SLpkEh/mjZVUeCXDz7SCTrTfPISPQGNR2nuO++YwwizsZBvvsG9rxI5amHbS27S9pteEP4sVpeRKUF5wadaOAUyj+AWKAbMOB4ABYARwBeq3gyiNKMCCERReAALNTzGUd8RHLDJIfpMDcElpBn5RdYKNQtYqOyTxKrDBBSODwQtTybuUREE2SpUqgWDTMuerDoIFSt8gYsCSJursNNdNcCuDaDiSmaidVFPBKCihOrzwfHhDgoF8Fy+qDgHhopoumHBImOL5HMwkG/XqEJRXbGE+J4igQvheEYJxpqOxG4KtQK1adisw0tD7tx1jPG89OLjZP/C+0s3HizfZPzzVapkiH3ciCuOXX37BDraC5/Czi/WFen6b3YwlyUuWwpnqxzlqWg3ZilmkRQcYTmyB5UvCosQAn7dL8w5G3vpgN5X/z37xVYbhqytb4YZbmjRrDm9XFMJJMJ710ddff42e2qSh190zlT9UKzlAhRRTuH6E7Bi8lEUueJfrxzBimYUH1F/yAXXwMtJYZMvBsKogYNfBMCO/RUaI2JqClUovCGjULu9ncBUgTUI+kep7tggiNlsMcZVcv/V5iZUEV4EAD0oNvcqoYwY84fZOvJH47OnLP/7RZvPT7759/qc/feNM37cXaUS09VmFY1MzdUoW5KPl6dpl3zXzqo8bY2Y/48Hher2NWKvsjL5bl+A/6GoiJjHEyIve+Pr9FQ2tem2IS52G1FArDs/UxvmCVQyo2jpbdXzn7tHFK/HTobnUVVlkjydbIBo3oPJrJXRQqdvGjIwT5OinlB0/W75MhKZ6gmtGtoqVp2OptJYI0fIE439heEHmCXLrNgQoZ4IyY47Ep9jSBJNBtYQwmbJMz5NhQg5c1BNXlfgchalR4QXADUoN8TbNze/CRNDWicMb+m35bGOSFrgCwbvSsCyy0kzKGw8mAseHYjGw3f7srTgborpJEYpjcAVgwaHnUZWwxcxjrzBs4WKW0KpS5KIpctcnX5D8hZw0N56PrVaCvO2KilbTUGWSW/IrjagCK6iKlKZ+WSbFzQwdfikAQF9rlpLfWPjCpefvBrG0LgLaDGzxLR1IaOrlRcMtCcgESeMgwURBgolquqKmAWx6T8GWOJq68oLTYLbVbomiwOlJBoymRaaoatVho8k1FfjmBbL4Jl1p5domG9PBCzbXlQxTRyBHCDIOUHzoBw+Arz8kdxwDXurhJZYval6zzPy7Y3CJ5QFxyHz91Pe8ZiYBE7V9adD62H0Jou/d5NQIMu82+Oyd5cvNhzfvvAaag0umFPrZl6++tWI5OnpsE3h//6X7s9KDZzFxZaDUJroFAjMPB2+/+CIDts6U674mUbVDdfvMn5HSMJzXsU5Pz61eDJ/GXTuWpWRExtkakL5Zk1HdozIsjkZz+0eH+no0ksbetmOlgsyCDH3rMv/Hhv1MHxmB4XPSRQ8YCcDPC+Uz3t2g3n9kpN8npzB/O1HZSFtpNZVdCU0au1yA6yMD1C8BuE5wl/0zuLE0iXGm7avhXB+gkYBAFMeG7M+2i0VspUmatWXNDRZ44bstYVb0u9/97tWrt3//m9/+zd/86ps/PVUIMi258/tfE2i1lXriyZpFsCP49rHtpTlJPBseKhz5Ok414M7q9owlm3Jrj/Gj0Xd74JmeHN3Yqe3CAPxnX/9ZZM4MpjaktljHBorXDHTqebCdgXj/qyePfPKBni9evDIn+fBewWWzXB60F6IrWZaVp90XW+3HOWimURjRVRSNS7O7PT45HytmZygKTf1hBzrUjJBVgM+R0EIhFrFUCOTnCdK20x5AGikFn1vP4LxTJ/NoN0U/O52gcVIhx7TmxCP2mQrTqipRyp9CGU0UQaaorTojNx63Um9w+bQVVZektrDfRQOQBZR1oqQ1mdKWN1SQuOH5UBXTuEZVdt7wSj3QiKz94SZFT7J7kyqhRCx+sSZUMix74G2SsT4Hg3g5QU4fh9h4y37kIOuugLIXW4UqX9FITPuoBE9cvGIxMmIEjPCCjQU0S+2hGWKJqjSxKGnIlbF41bJWyyMKNqq4TD1umlzTch+NSDqsFHfkpIRcRFyB1bClTilFUwwabumg39zuwWw018AR6A2qAmkUg5GQujUPI9M1c8TCqf2EEC4OWSscGAasvaQMZmYKQ0iD1nZaJjIYSRS5FAM0Xak0O7tRKwlj1aixsRZRdRK6/ZDORcnpdCDBuNzQXHoEMKY8Cs/dPgh0fKlpecKqNFOs0o0c73+bbs9+e5QSM48D1k5snwhUvRQotpzEyGBpHu91+eGNhnOtgS3EUxXvxetXEj8+8rXPvALkiZh3Qa737727TN+K4MH9vPurA7FV4O/toys9VN4CenN9Z+/D4e2xq384D8jcTZBO95Wnywfnp2fnp1nVvbt8Y4j1PRu3Dkm6dpZNVZuq+jeP/5SdWTvgxcvXeQztRsOsmWqZyUan2drhPXf57p2enFvZOIQl74Z5jxgl5FBPMjxntVIfSHfAl03vpBviNCc1VstlTOeAo3OcdiYkOlVLlLcm2S5EaUzRUeLcWC86q7PxoDdu9UdM3jY78dtoy6ThpVGUen252XIknUtJxbkZKhMspuazItRYKXFSh9/Ky28rEiDRU+FhWkW9fxvGMYVpmdjUBXeI7vn8rY/xnXo77fT41Nt3Pruhzl3nm3feZXAcL98NzBb+wd0j8zPv6sytUmZD9lrP59adlODeGc0AvhjgMfwfvnn293/Ioaq//fWvvnn6zOr3rdN9BqbTM9WOwKO77+7OFQvXd+fVmtkosQneYSiXRsTmabC5z4wtt/ccUDxZnV1ov9pBsjpFEnRynWOVvtQAnjzecRGVTzQ4LOC6Ah9sCFmKKXJkwStpqtnHe07Q5zAav5VQNAmvrJjPv5SxL+4/UQTPnz67e/rxrUq052n3nXc+L3bPdVeuhdh3bswH0e/5Iu3RnTN7594wutJwNLSU5rs5M5Sr17f9qnLhJCdWQoSrrnzFCmPqmVdr8rLcVNTsV4nObQkMkwLMl+ZV057wv3M1V/9iTClvrZH6mcrFsVIGGhDfS8lXZKuAPqrs3tCOxHokbzaLd3QrfxLJCTv0cR8uPFrOA2plcas65G1G9dqmtnZEO0pTLBZPzVTGmdBQgINpHmE0zJhgXGP58NywJlPMor4Xj1GwZkFDFFbIHDRfcgGFUaCu3PIIioJfT0mrSn3WF1W3GEdYZiJtdWUvTaMqH4aQOvDUm8TDlKUZKIyg8gHF69yWHMhwjp5lX0FRHEqDW5FLTxkEl56Phg+DHqVg4eKLrCj+AkqG3jQMO+OCxZaXj2DI4zWq/qq4JW4qovTy7FbTgUsslj0rikxIvuTwVvNGtWghBQ2QfHkUrI8SIyQHCeYAS2DxMJUJ4D4rYhhkGE2wmjtBwNCGcVlg4RtrDrFqFGIw4hqhvJVcGK/YhRFcjk0IxM4vDZ8+nnW1ZniQKnMW1k6emkkdHfmG4XvXEuUVpb104jqpmBdmjoCln7xz59wM896+ZdCrl4aSG/Rv3rwW5Ypc9Jk9eE780VZwpttN2mBJgnSzRp/arvBbBPD0JJaeHBYDcMhSjjyaZtjyQJpkThJnh+c2Jt3l5fqkV7bF37zpB6MtZjBMlzqlVgMptciYqkUf4HYTk/hGptef8h0vHZiwn3ubsVzRt/xj5//prvldcujMCa56yybFtD5IfRH//wTC+A+R6yczwZ2unBBBlArXHOX4wZmyMHXyZ9lq8166it1fDr4dneZU87EH8Q42Z69o7+6ps1S/+8Mf/+43v/nN737vVPM33z31Md0LX/9wjHiuwLOyTC50/a4MnPMxLPhD92nRQuaU2qY30zLoxlGDv5tlerb3a2z0n3aXuuoZdG6VzLNeQ4scYJeimQgayoD5xg2vraFRkUJpbrTpSA1gD8Ticll03rF6f/Ozn/3M7PzvXly9uzDEMuAlAozk3FzdHJm0zp2alPHFFXO4NisPiGE46vGrf1MHAyZykzvJeThOYA5QzKVanuVoHKbZugrMtLPwz/HtGINzk84nR1rl802XBetQbMEkt3RgAiZiLtI27WD0GVttajka5mNImjgU8ubVGwOws3X59lRKRC5jyZRZJH9SYGlSgKEKED7yx/6ZJtZOiaRkaYgCR+x0m41qLrIcbGYWkQhuEQFKU4xVRAlwweOS4SUBzegdH7K8JStNeSsfjSjwxmqTUDuT3UQrsNKqA7/AigLUNYrfqkx40134Mha/WAAwC7lLDCm4kT6FXYEp5m1md5NojmAwlgtmCYRfcAFymq6gWCwAfmG1nEMgO8XXVmKbUI0vSpDTy9dHVrElIBC+wotpkJzd1CuHz4nisJSrfqP4giQg6MqbkjBShxG7q3ATKj1/tyMWxdGnhVWxaJaDEcuvg0cPXn74B9NcSF2/w9fD6JFZMcLnyZxh0kffkNXZrju6OaLw5YcLymvcM0CG3vWV3PHRiQPMz++5Nv9t8vX+Wq/hvnomaXVoukpPX0f/jK2z/8nPSOt1qFiCLimpOuisg7JFlmrZjtLTRolaHYm1Jn/5/R9Mmlkpbl4d9t1YnZc3kSouQtJ9bZt3ZlY7bupPwrMz4dc4wcvjvCQaNn/RY8YyYFTMzxq7w/3/t5MpGn7mSOvmTdNKctsKqWr/g2khKFn9f5BmFyl3VjqzkOqsLof21G43pokyL3L7itmMUjJq+vv5z3+u6up5yWfpt+/e+TCRBxCXl7eeDvzHv/0b3+X97un3F2+9yGbsTk3yCu/xaYZzlrNayuGZWHXT0ZEzFm6pK6IAHPz8IvsUlLtBbjDNI6SaBgXgAATO7Rp5VjID6nTLaQRp6XnahWImu/aZ/RkoK5aB6ZaKNQ5s0QzMmZ6bvEfnXhefPP/Lv/zz//iHP7169cJM88nDBx6vSKUSpEhhG+1O5epUJKeZSNelzNi5kkndUMSxIbhRVR4xmg+z4pcljQMe0peitA5fSJE/RjSLyFM7uwSZGsrBZoG0pDUhfsVG5vQz1WGqd0gEZ1m+eVExxDmNN21RqnetdqPe0KekVBWDujaOy8G5VWtJiuZTRWdqEOE/dtPkMwvZmIXEGSmqFXoKJLkfFnSKYDCLbNNNCy9UYT6JRS4YcDCn8kXFlFMkgCS+HW/QcFIqEgyoX7UapAfrxDLjIMGQtqAXBj24Gi8hgDpRCLKfOVniV0KDU4EqaVNXKm0VHg1hlsNF7EqueMGabEneSJzeswktygKaCDlgLHw0ZVlBSJgS119yAE0Inp5cCdh5sajrFVjJCMpegkoghFNFKN+vlJQYTYl3uWhbUzS5lehKBQtXHSBrJQAuvoTELoGAMhZYfmkEVXo+IZy04CsHUDe6RyYAJYBAQBMSBHMzvpYjGLGkNWkZp4QeAa31QHb+rlSzvBKar7TmtcxDO242quxI3n9wliM71Mq9HFk06Vq11TlSk5nB2fGJp8YTm6NPvlNZw5pEV0kJccZsChT2cpQuy5nYuzfZWs9+l3nlnHY03sgvT09HTntVjJdv31mOGIDpLLfbjAAyjfOcwsrFADNZZ5MSxIZuy9dFxlImnfP8OAaU3MZOTI04Bu80+Sbjb8ouT0fHMV6IZ81gGtAktj7Bqcz/+a4yNwWz/cEujxUCh6b+f1osGgTLL/AjFhqmIbOkyhhLXl7qbN0z4bNZvtjx/lJxq7xK4t7Z8bmnu13sesBvvHJvi6fqrssw6PoskQft//GXv3mR+zSeecZkkWTn4/zkjPmybeDdN5udhhTVM3W4t9X2GXzyvatbN5hZtfhPsT40l3V2yrjlXK70hfq+6RKbne6I5HH0rGjVFu9FMYKOyhhh91Uhch6cxR+HRmYROPnMxOSwtD9Ds0qhdqSG+NasD84eHj58eP/nP//pn/2rf+lglQ935vqwOx99GMDXJIyOxidG064kZikstdQkWwofsnukNhE+JUJ+TDGh2AVyi0kpe/Sguql+6E2LReazrox4qZlBG4Ay2qmcOf1gKN6+Lox+15ktCRIoI/VTzbm5KAOm1TTmnmYSspBn+sCP/Ym3z+4liP09T6PeXLx7c/mOxVzfLUf2nHBoCZn0zqSwtv5UcFttfoxJKtP/IJEPfhLeURUGAUaupigBnwKZcVT4wlYcUm7F0hLsRXQA+rYofoWibDKEAMoFroQiF8GKhWmiUmyiS6CoyqwvuOuWQF0efIUAqgygSjahMhamZWn4XKP4TXcX2VipNy3qIdtl2SVeclSqSkOPEQ1NuF3GpTxKxbSUL1BGA4xGMi1qM4+rkJqdVtxKtKlUn0WQouVmITYJRbxYbuWlcpYoEkRVDr/46lOWlWIF8pf90a+MwEulciAFwWXpSkheCEcjFrCEVEJTXNIqBztXOfUrBIwyudq6kM3eryh3FeQR0D3tiz666dT2g71cE73/fl4+PjjR0VxdHVxeHr7LAeN01jl/+PHesV1nn049OzEu2mHuxR1Xl2/znZrt7QcZXNPryMK1VVGOiM4IR5fpblSA2RMbzSUdp/vVDX2460jz6/dZfyNOt2Ei7ns484iLTRDmysq8kqTDn4FR32EykbVyKrx8y2nKKNpmHqO0hZI0mXjGZbGmX9N1jmLv0zL0n/sexnRAtyqyLowCmShQb8Oo01DDppOrpP8sf3N3NFoFnjKnWhjnlghajzy7fmMJaW7f+wvNZw73ZFOldApPIMt2S6Vxn6vmdcoMEZwRaR5/spC3gJhWQ1Tm7mAxrjCRQdFjgFcXr7xB5ymvsfZP333rYwmcZ/8vX+cGUE3ko8JQyvu+zed5cOyTrY281WTZe1dEBg8d+scZgOkkn5ss0zS1Gq61PmX+yaqpw4NJPlT1OjltZluO1g+envCNFkSlEbMUNi+uTO1Oy3YEcSbZmdGNy/HgnBzcPANWHYy4gh0uDaWjpjH0w1WP1Hz8+PXDh7RLtVJh33tJz6mC7Je8fvEytS7DUSTmNMyMKFmlbhvv3PBmW//91Tyopn+ylFwnIQZJtlNRU+jMZXfbDMHmk67UPCldn7M2aqfaEHOhTJ0u+2fS5AKGE8tiIz5BrPmZhPyKzfbOIGtYnR3RMFgSqz++t+9C7DlUcfcL3644OXMve740mYMS6S3YGlmVx8VNCgHw78Kx7TzAguSqWOkbHHTMImpbJaJJ8ZDhFyiq/IJFNihJsbvM018E2ZIATLb6jCr6YUTTNFRi3UFhlPB8QTO2plIfHhdHFMyuqwJYAFzJKgTSCgPMYak+AGT84uvDFLAtU6Aq8XeDJSs7n1vsDZaeXyMUWZrlN7Y5qhrIdvX5YaLJOGLIJgfgDFRKt1ZiQDRtmQAJkSYKRrDCLdEKNCE+dk7TIq0s/CaxAMHlilRepd/Fw0gOQaMA0gWjKRdAkNsNgusaW7hyaM5Rr/kSVYEAQpbYxS47Fb6SADSPjWKiJT+53ho8BWUr2vfU4KwB3BjmoInFhhXqHBW5fWcZbOwxKrKY26ludA1eBlUgrOVxrM4nXRrOAweYT55++53t6tuby75gOoOmgth3xkei2q3r7+9pa7I3VXqNGM0atVvWPR09VpRfmVBro/b1h7wu3FrZ2men0UgMk5qL1IJ4U2d16ip5GhTz645dmiGbulZjkfcvsfQv/YrjJUNGgWmozvzo+GQ6Uw0ajPXS9aBSCILz5mcq2z/JtYCwjDUiVs74TChqlXJj+QbVf1D+yMnIMFXK+Gv4yQgcrZLrmkDtl29whGgtZ/fPHz9+cnqYXc3MJz7c+hY9g6f3P8yqzoj77dPv3YLi7a/vnj31jPfbb7999uJ5rz3x/s3Jowcu0ZBAdMtGpW1tbTNHA1RxBaVgPdIUq8qlkW4nuKH/kZML+PqNVH6p41PhWYNuHALtwnTPOo+ebdosNtUivdnkNwOLIVa7F2VySBGUMFHJrRn8dAsJxqnVnOrCdLcVku5ety25FgX6vEj73ke3P6g8ZyfHxuoPV+ry1fmDR93Pod7N7d3c5xr34eFJ8oGfLxF+y7e5gJdmmssoA35/8y5qxJmppBVz+g7ZrJyZUGa/SJDDq2zRJCDf84c5X0sb16aqNIQiKvPRT05vx0ql5E+6qR+09JegewJsPpug2gbP6w8n+c7SVfbPpYlVqQ/lvHov3WqyJLZiTBALpxD5sg+ABvAptHxpDvkGWbJqDL+57bMMSMliO0FARSxZFTTvqYddsA6ZVLuIgVlc1anKQa6oAvAF+KRRqwkVD8lVFJ+rPoWT/NY5nVAk+vIuCUhEVU7EjdvybX636M2sagULEEUxMDk1y2IvfqW1FGuK8CtpAK4KqV9MRZFN1LLVYixQs4Clrsq2WkPCCJKmTZAjaAAWqz02CUgy+flowZgXHhmXNjv5gucoUwcpqAFg5CCxlAteosWAi+fDUKOx5Sp9RRUuIxoOvUl99ZcQgia0hFfmro+FtGIQo6wcAORy8l7KPpMWqzdSJy1qWzPPzu5rq7d5q8lHOL1cnM15ZD6X5lyWXtTNCc7NXh/5JsxehmCPDXW9FlBzYnnP1rNDtb4SNl+60/N6omzRawGUqXNuHtJv2OqjLV2UhL2vZNkzyPxI1WOojrf0mP7XsJ0hM81t05vbCzx9kMdpnl93yzbVSEHkdTJDzYFtO+nd2dwYnaiRzRROgrpBWuG6itBlbB89Kso0I4/BMn53Pcz3LpY5KztS/cZWpM5clx3tafGjN2GMdlH2n+yUC56InNYNbkkpyuJH7Vaq/9wEsFSPGY0L/sCvTCllEmTC5P/HOw/uP9k8Psh58md/+3e//s1v//7p85dONVvvvr64UHrRyuWLZw80jYtrt7hAwGRq4LkioxpyJ0dRPhHd0Z0ZQXNUPYZmo9LkPQrTGn6rO/hTe2+OVAAE3RjP4YPZYhyWTX7nDoa0Pmkp32zMzFGpLM97L/VonPX4tlsAZGMjtY5n8+ODNMwirG21uxYqGkm7O10RZ+WXKUtyd5NK7NGMATgzjHF5+S2t756t2nfu86jmlCSBFKLyaFetcom0gd/MIG8/p5/Zu0opj5PxtF9OXtzmGc3mYp1sI2UQSH6j+TKW8NYtvEThFo25sCDNI+OH1QwlV0Z+gEwIbs19PW2SQabWFr1jls3wnEqkUFjoR8n2Nvi3KvzgN+qOqzLARjd34AgaV/wiWEBpNt9iK5aKxfLT9WyzKhbM1pwvVW00Gwx8dVVC2JUumiUHILbso0xsB0glGAduWmi4lToAY2PBojreLCGQxXtEMqwbD7ImaP8+VJ9sgT192zhRgtWWLnq94ptoE+KLWnAJmiMjX2MXQfVpUOrIStmEEFeZklUUn7VENbhL2Ta2TK02t0KT3KRxkd+G0UT5slP8JB4D2pADYyEcL1cyjPBLJZSFEQBEUQmw8Ks+wHCE1NETca0HaCw1wAgECamoAvArUwjAnCgbbk2uXJVT9sJLCAkoWQO+OgCkWBvqbTk0MkKyx2paOWJ7oLfZFU6vhA19DL+3//j+ganLu3cqfJb4Ht86rHK5f/nq4jXL6Wrs4Fouy4ZFhub6z36eCzG8TJxN6dc+XPjW6y1Wy8cnxzqA9jBZXE6u4aVIGYnyrTWTl/R/ezpaGKKqLQZHVg/v7b95o36y+x2Ht72Z7OxMS8QtDtSO6u+lYhWcFTpJnjSHXNc5byDNoKPVuYtbDYx5bWDv3Th7PWsDjwyiSSYWKQYvhh3dNQBruK41pHNE7iyCB/NP8+Rvl2GVGqCFxQfLeHRIhdnsSO1y/WMwxpiPltktrEstZXlLNNvZL15dPH/+UqHfd3TNbcmHh98/9W73a/vMf/z2m1/96lf/41//9W9+9zvfw/KW1+W1Fy9T8dzX0oNvuQWFNVSXvUwro6SZXOwei41xmCeGV31QGS+d40mWFcDWjQXkTKEHNWvQAWoZNVDGe6BpZsNakCL2vd7KzKuXyZA0YjA6RPw0JctTy2P0x7ZxdB1zVZV5Rl3Ne3p23PE1xcvdeZ+RNUknD7deL6JrSsAVfdb0GsGt89/OUsIYcS00STu7/zAPZnPL1v6+o2q2DqYOo3nx+7+ZCWLGY2LFowfQatILu84v+Z5G5t332GJcUp2hhDksqGOc2E3FCDh/nzqcUpJcR/5wbzQhr8EUx0AjTaJxWPhoouIkHSGRfdd7DT7VbLr69RdPXPs6bxlcOw6f7FQhDWRaK8srJY9coX/sqgAuifMFd2mSWnK3ceDPCBrL3zcdoOtYjTE/jYvNHgFiC9fud24+nVqE2SaR07DNeTFYAPXZrhrAWACBacznSoMMTAcS3C5ubNArQVYfqrMExkpe/mIvwMdeGE2VESRHck1xJdcUBRc9+ZBlxFv2YkqGskavTDCashdobCXDL1d2tipGkEMmU7Ta20sBNzkW0KpXrsuig2DAJsHnEGDnwNSuHF/OAWCBJAQLfcivnpAdqpsWsqYiiAAjaVVMELJw9SxMYAuoLPwGiWpCTZePPubeTuBQEl4fYKaPXq3TLaLUhdXyGNE0LX7lAMrYJCqW37w3C2AYBFi4ea+6NQEmr5+SwCHI1P+OuYJLcn3/nKmzrDEGHzh1krvqUxwHuT7hxmXxx/sPXBzoU0PzBvsd83s55a7evJaut3UP75F9/Pby/tNnL555l+PtpbWSu7a8bSG3mi4tiSdW6hTDlXn63KhrTGe+JJ4tVjmYUXTGlAPfkduzT3DoBpVzH2b3frEvJt07YDHvJ1/4WKEV93tfM7RyPbp0ze/kTk4Z3rkZX72xOSd33jemjrX+8WHOBttu96ArWZSc1UKuAzO5eP9GlbnO3smJBf40x2xdZqkwvViUSydGf1rWSRAGXGDSD2YDJDub4l4EoZfRTE8291qjNtLkGeV8kb4lqIwqhK8O84nCi3I2eiNB2KgtJpuD06fKe2gcr7v58Nd//deWvV7a+cmTL1hOef3NL39jkuR01e//+AdjsDPtFjo+x+FE1elhLG5S5GvLxDruJEWjEp+pyMxKbj42wlT6JsUoip35sqvdmMTGnIKbITJRk/0MXnnljJXnVE8q3pwvsGxtNtUHNV+3yYHlVykAmFrK5FSU1CBEqS+ibHJkAWuHQxnnCqWcGzYGlx2j3WOxFYL4zr7+NuvglKAy97nyq3TaEsqX8NykltNaykX5JF2OKeycpBLMONSimUzJ/t2f/vwXehK1kQOo3vZXkun38z7hvBGgTeVluyR0cziNjCnlrQJlX4r6E6wKCLLaNpY2Mi+rk+XM46ViP0ojH5P8wEOprKEUQ/wZleubHzMsyfQnCgww3L549fb49Fy3Y8/DJlaK+F6mDtlYkg2PwsFpu6l4ygTSYMRXWOQkv7P0YnAA/dGLrZu8ZHwEFDMWjUdIinDmA3z25+NNJQBRVHQNUU5BgKiKq0S+hPlcycqCrNIBdY3lS2PRiNoVWxrI1IbppMSyNridslTESlGwNIJi+QXAmjSfKzGgsYqhNPDgBmFkssqXjG7lLQAG1FUUxtLzOUE5AtSCaBbBbrrgEiPYdfAVLrZw7QbZILELLmMt9lkxiyKBj7gsCGjCQfKrABq1BA2Ag1djBBFzaLimAlmHrHjBpoILUikU3yCfBDQaEjJRMAXAS1SJ4WGa0zY5mAYRVzFjHQy3LICmUfxdoHhylg4wRSJTVcCiOEFbrhyMfjnCPb7TCtxZMD9Mklt652HodXZgvaSpw3Ng+uP9U/d4HL27SifelnZ172M+TRz7afBoPx4cHXiH9Ozk6IsnV999/8Ja0zEWPYqtQWnF1tuZTTSJinJEGSFvHF1HStYj2T12A77HUYy8f+7M6pxUtQqzQHXw1v62HaC9Ow9OjyyI0dzcPM/++N6Nk8zm7WRn+Ttb0HnJMTvj762WnGvN+VUdqyR5BpMZWmORlH+U0nqy6rln09oupTKyOHeu1CZkFNa00LaMmoUWrtzJXDI0tWUXADe4ACyF/0G/Eti0kpsKSh0WTJGtEpDJexagP3CGYzYk4f0HXzrw9MAH+PZ++/s//ubXv/7lL3/57//D3yrr1N7ss1tWupA0Nsydz/TyxzI2QANXz8/l7ySWijQ8Mt5hMhstUNRW3Vgr0bTJCi+9NmewaV2lpfpnviP13Io2Z6zAQ2OfOn8ZKsM0GU9/nBnA+EyezlNyWGxr1Ym1Yp+xOelO6W37t5SdJyNjxDwh1kR9K0wa86V3MyGzbrOY9qj0nNxLI3UmhyKMglITiGoyiNH/e+9vjk8Ojk/OHk4lautIjZp5vCDDWkgbJRwgo6gSGQ02fYJaSWULZilP15V1Asn81kgSpLIxwXQOMHUxzPQk8s4UHMnblXS0z//Z8PfrPasc5pijrEZfTdgt1gbgo5NzqxMVQi+VPLF4MwfK8YI4e/6aVS2MAjCUn1a6MBQO5egwTDt9ztSHiJ7mw6cn+tqhgMwK8jcbCI1D2pQArRnFLB+Ah19XQ7R5sFGTrKjloylcYAVr7rIsP8XgPvE5hgBmPoYTTEX4YTMuCzUWUHiRkd+0SvBJhynCktVHIK2achdDYIOVXDKUAI59CsAgW84qY6WLAG+dLACQpeLMXAcjpwQrDRc8AmTwJRaER8AOTQJ+iS2mxjEhFaxkALLlluQKEeTawQEWGQCjpPnwFSItDrKUDdZHD08mAKZ+AcbftY8o7HXqSSmrKmSD5OAV/Cx1sRzkrkOGvVH82q1agQGItSLqzQnIaSqZ5qlF0RmBzm2Uz0fc9+58OHDPe96h3Ptg9bl/5/T9nv5Rr0ArlVBLfnvlzHLmvw+OfeQwF1RdWibp9/NQLXc4UOn1xaUtULve1p0ZwZTazCYBlIxWrQ52tSWV06TeznTFx/5x7jfafA3p5p4OK49erV50g/c+XufJrM/AydHx4YP5GO3b1xcvHVi1tZ6vOMxaNvvP2T2135ghOSetDUxC1ieRlOe8mVjklZ3JeJbmTL55PPzhzuVb9VardzuQU96bboHaeswqT/9kYVxheCHwLiCjgtzCL3jQn3slW9VgSU7RbesGmMOZxFXPNBnVJhV1xAV4e/n2wf3zR0++UPd++Td/84ff/eHXv/rVt398dfzAt+DdonJCmoUsOaZJtqsjPAvoVD+VRLUbUZCb/i0DYpDJTogaL5zNWQG1aPSZa4q3asc+oc3OR/ofZPTxLBVB6h/D3rpc7cBxJ9+l7OSVSibP2JSWP/ZWUhtiS7e1xZwStLexccikorbPAJzCTflOQ9soM0M57WikLTir5TyZOdYYkoqiDah33r15n8wjyktWTg76xbLnexLamPX6NK5MDOhJ8m2ehGycoNzVIdNS9EJahjautK5sf3/84K16jHLo3ILfMU4aQpU3fTCXUK2mNMlw+DTjSFia3HZgTmUdt2Hcrtk00U2lHMtvCmq2WunDWH2lyhMlGyF4T8/Por22pyo5666xzIQkU9SRIDJmnEJkVC+qwXPNLK0aBExeNmvZxsJMXpI7GMHmYhSPV4xYqRS52Q9pWPRyjAEuT0nry1VpsHSQJg5xDVS1EDQNPreELOEA7CuqWipI+B4lWPqJUpwoq8+SUJmC029u0Els60jghBYLGEaBFeA3R5LgkMFwn9GvdFeKpSnL4tplrASYXZZdW6kWjUWghx9NM6I0rdphV6VWcWRVeGkoSE7ZNWYsTXFRivUYtJIlWsYGqzkfF/lNjo9lyWlsg40Sy8ETUrgpggsg4wrzF4yFWxgAFnpWGoB6ZDYvjSpLJaMXVcvAEGvMq9qLJQnHTY9pOlv5Xtvw7Zd51i4BvPpDDSHjkHz4+3DtPdp8hTODHgUs6PO0iXJ8SbgpyxM3f+8PM2Z7XuStpDcHPkezZyTOPN7/O/s//+onTw9fWVC6vsqSkqgMt7qtqVBkTf6nmg10enrgRkEPDiygk/ftjPut132Js/FjYzz9at7AIYpCcqTDcheTOxaVpSi7xm/HTHmurFws5KzpxbqL8dpDnKRtLzDTeQ1OGrGiTOQ8+I0F/WyQO15qDkU/u8Mfrxzg8uWo7Nq1OA5HMcK5GHdswudg+JJYQOBZBW76IeEQjAmy9P8H3GY8m1QIktEcybGTcZDHLkmGBCgrfknnu35uZZKbzeg7VvFcM7HdZvy73/z9//jv/8P3335nif+Tnz3eO/Me6thBWA+TE0L2QbK3WcUSm/hNVdQaSj/D2SeFo0ZG6hAnSyMzqDmSTgD+UGdo4fsOPUmZm4JtPDhAYDmonB998QXDgkjQZ4ZgpI29gwfoKzg1nL9n1NzucjGIKmzMioRs4Tg3n+rPbBKdVauJV3SMtpa7tKJiXF74ya9VsJSj5KY3MBkJfkYc1cPsQgNCcGxrNtdUpaVIqLdGpigO0joyN53Dh4myqzDLdDOdk9M8YNJjGyY4zefira+G4EvfPkcYqGfemi3ZXPtsvW3SkeptqjeTxEwRNj1S23sYx8GzWP3ITKOe2TYNEQm3UALkNSddIvlY8unviwv29A3Hq/0c42YWcmbCI3epyXWiAFthAZAVs9JVRvCcdArw0VCGT2rp+QhwcQB5QbDoZagSclBlMUxZJtXGfeYji5spoShgE6iZ1JWV2K5mkMMWYkCTg2SdRVbNGkxxzIKP3yxJaykJXq70U7LBNZXP/OazsVOI93LXwo4jpGpjXMQw8IJNgv+ZI0AlQ0DDpSQajE2FNMEKXCqtZEUtGEsJyg4vs5x6A084zC69YOmX2g2qFsjqqj9/BG1mlMi28VFMdazk9gIoBTkpIqtwQJF4YZoQHw0JALGtjuByVQ7i6o+gRgA0Ci8A8ZJQgBxCFhK9IIey/q4CyODr4BcXSj1YeFOAtJdQBkIZylg2OjoCHLws6MuywNM28pxzVJUdvV0aEm69TKbyN+mXDOPkpmVnW9dRBbcYHpscm+a7o5B/c+eegz8XJ0cX7955ZUPHZbxTya/ftb6lN8RNGb/+n993bOvw/qlLjtx8EGUNjQCfWSfeHqMLsjz7BeuxKOQIq7sQdau6IAchX5vBu3LBJ9qYIm8TWv4qJPk137p7drTvM+xk5t3RDDQZ5lqYJhLpoI3utrjNO4QtBCxW5rA000jDk+U9w7DLgRXWzY2i4cDMUsuQnGxsqwe4rrHgH0dtST7/dSSbVdLXb0dofSMN3Xssawo3tcVoZmoRoQoqe7Z1U2gb2EzGBuN/+Pf/g+2B1y8vXFp15KTb2fmL63cowjsVSelmXuMBgYWT8tDDSj6CAgOcHme2jdAf/VhEDvGnMdheYWSMkwSFjbsZnlLJFU+2Er1bZHKgaqF68uVjUlEiGJo0BE4UAzuPwNKKxuA74693GGJ49i9ZHhMM6IYZ6ZYxg1jcqJ0tgjzA7wXUo5J6kn8mKzmknxGTAZIit8dO9MmwLdKgHbwmF+2N2d4NznicmslIkrnxHnL0T3VKOptm6kuLWW7m+ybyO3MLPaRO4M3Fc1wxS/oEHyf24NTRtmuPUyZPmSaYHdAzQlnTM/K5tSodyFSF6DZFY1IzT/6nbROpMmgP8xYMndG05JLlMW8mZB9vDb0qBs1Oz13DcnR9m/4tt3Ep8aifiRYMX5NqQnxFM/JSzxmZ336yCaGHlKOStWjqeyiMpq68iEuvIGFw8Qtj2fR6S8TiBKCr28ibH70FJIklANCM06GDtxz5FeRILnsTLhKseMDIqFK3YelHZ6a7h1d9i+c3RcCucz9rg02F3xT5TWsRN1iyhVxAlRELoN4K0hNNeReyXCvFcpXRdrmky7LkCDJR2SG50izJBYpMuxxXJOHwTCHI4S0M3+CuGjBVFRkZfK2i6lWIWOyQ9Im4bVVYQowZGKstYniMfFyVDMYFJpwPLgawMBULX7dLD1MWArkGJbEmBIu4vEvPldaSiYsT3BVFZCiT9Rz40RNq3mMU7dcIxSjvtds87pz9Pkdg0ddWfo1XBm8ySQbf3vWqzgcPYq1kbUYhsy7TZi1AVFzy77qn/n12IV3tQRVPXlU7iZoO6Uo9YH5zmZachfKmf0xTVz90ypZEhh8X0kpI59zzaMdnjs5k49EoayjO1QqzCPbQ7eKNqyNsGt+zeWm/fA6/mAVKyX7AvhHo7NBLnO/c1SSDBm86G9wsavSvFDCOyViOciuCuS/DSCSvh7dZTtshdA7l0ltZrJV+NgeevKl0ea1P1Lpvshqb8qrNmVe+ONJW0QAaLKYEuzQL8xlAWrQaV95iIBdeuuptjy5ItcSN5dPaFwO80etOseODo/PT+3nZ5Opm/3RzeJNYTgn6BIdLCNXz6W+zGKqsiAInuNVkze6VYIwXPBkFmv9cuEzG5ilmxl0VzFgzpjLLca/4ST4amG8Vbqa2MgKuGalEmqCs1QfUlcDrPPCJMldUJadmzsIX3FGUhOAzgkSVCQUBw6oqm81nuVQrgmz0ZjzvGY45/YBDdc4QN617egBNyBMOM9Ds0mpYivrmbi7jVBnswwDGIDGsD0EQTYIbszdIbWj/4PzBA7EZY209GZKvvQFkZeyUlinQjW+ZkRnObGVk/W3dTazSl/3YcqqZaCvayNn2NsuGMUoqOC953tD0uMA89zUAU9e9m6xqh/x2vs6U2hX62HhYRue0jgiqEBYDz/vVm2xGy23fTklkn1KsLM+ke3X7VkjUGsWkXnPJFIwgH7yZ2JJbR2I4pjMtwIfkSpCDYtvESkAPjnGHKuwoF+9KuBg0BaSNCwxTzRoFVXx9BE13EZdl+fP0ROiTkkuNYLcOe111a1qlhAHQp0FlA+ZXK7FlJAlBiRcAKRbx8hcBZBMXBeiQhnFXDhj9SrG86JcmFQKPEp6jG7jlt5CNKhfiBukP4zBt5xCfCcfLlYXf2CVhN++llCJlqg+MJBrkl6vS+BW1Zk4oKwG+AJ98PSC/pq4oeKJKUwzeVS7Y60oALnE14QtC0ispbtUDN+iZ3PSufR0nzT7Hm7wSjNHlDnlzQlrZsMrZxlTyMOqSrUSNhkZED9ntm+3fPcrj35wFSreLSd/mLzmafUFq6ap0lXJrKTQqjQXGi62y/rzjEaDyscAhgc30ZqdnufzIsWcdm9H59Ghfn20prFMg0yeSFMGJAfLmQ04ss9MNc8UYfJP9r7766suH5+/fXrx5+Sw3C1rt+Hd714dhfHTGaG2NLkfyiwOX6UdmGtmbTg68A+XhryPJzl3kbZSxYmYY0pFLWX7vDkJ/WY21ekzS2y5v2/dFoXEx+7ZAC28ifvgjdQgEdS1TGBWgUYASxHTjBDNMJvObJCRFPS8dnZ2csX1GG7nDeHD34u3rPlGlM4GGPonhlVzkjAXBxg6LBRj7ou3NhyBk8xAhnwyI4bb0mUONC0F6jLm6ZZbOScVDAEWo3uS0laf7ecqQRHW5Pl04QnCjA2cwmw1nGtZBNorwyJrxumtWXNvRFxUyQjZjuVxnuyMl3B5pxo+smHFYsXaHLMake60KznqHIVXK3OqVeUb65TEBdNRQjVRSswonwz3ZtcWSzQqjcb7JO3qq7N6ay2t1WuvMQWb+OwX37s0FGuva1F5fenzwQEZ0TL///W9TiPkifYxgyoJZ20M2MiOZwGRyrMEyC6Z2DbVoosGsDEXR3oBprHX+KxvdjrzNJyajm1WQF/THxQ7cpisTmvo2aQlQLJZWZDMhIDPIGapwm0NUK0gucrbVmP6x3zhRfvnVnF+Yv9wmt40rdeOou4hIR1Ch0hZc+V94QPF8rngSiq8oEkSBAewiJwCianR4GD4a+NoLe9MCcJWzC+BdSPikPakTFYZxpKHZBEaBsvBLjEBRCQIwKic+J6hrK55PwohP7qSLgJJDGA9BacQW5hffpBeSEJnaJj0VXkrjSslHAFGWUkoLhm6NbRR4iQWjFAQURg/Db2mC6SMolnyU4LIDOPhdAnATBSiv3YRQln4hCV9OLDyuWhKloFj4pQkD0qHJQepAFzv6xYsRV81VX5BDIAolRo4ESARpMhl/jS6z5ZDsJhy7fVpD5OEZelGOWMkaZQx7H/by3q/Cz2LKKOxZ7Nzqh4B86tNw/8prSgrC+z9SOjjVbPcOTu/uXbzLXh1KOkzW0qN/uDYrZecUnORkhcpZx8wK2Cu+JwZe5TMLpnTWXjk6PTKQO9ZlzHejk4WsOT4OC4u8VXL3ngqpO4t9mTQnnaWTve4vnzz5Zz/7+uri1bd3by8sBB00MsOYZX6eb+fGg5xCssE39pBohi92pLas2PvU1VDWoO+yj3ypMWe47+SMuwwYv3W67rG8541877DlNRL4bb4+AUWGZ+sWzRbxg1/mQqA0W6DixlCfaMRy7F9gtnw/xS6IPgaqVqTjg0xdfDKOMBuPJDN/qkvrjAQ2rYZI2NpyScroHqfWTEy2+AeAN0JteTdc6tUk2u9vZsPZEVKDSMZdH7g860mraUezojo9PSNBwm3I6J0DUPTsUJeUxwFGUTrSPa04WkUvfU6qurJdTWaWp8gdes9TxU4Isjqc0dvONnjw5lbp3JpEgDQEc7zUkZlJxkgKRBbyL1ePCduLvmf81aBV+hz2v7wxvLV12LsC5JWe6X/soO+pZVv39dlPgfSJoZL45Zj345//+V9GyWxHO73+3gkpzqBpLpmcbXstvBUrlZGTTFAeEhlfkE0A/FiDfQb+4zd/MpPExbzZeZ4H0oLvUqiMud3GIGt6OZUD2FGeGNMOMjnJS0jSlS+nMqJtNNGqikwQDQkKlD+mjp7wdeAGlw/AstmfLBbpJBqv9SNZmj690smd451KIoWkIlFI3+U/Mj6FiMKLfjoiJZFDQIKDybpHKswhKJUwT/fXFKuGsVAOYURVIIzgUm+UigfpvQzSahH0kMjIAS96ZMUnatveRqOYdSK9x3yCi9Ohel5Ty+JSOnxBrjqUTKKCdYQsGulWMjKxuMRykPxFBk6N/PDh7dsLRuCKGQWSnFhwkeRgBBOiPoG5Jr3Sku4uBnG56Fn50totU1s68OQ0CVEAQUCyOm2gqbQySUgQnlbFrCRKLFhMVUIDX5X4YLGQ4LZYclrKRpSKreRqUhYaHh9mKzVRBgsDyLSyzEtl0FT0yvdDr+0Qn6f/dcLu/eWdN+kolWP+UtgZtcxmHVSeder+Uc6MIEg2bj+8kimTVqUm716SvL1+994MV6HrffyztXbI18Hl+2x3Dp+eHNwevb29eP3eV9w+3Jzs354lZ/ce3r19fnt98MXJ73/5xjjqca03ij1h0x/odeTi6u3FKR0/XL+9uPOv/vnDP79/vO8J7N5t3jU6ONGUWGv/9ub8jnsk9w4/vDv5cHjqwOy+z6nm4v3v37z5y3/2V7///sUDX719eP/NzZ3Th0dv312fn95evLrz4Oz6i4e3Bx+ff/Hk8C+//udPv/3mD7/93ZvXPiZ76SnZ3v6xjaI8f/vw8a379rNayLsmzMna+inTH1fyOT/FRLrPYxcUHqoJuuzbq3dpd2N/v+mdry/vuLnfWVcPy11hwakWpUG2d5tnrnHbhSQw66tpvxOh/+tvfPuQDSwCQopRPoDWHLEb/F0nmMfiKV8rdhNhKzFWzoNu/aIvFuF/48KmEw/C39+98i6nAWajQFKRoTmgNCmmF5JKhfNv5hkzIKNtNNeONjnxPGNwU6M0nBnT3+85NufBqK8Lq9jeLzrw9Q42UR/5jhPY19cipKLau07NGCNHRsQTD+p1mGnZH2+v33pKbPHtZbO0VnvL08Bl1Ibv6BbV8uByXNRzw8q2P9GNhk1B3DU6vrWFYo8k27x5zDdP+m49EG9G8eyl40/rSy/tI73RJ4malUa+S58tcR1IyFpYm50UPSPa89TWe8Zv33UKR0ETwn014tqxZ8Knj1KJNSjGMZwz3IyjoqzwXR9C63YOgGev3yVRuzyHZ/eOXfT2/uDBV2rji+9+GzKnKLInHakJfrj2GCht3szTIQ/6Qc6QefPh6uzBfebKB9o1jbMzD5qfv3r94d7R2ePHp2f3fU4jj6JyG63+447PniCevlKpRx2eNAxM4Fa5ZMbQPgOqfsDXzQUNRUrfh0jpc5YH2JudhvQi07cn5wc+76GL00mpUXr4zVxBLobsU1UX1aJMv086oQWK5bfeb+rBtrtH2W0H+BVFFcQkVBUARwKfZqIWMVilnPiM3AACG1sWXFhgSgZWJGg41be6NdjUwRItPeIqIJU6sU1riS09v+pVIB+GkEUM5gjhp6C3eQFIpWQGNrEVBQ+WI2rTAcEiq3A+/YvkyxThTVTB7wpZ9KQVrtgG8Upo0axUluYraTScIOGNXTRS/4wRDYd+AZVTCcVrzDVIg2BALVDeEmMswA4LQADPl24n+xhhaqumBcMtUUtDj0iDTFFo/um6Reki5GJWsdkiVtsZ5f3sOO3lkGO6eIrIUoSDXDhgnq6QTfWdzHx3kb5pNPRuEYygBADEAtJXnuQjDZyCJkQxuUKEM1FxMcrl/r2ro/0PV1cOG5u/I1P4ZhLnd8y4Dy98RUZ+Xdl/ePB2Tp82O+TT8+z4jg/RWCC5actTXle5OW9koZIeJ/U8nfU83NvUEOOnV0P+1b/45zf3Ds0w9Oz3z0/1Ql6NMhHPWaNcR3Dv3N1aR/sW9LKvcv2bf/2vfFD46fMXL1/7hHw6Ca0qz46ZTUF4eJ25i9NjH+VUfl+/eaf7VVA68TEgM8Qgx4dnjIM4FldDxKV/iRlnCR0LpzKHPIwdI9iZC+0UPWDXdUEJszsS7xL8GJZKBYoC/5igsSXbJS4llTN2zgCzwWzUY7zOwjdVNLsBW/mA1J48I8gAJ4/KEQw5D3qnK5jRcm6H9LnouVhDebqX26rrKje5KC+TZiZ6f5lvPzx4mAl0G7XuQuG2g8yJgPlIkaikxOXE1L3TbbVk/iomp/DVpP6yDPyo3/4nGRXVWFHYwc0SRg6G2AL8lOOYBaMgFm5Y0lRR8p1uW2qgRxCDjMX4uFYQwKEp0Fh+3WIUlARLyDf45OifqW43eVK8dYLXZlYGBROLnAwnkDJ55fj9e7b1I930vodHCpnNOU9zeoyN8Jzm5k3F1kirQFWlRjUULD6Uk2s+pHh4GW8sAEEV4BdeGYyV5goXxJDN40qixPVLgGazf1sRfNGlWIZb2hRQOURxDSIGcDCSbzYarJyV0jClgGHK1Sg+RkkXL6omJqoCR/zGTGJ3HS4ZJlnhIVYeuBBgqXAElQNIzZ5940pAUBrBps7nGkvCAopv1vh1kIDyLkpA502VXGll14PTsNUdgLI27MBMFJYmij6a7fYCEyw93hJULB8XfylT9fiQUmmWBZGVEr4KlIVsZKKWD0DDVYfiBRfBwsM0uWL4BeCr7cJM4pvGXCRGaizF2GFXPpoSaGhE7d3L9AvBHOfIHpwZra/9GYiti3OsVV/ueVIWO6O2NRcGo4HcsWQCskmC7+TevDN2zRMHgqGcmbo2moyThBdg4FMJfasuMcb7HM7ME969vUcnRzfu5js5vLo8sXF2/faNNY3npr5U9MFa+XDvweNHb75/RX+i9g8PPrx9b3UWLRybsirZv/PF45MnT57k1v39u7agT6zIbTobgOmsDtzNo5BMoOcCh2QZs48q5jNwhz/7yVcfD07+9O1TB5gvb+48Or/79lUW+r6b43uuZ8eHV69fWCQ4XK3PevTogcH1yeXN89dvnr+48EJIvig/dY/YQ2v7e3lupxLryyzxs1DsK7F5Om6VqNJYwx06h5bB4MpzVhuGDBnlMtDmsd31PXdpp/OMcUjMZudyQ9kef+reGnlrbPu5G+A/8ZPsT4UswC/xAnZ5h/KTUEF6lh1ZhuGpCgw8Ihu1GYM3YmcGgYWbJKb8QTkOnIxzeU666a7dtZ7PJ9hw7sUaSnIYzc2uDK3GCxnPpyRzLm/f6GsAQADJXArd4kVN05mKZb/YUV3O0BuakG27siYdzDijU3I0rlFaE2Az2KRPSFxp6uPdpS+MSuzUggApxHGVCQ8JrvDKEb8AUWi4CgeUfdevhPrwkih7MSTj5eRd7P7Rwwyux6paLoHQpc8Ed/Y4P368vL69vM6FBxmGHJw4Ob778drNM/KdfayPH5149hqCJBSKykgffcw8Xs6zfKkoICmSzKGHGXNunoVRCWYpCaZX87hyKhaWkALgZiRWYK7YJqZHz18CkQnWLXo0rS4b0rCOW3QriH/jbOiN0nyxRHASlg0+e0F2BN1GZd5EYO0eHbc9b9k3So2uMMhgAE2FkAZJBmx0mKTRcOiLrBzCC7RcsdfWKMH8sc8ntSNiojAWbnJVnk/4wiAQ5MNIV2wys1NHQzDJL3YYMJ8Tg33RF2Pa1nnDIlj0MOBarzD22kEQXFeLmfRVz9Eo3cMKktBESynIrTJquo0iENC0qkaFwCwCeNIaCw8mjb+Sq5DqVmJR2ItZlEv+SnSlu5uEF2pltcLD4n+2pG+th9+7H/ntm3xG7dDbnhnRzo7337x7HZKxOeqWtDL74JMv5GRfKDdOGP2opM1fzR3IaaZrDqTQHTaZxi97YH+KwTJwHr+7yOd2zyPT20NfTMgzN1cQ5HNmrrDwpHTPu4Z//P5VLUMmB7bQVK8d2CfuwYPzBw/v6y7sL3vY4Smwk/OWsCGLKeXVXD29vdDV9Tu9xd17V67MO3+49/jB+f7R+VePzh+f3/nDd3cuXua8ycPjO48dsr1zu2+UtmEzm3NXuXYgTyLPHxzdf/Tw/MHrZy9euRro7bWYt2qL6njsc/R7B27HdcGlgcBA7kTZPP3KhWAUyWjr+uB7Vu22Tw+c+DbVmM8m574IsUYre2mKIE8e534ms4noj8ccGLu/TIFaYTJ/ks1d1/a4i9mFV7kvoCX6GU0KeseVuIicIYrb1M9Mw2JpDpXEi09bFtLaNhGNyrQp9daZca+ylL4NHDID597eqfvJtp8ONHba3tK9ayH+ciZeqVsuf3yP2C3NdlCUKStwJBDsvwSsfUeY0RtK9Iz9sdmtUSi1Yvvgkw7DOMzJwUjYNi5BM9NWuaawk51N/2NAT8LDGD8Pyzeu+Og844dEV9cE2fw6vYx66ZCWNh0CZGFAhZdGc6v0IsnhKr/0UqEwDICfF54UkhF03ybR7AtNL2uamxY4zuQyQ64Zr+rqu6K+c5jl3+2ri5dv31yaDp7ff5CBc7PxyyImTNlFW0pKehcWlPTSc8ELQ2fw1rDJoK6jsbu8MMVXvhzBgBss/a4P/49uQYuTDFc7Fl7iqBKpkw1IsTCMo8AEwU0boG6BCdnlhS8Gsq7yg9zOtuRZkcNgV4odeCCbw7KXi7QKNOshDbxSn5h41RC+d4ciq/L8xu4KBNfhAiwNV34B8KRxaBAIQgp6+FPhghzhDVZ/xPTnc8UDmlbVWPhVwCUjucCuv5LAVVgsOWAAB2+fBoaTStNqVIOlAQOKYUPE1XxXzoLLvoKI6SbYfFUOuDWhxHwEHGK9VVsguGojLmaRVUi1qh3KXn9iDajvjb6vXz2/Y7p8dHD35vjeyanlg6DEmh7KZNmf80NXn15jcFOkR09N1Ad5iTWS6DlVM7B0OXWv+UJmZdc/sa47zKoqj6PdK5SPTVmde5XXOvfGW8FXH71zgkw3qvv66DByDjBlXLNKtF2tYM5tP+cuJHcR+8ZSFoyeMJsQEOiY6cebJG16wCh5+j0jRCYwruzyxcN5Bv5v/+Vf/p//2//Dr/7+93/607dfPL7/xcMHf/bznz16cOrsmHPAua7i+v2TLx5loL3KA0h18uuvfPn0sZc+LCCePXvhG3yupL++ukgj2997eHhy895rISqPoSw1E2UqUd6u0XgzCM1/wwRVZC1rwJx8m7eHZxi2ELeNf+cyL5ZkvsV1gFG/lUCMPIeJxpfCOAPcf3IVHEvutBQ8MFyByvhP+x3taDRj9Ay3MwdInQinB4uRthnBNxvwyXmqxNRYJajEdW7KQKFkKpqlrQ3nHLA6se0wDnJG33zlQjVxUbilm23n45PDRw++yML3o8fnVz5LXP2ZVN126k7Rc97r7Z5zlNmswuXdPTFp+JNmZxKbzhZ76vYPXXgnN37FLhf0GI2cxsBUprPMaohYfrnAGD+TUGJ+3iWYdOks8RIDtPcK4S/hZKaKTOrlQklI5cPXYeFKaWosfTIi3CROlYp2987vPzKAYkRmMNbyXSbviMOJ5zN7ORj18vWrjr4+/6AFuaFJ65nCzTt+fYiVMfjDZqe9uaYAmRxgtIjygMYCZoc+1Ybaze8yFE0KN4pBQp8p3sZ66EnjF2ha/Dpk8JthvDwiAAsWRFHpFSEqqk1Ria3GwxH9BCuBXwcDT9FGQQ53Shp+0RTZdFMOQ1+xSXFbNmBqLB9AQmObUFUFl4bYwkwDwy3Nm/TyRaXMttkf2ngwEixZkaXh2wIifAgoFVey+iVu6lVDptDQsGKRwYN3M1tkJTQWASFoICeRzfuygvBoyi64Uix7MVjkCyWHQLBkjeXD8xe+NIIk1xdbZ80EwwmK3aKTLpjfBqkloAGjaWbrtwhouywg6Sof5eYha2VWw6UJRmRxBsep3DkLfHf/3cVr698bY1IObNy8dXAqL5x8tBNImtT5NJGcKVtuPipWvrLIcOCXrBAZQRNjgM0mMT5ZCwDvs0M5K5ER6dZpGR0lgfpXHaXqmzFF56Da6C/u7b29ur38YE7+8dGDcxaCcnTEcgSsIwEY8ZnKwlHX7Lnvk8cutTT62uZmWFcMynG+eW6kzorKN+cM7TIq6ZTbPVvhBJB28/bi/vHRf/Nf/bv/6r/8d5ZlRlvrKmRvXz23JeAzEWCbcu+ur6yHFYfjNAZ3CpsUSMNNBKcnXz9+dObbfN9+972Nab1IzptJbu6mNgxbQqcLzNhlhZzK4BUUqrGLRd39uVvqzbvLwzyJ7xItV1riUk8tTlQRu6gKLlvrcXqug7kbOZWNeVvQkZ+Z0ib04x/ahnqqboHxP8dAokQGGLfpARuokC2s5DbDTJKe6pRcDbISVK5kl8vrvHGKSFVViTymVTQmLNqU41R9du7A74xEUmCC1BxB3bVCP1TOuSo8H1ORFWWROdXoicJYbsvAjMbQGxu1mGP4mfykcqXA7KXKggoggegxjlYwxadybGPRgOEbVaCMZdEatAk0RKVpqBWzZye2ZPB1FdUN8yUhq4jU/rhF1mAxu/BSspSCHaRLw4bUoMBSmLYoPcopPQPMXxqNkpr34oZ+b//k1LTx6Oz+IyZ9+/IP5kZGX1vPrs989OgLRFaCaZ+Kbipb7DDTrbS2nRV5U6zm1KgRGlw6q+Hg6lnNBVFyLYvqjyCJ2Dmb0+ClFMWJ4hDvSo5KQ7/Zgi7D8sVJpswqB6AYBIUBNKiWMIIISilYoMRrABDksPDRVH6B4sGc+wCoW2nwTWUJF4VGsCwRMkcVYDjE8IAqoHianGBja7WQjgT+pBmviS48OXXFFCZtBRm0yS0Jglzq6A+HOpIxikJZdnA0H5e7lkZnoeIbJL8qNelGNTvF8CE5lPC6huJLs3QoHhIlH55YrkE+Lsi6wiUDUwlcV22RlaaMYA4BPAJ+xVZ5RS8hsaIKKJHaBxcaMEAURnCT2PUjverp0l0asem88nIN2aR7dEn2HMNyjUQ2EDMij0NMsuzPh2ycNM4EKx3kOB/cquQDp17Nk9OveICb+WgVwAtz1TpucXx17TFeMug9Hl2nkcUVPJ1EWkzl+p7353qno7uevGbSfuuKaT2sR6NWke4Tei9KOzEWPnpwZgj2gorJuyOyl64l0Mg9udXm3LOVJfGmemfprHDU8ByQdl5D/va+f/rNy4t3vgTrG/Ms7GKnDz5p5H6Dd2/lIfdazTEQn4XwjPnBQ7sCRxrJuzz5fjfT+Vyufvbk/uOHZz/56vF3T58/e/byzdtLJnA5pUfZe3bSvX2SjtBcw2sq8Q3NyivXieTVl+jkFusPd/ZvD24P57Stx8M+5aeTEcmG2oYXvG4/7N0c9M1Oj+jgGSD1bXfRm77jH3GtVyIXsAhhuBUEKNXaDXnx2+CGagWXeRdlgQo0oyBKRW2zYgS5le2HDx445Gauw3qmgDCpIQZUd4rNFk6ylnXbHArPJ4Y+PrAT+sCzhqzPNL8ckjs6unKsfBxm9U0dM+Jnm8QLPs40aETJmQal3rON4/k/GIBxkUZDgIxV1HBsPJMe+NbhndjYJ+qmr04KgmWY4oocUTAbY80PJBYOICoVAFe02rhKqF/iwqJJg+FkHxIjV6tWjmCVRCCJwhhz2Wh0ykLb2URRk9i8epu309NszQKZ5ez8zNB08/aZa55dCuty7fv3H7qJK0NmKjBC+ZGjVMhY2EbGuBG48arkkG5ys2JXvgpgLRkW2i6WpTkCWSs7mgWUYCN9+yO2SW9WwKX+zG9KKYGxggSw17AAxPUrq2lXaFMpgeQhF32zIcoKkum5prLS0nFAdn6EmBMF01TAXOWLAmzmj9PRt2gjdOiHO9VOm6GG4pcomqpUUeDJ36dpXeVXeb74BUtObDXh7zppkcwppSUTMbhkK9GS1SCiBGlbsZDNryCt4Gv5iB07wMsFf7ldHX4MIytj1cOLhljm5SAFm6mlZxXD2Kj6kGsujIucqlp/JYGrUQCaw++KFeTkpaYguUCVscJuliGLrybZ680aJZbntseEPjr9eHP19r2LMZwU8vlAxWgc9Cnf1+9qYfLz0cEpfbBUamdv3djCqhnFGlvFcpIjv/QoTaupAajxbedi4byj4ygma3j2O3ry04XaNLbZ6OGyy509R8n3E9xOQLGr3K+sYe55lXLeoTg/OfTNTU9cBfN05b2Byv2WjpLdNU7f7uW7aSoqtOWuVeKRJVc32V64M8tnJW+8F0Xam9cXbz58ODu+T1HdY26WPjl5++7iwi3VrvjImylOSKu/B4YUnZIvThiuKXvlscT1jZ26Lx7/2c9++vUf//T9N3/67o/ffK+is8Bc1Zdxkq1tFHjmq3aaHzAfC8mJrWdPmo3rNv8M1Tk0lPLJU0+TyXt7875m7kGWjxvvl+7dvTbyOn9UO/+T/BbKLgvMj5EtvpKpIyXgw6Qef3JrkNYjmwakJ5E58SXmmyFOLc2GqtFRBYjb8+1JN42d3b+fHWdR5ouyx+yJzOEpZeC1LrKyVlZejx7lMNFbHx7O+Xmr5bzpi9Fr3nwpWqGxduo6q7OeuZVQBp6UAhi9SMrwsZS4vqB0my1wmxUfHHlTjqVcWSvxiAqIkgOYA6DkikzcjiNTiCZce1HPgBevqBIAKq1ApY3UTdctFiUhbNIG1a4YjeCIj0c9zSaiUoh38lnEpJ+tBQ96R6B5YRL15sHby7R0r8Fnc+jw+MHDhzTM9Wc+6W3abRPK2hm3Qplpt5vw1FJ2W/oTKEUOoP/ZxYdvHJlT0LEVNzqkl9CfNCMwgnhVCZnyGdFSVhofWTHUBnOQK+loI7BENwE+haZujVGGh6JSev8uilYDQLqkkaDDkm00ghwCMumEAMw1FfkR2yiqrFiM9BPVYhbksETWFB7hzUZVJXBD4AnNVn8YxMQShR4NYr6MoIFvsAALooFnNZnVwMDLTbLxEFeOvBAbtSYVwcJYkJEsCDY6LV7AEkhO6UlrTuVd0pqrIHYOcbXll77CmyJpKFshxIqCRwlPlKiywwBIg0HmdJJYNIwgy/CCCgumKVZtLMsVL4iGI4ffhADwu/ogXuWOsjACTurSAuDlVxR6pqtA+pTM8NbiWM0DUEr7uFJspnBpvp6s2fs0hp3fv2/N9vb1a2NC9gf3YworY4XGlpH88cPFxStm9qagQdJWdTKVwTjyqCdz1qe4OCg6SDevAGmoPhNrFLnevJKOkUHhja2e5HkdyFUUMqs5Z9F48zEbYh/fHe5lFPTA9W9+/f3Jfaeu3lpCOU59/6FLIql556vHR4/v3//5T55cvHiWW1Q/3riq7ODe8SwQc5/Gx3wN4o7cWGh5mvvuzRuV8/3dO0yUR5Cz2spw9+H6xPuc+qn3uevSEOj5M2rFrhN3w77s2MRUJQ1+ro/1wpNFrM+zyUfWSDL8wTvP9zyB/PM/+/pnP/n6z93i+OL1n779/unTl263ZnIjjUeUL95cxSTmr5K8Mocw9O6f7PsiRQ61zX6+SbCN6uPbI5a8++qC9dWtHF5TC6dfp9Cdl88vPO9S4IyPz+wD0VSuTecj1ymecWAK1lepAJwYfp+aTZ0MvvQkkRmiuA1jAyY5BarLwFPzs/c6lB4PZNGUNiJWl26xpNKrCXOThgaUZ72OsyGgNAL1xIXHeT5uXXvmHev0cjS7/yDEFNCuGdYO87RTH2zwEur7o7Os2zwigeSwS468pJuLblLhu38wYzOsjuzD8UHuJwj9qCebSraZLWZM8alD1+bIYZZRNrVaRoxE7Tc0PZEVMql/GshHqU8TfVzkc/DJ5ziM6xkwyVwJ0LR7XMQtLEng43N4uYoVZCLE7Mbn0JOGwMUOYqO/biffUUkqDKK+OV0B/+bNa/NdExrvRXqt69nzl978PjvPMy9nCRXq3seDXO+aWWEkjT+7E6ODyWd1o3mit9ulFCMBsj786JtK1WyWHlm5UuIjED71ZxxKk/uKkkFRKgpRMgiAT8WZkXuxxMqVWL+p4pFDZYYHzDUBACQYgJHc6oFXsPjKCc92/wEQy25HBWRLsya36+valoTYfrrFpsWvI6pAGPNgLKkvJ5YTQ3m+WHpWph7Tc7FiGIjyaCQhs5C7joQGqy3hAK5IQVyTzsYrPlyTU8BnLF1BFkmf5WbA2NRyscnQzBsAaIppXmjL6Y4JhxElyJc0lnJVjfrFoKnmpeQL8kkoGZgDR/nxAeUtTX3KwNchqxC++oCgNILIVhRieMI74oJhBAkvXDlgTr2ELwEaQsQCDCcgGW2Qplm5+MD1+2tXDVn26SUvXr7wTitjEWG2a+uqTZtAJW7779mzZ5sV/KjUFAnhDuazMIUlx1xLW0GJcrVzs5anopnHpLkqi3zP2/jkXR2Poy8vHz558G//zX/xq9/83fff/z9fXLzfu/c+Hyx6c+fjWyvUOz//8s5f/Pyn5z5omEs5DPRHPjqQ3d7c05tM5fHc0Ym+xkalF3wMXOnXknz6r1jdjH9e4Y/CVLljYL6gJ6eTGgOCorO27kUiG8is7s/U2MBrYexWgfduv0DkdiMdM3F2um8+fvnEy8kPnnzx2JvDHg8/f/by1Zu3b9+/krscDLvrm4n5PrGFH8tfXV5rl0raTEjf76OxHzOP1NA+ekeZoezw09pBrYwh9KaQQdICfg42dUSfHPE2tS5K7zRkETIFk4z+T3BMtpUwNovAiFVDaASVVPKsMIbiXGapZDmwymNAzUvbp17HvaeEFQcLeMLgKb6PM0PaIXCu0wvA4M7flKFVpb7HYtiaOHs2ltLWu9JR0u9zqnxdpKyALx1Dt2hTXnPqXLpZE+dv1fmULzdqfxojP7MKRhiFHX/qeTu3dIMKSJGPG5LJ6rYxoh/xn/oEhAqx8rdcCQ3ZAjY9Sc21GrVgpZWdnCUBwErFa0QU49DDNCpGHlXZaVJLHTDOpGXs7TnxoCoBTG20tWfPnz5//ty02zaPY9OtYyPH/SybRQibVRkmDTDfR1mKVXMlujRE0tj68EtC5azYUu76lBfUaCutOeWPkE1RNrM1UUVt9jEEFlt5dKy1KWpGrB0lYK+Pz6FvBppA06uQMlYOkwFgSlZe0hQAjHTFlqCAeTqgZPwS8ItpBprKhn4Oye3SN5ZZyyUtfQGYDtzCV3mMTb1pVaVd31iwGySHQ0wUoFGfAQtZfCVLpSnyYeqjpF4zQiA8jChKqpqCYgkRBc+BIQVnDMjEQhA74kY1RT5kWXTZRRJbenixbFKBJUNTt4Irtvjq9knsSGsuECyuYvgo6cmXqLQWpllD39wt/QEtJpSNxU4yfJ6oGiey/Ih90o3dU44+DHjv4Pj0PN3VXVfOOiRscfzoxBZwEjWINb/6uOxLpYGGkkoc4Zyggxr8gWera/ukCiZJWyXlswckpSDmGFN21dI6Nnf7eexJXA64OjZlG9mE7n/5X/67L7744t//h//h//X//uWbF/lczk+/vPMv/uLnf/mLn/+Lv/pnP/vJE6etfIQ4h3WMSB66ZDWQDRWS317JilcZ3zn2bCczumbssjg+zkFtA9qs1XK/ceJyjwek4Z+2albIU0+ytM9NV8mbHyO7LXNdOk3TH2fJZ2DIzjRCVc4HHQ5OT7wq88XD++f3zz1fO37x/KVu7uVs19urybBi3Zfrm+75ygx75nGzU2dsFDPOSvP2jvtFdLnvfWTpZh76WIybMxibZxjWEBnyQz45/CGP7rmxaYuGLxfLCe46qewG/7NhzS3lrvTylx41QSMijWRcHjyAJa3KqDZeJPOitrfFPGsUqca+efvaqSs02l2fdIAZh6oqqeuv7E4Pu4qU45wZu/cPjCISUHvVJXxTYCZz5i4pV5Uvmb9jn+NwarS8qw5U9JMyNGrvOmlJdPkFdgnECmotBcAAWknOw1QZERy3XWsOQRIs47ZPK1HpKx9GcskLpwZtiynBGnOaFRrJxabb/g3AlYy/C1MKpv4SEv3TzBVMiO0egellJkqk/sGobesL8Oq11e9LRWMwNhFFbranMs+WDKuqkxnpq3OU2OqvjdUyYrlG8dujbvK4jRqCDU3zNTHx5HTxAoonOcaeSUazBl42RFMhu/RZ/5W5/m4cWDFw7UDHOh+9yU8KeCXT/ggZzHKVJggoI2lcc1idFnGDYgmhT8nAAOz1l8ByLRbxjSKZa4ow7c13RYmFvHz3Dg2dOYDkiIKvHdAL8isHjEwU3hLAN+kmWn9Xw+pQGsSlR/aZGREQKNbbNPxRZ7NPThkOEhe/QBXDUi7sYsH8piXdAssXtWIhSUDTrGHU6UNWfmWCYcC7EspSroVHOb1M+hpDhVhObIkBCCpnYWgCbpDPGsXUb5bBdaxBIOHkzAot8rUx4wd/szjY239rvHU9hXuVzx8YGbyW8PrF8+vbG+PgoTd9To9t22bWvH/v4fHDpkg+9fgGEBU5Yn13b55vtaCDGYe+lFWJJgiy/TOXHLm4VFA+0EbkPacf33n8efnutQuhf/GzL//FP//L/+Kv/vzLx+e+rKpD/zM3aHzxyNtSvnaXk7C3To/lyj+jqf7Zdqe0JJ0qL0eG53wkMcviaKWvnpdfFDAL2ApH2ZEZ4uH5Wa7Jv8nXkanKPtEqrx2nHKmGnOx0Y9lK+Hh0dmppqueyMs1esQ/sJYcf3715ma8N3ztAff9k/+BnT6yJyfzTU1d4XLx8+SoPzq81z4wkFi+2qQH0yWvAXnTNZowJisfCXsSQJ9ncu83GtO/emDfcvnELSKZS+c6Ss+HMZnAzU8r586ktfArz6cTV7LtFUMw/1WfJYZGqXOqiEwIyT8o5g68HAWnvjkSpcj//+c9b0CYyUWzfnqhzBnmOo0VqsaNSWNs7Hh3m3lp/YkWRYOjtkebcxDjIyBGXssg62yafL1Y6Y4BOMTk25UO63hybyUGecaqgzNDSazmGd6cjBdNnISN6COA7mWBAGkIqQamr5XzB8dM2ARz6y6vN+i8ipvlDcql+G/ppL5NbYj1sKEF9QoYvxizAB4utz5gpztFn+WKR1S8XeKi0bs9bZD+R/keMMvK5zPfvPavx6rzNIRtaL549xfDY10duHMvqmJ3mNEYjMrkjkF81wGTxydnNftUQpeDEIl5+AbqUkb+IwYQgrkMJwyGAB0hXxosBAxYNmIOsNGQxEEGwC6jeKIqpXD58zkWOawK4ANna3Y6XFU0gXoQsCOCKSeLjiqxMviAJaBwU4XMVWDIcgvLGTJwkYDZRs4IBYymmfmWCEaffHIFqbUXxl7RiRlXgxlIYFwYSvFxsMGmFeutgtuBGsV1lRCFIr7edIsgOgUSxHgC+GFyIYagqipJgUQgAMoIAbJ6Od8UaTcGVxud2U8dYmdWwSdSv2EWMoPhSlrEwmSsWnm4cTE2Ki6jyLk3kl0ODF3GD1gpNDlJ2iMLIyWPZ4cFNC5cnj2CTXIZQloYWTvTRPlPYDtUNudHCEHp8dGpX9c7b599dX1yoJA8fPsw9F3Phrc8cSF1aan2kzbqH5tLNx32NcTav8+7tdBaWnMlrXF4jskDUiecxmtsmD6+p6fXE6dGkQgLJZgKGvneXFw5sOCn7/t17ryg/eXT23/4f//fzpaJji07Pc23d3ly+MYrS9d3lm+v3/TTbjPQf3ShFzPtHj+97ccWJagsswZiGAa0gc0xH+1boOWRNN4rorOfoN8OISnHEjPNjuWpVCp+/dkvhzJVeBgXGTLem0LzIa1xBkjco9MZvLJLl6/Q4f6Sd3j93UNsnzV88f/X81UsX5l9evrWipZSb/2IB7xZbf7uvQ1LOLtw4UD3XC0zVNc6as0iIGlK8cq+RRXCXOHNrkKPXmPyp7Iq7lYd0pdPqx+daN+qL/c93tEiN4bYdUUDH28ZOAPNwufAQyj6z5xRfPHos16yOzCpZlJWomub4lbW8Qulln3q8+2fn/Kvrt5KwTlYaht68eNUhyqbCrIY9xqM/Q7e+eXM48rOM8bSWCbI/vLKYZbmN6NTSdNAzfCdSo+CGbNOWp15szDA5CgyYVaNfLpR+pKt0GXaTqc2MJFEI1LHavKkUCVYW/AgdB79x20OgxdOqwNJnSxfhHCEwhRcAyQj8OrGN4tuvn7wyntyTajppquhMhkp+x+7Q90+/tfOMxakd5nZwXKOI/A+0TQNJjde9K7xxTWLynjK1F7YSBVR/gCgyGrWCkDqJhS9Qn9GQgdHIS9n5TmnUFMsyaMCL4LMUY+UVB6gGo65QdMIAWR/gAruS0UCstKsKv3qEbXQSO1HpmisQhoTSF+Zz6EvDVyEEi+dX3QbVieXKhfL6/eYGqNLA1FGGNMgqDymoLs7T+7lCYHp5ykiiyi+ZMIibNDnFw2xlRz0wyXXFl0BP3kT5MItdS65KWNDTpGRaPdiA2hRXBhGjpBjiyqFPXWViF6w08NIBXAcDqFYAwaUtI0sIEmbRNEU+sdLlSlCaSigxTDUBdEDFJRdVrOylhERDFJ+rSnxaIWgSDaYrn0lGicEdfnwyV1+kLdK16nreQ5Bv8TmMTKQncxeXrv3zVs7hw6OvHHn6/vvvfVfl4PoqXeTZ6eXbt14SNNjnfsVNTU7SLCBpZ2UoXLNDVgeqVp8Gm3e5o1LGjwxcRpE8/9abQXJkWCo5tGWo0mYpo3c+ODx2Duvm0DTCfY53vEHrW74eodoN9h1gD42NZnSwAlI2mrpjCVKgjOsZZLf60IHzmLB7koqcQTR6jFad174JkfE0RjaTyMLa2WQNPnRc4lRxBGYalpsSgNVjsUSieWqpQ87OnHqf2vaw/VOHpQ8yDbI/bvX94Pzs0YP7P/nJTwzD33z39Pvvn756/SaPQmnAGccdBp4aQfOb3P4f45ArU16hyqtUH+96Wq8jFT8TMQNQZhEKJKv1zZZjciSnyci2w6J2HVXFglOn/ymO7FlKVds24SSkyBSx81UdevXmWqIie/PKTkbWQypG6kzW9+kuDL0WsOzrW5E6d5THuULbz7SjyYyFr9E3yn9wGjHPOHOQbZ5Hype2AG+aYQ5052jTvbDCNJwo42yR5wLJZjIps0onnbtcYye2lgHAmCuEaJzYAnzvFW/hTXNDVgI+p9y3wGb8QI+GTP4CKA+uqEFvCNhuhGx4KVMyepas0orks3NheElw1b/EK6oJ8VVG9T4tfva67T4rAQ9Lzk/PdJLPXzx98eK5jCkvx9y87u4YvnqPMQ+CPe5MZY/sMWF0bnJsZnIC70znUkBGMEIia6a2xLFwpMR0G/0b5K+oCq+EitqFK6piyyIWwK0osRmuRdRvdNOoQWF2S10UZvT8AjB4W0v44OLVKlEoTTAJaXEOUzwYsSVYlILwK10wyiL5JJQdLJVKKH0FFiO22ekWa5EqARZR8tLsjGKZbpceGQfJLfngXSS84Gc6kFCacmGpMiXmV2B9QbGrZmPUwiGXg+EQcwA6p9FuHcsAm2LVGPLNJnkp+UU2m8hYinyMVawyiWreRaEvQX1BxLUwFsjPXOkRcKIqp/pUQwSCTTHdylQhfuEWtyCCagsgB4YjLaPaHE0nTRIwCHIowx6qXkkvRLhVpF5f333HyBRFcDoNJZMPvnhycHJ68cq1i1Yr+jUbfB9yRY4bfyIl9VPXSKyneuDcH8k+fdCbfBsbDIU+RZ8k8vTXvqUHnI5cORHtiaZ3m3LyOaTqGAlVmxVPT05f3LiO57VLGu6f3Pdu4vff/PGrRz87zmLU+vne/ZPjy3dvrSONn4qWBMzmE2PhdK9M5I4fu5iyI1ZVIRyhrgeNosxqjKkMKtJltLxtK0ofndNnXka2Ds626f7ma1qzRCbJmWQGT/25fOdGpzijuPeIbiyuyfnoELVXobxNnmHjMFsKzH7PhxuevX7vBWSr4jPvsbpW4vT+o0ePvXn5zXffy6BFe+oS42oH+VqGfELk6ii9AgkVLnVq0c4gIo8Yjd9Kn1Uz3VaumceY2SgdJZ7H4RQgLew7fkj/ic7wWQ7ZH2nj+ajivX0fX7audbbWMFpT6yjcU8ZcALMNyXlryLfcHz9+/N233zKO6SZi48NIiX9/Pi8YE/u8fE9hGeFscth9SS5kJc6g2zv4HH2Plezki8ukRz+pB7Yxrc3niXxCqd6sx9/soDa5mHorkRqQNEzhTnMDc5psLaaziVLt25l+63DtOhknk4PcEE/ng1wQEsBHUAEemdSVgErlKntZykUrNCUoC3yRMch2yzemG1caS3jVQLLTUpMZYkx7TFaVyDunFz0EntPBMzQ6BMjYqcP2e7gok0c3qV0zjwlychCAqwIAaVY3SkJOZNibr40y8/JIZO5ouCgL1DIklIV50II/YaYTgyw9PAeu2M1+b5lLUYXaOSJtcBH0PTCpKrmK4GNEiUUyomAWezrGaU7wlUYgeplHXF+wqaDx6YsSVBl+kxaFuPOpEpPGESLIfUYP07pFEw6ZYlNrbfsBNA81AAuZnCSIqoQmh72YBVCguQPsZrA6VA149GVZfsXSfGIy6GIhRNDs23syCCgDw1UyYqqSuSuWnjKCADFX5QlZTWiyEqtW843Aea8TFyRisWCUABJg+I0qjAtQCShL/xlNkxbFoS97GbFUrChK0q3BSpD3HtPfrTzV02CGt3AVIAFAHb9GQY5xWU5yZjHO4r559xaXpuTVW8sRg9qby7e3txdPHn/Ber/73e/cj/P44SOdpvHTyo98Tz7v2araJkTDi6uLpbPkwBJin82Yl2RjqIXPMaa41Hadp2mhQnQPtYC9WUebf/GLX4h2UR4t/+U//8vn37x2/ljyF28v3196Tdfbu6/sdqKxXSkrcqXDzYhqcHLSZw5SsaJUa1s71baqrc6wMG66FVbJM8KUna/zZYpgo26GMiNuCyLEKWgRWVGN/rkOwtYrFxtm8TsfJVTgPvfAtF5jjkQbDBJPGsbi+3eOzWQ8TXfQzc658Ul+L594zWPP7UPPXr40JcnKW9WdYjL6UzC9kQWcZG3na1l+7KxYnWQ9mKfgGhOL5STyrSKz4axDzGpYlpCrp2fzlcdNHreDQdT7J7oOwORsRG22BvLig9dzPadQFqIY+eKNj2pcn5+kwnhsr9zVVXeKOUYgNz/96dcpoY5D84g9jVAznI/BdLOUauYZMW0e7zt6piKliUHWxRTjUgYslIqMOF+AVt+KjM2m8EuJvfjlx9RbV5pFCRDDn4TSilftpSp4gsE3I4gVClfFVPuKQlZzNVj6kjny1N6jCW0VSaLgkZ8s11VsUp1GhKYASj0AeLGXN3hr3lRTj4JTAyWahyZ7937z979+9yYvKz5+/BDyjQ8OfvigEN9cpt5LTnUicD9zWhOZ7ClKbmYjEBuVRskUwUouNONCNE4URzgH8O730hnATfynhRBG+hQZxsw7N04UqGIxAlYqYFGQd/+7/9N/015S5RNOhnv4ZfpxQTwcJZElOEdXyr9NKGnIPwK8+j4+14yFY5sZchYjwDBDQlNHIhbXNH6Rn4qTHFFNq/jqUzxbN6ESJEvjDLFgDgtEGdF4wFZ6viACSfMF0XBLOAxYPDJ4wVqj2aE8TF3lo4kOs4U7jJG8GAErWHq8BC77ICgXSy5jwiz1GtsOmqEMZsWQA9ZF4iKERJgCyOhZzKiWnOJCAL/yDoNGQcDb9aom9UVxYFGVg2sUTFqiykV4R1BBDp4+agIW9GJhcOnKLTgWe5KbOYE8IhYkEzGAQ6YVYSeweNlUrAiKx4tMFAeWFudD9Gh8o9Bg9+rFs7cXF96CPT10YeSHqzevfUDYCJAOMbcqZjJ0tbeu6ky62eYlckZBlc5zWfPuvnpIOIKD0wdNC7zr6E9PmGaT2oRDXucwcMZQ+NqBdGMUIdT26JTdZIcFYnt1chocAMGGBtuYdzIa+zSqvgklvOQUqCQgWzcqATHJdWhQemOqRV9jylQViO22yyC8YgWxmDdw5jq6ozxmvrtnCu6V5qOTc0+MX76+cJfW02fP3f6BWAY+vE8BcVVPilIXxS9yKYYA/PbOPcdZX3tTy6MGI/Us8L0x/NAnJtgnj7FTncAJKvqskjerDRr6AiBVETgEJ1YRWB1Jy+PsBA/2zuYztK1LDrgZbh1QNy3rnEktvb56R0LXwfScI2PKwofh881XPbjXjTJaz9vA5snEmghLEZeuzstGYJq09MEcVelQuPkd9ScL82nz0iup5kW6zR2Aoy12TlpeVxNVUX0BT2qaqFkCy4Q9m6vstJk3X84WK5aKAkiLQ8NBFuCL4k72N5twS22p1xEuUxx2cOltVYilG3p4SNWJY42mWMnoy9VuoYlWQvNCrCA8UdibWRiMNQuySTyPLMyEVBJR3NITixT5yCCbNQSAaggvSGZThNTQ7ETUGsgQSAughtOzyaEnkyujD5NW+YWMEnNOpWKrD5oqIJXiy46SWCk2ll+HHgGnvm4UajJLCdkmqHSLhxKaILbi+Ssxm/KS4SQJLz9RM/YSiluUlSYKkij4KDI1hny1GzwciSWQj5iNikc/YsMFziWq7cu2JoBfThSHnoMklsUrnKWWAlJZMEosJYYUpHBZAKIKlyzSx5UdVy0At0yBkmsUsuRxrMFn5NyysKNhpaFHRgK3RAEIsW9GDQCCOjRE6S/kSBRelAsYGZtCwVVN+Opi1QBzyAr8YwOwilEFcNVJAiALGAGUqRyAYOmL51c+40u3OaJMXTWE5GAQ88HVh0/UwhO+uFBywxdieCqRVl6d5t2PDwXev/NE2OrNcvxQN+UKxyzH8tKSVfH7jydnhGTKrTH4nnmYk/rx4VzkMkO+RA2ihFuyKRixYPQAbqWIrBhtCoAARhLKBVBVk9l5ZBvric8T0I0TULf0nxVSCYWJMpajw1VHICCYm5gdWQ0Or+lxomDalyFoGmWpHIrRhWSxVXhJQFB8JfM5uWAv890eoTJzMLz5wMP9hw+//vrr758+8+j95cVrW/6VT0KBZPPDrbXmCIg1OFHJmrXwvezrHntWnhvKmMvId+fM9xRnm9E+OWLFp2o3jxmTx1XJwny7xNONWq2/kzUWsFdw8OFAcXOSMO56CkAII7x48UyOvGvkzLPLVsyxnPTmR86TRzOY5mBpzqbNhKaWNPqyp+S1WKlPjUu5JB/jyARXpZTzuKVhFVZOAI4a7IJl8RZTJBhjpOWU77aSbGRlkqIdkRR95oT1PPNM/fdcu+mCpYKDfPqnqk8zbNJ8TiwFKnVpuxuFd2Wq9C5RoyFYVNmjw7ZXhG+6FQIvWFf25rd47KUvQdUgVokj6wSI2l5uME+qGlWyokov9WrSFCuKho2tfFxouDQiVWq0ai5Kxu84iKbC0UDyqwyADelAflmwQ1YrmKbOJ6HBSgCTgBj7ose16IMVqFzMpVsZwC+q1ADEZsGC8FyCWyUwYq+KrIZYcMom3IiXroQvmVWlcviVWfpKJoRDJqr4iNs6SEkIEQiuK2OTgFlyKh8xBerEklz56MEcsjqUI3DTopqKKAAfF7/0TXdLn1AlAIqsT1WpSFqw7LoJtlqmg+cqHFmF8CErCqA4CYFcSUPCLAJwMSSUUlQJGrWCS0iyPXaAySHM0b9+MNtgBRLOwfMrv8JJUIlVcfQyVT1FAWBar7DoJZFNxdg8H2IKjnBkXKVVPl8UXsIRNEU0UXcwJUYjKBbN3lFm4gyr95SQnvedG6Bub8ygH9paPP/C89eL1y+t62zUuELHSJV3U2yFZhAew07PqUfWBdDT6Vi7DYRXvuNJFCjc1EeXjT6isLQxMwKXJ9VTIs2LWKMAarx5A3heEqU/LYz78G7eaiwkB8ZYsaPCpvbWvDBoxKJBCa5iNWZ56zcKZT93GMW2mxaiuBYTAviYYkpNEDv7y5EqZiVvLz+PxM1UbJghnjNeZ75p/OVj12s+evPgxQuLfeJTzx1bl1nD1sejvKaVHPuX7XYjG8HJmiNpUiT/iy8eHc/kcr6pZm39GlJ1aikYbzjv7N3xZeiMwZsuiIYcCcrLqtcRZTJNoTySqNo3VznfZKH8cPusNwnf+fjHP/5RVbR6lHdJmK5NxjfX9knXCbqIdgun7M/7QihVXNzqI2JBUVkE77ikPuUV3nEod92nifM0TyTUI4rDWEoAycW8f7ep9nnYGbcZLxWQxuE4w1ynYOMmRkjSHiekvmR+oNugmkpEtgnDsMdDmeo3DkHxeMM+sQgosLK18Im9ktKcP/AkwV5Spqu3IMIyd/eg3QawFOZxNnlNpY23cpLMdgorIVFoAITDA2Rc0VNAK+7oi0aBDF80/LHDVScKUL/0gthhaudOeaVYmqg9bad6VpOqBI+GwVdZMAhppVmpDM3GpJA0x97Y2lZQYaljP9YfWS4KJ6LJS6nSNSBWEL3yAGiwmWmqTUAUQG1O5zJ357Z5Q05UygYvXxAjuMH6MCjrUue8Qjg0iOsarD5YBH+Mh6x8UbVXE4XnNoImdXcyiKorMb809WkCExXHidyCsUb1jNAdsQgE6y/rVyy8tETxmwUAJLgSAByxaPCKbSrNRdnhITmx1x+z5SgWy4qFr+VLCQ9oRgivTEhJ1FVCZZascGj8/6ETBaExkMkJksmvzoWJFVT51AE06OtLmhNUzZoQv+IrByOAj55fRhjZATe2afFLQ0Kd2AovgVhBM0o3/73Np3e9LJvuKq+XHB08dT3dhw+2Uq1/7dedOkXpOiKt5ebK49SobPMlOiiwiLcVeeCcc741eHq6nZiS7xh00+3kCYyYthw1EPA5yhdgkAIo2crWN3oY60IYDiUkbFlqKDSNxVH6Si7M50RR2BMyLBx2aXGiwOghMxJunyXREIvNdVzw9B++zOei147yTRQNp/8RnO4vNM61ZQDOO0X6FZ9C8gRXN5+8P7p/9PD88Ogw7+oYDvlsmDe6prLYRzQhV3XyzQcFl3Nt2ct1kRmFjX+WsPfPH4Dd8cshzGtb7hp7/2mApE8G362riWJDV20eeII9o04eoI7zWPzy3V/++V88sDt3fo6MWE8TPIBQUf/iL/7CeSgb+GPYed0ozx4OFcSRQ3Tznvc8iPfCmwFSqnm6yBRantGhz9pp650wZWkUMuEz0gWz7UCqRTK7dUYmtUeI8TZKTklhoVKzA8+YXAmUWobevG0tfpDT9DL6tqDzmHmybFi0YJ4pSHkRqJ6ZW+bTmXFlGZW0UKXgiV+SRb+iqoCKVHr4Yd3o4yEe/aPKuCakpVb/bUbzW7EKASG7wSCGlHfwSm5RVqDYPg4w9Hrf16yLJoRXq0rYJppf1ZhwAEbCweQQLrjSbRJNVCRiBMQWKL2ahp1DxgHEcrbA+A02ig/T9h6KiQ3n8DaIpnYTBFOGfJi6IuGxZDnbnG9jN6vsha9+DaLJB7jHLfqyI+DElB4gh1zWFaNE9R7WzUgALgurMfFGoCncToWg+vQDm6OMFV7K2hpmN11RTMPJcHPI51oeKM3iq/Bn+lMGplFVsurhbbBR/DIWv+irwy7jigKIrY8AwC87PGnRata1RVb/UrYPpTyk/HKO2pa4PuOIreZ8XPVRAtgf5jMnFSkiqIbVBw0kZ+ZaVXfxYKnUNZbw0n9inMeQglRqbQNTG31ZBAFtqAAS6ABJrLwUQyYMDTkEZVwwTGGMKMUuV+EJGg8igsI3tmf1tmaFVrJ/9os/f/H86Yvn3mG49cXAB+ePrU190JCddFtZfdIuo+JmAvTo4WOq0p8w03wHZhLvyfE8MxtT2H7XlaQ5qCPT/CWvo7mTr6GN/af1pi+QwTHXHNAcUiu/DX4+XrTsucoludgWCqAman6X5QVrMUDt2XQhGWqRYUfAhzw+yTNaBzasu/RuSzFLTQQcSXJN2mz8eu05dc8wn3szp3ZlCexE1uW1OzXv7uWt1stcTpk9D5J/+tUTYt+cH9k2dI31u7det3KI7MOD+/fZ0R36l94cNOTnCk+Dyp2vv/5Kh2uSZAv64mJzsgbSeGzT4rtnT43l+uLq78bHt47DZ068xoPkkhUyNqvVytv3BWZhqvKzhnd7C5CgL3Iajnr5ZMWbV75WxGk0cmq2YjFrv+TaQ+xxKYg5CuAXrBYNkE9mVRmVIt3hdapulNj2fmPDjXox6BgfDQduMYHRF8NuQ5UyEsvnYFqCSTaPJkLuf4onLgVkIZp6ODWLbVv0tUMTheE0JAIzhm0TJZ2sdBbq1dRGQFsWIKns+El46+DXM1F4DhdW2TIzEWyvAEmvrf6bmXSJixdlSkdaXcVPvtInKD4l8s033zj+5jScuoEL+5Z8o2G5iAKI3aWBXNLAXAkAVsCcWAkB4DnBGapCSZrgimI3mBUENN12rWWX9CJoBouH5EpfvERFQfKrVQpShptG1SpFGzwMN3IiCCwvlQguho+lvRVRzUMThidH2k0eZdOGrxAAeshqM3SblUHlY9dgOs2BIb++nJSgjEvDpks4/IpaqSTdsUiLmc9VkyUBDZi/AATSWgRNQix8kbtR3a4sy9KQJpWAqyVdsySNeTlkgAiUQSohbkNqFuAhMcJzrMQmWBq0emD85HbqWdXjNxbxEl6FBVG2AhWDuDTL38WD4fkElmCS2vTvkCWAbFqyxlGYWH5rlygEKLlS8isWsczq9BAv+VWejwwBfOVgbwbhm8diiiQBnq+7NGmxwrHNi90Q6+0FTwGtky5evfT5vXdX713imC/5bK5Q1n2kO4tKeZ82eznOF1ARzpg9h7NS+Z1abpaliLhq8NcMlz6Vo1DGyGnqnOWeSbko+njuuFVYV5rZPQl1+dTiSA5Bukxxut07bESyFEtce2KBpE+TEKWliOLAnFxQg0NWO1OaBWWbcgDlRCbXqly4fQNKf7iaRIa2A3mvAD34vApFLAV9BWnf5x5SaO9uL514O3p0+vDBKWVev3nrhJXbO165Q//evnv0XC+i8/3iyRf6VtrZrz71CNiLT+/zcpbD7abciojp7rsS8+xML+zihRevXzluTZPmi79RdX6EPMM9zpZGJhY5afXIQau8X/TquZu8Xj1//pQRrIN9lEn6CsvJMU8A7Gk/cufk6almh5ijM3/b1mJA9Vsi2ZL2Cm/uh1AEqoXqnhebPtzmtaKpF+qIY9z+9rMk3nVmbvPqHO0N8y1o8c1Og7HdlCM/Aqc1oRHiM5RqRY3pztNl5a0b1TEnzzfPpEyT8KqveFGCU3OA7bu29bbyI4rzk0IeeEJwhHOr3NEvF5bJWmnKCEagygHKLtgomrAkuI5MAGSkjFaCZcGrDkMyvtHXeYLe367I4Ps8COVyTZSvWHGRyW2S2fZvu/pgbNIu5gGTqWRh2lLS2LdOFLDEZDaIBcxfMuGXzBJXQPTYalKbwCOGLC+uugYzXgqjIAVQX5ZE8+s2HPNThYqvCCzlImqZGEby0x1k8YqyjGB4lApGthskbZlDwy4BlhJgBHAAXGUUO8n+wGteGjXKJjuChetjh1n6g5dbLIDFyBLSRQ9JPnxTAZS+PiHF0BPAwTTLeLHAswZiNUwGVTI0kMwvWHpBDiN6DmOzXMrqoD8y+rJzifnpX+f1HsgyVpklFrLyKwGew/iZ/iuIfcG7APalG4GCHDl8wabehKgNaLo1ghQrClkBscbdwpWDEhK8FK4mWJocAvQVhQaMuBKaLmUCDMqJKhXIi7+5g+jDx7dvL32rJiez9u599803F289W7M7vee4oCrBgK1lmAkxAFvV6UsVssqakvR6hGEwbrO1Faptu6rB+fThCiCdjGTVm2FrVKV8zk1tDmW4l2oz/0vTsTy83RwSxIhm2YGoGqFWhW9UzdIqHc2GRRSYGtSTL1k2vsGofmrOsG7Y6VmLkYMXCzKFgrJw6qr3nBRv7hnKkS70uCLftxX6IYHMBW9PDnJqSXJ/evEm2Z+XDz8c+6DQkQHOebe//c3fu0jMQPT+g1c7zr7+6U8fPHqkQX/zhz+amrx59VKK5/ePH9w719s+f+E17rc+mMg5dYj+5KkLQL5X+X2lSercppgDZRP2oeWsG0POToyQWZvu3Xl/eeW2EwfECHTAytLXW6S3H/Io0aD6b/7Nv4a8ziftvL+bt3sZwao9V0BPlUZmjjJ4HZmPamTozRaJ669Tt7fjXKpkugs2XXVbXjj42qqA4kCmnrUIINEggOTEgusEF6ASgbUwsuAxCRp9JeZJCiNkljbbqsSOJt6ujm5skLfMfOsjaNX4087fCIycAZJxblTe6ANfaSPwU48Hb18pbFMHyhjp9jJuNovOTSRJNLdvv7+xDIHokTIvX375MEOfhgMjSj204aF7NItS+mjUxiZXen5Z+NhVOUCFJ9fbVlb5SoRD1iACZl7EALFlAXMoBSu/QU0GLwdZSjR0EMuvaxQ8oBL4nLyUoDILw2/YJiOb756WUxxANGDpsTDlzFi0namhjNbjKIqyxMWjF0Nz2mu6HACmsQzHEVUtRQGYw70mAGSiSFMkGDUDAJr68Gg2ZB5ybB2ugqIAEopC40gT5AgpUE3ATUgq5YUpLwAr+rrGriQqBEGB5TdHlcBvuk2CVsiaBeYSq2c0309Otk8sUMKzQ5MW5GoNeGSiCGE6vHQWpYJylKwOyOoExaIkCoZPVGn4ldNgo/h1kIAVVVjS2MF80vgywpFTsZCS48pOn2aqLOWlMKSoCgEjFiSnyfErBxdRyEoJhl+xBURVt0YJNlFi0c9e9IydeTaoHu6/0SO7zvj2zpOvvkKgN//m6fPzPYtvA7ORz20Zk5xjQvubZz/Ty8YU6Pn5as22wki6qcNzqqhgs0YKDK2Ui14QXAwCp362UVdKpZ0ZBemM2JDmeohaQIrNSOnrQ4qtq1imq/BGgY24tTMyKZZ4Gdl2EsySvLKAABeZoiwQBSvn48VbSI4k5caMkkhO7zpPxy43gIm1L3zrpvWffPlYLt6ZTPismYWxL+meHbm/43/x+N+54EOztPlgwDw+OcPgU05fffnYHrXdZjeI3Xl95/zswWm+rH5fF4zy5cvnWj/FfKlJeemaLd6ZNgp9GoGZ+sObN+/O3apxdm7In8eeKmZ2Vr795o8y8uWTx3Kt0Wlunit4ru/xrzeYfHnC6K5nEqt/6TCsFgjKW7ofWnrnOw+sfdvYDRv57KAcK1Qli4g5ogz7hIMiKoo9E1eczkxlhsdlZEZsYZEPCWbnxoIh6yJtS6kuJ8DgqVFTFcmk3jxcp3NSnqZnaI4ediPydP3DXErphezgInZadMVGh2mD0WAOlKFZUWiXMmHecZEzAuFAqlxr3SKpEP4n57rvcYrDL7wsk89VCB9SUE0WZbak+uncjL7wCh3X7i50NRBVRsGKwg7mi1KHOcFUgumgkt6MNfa8YCqET/iKwshVveqGQD1U/fSiJEMuvCjExfDB5FRgkWA6gOEjd9sS4Xe59tVO1U4a1FJBKSQo/xhUX0jU1WkrMSpCVhB6NPyOprjAEgbQmLPJU6AaYBQkARlDQ8LwV0IeG9GYNCqhbK6kpZ1g4VA2aVxRe+4KLp7CkM2OJAS5RVydqxuxu05Uc4GYBLz80tOBcFyQYASCorDz4QUhBZuvhSk7v9JoIlOCVQkMULqSqoaVXLi8CyacEJI5WaYbVwXQQPK5dPdjOrGE00fFNanHC48AsvooXzWboQSVgij68Oljjw47WL4AVR4Zh71pQbaAKANflSQhFgtLVib5kLVejYOY5ApHRiAYO18UYj4JhKMvb/UUJIdYSAQSEsQILwiO0HGMYOiFGVE2dPMQ2OLl/c21L8iZzXkzhc3Q6tk8yDRCZNvz462FlnSVtIunfMimajCUpZG3BNs0ktY9L86mBtJBmRvsago24apz61Lz8ub1q9rNWkSSNZFrp1NoGdfzoLquuXAsqMITVASksMxcH5ihYPr5ae50iROHXorgpk7VGGHKjmSpizWk0ZOG3oMXq1a0p8OS9j4lhVIUuzu1JFFcHuImrRHVZiUt8i8v+26M/sXWzmYXBD0J796+YXBnkB1Dz03dHq7v7Zs++2qyXW/j7k/cn/zeKueNazgP9+/60BDFfv6zn/z0J1+9fPH6xauXl8/eIjx/+IDxbTtTtc6DZqu5DwcumlZP0rVx9GEoFyD7wISzdu7rdwRPWSsf1ePhk8eMJ68GcqkwgrfLUMrC11981bVBpbDDZCS1C6Vb0thKVMbdu/ceHNxXL2JMX0RGaTmsrJFmsNtYXsVUf6cKq3teE0o/hmy52JGszbPXjeb0R9ao5qXVABzFpjlM7KZH8gRcpTFxOzlL49IniTUu46Ien0n4yqJBJVL5/Dr0xCJAxp2cb57F0rNqTLIRWEpBylQaZD44tHWlLEE7gcLoObAUdZoka6efJEwUpFhqoFS1OBXy22+/RQmWQqPa8JVXW3pTrPCk4T3p05yJARDIB7fCS46QKLBtlWJHbGqOjCCAKUHscJLTDNj5nCgOvToDyTUoqpZROQFNSxJ0JlYQsimi1JoqipyqJ7ZZkG7Vi11kTwRUc460KsIwq6i6MujLylxBRfLp18QQR/dpGDD60naU6EvAlBW7JOwCDLFJaIpNWhVFbMn4xSQNNporgbAQG81mBNKEZB5SXiTKfHwwBy6AEgGxtZp0K5lMAJomhwBvo4rBWDkNElKHEXFrsShImJbcElKWxTjV69O+cfEl5gtWDaIA0kXvmSZ9wNUKWSmZFI0SNFNBbyiVOjL4KoasGMjqRj6gQZIF0cycIIsh0sTCtJKRXGllEcthYWp4ylQfcmDUTp1maeClC0bWFPnckoaFk24TxS4JjunUTEixTYsceLwAeDDhTWXhT49OM6pt+6M2NDnLFw6m55Wdg7mC4/zR4+Pz+9/+3V8TdXh8uqdrnjrszUqOWKlozULqMGOoUXj1D80I9Tg0Ja6S5aIMSrpRI/VTp5ujYfnuEHof3Mzh4FgjB2hhts5m9WY6Qk7Zt1H5HbN97iErikDpYqQJWNJVprFUrU1sJAMgqzaAZLGCRO1KEIT36g/fgWl0ilDpsIW9ZsiHDx4wjpwqa8UncavV9P0mxTeOK99z9/XDx1/cf/jYEvjXv/2dq6ZevngWW5+fe3/4w8cHkr58e/GU+/al71l94Q6zJ4989cE7SM+++z66+cLU4clXT764efhIzfcFWCzMxFDVnF/nES2FqeHEu31rD7LSoX9z8eTLr7qZKdYQbnjSpf7kyyetbCm11Mo0hNrNNMnD3tZqpePZES1CcbtvFzcmmtewDLyZTynf/dSTEGybfAuiGFFcDS4JSLIRcGB4jr3Q0AdmyOOVfTC4/E3MrHE9WXG7ncLVdiMTEv3m1aC7J/fPyaQnB8DWdAOnwil3CC+TZugmv6nDTAKbYQzMGtWheD4JXC5Dn5w2F3y8hSN32zxRlrFC4BclPKRqw5eKjCN+8eKFWgRTrhJX2i5cabt+By9cTaKaEMtVVKNW7MfLPAdkvWoID+Z2LbYrf+FRcrg4gHSrGGI2hCFkqQqApwO/KqEBU4aPuABRmwW7sB42rWuWGi2VSsTQsoya+tzpMqrErkQ0hHCQkgzluGL4ZOrC4CQvoeJLs+tnN3Bc9UZcV5pmrAQEUsNRmhI0SrqyUF6+oESRcWDOVBk7XgpjJLbSNskMBuNKFIy3NCWucJiV7iJGUGKYJblFKOmKxdik6cl5c1Gw6gliVDXRqJfoCRErWLFga6AKF1VHclDbZgCJno9FNjlBrgTwMGwCg2BxVQjM1TQwsZUDw5W9WWguKl8UCfwqWZllRMyJ5WDqI4AsAQx4GbPETUjUSkW1rFlE7cpBsBJlKyyCyWy+EM/Fd26Il3MxFvoZPTPRPMzhq1k3pIUc/OIv/6prwTvXeVfPRTnonr94oZs28hMy3eRMeu5+zBd/txMCaWmEEk0pzhRBFrgaoWYXa8lFjduD6CD4/mpujEoLzOcI6DPOvbU56oqmHVNzKo9iwRyYX+HLn4iYt+aSOk1oWDX4omShOpeGYtwSC6aVKHYuEr0g3gIE2njgLKXVp3vGN1dV371rVLvInvAb+wEmXWcPYjfOQWXJkeC8lWpmA8OpF5Off/FXf/Hsxavvvn/2/NXrb//40jDsTNyXT758d2K+pUO0GPpw9e61k13GvyePH2cZ/z6b4W78+PDelkQ6Tba37b3rWiuaTZSe8l7b2r/NZoZrnO8eH1mu5xHycQ5euJ2QFY/2c0oLi4VsTlZtGma+8Jgse3A6ThY0kchv67/z4eDDXZ+r0KqiQJrP7C+n0LK7YRicWmdqZUw0dud/Ht1OlIF/CGVj0z1Ki3D2x95SGKkbqsichtNCmeHVwt9gm3tcphOItlL1srUaoBXmN69CmV4CnTPHunESyoNxecn0DzEdUswkoGi64LoGi4cR5AAMwu/l4ck/u/jZRgqO8Ubt2aKfPiu7VkiS9PgVGDkzaCkmFc/Qqw2qLVZBxaMvpUQX+64EMAJkNSCyuiYkViWUBYXOARBzgAWjX8SARvHBJeYvgoVZgKpOeUFp4aJGFZAcJLj+aoPwKLmyFCA/6/3GVYmqyxfkUNevRGyC0oukcQg4IL+UkmynCQmTjuZHwxJ1K6S8Fb7gWmGXgBRi+ZB1YPTN8ygSBeooz3WMB2hN1QSAnhzIjsqCrQGlqbTSENXktlIzWsDU0aGJlpjfPC7dkC1pAEL4UiEEjcLjV7KK0rREgavqUnslVIAQL4SIrTQ+h2XpprrXtqUhuRW0wikAb9iQ/bJUB/7KjoEHjeRkAVIUrYxMkDBLn8K72Vw1nnDE0qqEshSGX2QrtpL1iShZprzKhYYqEr953PUp1mCFY+HA3NXbbFNLRWl7ppu/PD7LRm/O7/tm0XFkUpJlAD998rUt0o8vXgi65pB5c0T4dl+ncJbDsfes5t68vbD8YgRKzmBhUzufwtUZ5SmuPtUSd5a3Wjwd4hlebRdaAM92pYRY0gwhy9/pelzENZTocg8ya7QsXjtntDM1aRHw5YiPLJmcnqWAjpVwDgFXmITGNsggMBuj7TlEpKOeraPxiczWuC/OjBo2Xf1hxCI5Hxvw1uuBDYDNHqyak00OH0RKQX+8m82DA/XKFVkXrkl6+fbVl19++fNf/DO+Y27Pn738/vkzW/2/dQjr8ODJF4+++vLLN75P5e6p1y/fXLw6f+ATFYdHP/nKi0zZf37z1hByNAejNdZ3764k5ECWeuEQVZ83y0g2FtKxJFOU5BwsV0A+nOBFI2emaGvcffzowXfPXzEZmztobWXc0vD6mb5+hjFLvTHgtgmw4JhLzTe2GdYUjPMEmStJllWckZcoWLMTmYvBti56TAHxWa+OtBGYEEJRfHAtLAqg9jJmo/gFSpbvNnO5JXvPY11nmtRmTAor43yqXyQbYL1/hdDnQ8I4Xc7EJSOUd2MHfIVXfm6fyUp3sxaqJlUVgZZIrOBy5dWNRf7UDZjWGeYFrMqGYFOXtGhHAbauAvkQiEkGy7geCUZjV6+Sr638prLl/oHyRS6BAA4vRxn2JLxB/ia/29VCc1T6ykEgCF/KwtuoaMvp0IonvLyIP6MXlHrpSQOghKRDhVcZyGafnyGhcei6rIbh0rq2z5AFS5OMzYm7lXATq0RIAEXXADwJx3AAQuDRC9ZAiInlKh8AgxYBhxIXTFnAnOxxAHgOI1GCaLC308cLqTWiBIjSMSEuvoNf8ZBNHSNiQQCZHGJikXmmAC4eMWTxkFwlVMMhdogsbYO0pVLzZWiUffTwHJrtYJNy4kiDBNRXFjCClFlaAVz3A0lmxRaWwaZYIZUgSlCdrpwGKVCBlSnIEbViiQJD1gg1C5jMCpfNCqyPF8ABkC05S2alEQj4zEGWt8JXbJG7iomC3MWAZYFfs5PQWGOkpDnBdMtTB8R6r9Q8O5c0+Xp8xhLvjFoMH3/z/TM99dc/v+8g7rNn37++vrh/dvbw8SNBOwWXb5S/7jzfuHUN5Xs7OG7inQpGJuFclZciPLiqLm3fvbsoUhtSLxU6Fms0PhqS0FOVK6AkqM43AAIg46Y4SsbHRmbdtS8G7oy+aOERqG8oRXF0g69NDHdZk7mByy3H8/3aiqVYDu9k5pDPr6a/4TSEfEjRV0OO7l3dM6rpFgz1Slo9OTg6cQTDWeJnT19I5fGTL/7qX/7r15e5PNqZc2tdp6m8a3Ryfl/q//2//w8vnj3/8PHpwwePHz1xHfNjix6Hqnw/IwbJh0nO/uz8p+6Xfv36wmBs0nN0cmZ0PJuvBGoOL15opIptNqCnacR+A2SovHvXF+t+8pOvjDaXby7evnnjkueLi3sGXRk3H9MhGHRl2ZpUnfFkzGiaXMwpL5YmiuksULOsm8qevRPL2JSwsK4vLYX5WyCZx9iK9D2erUsltc0xVZFBpnPxhqwmHJDDODsdm3qCHSufpdusktIUX0WGvovMHGlOuuNSK5xayO0cM1vk9Q4QBe7KcwKlxUdWaXxJwBQZ9uk24X3yuDSiRscoOYSfPJjldvOCUdlNj6qhpYefKrPpBFQlf01xyWoSgq2WqpM6wFcKokjjS0v2+eSvdItcZhFcMguIkimuLYuPZuVIbLNZTEpnjN+ECvObXGMb1FEUKWuApYBYaZHJoecATXSXRnLwGPXD6MdWCWInMA4WUcWBAYLwJSWrmAoSVNT8iqgvqgmsIBapVg+iahFktERDMotTCFlZEIMrNnV4a1wsxYsipBngC4oiWaz+DIYEQfKXHEmUBQYxVzKpI+sUAQs8aYjFgps0vymSYAuLBNriKoEomJoVXGIAyZxnsNWwAuGrlSBpYK6al5dM0nbTRSCIuEkINl3IOvR4lwJlR4+skkVxaCBpzpdBGJRJfnpPvmAdsQA+CTVFaSqhGC1EkKvkxQgj13y8HDwdEOsxl0x4yDBvD4KVXbCA5Kw40QC4Moqtzk2RAeHFRspMFksJTwE0TeX0MGcIaig13+qu9C6EsGxyjQDrWz5Vph08H/Q2FO8fHB1adeUA1Gv3sr///vtH98/14G5l0h/bdvUOqHXh5cVrczEGqRqEcNKlgyzzBaUOU0eN62w5e3CQnjTU05ULVEmDAMNVQx0IoK9eNFaQhMa2dGpbwhf++etXlIEvkg6Km49LWgD4AmQi8zkKBByCMtaMMoWAI5mPC57zeUT3HVqVWl3Dr48nPnj0hV39d69fK3v7ySdn5w8ffXn/4aOf/vmfK0q9qoH5t7/97dXNB7dAf/XVV//r//p/9dvf/f43v/nN82ffW/jev+9qyPtf/OLPvn/tBLRXhS9Mj2wx2OP3Au/tg7vffPMdtS/eGZ5vDeSnxyevXrzWFKhXN8ah0Bz09QTNd0durl6/fOUZtWz6zqDFl6vQjLpUh6F8bon6+MEUzWFptUbuIOdusuRXciTLYeRn6I0zyqbaOQGu44ubxM1UWnUNX9uPecDUkQMgvMFhqLBKjq9cuEbVT9FM5WmwypCQ/RUNcxK3+iaWEtnXMQBb9mYNPFeNQQ3R+3ll3f7GRn/VlxB1S0XNBvWwY8t4EefjYPylTIine+liSdIwQxgagGGRDlVDbCyTa8IyGqmtY8JNZ44PcbNDCEfzkRDVFArYXC0n22eZQYKao3RQwpSyMHoOMlImtsFi+Bx6TmzsMzt/BeqLQlNuviCaCW66IEEOMbICfATbviS8TWVyGq6kNw6XWHgdkXJsigg4LI1lMfDibayolHqTLPVKQ5CNyl/kaFOdIrciylW4pQJGucQ2yUqArKJ8GG4FARXlNSR4cAp5WsjkMR58M1OCsqNpEiRwkMV3BYC+DhLANxHGwkycVIjFzhfLb8fUKNIQLCOM+OSrAPomV7+JNrZp4eWkhRLQlZkgV3yJxYduW5ykIaAAX3ESw8JgxwAAiENJREFUBeBPn7kZkgU5lJVQTQS55gVQmcg6OJVeLCdH1QESWdUjqkGC0ZQesukK1g6lJx+mbkSmuxfF5yqqwCA2TaL0BDZ1UXj5GGE6qlVIFaAYO+ymIrYELaalJyGIOcQd+AsLjuwUvV7eAkh33MLVrVnPGWudEiLHZQ2Gaq+Selx44bqsp089SfNuo6WLI9Rz0+7sKdtBHldNkuQUbrMJKSgvLb4iHaOWz2Zzw5tt6zb1HNkTxWIabz5t6saPnYkO/cVOLlJRm1aTaJTYGrOSt6KSHB0gSwZQDcpo3dkCwljeclU4xZocoGTx39/cOfBC0eHR2YktfKWlJ//Tn/6klz84Ov6rv/wXrrfwiV9Hqf4/v/wbEz5L3J/97Gd/9ud/4QKN3//+9x4Df/fdd574Pnrw8P6//Z/rZN2xoNt9/vTKKvf4iwf39h5qlfBWvVI7OT47f/DgJz/5iQvF7jx9/urN2xkEVUAvJB36TvNnTj2itLyoQkb9Y1dSe7vIqe/cGXl4Zde2U/OZ32wzGPukFLq+3JG4mZzlcUU+0Tgxm7PoyiqDnGEmV1xYtX/qAyuWJZkazGhgvMUUgI8bJpiAP3QT/WkMqASb/IompbMZfTsNyopKPZprwVLJ8ZZMo9lKVrWaQBCRkMpGN0gRUg9GZ7B0FuTKw56h2zZnOarzNSR5n09SbToup7nUeHbbshMevizZ7bRsn32TtpsQCifm1A2KSUtQrCT4KAEwgMqsD7PUA2xztMmk2LraoXJgiGpGwAWKKbwkC8KvKMGtC8kub/VUf9qRroSQyQhXRqJoshWymU+IRQZZmfqxzcS5dKKlRKLRi/RWXBh0TYZEcjFzSzQADTz2GqXZAMPow1vXSSCn7GoPVwkVtQSWt4omma2rZD5ppSm7mZrUuQYBYttBS0IusBQJEHRIpApDUglxeSu2lPAUqNNpN7klBw1HVHnhG1VgGUqwBgRIEYyrSf9Q1Y3dSNOD8KWLGCwXeAXZAMAHa/5l/8xnB7z62QqRd50aJVVxvljsBHKjfhb9gMrng2tpr1HKAmckIxA9PAkwAImWTFTlUJWjWJPoeM8vfWmwRPMpGv7KPjLyq0OzSSzF8NLflIWvKpZFFDmiBDGKxQuJkXp0IFbUzZtrx5ngG8xdGgraCZqZdhBuBLJysO1cs3jKmKY312N4SGkj1CEd4/Cv//avv3hw/9HDB+77c1LXvcR2Lw3hb7wMOk663EpXLmocGJKpioq2x7MDAaYGv9bjy1quEZ62PYVi2zdZ00gWcQWWpdkJwVigQAXyOXg+fLkEiQVTrHhyKGyIh+FqNMjyTm4ShaWY1pDzY80tq0z5Oj0767u8bgwxMXFeyja+LTrXiunMjZpHJyduMPrN3/39r3/1dz/1Bd2vv37yxZc6f8Ptf/zrX6qQntFaEPNhOHXM2KzmnFlAP3zozaRnz168uXC26+L8vm9n5MWi09cXMpILsj5kPjoZ3HQ+zSxk8eQb5h24Ojs9dCuWvV+SD5wZjtk/Hs6NNbHAbJ9Yf+PKOYGtg48NfSBjzGjWZbubMeBhEGuFud1FS/QsdjPly9mxMWeHtzSimr26lRHvAmwmcMva6EtZLkEOQZEYHe0qPYMDRA4uxeFpgZo+lOEK8Wx1FlgSCjAFgBocgGtCVvDl3fXFIqscvqg6CqxHMGxVxWgC4FCO4E09bxKq2cJXVH2jr9lSG7K20MpGyaXeLvFKfWm1MIDK/4yRqOKrEjI2h2Eyqi4ugKAo+pCwhOAKcfrtPJYuGQCmUbjShGfLsGXXMgUjKAsAI38BglUDkMpG8v/1//K/FVhuEo1HdNOrfjAYuOP9zWstpWFfjlBRpQQDKhCNWi15saQBxKZVzHdqxXLwkTvdiqA2J2MAZYMejAVQSsQcmPymosU0yN8FmhYMQBUhB4tUev16KRkLhvymgqw0HXsmZwcm1DSRqChc6PmlBOCtE4tGrJpUO8Bw4FZQUQAOGRpJN5ZtyKFk81V6Mg080i0ZTcqIxoEV9AAO2RJIWnMKA2g9KMxncwRkGo9LZqsTwMFzSweYWkPqKiU5gro2QphFECV6qfMp0FPoiJtxlLIgs94raLp8lJNUCpGQJoemStaAfLGc2BoQI6SVawlETabb4+R+nKZFCAKwWKuo8vIFJcEvOxtWOPlc9eHfHng5xXOG9zrcYxk9OM569/bm8O69b/70hzcXL3IX7fH+5dUbtybp0x/unxLlun5iDeQkOPGSbcBcFmj/MgsBCXlXtKW2d5mhIznNmyu3knJ2mGFdaDH4dPdyhoUjzX1YhUmTCy7qjqMImTLO2hCi8L58nxmMoDYGDzNROXVGDphPbTTWrYIfvMISlzegZIGRHdP2ULem05fO0fFwZRv2zp3nVypwXo6Ibl0XWogeHVrieu1IFrwylJ40d+h7L+nB6e21ha9bq+T08OTYsEptyXz3NJcLvn6b232NzWipiubm46Fbf//4x9/bJPV9QJdpuJLj22+/89mMk7P7+7YsTh/4vqBXiv/07ff/t//7/+Pi5R2dyM3H/WsPa1VsJ7gO7h18fH907+Z/97/5r3/+1WOXWkrXGs3pMIPW8Q9ubhrjjDU8hUgJebSw2b1PVOxzs5kpUo+Ta0bgawUr2BrFpKLmOFQo8bZEzBUUhCjz3gofITE+AhWj7JAVGOR23qOeVBRGDuVNElkulJodUT2bldoXN6N6Il17kQlrJXePetAt9HYyG00IF/XuvW354LlJJk+b4zIHSJ1VxGqWWAUE4/xEIrcLA3DIpqaBASgxQApye3fyZiOMLoiD1wupw5Abgllg4GI02YeUcXJayZkdBn5023iE1Anrg4rd4ja/uEAhGG0bhFHlIOnf5glDGW6sFA8lgmS6vcd8P5itqiGtsJSmhSUKfWGArr29JQwJYhEXKKMkljQ0nR7GoBW6fESQJMI0DTBHSgUVL4pjMrLgabkkbOmTE04QJZ8J0gENPMI2AmGkyFXOblZxkQDPl0Qlo0xa003AgLkiAdVKsEnXQGSqXpWApqkL1omtHNnByKGBxAvfYAnqw0iFQ1YMuABMkSSTAAY0X8XwS6BzrmR+9WkelSJptWqjsAh+mKuIKkpay6FP7PY4JeGqe5FgjhB1DgZvywvvShqMV5RE+URJrllDI6pZKwsMRyYkoMgSVzH0OWW6NSBRaCSKpgqstKpYeZFVAXrCgJmuXE2FEA6ea/vBThSHsnhCBMGVXHZwMWVHA8MR63Ji/RSy2UjbbAh5icOS98lXX7pB+sWz79yg5ZGwEzxGqZhYmTpyku2VA4vmea6Ww5yau0d2UpY8/aP6/v7JnTxKEGumKN0NzJ4WX9PXUKxTixYfmZPLz+vzJEtA9rWIXQYxNjZTGtjkaWr1fNmpQZTZC5TIjKoKwaKOASSXjifOmv2eqxxrwMyY73i0ac6ap6ePfv6V8fX5y5eyrTc09Bq72cHK1WNga9mTs1M1zYj79CncN/+zX/zc3A6jmZMXel2tYMR98PChL+924QvpSbCh0TFpz4Zv7x7/4he/cFTKMPzs+dOXr/ICmE8V2ZL47unzP/7p2zv7L3MXpRH77Nx3D99dvLcVYw+VQWQ4RZnXgfI9YFvcapebqa3XqeQ7kgam/f9vZ3fWq1mypAk5duwde55jzunkUFlFV3UXrboBCXHBBUiIf8AFSKivuQAJ8Zf4HYBacNEg1HXmczIzMoaMac/zFDxm7/d57pMnT6HCFOHb3NzsNfNhua/la/imP75SLdAbzjkdMWTk0yM9EiYn09o27aAoTLpDSq07q0ZgSgmd10SBUJNGLSDGgHbISDYqUlRjrmlYYdh2DHVRNXqNVnnsm7jxKwQM56082VGLeeKRZte8lBx0VeWqo3YSUlz0QV/IIfFRDl8mBdfxcFb7xw3REZbn3jMrnYZNRVQQY2AHJGFnuNJ3R1EjID2iyGjjhVw8QIIQQ8IGLuFwFKGUF4axlY2J1BRMyF2mrOjD4RGviI5sgsSkazA98uu1WO7Elr4b5uRxUcOsqy9FlGFilAY/fIqUmhS5jm10qA1iixKqFsNUKwxiAEIWM4QYNlIoUgGFGZIgEmLi6U9BJv7SBJAtwCrvvDgmcOgr1TRSu03pvLRIMLtiFVKQbwdQ00tG27RRgCRODYFXqqpwwCJPc6R2qWzS9sthrTqsaqKZvkbGjnAox3UQBJNhnSAJ4UhDYogcgwiDTygMOIlTilLBoR8EcoY0ZRWFZ1twLVcvpbGNSVKlmAaeNAXXUVMvpcIGSIcw3oOTrCKEz3SgiIkuSzMGXBqiGQYaRpY+d3EkWjzDIPNLhxDRlyWPISZQsomcITU40QEbijujKJgjAJoM6XCX+op/dBCo6DMPTip+7Sahmva3i2e9NBzyEqWmnZlzO3Nhaf7tm1cv37xdXVu2qfrhuJ7a1YH1k08X57M+2ujVkNqYrAPHI0GJx4cNZz4sGJqekhVPhXRde/5KaaYdBNA9wLCOkQSm+tTCJ04S1MrFsFUvIKBYeRLKpYkx6szSk71tUqMLaVlqZTPtGrB+gaGkPcdpE2oV8Ic5z0HZzHBUBrwB6j7nwc6upoa0uu6tnvsULI1O822l7O69t41svfTlLHdqLMGucb/55puSrK/bDXYlWhvNR0d7+/uWW1erdUG8tmpttqgjzbK29cTy6pqY3x/mZqk7O9d/zh4319bN294NO/Ko9OGRXyn0TndT5uJ6lLy+S1OXbD5IVUOlZpXr+jUFD455AssPBNfomY6x6p+aQwqldOoJ48kJvQoS1tV/v2aj+jTRYDS1hkqL0UxRt95kqlSKLI7cZWiZSITUfd1jI8vGrRdPqAUWGlvZEITy3d4zv3V31MBAnFdab/9OdFjrbUJ5OyGYiqT/JNSGnywbJCHKVHiUkvwINh1+U1EBOn1Ji0SfJMQq9TU/hA+sAGDSkU0jaIc+DipRmjpOQslh0O0WCUMKMU8aecCliALS3BQQTBQ5ZXHiR9EwJy+rPnHX+BQEJsLYkscKM1Grn/OuxpSNbbqMlcGQoniJi9FfihArgCguaCYbcIC15sVZREGhlKEW7aRx4ztBdEBHBxMTA5oETy2leJIEADz4Ssnx/OIjTFZKP1th4VNhvNrGOwWEDyO1asedlDzxMNHNAdFeKCaR3OZJEjNfQeaLBI4WYOgCZ1A0Y56UCU1q0rRkUlkKSVNfafClkXBE2QVH8OlHR4oMaBKUCKWJMOYUgsZFzLOlo5tNZIoyOEydrDSFCDGoscuWJvCAtJ/ilTLEDIqOLEfodpZQtvelCkCWeZoiIAKLMFWIa/WiQ0FWPIqCnNJgUgBITjhs4yIKgZWGaALUnqkU2ATAnBWeAkyl6VxWJHGhXW3AGmIlrDd3PRjVR5of6rk8V7i+seUYPDs/Pzo73zk63raFaM+5n+U/O63TeRGC9ZILhHiU+vSGrWzzsfWjfNUnG3PZym9tZvoWcdeuzs0xHX+9w31+OvlwQeqblAJSykUWSE4VSevzxG4f12cY+oSmRnHVTlWLC3UMtc46Ibg81g5lXm+xuHisfRGYxs/Nh3nb0j5EIVy+kKgufFvavvCKnyuo59c0cf1wQX88wVXvm/fv9vZ3vFlkZXU57Mnm73/3G8uwopX1NVelnqWyAFtrXR27Twxn++GDjz/+2O1hO9XuxzuNcWrSDbj45ZdfCoMm8tGrhz5P+eChG9Avf3i9d3Bsw1qD1qFWnxeuhlEjQZ4b7B98XXJeZ+ubKx/K8mq1l4b6k5UugmsM1VHuzkKPUo+9zzi57PYppJ6FfTPRK9t1k7dUNVsZTI8y2Qwq7tIjJBiUHax0otW3txpqbXDfJl5zWE8iMDDcnJ660LxcwNRf8TVgA16pC/ZbVDF1VOk7UThEaGGiBTMg0rof0usUE3H2waR22u7WXOQl9snBW6pZygt8ev3MtnYN25hmahhntalgiLvZAbJaMrWvpmHiyKoY7kyep2FrItLdmZnBkoRSI3zpp3a9vg6J5k3RaDpq0ayDq5vYEMJQiI5s+ChIQ4Q9iutkHaO5WOkCci5uU/AtdwzJZZlQRiSxjT4JnODDw0SnVG+FFBcVdxNbpZObo3EQuBSMgBpkAqTI2K20Qxn6GF7BRnniof8Ixl9o0gwOoaOBkCIIWoGCVBGheQFDThjbEUmYeK++bmpv5Qi4VDfHlnkQyOlYk2QVyUZBSt8cFI94+HSkAjAeMHQQJq2Midq00av1KcBPvYYmCc0hDAhJopXqOMo5CDFBiD5lmqFRZLh3VDXbDnCaFrbIpbJV565Cqsl8oMWFrJi1kpQEkSBWEDhlgleKj0RKKI2yIln7mAGPDoTIIWtPFHNCpPERfQj0R2wkiGbMHaX4mIuBMApJK9aZGddYzCMJbCJPGFJFWjUNG1iYhIqYR8LEL9iV3/qSxmQf223Bej60Pim05A7ZsU8pLa18/Xf/8s2b1989++ZD+2Xi24zuh+XCC6BPXAW2rhU03d3zG9+Z1hcuq6bHfI/o8k7BAixFNYd13clNCDmj0VaE0tRdqj1FiyKhHB1TZn9N3JPAcE25mfXqOjeahYP7U2IuMlMy/1HY3N7W2b6YcXxyZjHv9478wtHi11/+C4cGfUujddSFsnvG7i/Ysvaos4tdj0O/evH9999945IXff7VlzaTX758ufP995ZYm8xb29tu7nomwHc2XDcbqEcbfh14jRDC/sX1Rb03fGK0+dkmyJ88/Wh7Y/P58xdeeX+x/9yD1vbDP378aHlhcWtl7ffn72b8RH19DroqaIw7obu8OV9ZXLIBUEuCHYd6DM97B/U9LM2gF5AzjiKPTId6I9ftA3Ng/Z5SCmuBKdJl0epcNREEjU+eTiEnRBrNODRWPQZwPV/TF6HWZU5TVte1/0oIcx8dziC2tzG7kyfx8GLpA1iMXukZxoQEn+OKsCT9fxK4yGvkW/diJUycfz3o9Xfe2tIIrSa43Ezu9btaD1zd1BP8JIYy6dZwxgaJuPz2ESQq0XYdJ62RinDNRLNUlfur3SQ0M4bZkpOEgAxSBIGcpHy0Fyl9thiYEU6tJ5qZCgYOJiCJXBZskNO8BmHCSxGh7DCPi0n21slKnAZKmnjERjNVwwg5jri+DZj4maBWC+tLpm0/VOND1tiNMP7IU5mLk7p5Hj4YUYshydAkb4XJKjIU2Ka9SOgk5UWjiFIRK1GlRdJeUpKAD3dhUtt2NFnqaCLxS2OS0gQgZRK/kaeChhGPmWvw8UhydVETJR0SVklJgMvGu2wAZZlEX4riQhpDavikSgmTHfI0DvBUnxw1UkFhTNdpfDqY8Pw6jGkCtBqFUZQWSAqwponuO0WJfIAHLaU0ZfnKqAoOWCb0k2oiJGvKTy3YohEqqxAhJvoAy6T7N1HFJI2GTzyU8YQxjMkwVDSQucMn2thyoZqEsqEoJ9rUGgLCi9zT1j1H6eOatTWwHxBwWSlM86fJe+7enHpenF/ML69+/tVfX757bTqyQtfdLakJxgtMpmaDrZ+I4VjHelPD1ERwXb9EUENabQRbuB3/yvxKafRIy/3IEW318q2RliwQwtQFk14g9BTytfaoaMVb63dwgGOqO6pRGerTqrVPUVTj3PVwg18anBWjg42Wnx7yGpQTEQteXe8u1V40fTvCnr7jCPkBQZ8tOj0+8eUN28j1w0MLC1tbG44RV73v377Zff/uyePHbsR+9dVXFFwHf/fs2c7urkthmLnp6+xKkS1lq/X9R/WhZtfNfoLl3Zu3bhh7Nqr3tDc+/+wzH8t693ZHnVxKefnJ459+9NArUbV13J/E1ZrVDtaEyztus5+dHF+vL3rNQVQ6xba0e5j3VupZiiJLWG3P9g6e4/HygmQyTmr/thdmk38v1NVsPRonbdjHOyGYasTpQC2J++WovxAuq9lzDZRjB9892VeBvctiwWPOb1n1lgw+EnVhLI33cpMFyYV1DZsaFSRSRAdyZ2siml571s9PKWqaHOM1jO/UBUab9RxS5nXwIj9cUW5Qj17jiG1tj9T4qZ2VqOViGm8qlJZ6k2gTsOFBHh4zKmUDIhN7NUyfwSjVjNEMSABhYkgURSdeUkoui9JlstBK2K03DNmiKEtjywRp7RoG/bqakBx63QMlVJo5Jy5YoZgDw5NLAy7F08cwVPFCbwRygnR9TAQZ/URODSwawroGj2pSECFKQxLtSPDkgygX3nRBwigilHZU062elsQqJokMGjVNEKdS2Sg0arWyLEM6MZcOZYwdpOhHjVWQMbfDZkVejd6/HxLlYOIHURPAbUfho4CPiUY3oLmIPIMvfMwV0eRuVHMgDEDhUTMqhoROWoNt9FMLKYqaBSdFiYQ+nKrX9Dw6sPSDpggfWGkBNUGTFXlwpLJxIR0uoAUwRcHEq1fUbBwYcCCDEDk+bSIbWNn4jTtCUGILICHNOAUbHfoUZIOglDwpiUOIidgo6Isw5PGSkIYyeYriUTCsEKtaJSeDc7IpIuvIdjfw/MKjm/eWV5ddt73bqdeIP/3six07ltd1W9cU5YlWaxYMV4cek3W3wtVzz6a1pLkbpyFtz3X/QK1u4hoBv3dn8uEOmrrdYZQ0cZZ590Kqk4okpVBhTx+q74nQOlq9AL/Q+sSMcttWOShNl/dYnJfWeUMtyzYoZ2/mlVq+Z49Pz01mHnBeWVvfvl/rotXYIv3Hl6+Ze/beJrPfyl1dXXMipwqubi2iiry2YGXd2Fx770PPOzt//OMfbS8/evzYPw8B2ky2z+zSWetZcR8/rXeTLMbZf3ZWt7B1H7KPWN25ry8ujn150meBT07vbz/8+NGT+5sPXDh/9+zlzut3fj/pcHdfvSZV679ey7NaqMzB3v7zZ99fHe8/vL/5+OEDA9ACXB+eqJ9DnZnvt+2r1jkuWMw68GpiYVuNVo+K10irW//T8T9x1O2owZGWb+U2LGMnVosy9SNFzsq08uycUWsOqrM5gl7OKxg6jHu49t/JMkYeQL7IB1+VC9W9hckcS0BHauT2aCmNLKUdYyUqW9ImPVsR3hpI5aLvRCiP3FBqlbbq1bf2+evk+9ywKLV+wbeTymqm9mVd0mq15dI3o7lKu6lOfPPk3ohZuH5vDbGKIzEYq3iSVPnHAOBPH5ttlDoPUDoOiqHPtmLrIsJQB/ajl+6xmlWqV3qZlOJJhhc6JLKZavAJZsTm0FAaK17YAkHU2Mr+qCnTpZHHJAqUMdLgUwgUyWQBxt0m2mkjDO0c2IyRs0sBUZYqTc0xtylQUXPgxZ+sRUKRqRPRZ6tIBRRBTqnZLcHFHN9uJ1sQI8jhbmGxFsKEEUBpJJoJSPDp88Xv3flJwykaMWBM4pHg4yU4eowECSMKUqHKxil8+hQw4Smg8FItSTNnoCmSDn0hgUqo5ebHY2yCmdYj54WhBae1Jt1ZkiaOyGVB4dNlbG/3HZ4uNR6l+AQpi1ILCNQYykaYdnNZk2GXbpKqV3nsAxJUlAEiIAOHPM1FAoFmHA0vhBSUxlZpXGDopEgpXyQYgFIkgNQoFcHTMd40qZkdGmSEUQXxk2NkgxNAzybXjuvkKDMp1LHh+snUbT+Uyev3O/ru4ZOP3ar89vnLhyur5yenfpbu0pTrKRuPIHhFxjeQDw8X52b9jJ236MyBwq35q1+f5QgmKDF7auhe3Z+t02SRJAZpV6hawPFFGaV9wpOndjSRWsiKSlPUhjl8P3h8945rOtN+V3rubE5lJyej6msamesHzXykkT5BfdOqbo+6pzSH//jTx17D9fsHTkgu8xt7d8jvfPzJZy55LaKv37yzalo71+zIr61qz4O9HfvK+/v1VokN4Fzmvnn7/uDwkNSFrP1nl8LZu3727BkQCG4AuzFsxT2oL1Ee7u7ueCnCwu6l3y9/8bkLWb+DtPPuvS5c8lrxvfn9mw8+jfL6zXvb1PvvT+s7PXUEuFSuZ84NZE3n6svy+frVD2+//+bB1sbXf/1VnQM8+cj9+/O5G4ewNqFWDV5DvgYA/SI9kFXHVNj3/r2mCD1UqlPS4OkLReyglbWh3r8UqUeUGunksfXdEqtva9bYopnhqt3Ss9TSyxOc6ZwwXGNY2aOMhHJM6GP6CJ6sdtSUZmw7KCkYGIhbbZOQegerRg5N4xvCpAodbzkSo1W1H+gz+uym8Ffrq0O8VteKopDVlze6vdeiFRyQowphoBUVlLW8XkPKkz1GLLHqI7xgEAmrEBfkAhMjiSLMj6H2gZwwktLBoGjKTis+edy66jr1kllIMAZDeD2CJgfm9DYwBGgBZN5PSdX4AR40TIhh4ieHg2/55Dy4219Visj5zbRGLkso7NL/H//b/0IqE72YkWQKSygpjcKHy8mLoTTjWGtioGMYYtIiphKHK3cCVURfEU3XEw5F7yRQRqmqNNnT4yOaqTAJp6xy/IBlDgrhyZEFIE0cZThKSXjHUwsleDpnJ6fho6ZUlyMu4lcpQwh8KXVFQxPPlg6PGHJVI0eyDBErasyV4pMNEwnNkCw1xMrOGStqePgoygEkZ0JTSk4zD+ngUYRJ6bDFC0nfSSNRkTgSecCB3G4xjqIgZY4gh9dxjhwpiSkyVqNUbCWZvocdQ0IKUk0KmTBqUlkkBrH1yK8VKK5TlEHMHKUlxRydBJ+jBW+VpUBNbE6u8RD4wvCL4QWyUuNe1mBg0ifi9YgTBAS59O9OPszigsVpFUNrkJuSAF3WTCiXSoC4OHq71KPFget70X484M711fzdGT85q+MNfY8/1zs8FUF13M3yqvpyxJZMClOz5YSGxJtMQ0jt+qrevUbU0g7J4tMpbEkCSOLeo6rZkSGh46cm/JguxsRHi6aTiVqpuklhnlwce3aMHJysSB+6LP3oYxe+fgjL8uyK8/3uPhs7xPWa0KxPicyZju0b++1eC5hWcvr5YHu7pmy/wOD6dm/fkHOZ5y7s1oN6bclaaG3WFxZCC7PWpvDq1avaZL57l8TVcD0Rvby8f1G/ZmjFFq9Hrh4/fMSXdZqmOc1d42fPnn///GWf5Fz52Nb/8cIvzjvhmLH/UPuP7rHfufJ9rI8eLP3VJ4/n71x4fl3f+Y2Hf/2v/+EXX3xx/7MnPF6e1auPhsHm5raGqtjm7vGrMQnFSaixNIgfLTbMtI9OETYoJdQ0YKiatLP6DnlSgNwcIGUespMvy69D3H9Qhd+ED85PUoaKEIaiUuC86z/EI8KQR01RFUzpR7R6XoqPyUFNLacFwgYrCQ67Vrt77cc5eupw/kGhDzG/fFVHqKlV8xJSVgnxaJCejap2cIIJB4I2dLghvCal3O6uvCU+jbHqNfgEPAkjoCz7mFVEPhTCGOTxyKlSmiomte5FoarTROiv7uaOiVJZQgxD9ape68E/upJCPGKqvaa385jXx1e6y5IGJGnMoak7K1nVBwAqaLfV8LcR4kgYk63UNM2ImHZCFEGwkhKaOKgZu4Ggicph96g4EEkAqxd9LqclFGTJgVi92u5PEoDIiTN9OoGVFmKPNjw5PppqXnQvHzSooyIxaD6kJGqEnKZ16BhSJOhPfPd2KH3CYYWPJgmnADW0NLFp69QxUPgA0hzINIMQSZADSx+JSl1TmqpJqbFShegkJRE8PgpMqI0UE7SkNBOzYIZ5EAJOmDgDwlwpoSz8oIUJjlTfgUVK2Splggx0vGZBYlaE6NOUUkYDKibRHwhR413pbeVAkVAIDQll5oR6JJiKaJILg+vUhdBcEF5/0VQa74riLikdX8vS8BYVE4tshddfb6ZgNeowql6Li6umJXqzCzMra5vek7k8O/1weeFLTr6AvH9w6Cp0Y7Ue/7FMWa4y5hMDkATAOygXIvX86E3dhFOE+ErwFU/XQprjvA+lGvxRo5kGdCSaLI1OEsG4qDX5ui5zwuQjX36XV21kkb/Vie5zq8XKiqXIT/CenpzvHx69/cdfPnn6sVdtCVfWtu4tuR371n6yn2Zy6e9Xz91YtRjf/7DlwSjr6/HhvgB9GNL3s7e2/HjgfV9yVnnn1h6/cm79V3/9Nd566cErxLuV2FNXigwYCD4T/fqHHyzA9z/6ZNXZ0cM5m8/uIh/t73n7aHl5defN23e2tHf3To4vrJAk3mfSLN1OWkrz9T7DpOXuOBO3Xs37jUEnPnduzo+P/vj739kV/8X+l59++qngl+cXXHDvvHnN6dbaula121AH4PSItoaI0/mKhs3w4E6ronQfOaKTUgzKN6NdlOOdcVXv9C3b7uJu91Zzie6vUtvswRz9iyExMFpxMjtFWer9LgpIqCg8nNp54qre1C2aNEu5z8wgdGtnnexapIQcBeGbkEurKyU1ymsPH1CfJNS5TT3vpQlq9UXYOjh49L84HotNUBA61A+uqlRZtCYBEoebAaCvCSkX0HRuYYKckEGmLG3gmgy1swNHFkUtjDTmFVffrkqDkNfuxy1ilRxkOqyGJIBp5+hUTK2giDyakUijk4U/7jIeIq8DfLoSMaSgNEUDPIyYg4wJrGxIlX9c3qfC+svyNvptXIFqXP7SFlKOQ9Q4COHhcFCTWlfSwT8mwcyb0Ym7WBXmdNAOpxTgD/C0FH3KSBGF6EiFDXy0JqsoDLUcb8xJmGN0FX3hxSMJRpG0YPvntMrTkHQw6kIzylqDcrJgg1MxTUdDJEMnRawwgU1R1IIT5GhKKSPg+RJWNFMaXq2jI6sR1Cj1osNKkb6Qchfk+B1e6MQvcyajspgcJOpLB8XpwHE1E5M4ylGEtwBEAlY8AxAO/du+mNCkIzyMoihHRxocrvHUZHVWMKMvDYG63Q5wtENgXf4my3Bgxko8fb/KXOr1Imfulqp6fopjLac/qRkRJud6WHreRcm12c+gWVjxvM/S6ezh+cnJ6kYNI1DX52duAFr3Cq0+OzMZG3iUKlTX9Ef8SaZNOjng+YoO+eg1aqmyUrXoeCbbic55BJcji75ga//j5s7B0XFVs+7v1rbiZFUwy99duLg+vvHlrsUlF5zmvNpFth18fGS9Pjq5qBeKtu/7AJWPb1jd/YTRydGxachau+LbFvkl3Z6bHdD7e4cXS1f62k8x+A3BDc8t//AiwWh5b/cK23bX0cGhh6Wtvp5d96izVsvsrGTnl79+/OiBTemlze13FxfvfO/q1WuvPb149v3L12/8RoMfelhcXrWpYBBp4bqrPmkqreIgrav7+mO70wep717356fqgvtwd69eQN5///r777/44guf7lp33l8j0T1wpyG19dWbBx49r30jrQcx8w8d7Y80NdKSiqTkGYEpKrn/TQWLLLR9F9a7XRPzfrKv+qHNre+gFEkDjkdleosySttksjBHYWJSz/iVPkODlHCYOvSTld7cuci1uFKHAkyd4nzDYVSlTXajIcuaYwqtH662+rownoZUv5QcX9JSnB6SIOTYM6Rtq8Tw4MUAsG/mzTCXhFq5azFBox+/hGiEjYkkzdtOCjYKspklhhqmouW9Y4takHO8w4lC6TQ4JvyA5TFqJEOf8tDUpwZwDsOgZQBIqUkR5XjE/HmNohbM6NOhCU2NpPV9QcVIAY2RKsPrLtopba067KllkyFBB4icPqsoBIeJCuAx5EoDRRLYYCYlRPRvh0GilDLq8kqiD43QT3uSYJJVhCGBg0H8Rt/xRm6DS4qGF7WQNSeGARV9Cpg6VltZVlFgo0M/OryQRC3uopAUSEopoFhFQjn65CPUFOmXwA7bhCclGZiDj3xUSsdF33QnZvpxhIkOj/ikA4QEDb/4KGcYQEic5JiQg23KTqangpicINeCKowQIWREf3gcDJ22m1wjklMj5JRJioJDEqGoyGmiCNVUL8uSRye8LBDmcZ3ZdlJUIOWgbn/5HLOlqn6g111Qz4C6ZVgbJvU5Ik1R9w0t1UKqx0OrgX2mrm9ZeYPNVrjrwW2zT/12vWXX4VUN4ueA+BUq17W0+/23KSUqgXE6QvLTugmSVhdNTkosFeQAh5VStLpaO1JC6rdF7tKu3cPrm9P9Az9f7NuakN3bZWtldYG7vLnt0tY1qB8CnLm354eG7DQ/ePyRq8P9o/qopDMVt3gtuI8fPhT5zt5BIZzYnD70c0VWUL9Y4YcVVJOy9c19YD9ACLyK1rcWlhc8eOVNJO1/f3PLomt1vtzatqVsSL96+dKB5qTNc1uyzJ9982z37Tv3Fbc3N3xjwAtJL5+/cOWk+ewbUzbVgPLo9NnZuc51SlMHJeqeN2hqafMjK7Y9hXvj0403cJxgLNyb8w7x6f7h79/v/vDt91989eVXX33tjaiF5aX6flm1ZTWmL0f6WQ5tWLfJa7qbzIQZUfyk2WXp4wdVDEqnQnu0KYIpwk4rRONXmiwFi394KcDpwKjpmCSaFVnPNqKy5cBKkf8qSEMQBmovu3VuOPnXZ3uF6TbIdIYswIIsc1feGkZbVcSOg3I+m+hpGJl1nLnvayrrF4cUswJQJh2YYlA5FpmEWqdaLEsvc93q7Appseqvqsqk3ZSiDPXspOLhANF1SHezKq9Top8sedpBNoA5/C2P5IgF+dB37hgMRRVBtycJjxVEzwYQyCnAJJn6rHgKsSkTfkqnsvoLJ7asxEahfJQX1j8STUJ5aiMNTuRzjrcoyScgDMrEmnaJJR+hCFnFMcZBIlBMqqcdEbXoUIt7QsgRKk2YKQqvKM3aIUy6Ld6BYCigYSgej9GDoh8htZyAO4aHfpzeNlQ0/AKRNSCiMKJlVVVo8MTDJGHQV4SG3yGJOaiBPxg6SBErQmnQSJAiwgSjSHvKIkWyUYhJaU+F4aUZKCMLJ5GYuMNLh9UA7Igq4Sh8rJJNGqscMAOKcnzZ6YphQCiwQvpaGqexooDJAhm1gGhGDHmUDQDIeEJFaaJAVYtPB5X6qkUkQ8iQPMMMQg7vqMGnho98FM31PTCn/OLWDKpVE44nmPt3Xmurs659zZtsy3x2Yd6ptVneLOYJSep+JIhH461mWq3SFXBr3Ms5eYfNWDB5prmMIK7xF+d1vADsSa1CFbnKXt6dnIAOiXag5hDDaIdkaTJXl/QXt/U8mO+Ef7jrNp/ZedkV9oxPQXkaQMdbn5Z8G3nm3pxL1Xt+bmijpsij49Oj43MjZMme+cLS4+XatDBzuQVr99gyaef204+fcmGWcLUqPTk60APViTPus/pM2IqKI+8H+0kG8u2NRVvNGgSCz3RYjC3MPhL59ddfk9h2ttJX8De1R+Km7+bimu3o7//47UttenOz8+7t3s57v4O0vLqyYCfL9ez8wvG5t8DqHUV79nUuZFWpzd4+ldFh+sczGX4RcXFh9vrGB7Dq2POlyZsFz6VvLK36rPTl0ckffvWbF98929i6/9nnv/j0s8/Wt7a9SOb8qj/w5obyrF9rEPbJ6aEWDqXLND7CQw0pTVGpTbgaw9NS4Uyv2Gq5LK2pqS6vHmQkBRteqTZRfUzwpPjq36u6q1rCqWEbZaVpaEtvoU8wGTXCZD4MDtRCq3OzamQHQqZ6pXqT2zraJgcupHpUTZGotSXcjsRKWSe1gSp/Ta1W/g1XyEaCQWJo6SxejAcKP0upO8PGnKQ0A0seksVIFQ0JPscyEENz4FPAE4pEPDEMAomsFI2mJqEsTQwDhw55oKxYw7Ct/2RWia+Y4zs7CZgVhFQnTFzjBS/VBSSTawgc1QSNB5QWvG2fmigCbZFLNQAZtRqabdwoRdRkMU6IApv+AMKQxOEXwNspfWd5UsIQPo5EFTlYpJQEeRBVlrtIos9EhGootp4Pa6rilNynKCfQ/afBCi3g0kb98SrNmT4EFBcUwpAEhyQBqCDCBwoOZtAwJ4+OYFDvCE3iH8qQB87wktLTs3qqKL6SyiZ+LlAMUwsmap1IyNFACxPDwSfLCo50ZCF0G9R2CF7Y0gD6LVhCEgMGafa0s0sfIEGOJLweGSAkQNSCJltyDCIEKItSyl1IGCQMrQdKoVFmEjlGABQMSIBM6BDSp5CASeI3jsTmMQIqrVYXvA7f/r3gvpVHXP/6XQR+zIL1vaR63bauFuxFexhopj7l4Jnbg/2d/b0dLxF7ecns747l6cnp0nzdcus3fVVzMma6LlU1tVchCrW9WnvbmnFfNmGrXetU4ISpDkaoqkMHzXLl4DUG53pz/vrcMkXF8WzRslBtbm375rJfefLbup5PPvz2uccsXAyvbWxpQ4J64ujA9zF2HZJ6zbqr9Vzdeh747ev6krPssvOvxYWj+p2iw72+gH706AmRHw5cWj73TJN7vX438I7npQ8v4QOBZvV9/+ZtuagfWq7O8pFnE4JHrljoKdfNV+cXXmf64cVzc7dV2YmL3lpbXvH+9cVsDV1tohE8NaJvrg8PVD8EULPVhaFvLfUX5ik7yfIMuh7xLLAdDVOatvS7GWY7NxWOq398QHPn+fPnf/X136xtb24/eCQGd1l0Zl26uZ0/nanhZsCk8cUAH/EbCQZpe53jXEgyDaz+9mtIetzN2hreOjqArutjmDRCqQ4d4HwhQhL9OtUpC3wIqkB626aGt0Mw8jYxUit4UUy0uy7Mr27M2wVOLYdMLc0aqt92LeUZg7/OOXsM1tlO45S6J7MIa0O9rr4nh7+aJSpPQ9KkZzTx3tNaReVnTsi7BSqYMu42bLW6vYV0scFgABAaPLdjHnwGfGwJMXEnDRFi4oiX0Z4JL34zlqhhIgkgHrHVMkpBycY1hdvIcdHqpaB0KESfAoqCosFjKAxYvqL/J++zRpsZvcHTo40El1YQvZaiFjnN8IledtK1vWFLM7WFg1EaQMq8MEwc0gRNP1FSgE9OZ7RXAoMPJxQccsrkwWHitJ5CEAKIJxn6P/Gr++ORDn0IiF8jA0OiNB7hhJSKFo4iaQyDz4RODOM9VUh4cR0vWpdh5PRREGIVTenQiS8SmklTJFVkNBPyFRx8egozjXrS8T/BpxAiD8li4EjjAgIXUkVDYqbGI/I+mioAZDqLHA9BGvPElpSQThS0MwZ+oCjIIn3KUBgZSCTJUkBxQULOy5Ak5rQVW+Ak1KJ/uxbivDub83QXEfV2i6exzGumIPXuS+K+wqjZHMbNRX/sQjh2o91NcynnG1oXH65WljeXV5xT+jm7s/opIhfiC/O+e+wSXgDOBsRmu1oT8Y5csIrHmE0LcEdIc3Z28oWsVCH1CgJJKsiMcgydz2iZumvtGrpG64V9V3vjEvpq5CQ035J0MSaM1/tH59fXfl/IfVYb1Pf7DoUTJ2utC1/XLvV88qYVcBF/4fcB39Vzy37hb211w02+m5UVcg93udCpX6QQLRfzC65ynXmJ82Sv7gE7mrY2Nj399GBr24Wvld7DWXaJ7Ty7PvYFD0ugbWo3ht8/+8EQciVuHKiTExEgznNUh19nPm4LGFleKVajo3r2031NrVJUTWRsyuo4/+tpM68tuF3s+ydXUpqnB0fV2ou2xpe8JHZ2VQ9df/OHP54cn91/8uijz04ePH7kATqt5BMZIvehEq6Ba16GiJdy1CSkMCTUdIfZ6k+LJsdXAqRA05g1Y2Uk2/AmibyLyhFYpOIk4QllkQCCD8TPfDRfOl37hq5zZQfL5NzOIytKpyaTlcC4JYm7eE/r1XCqXe0JaXY3f6slC71vVtfhJsK5OjOKfPreMMDERlmcstDCODODiI8kCtJQnGlq5trkVgX7ALu1IsBkkpRVeNkgaP8STrMRJqXpQBMAwhOGj62U36GZBq95YDq/KYpV65S5LGIIVoriHWyySWVzkh1wKXl4Z7HB4YV38jROXSOmIGmAOIs8xtKYaazL0/piLU9QgJIDcjSmNWUhKMJQc8DToQyBPDpxL9uVqiSuw1x5eqUnWWn6BhTSQNS6hjXESZKSCCDKYgaOJzQIeGelp0kU2cSTupMFJySGKVt/2dLhlL5sYAlDPNKXphaplDpSRoSBCiYJhUGK0mgppRw0DB3K5Jh4xJAPHMxtWltZjSPKt/EJgyPlS2lCHfjJgqIQhKGPGTz9QJFETQoNaYdE0uqTA8OJztAc8WC0vFpo8Hjk3UiQNTWn7sPLqB2cBJn2STZq0jQgRhjSoNFPwMLD0FEahiTIGQaKwKYrgx/klhuZtQb3DbPJsddPtPYCrD08G+vRYk8P9Gi3vLKtx6g8GnR3/ubqzMvE33z7rZdj11fWPBx8dLAH1pav33LYe/VD4oGvPaRiqEhcutYgmc5T/cEOsIpSF6WJX5acFUxQ5ClSd4zFTgXrFrAniSzDvcnvYamt+w/2Dny4Ys86d3R6ZjPZqz9e6l3e/mBFfPXq9YubG2vtw/tbxjyoTz771GPMVsp3798cHtVXrtDGw4euX92p3d/ZFcb21oO1fgPYVTYQd53t2rqcXiG1APcrYavzl+4xu8DVSq6DZ+/NucTUWR7gcgM47wFvrW+oiLeSLMDz53WkOCAcsIuLNVdY4A0Sm6B+OWthcdnpwpmfeO67jC7Q/UCio7DvgNbpxSB1cfF09/LYYdr/6marw8DxrOmsze4ue1LIiY+nrN1NdXW+f3L04vUbZxaeAfvk0099pNp1tu+V8QVWarjqDiGpeyQaCilKp+gR13cR6qNp4cS8HuDr/upBWh0HjVpJp0cZKEIpihADhzJwyrJ4VMK61DZlValzJmkdq02l0fSnO2rVtm3yI06GFSNVUDqufeO30h53Ks2TbKKrm79N9TjfLUqoPGNyoOF1d2qKN4omIXbFB58GBBmwWGnw0dQkowgDXHq7rRK/1QzmJLhubVm24omvgEgjgQME0aGA4RSRU5ClGcCk3nvnSGmOPo6oISb49JEstACGGTyQ8Am+TWsBJmeLZv6H/+Y/HxqKK7ReHjJRBjrVC/TaUn3QgCW1OmJ6fZW1BsMVlpRVKtyVEX/NIIlAKcN4TK0osyIBa9a2RVwRtE5ZZpTcqd8Xoy+SzLwJprzY6+l2B4JIqIFynpuLYIFRNg64huY94yCw4i4VUZStD0ISagLgEflRtdQRSADVhZwXEjwTfjUFEzxJYpNW9NPdgrRPqp9WnbTSnRkzDqcwgTBPNfkKWton4CSmPJjAI6FMQYRp3jilhoGpgvl5V8p0opaQGFJgxW9aJn596ZdCagEHsVVByqkjNeAk1FSKkA5eSi4Y+lKzsBQyIkFhEnBiY5IukxWbIn7Fo7PYAjcI/aI6hgIE0TJRRGeMT3ISRUgAlxe1VhGCuh1VstwBpwaTJN4TeQXaB4bBg2ByShMUBRI8/YwrvUMhDUUoeIBtV4bBESHlx2vnviyBc5PSdWW9LeMTH/07OXqHjieAXPfV9xb6yNe/XvW+uDzzrpMKu2UsznwjgjtPAFv2dIjLXGstQS8BJqY6pN1/s0Yvr65b+R4//chRatP4/e7O4cExF/2+0NasB4r7nUWrqmeoNJpvTtVd225eTyTZlc72vj1ktDa75KMj4jRK/fJQftHIjwPauLFh6+lpe4xuoNYl8tqawXBxti8mC61PS9pS9hTWxvKqoN3KObQu7+56adhzzpZeZ8f68cNiHbNCSvepkdZGGl/Dasnw6Szt/L/8uz++euV2+51HW7VRasvf216fPLnz9//q7xa9iFynRH1e4ydsfSLaaDmteTx9BEQfcRGJbEZOht9nn33mYelP/+7vyOtRPM1r2XGIOw3zXqnvH7jHXA921atKBeX27MXF6mU9xOoECCYCmNMsRa7m1Ys7A9shldLrfm+YyWR96deTNIAIHT0lTVqDubLnd3vtd7LRk3z23Ks9L5329UxVPyxRC54HwWvEzuQWT52rIXFWbXq6L/C7k6MD39dHl75+xlbYdKLfVai3WukgYd9Or06PSXrg1TGrBw1yBzsdPFKqvqoPkNAtASl9BB8ffApd5doqo6mjhUEhWyAYOCSJXDAZCYQJlQRfB1pL8Ey4oB98sHwFJMg8IkJF5MYqZYdteHJZlCCZZPjt7h0kZllEmQ5HvISSFQ+mQahUkNJgAlQEkJAkmqkanBrfMmoSG6pDIw7ikryAGzSNhSfU1kCR4OjHXBE+aorK7JYET+ggT2RRE0OGUX1RfRp6bBWVfSMkJF6QUkUofkm0ryppXwwhfFbRVClFoqVPqIhEGhDBp1coUwspCgKoct/9TahUygWreB8BMCeRMgxIXLDV31KGShE+RW6+YCKMBBqikFBjNYoIgSA6wZHyRR6cUS9CEr++QhlI1DCRMx/41FA8qhd+4MeWMkbrDXA6AAnDhJfFIEImKH6jE6E0MQiDPqKWwZOjV1SK0l94L5hS0HpSsaWbgCR45ngpzFBgCROGIgMMkUNgNfRjS05zal0dFFuaeISpKk3Hm6UXWrIAxWllsomadV0RoZQhHfyzZ7/zkwP1qwPvd9yC3Vjx1mudariC5JRavX8pBPNJvwFj49oTYT5UWS8bE1/XuNXDGqdWX1+h0Bd3a6vGCx+ekjm33dufInEz3nNIrk010Q9v3p6c+T3jRy58Nza3rXY+y+zJKnPl/MailRLZC15eWrI2Wyw9c2X5UTUv/OozLUAz5ygf7pVrK7FV1prtgph84fDAk9Iu8e9vbZ+enwE52j/w0LJGqCV1cdH3NBbuzu28f1+fq3ROsLjk01cvv/v+t7/9ra9cWTlU3OWmQ/60n7lNp6TRNEt6SkoSnjBt+x//wz+ozouX3+++uzg5r43Xjx5YgJ8smLk1oeURlkZzsl5fVPQKzeRKBRRSGMBqzx6BsjpRvf7whz9oJa9CeWXZC1SqfHZpD75vXviyR32b0w89mDWu+xTIZ0i9b+3ucg2PydUWzm5JL/BOqSoQrwKpgjmtBxEJXyRdx5qIeG/KiKpxWE/eW/OL6gApRMdZtcSt2aOOvB/HfGVo9kMLXAzYCqAJyGTQOifq6UIqEgo3zlRuHU2CMeARkNvmwazwpke3vsabxp2ckWf2DjifGCGRJ3WAJwDyQAHnS5YvOhSiPBRk8dQioTMocgpK1QFOlOMXj7ltTh+RiAFhQPFISBNPmJW4FUszvjBGKR2kaMSsFK80JBsoQmfjEUaSOZO58ZaaavYUJZ3cOVAmCCLFtKVZMNQNehBTzxmfne21lrNoUlCU+gSUBCBiqEOVkuPJb9eEhBwlygl+OyOMXyDkUpK0nXKuRUiB0BinoEhKgjIUZIVEM1CpXRQIUSKBTF9ls2YHKm1EmVpMAjJScmHAZEsYAkgORxYjZUsNphRPASVO6UTNey3TOlJDYFNfOojhgFIKQZYc3+WFQych4ZNVmqpBUxqig4lCkKmRkIPFUDZtRbmRKlGKaKZqYCngSehHGDVysKDiPeCjKJEzJw8gZVndFI/QSBRlPARNSsHwJXeoKwpCwgBOIWhgUdwlJQcYHfqIQjo3cjyKIxKlQRuGbCMUG55OdiaoIYAqZcGzBqcWFBATZGywuvPg8cmpjxKc+jkBX5sy6XlD1+Lq8rGugxcXXN2apn24yjxrrF/1ryR5kKiC1fj9GroLLK8z8ej6eMYzYz44MeOB3+rKyxsfAxL2rKeqvE+kByztvmDxzvu7XtE7O31w/9Hnn3/u5uuLVy/tu965rJeNbAu76pWuLq322rz74vvnnreykbu6uv7k4SMTaz1Ztbt3NndsqrUaqc36xsbsnLoeH70/tkJvbNUTW/aWbVp6jdgNn/29s5WF2fO5Ey/73ncveWXVcuu5J1/VcOvn+XfPfnj5Sovll4u6CSfnNxpcXVCaThsqlU3PpuMUyT5eW320+tVnD7e9KHx4sLe2svzx08feL67n0t0Qdm3qrWeXm3UNa8d2ckLMKUAIMIEDxEQoq6cMg5xznP37f3y1+eL1kyePP3q6ub01t1A/0ejAvvb4kj1tR9Bd19MuG+fO69y50BKn2I1Fml0FPelDK8zqiHDuYkxwkWpSSO0qrVUWgkPGYaURehBWmAp7ATMwpgd7rZ4sVLHO13o/up3RjoEHCWKlikoIO53MGLJ9x+Onc1oaWZgNVsnEdR9ZCZuwKkKn+8XAdiAY+YiC+dMRCh+vVTGl2VRX9E0wK4DpxJgDKpq6I1m2FKing0ZzjQEAJDhJmQ93XVIuEEPH4+AxiYcXfR3beMwErpRJApYGKmpcq5RpBwhfrMgTJCv8SCO0kJOEUprgBz4hF6kgzMnpQKqhgF7MEkeysSFBU/AeKAq6WQGlNG4Ih5oglZKnAqmM1CEdW65l8eqG8PESEKlYoWkCfIiJbDx6IDVtLatUGhypLDS2hKkwTb+CkiAjJEd0pDEUJ56LoI2wMUwQhj5laIiLxKaIidIYhpdNG2LSyOSsIKRNFDOUJR/R4iOhU/H1yhEoC56i+JKmmpgRcAzJMRXMZGarqGRpIoBphAj5RYRMHEv4hNrimjUSD2FsZRUltvgtR00DlmYkSSkgpQmPr0SYlI7SoUkNVaB95y86Ks4X3qHu4E8wFGLb8IWvLQl5SZFSQsqONM2OzxmDABQl+AGSLJ2gSUnUlEKISeJXNJWVL2jpF+HFSim57Kdf/NV33/zh/dt9l4A+wPRhpq6qHBMWNb3aaLVQuESqkGZrf0wA/S2H2kyFA0SPwa+PNpvWry6cJM16LGl5xQ//zCwseZBG6emle6XXa2vLjz/+ePvRI78jZGX99ptn79/tehjKyvr5Z7/wYNTO8Z7r9Tev3pwenfoss7uqTx499mCBy0qPB7uQtaZam9dXV0RnO2pn7/3JGZv6RaPt7QfrG75I5dayb2Qdv3/71sa056Z9QssHoq3Zik4Pjwy5+rXIJRe4d9+eX7x68cJ2tNVXV/mmlb5zzuG56Bw1biPfblU8UmutkY4LL9UXJFd7OyJ59PTpJxtr9kOc4yy5S+HTuWcn9Wkxd56v/aBCbRJYEMm9LmbcGV9ZEXqU6SInedqszpXxfBkdznvpv/3h1e47yYvt7x48efr0yccfbT+47+PXoP3clWfkObg6u7AUlyFru7oGbwVdfwVeT4KZJXyPo5bdejCMlMTQUStnT4xSy/JtsHmgrzbJNY8OF2qNzEIT9HRnW4vVQwn12d3L3hfH1hNZxg/o0izQMilJUbVhssYPIUeyrTI51gjRbH1CowkOAOFY32sdKHI2I5CKpRyUwJeucAx0n0ND05kxHFwkshXFNIZy5xADMT2g0oOElG/3rzBIYksHT4cwjirKKR/byAdOFIJQQbe7lBIi/MjGixQpCrJSTOv+qEwIyjwnJMoFMSWaBg85wivFRCcLU0IKpvZnlytsynhy1cfI1gLMElzyCtJhlEhQ6kONhKZSKWEZ97Vah1HuI2GCCY5UbJFQCEVfbw0XATc/6kgvnrMiYUWfI/p4EQZfqSxbJACf0sUoogZT/TEMbbJ185VEKQnD0uzhGEmqRgGTIMkxw6ksE5LEjB+xBRws89Qly6rhSB9RUCRsxFzVhpdRWrE5wZ4uBtC4iPdc85GIIci8KKKg1iihJjxqKSLEIy7wSPuFqZhayIQkE32EbGMuNWkMYZqUMkDxYIQqGAqCSTsPcAx5XEtBSWMi5ZQ+BSkejipg6GgEqWwYOrGCQNn1XnSKn570xFFggQRWSDQHvlL6QDAoarKRBIF+qiOLFJEgfBhoKNECiY4UTy7gdL1FJcJhGGVOD07OPv7sy/sPn77zmPHh0frK6trmkt/006ZuIZ6cHLtzV/gL1jtvQFVlTVm+AuIvnY62nuvNLzR5/Ks/L/lhZW1+aWX1/v2HLohtDNhFtj/s21Fraye17bzhSWav2Dx0xekhrF/+8pcWWssw2ji/7y6sq2QXfK9evHRh6pEoN6eXf/G5O3kuWF0KU6jr4/X1J48f3rn3wVtDFlcXuCpr8Vu2oby0sLMzZ7nd23nna9h+Zt3F9KIoV1Yd1dR8nWvnhzces/rtr35taVeNOv3xalEPHpOR4aeJKtsfyjQgZEebWwTow1GqSeuxIMtLLVXXy1riaP/y7MSTYya2D+eea75wqmJZ8Bi0Q7/e7a211yBzGXiXGyAYgyG9ViOgH6Mj16NXFzbEf5wBPKvl2sAX7394+eL582dbv7//V19//ennv1jb2KyhX0fHh2uf3LqpFybn7rl09jABoWOqXpsVqV3p6v16c7wqZZ9CRVyW06l+tMNEs8mCx7JU6updRc1Czs3qMBlUc1Ytw/2PtHKlWu3WI78Na+iWsJ+lL6YEkzlNxXmTlWqcCqZnyB7OddlD3iY1qjFRpibLhcGZ0hTVK3a9nOh9OKY1OpRRuWynCQwO8j3ymKcUJkApUiqVRUwodDrZEmellCQU5AgZEuKlwcJQTimG8gAnxEvDjDjp8Duqr28ZImrxlZREETXyeIGmKARNFqUUYHYCtEyIOStEh0nACWWlJDVWgkVErxqjw4oSIQp6SqUBooBXFEqWLeHgMbqbwgBJhX/iIgoiVh/mQWBCYo6TJkJohGy1CJowcxUPYguHTtRuN41SOOS89454RRgTcgskQw3HEQaxVVpAPT4SklJZpS2uj31Tw8evgSgeWZcXGMRKtCRhSPANX/iKpMinJSuq6RM9GGp8ITr0peGlonLRo+7RJwkgeZTjQpY8qamHEFEYlGwU8JQBxpcb6GCDHIWU4lUhrkkwUkKahd4UhHhRhCFOjaQkskMOjW0agaEiWaXkGgRleTu+mJyQQcvRTs3iQYFVwo45vl1X3fEUZAEOtfhKSCnCc5eQwLb5ZLTjAxt9aBgI1OIUnwgDQojwaBjiz86v61cKlla3Hs74oQOjzfhanF+01WyQ9ei+cmFOx06k3hZPhVH7qPVgtLRq6iajR7c8Vu0t9rvzLoF7X/Tu7sHh0saWf+vbDww8y7C75HV5enruUtUDnF99vbZ1f9tPI1hBvQhk0tx69MAiilzvWhqdFrgidyuX5OH9Bx5+tpBT9pTUyfGxpWjz/oYfHJvbu2d1/vb7b9+9e+P+6EM72ttbPjW1e+fDwf7hs4N9LzX19vXq4637L168+PaP31rjUd1UPaufE7acay6kcq7ncspSNdVY02NKc+kLKXHaoQsnRxyecP763LX76U09tEgToNRXqec8izUOlnpnzCa93q0ZOl2ToZte4wVDnm69PQB8/sNPa9TzTGc+zHnse5Z+5Pg3v/61q2EvLH30yadOZeZW6oIvK+uNS+0mtwlcopLnXr6B74ktLqy8vgzKoQPR9Gppr9iampmMmbr1Uwv45EBWhBxATPzpf1lqfdLFYLDFXsc+/LaYjFUSwG0oKWo/dWirqTD7Q5Ol0KO9mhqf+MNLZTUIBa09KHLKDjKlmt0WSGClzqi0J52Kp8GloRrGUxcYCjAFEzkT2WqlPk7jLiBRFkzMaaIhpImXCrTqNZ2FokwSZSk+VPZNdKImhaCUd06R8i6smS1yJ2nkUeNFxVPkQiuwhBiGKoU8ixk5tbiTQshwpRz9lNKsSwQuWUYvFSNkM6IcRaV2py5WFNEJKc10zATJpg5J6XATPkV0ZEfNU0pNF0otSCQYgUYiy9Dki2HFO54EtbsaQDSlihAm5pQFhiyxhKhc1xMvP8ZDB04kUoaxFSFlRBZHkIdyGjFO8dRYySJrQ/zSb4C6wxTXspQHPlixcYRBSvGsKAhYT+MpKLp9tQoKwcmFdaySpRlAOEh2MHhWkBE+Vo1UTYpIko1ODMkxUrBcpEbqSEifXJDkeBR3w0vQCAGGl1JmRQIkJhDI8SQYyEzQMBkKrBKPomBKE0BMpOIUh1SoslGDieFCM6YWiSFCEow0DDWlUs0rBZJekIqQu9gGPO2Ap4mGo2TJhTo7v/T23a6u8Ebs9qOl/feeiD6qacfVUz+XZ0tZwxhlAqiqNY6j3nTryHRxZTPThxW96WQy9kbv+sb9mbkF93f9ZtHuu52lizsPH875zaGPN33a+dTyuWu/u8m9262NtQcuhB8+dCPWAmxp9IC1ldIq4rOUutFnp9TUdyIttzaNrLgucI/2l+qSeuf9zts3j+9+bK7xFWdx+a7G6dnJu7dvNIQ1e9kzx1ub7m1TPt7f880Ft4sP7i588803fhXYKq6tPIp1t99UtsxrZy1iWTIZKdKYdR2s1bqD+u9k4sNnPGhDfNK0qpMvLwm7EPWrGVY0q6xGq4vMixqWpooMj+Kr6Uj6hIa0h5l+VCQSlC4DTiieHgLXfupKYF7mdjysLy/5qonPl9jNd0H86MlTX820EvvFJ1f8ajHv/v3cZJjVW7o9U9U62ZhS3arOBiR8AXDtKrjj7IAq8v4Hpy6Ri6KWtPL3lqLq9rJQEZkkjL0BHoYJoXpFIbXD1+hqUkqosnKYgNTRaXOhxh5V6pPdvuL0lx0IHmrFd2DU4VCP6PezkHDijsfUDiYCnpGcLGsELDFI07NShooiiRCmViUp793veGoAtSE++qN0IAdKFsWEPrXYAscLEn5sUxRH0gQTHenAwRuiOe8HpeKGhtIBxRYf7xhVFiorJBscPAVpAguvKMHUdB84+TQfiYAIY5koA0fBAkaTfQ3Tnn9lbzujj+JbGsdDCByJMidQSukwR+kwRST0o4nXlIrMAqLCaI50RnDmzE3TxTsgkUsbtRKY2i6Vcm4Lc1DqIquhOU2V2ZLguXMaIJUNWsBBCYMCZFYU8ITIkRlbaWqtaBBJTKCpBdrf3SOBoIiQJpDcMOACKc2WnVJZPAk1NRIMBI4w5ECQooQ66ab+fdOElOApyMYXp+ExSmMrjZwkpaBEBZxcp1PgN1khYaDFdVxIxRNzacBTRF82EuAYhtDSCHiUAKQUMrrY9nC7dHySSEkQc2pMCBHGNxeDABAJOy5EiGeSgBXhh1ARCc1UhF/xiyqYhCQUDJLcYpBFafzEEMBErggT/ujk3G8J4P1UkL0LP3bvW8QHO+8Xffpw8a6bLnZQ3b+0EXp9XsPA9yMSML7uHvo2giDuLdy9vDm9uPywfzgzt7Sx7abkhmsvY+jyzj1r6u7+gcc1lxbm7TyriznS27sXvtLcvybpetXzRGIQ/PsebwZPLnn9zNGLF9/78SP3Uyl75nxjdc3KrQouea3j33zzh4cPH7tPXD/iu7RMeHRw/PL5c6ecq57N8rrwowVXjbue8T442n+/++rb58BRrQ39bWHtnKbQXFrSlaKupECYnlLfdCWmmrWJgr90JvlePPC+UGm7yptR+sKhDNOdK0quPErftWuvG3U1aWwYIaa/fhzalq03uOpG6lyd0fglJ2EA0fCiQmxL83LG2YGeEozhsjg/s7m+6gnrQ2co+3u/3N357a9/oyn83NOXX37pVMa3HXVS9T5P3e0GZd357ZNyy2p1Y0+YGWNL06HLpKxydNx68jFtNdLZm3tVBc8JAKoRMaZ1H8qo2880o5zm4kVdKFaNeurngReVrUrWzeTJA7O0CC6Mnm6jBCOlCaQPMb8pUgeJftTKmVYMEZt/kA0SUOlHvJaEJga26XFZVld5XqwjUTRKEzMdIJqaUxK0tDh56jhZaUKiCRY+/VGESa0hoFEUNfoo1SFB9KklxUDGB59m4o/aQI5OgozQBKJxNEJDTpYqaKpGomg4Iol3TDVg+8KnZaiBrfeAa+qanhgCUoxItLVmhWhRyWMvsks+QDBt6BFcXEZOmErypHvMiiSqR2cokOBTnwpiulbhx2tICZ2EaxOHYJiIShr8mNslSsW4UNrjpmZ/LvCpNgUmghG/b77jKQcEPkApfRR5hKmUkSMAEgEoFUmNqp6v4UPuOs6Z/uhnFKZUVjxSWXVxbYGHg7hG0Jjn930V4X+SxpwaOZ4h3qwrhUlfAGJTKgYEkzDjgzKnSHtGWTVp0gmaylIGRYcwjYA5Oj6AwJ1ShvGuFK/i5DFhDoecWphEC83Q1A4UEh4rsKwQhlxKyAsmYbNFFJRKB6ZaXF1Us5MTckctoaY7CEdUOh09uP+EX2qKAsVWVOYIJhSY60TI4nQWmOYiFLOLNsqaVG8yB0KNRJAQEgB3hCmKCwqIAhASrQGTmhbj0Y8OqaZJwyM+vcd8teit2YV7Pzz/nnubyxtrSw4A66WfcGDlvYfTY8ddvVXvQtUyWcNvZtZXFK1xtpfvzS95x/f+w8ceDfem6+v9Y68Tm5M89mRf2KNe9WLS8cn7t689rezXr825Pj3h6tbR4OJsZ/fQIqqyW9sbjx48VBd7135K0APShpUqUHYDeHmlHnOj9ruXfzREXS9/8vQTDevhKb/mfbhXv7zozvFHT58+3H6oGX/zq1/94z/+I5DZm3o+VgtrCu3mVCDDw4YtX8gCrF+0SeTWPUI8fR5TpBSCJsVoE1FpTGgUNpfqiWXvPil1YlKjqF6Qq8EG2VvTSm0Ck+slaBbZdlsukFalqVTXpNfoZ5xEzXkDgR0J8ZCI0lElWx3aL1x4/ddOvqX8b/76X/z93//9x3/7N2DLkS8IuVDswxNvhSdU6/59pPJuDVWdO7522aSwshOqy3GRqJSUnL5aE1odMcImoU4iEh+ujh3FgDRkr/89AunSp9NeJhNyxdMrfZBNihSs4tohjaAco9O7bWvwx53DgVO97ITs4qw+pIN4pA8BGurwJj2b+EnQZf9eODVCjlCF0USIYg4QT8e5TgaPw5OyUnJdrwXSCITBhyFOzST7E2JCWYoSBuXy3VfSul41DYPq025zpdwZFYQi6eiqR3i3T0VnqAEkR2DFya/mknInSE3klRqwoNiS8wgtjDRQzOmAkp35n//Nf9WBVa0UMCv4phhTdUQlMjo3fU9OefHTuVI2bQQUg1JKgWl4EVOT0kGxxRC2YNJS2YImEQnCJLxk0weEMKXQPM+C1xPU6IhTVgBgg1yVnK79nNavhU4pOklZpb4KI0moFiNWcUchaBREopUpk2iinA/qDMrRjw6QSDBAgnMbv7+wOqk7nRBYvvAYJnxxQaJeXkRJs4BNqBRUv54c6TYBTiHCiq0/0UCSLg+srLGSCIFHGThMqwTJQFAUQJMOBFUW1ahg1IRBiKcJXzCIRHZUgQ4FyIoSAzSlIuRCUSqoKJUiUarIqsEdNUWYEHnMuQiRpOje3JIi5jEZAchGotR4pgAwAzU6UeCdAqKAFFETM3BZ8VMjjFPK5BTinZAEMY+mo9qusasyJsAsEHjQfmbAe6s2eN229YsBnndynmQVrAnu3nz9CIFOUeObOn+ysno1iHe/nWABPjo+s4/pCtjDyj4PeXHnrq5x6qC1NnqhrQ8xmmhm+zdZ/R66Hzg6P/HKk7XcmfTZ6UW52d9PHxlNRo6LZ9eke/apd3dViD/Kpl1h7F8cvulfULBzvL25ZTvbc7y73i16+07N+8uFd7jwtBfMaofLH09cuNDZ1Y42nKc7SdYhjaaVCLXYwvS9/7SkyDFweHe2BEAMNAnx0tWlmlip2dGqtr5TW3HW4/W1TW9xdUdU+/toBnD6N+eTLxwpCqXv0rNwEDkgckLHi1HhQCavXrtXGzxWs3o2opdh065rXrEpxS89euBq+Ouv/8YM4EuZ55cXNY8vLvtpqdqhmNPT8xhoYH3nZL6ngQqsXmGtdghNgqtn7SYzW+I5uegrVzNh3SGuj5KrnArmjSMLMGHvtE+nNcHV+KxDcoo8wRRDFmBFXVo+f3ILeRKnNr2s3SaO0lygVNnSJfwExlb8UlAkFcZUEr/tZMYZ0TQM5ZODlKHWIGeY/qWcUqdQJIpEIk2pIt4FYyQEjSEQQbJXShjzkWbMpIW7sqWA8I5KR7FuAkgBT44ZtZBNYLzbIFFUTTetLASlaR98xdD7snS0jzqxQoTlr2NLRdK23AU82ck5PnRK4BAboCDUQZRSEtrJen9xNEF8JBvb8ENhFKU5gFBjhYbmEAbBYcy1phm2GPqJMMKqVjdlTH7Cy9I3bSkFri4kGUlALs/ryEEVRFOyad/gRx6eobUKjuEoMOOGpgOPJHOxLB7xgkcJGEiQWWG0gEiYU6OQIgx5+JES0glBI088Ey+9VUPCO0mK6MgyRIQJMiE5wOgkBpgJj75SJiSKWCU2cmM8alWTJtkoJBIBl5vpiGcehViRR4FT/BSjajEUBmw6RRFiRc4kvKwI0WWfp0dYek3xGJ5VKpKmkFVKkhN5OqPrKYCVKkV8xUTdk9WtCR6INmSbKggDTxnyECbaUanRwkwQOaKsd/VAC7SAXU4VvJk1l99cWeU8V3XsNxwOjhfnfYFjafv+4qFfLvJex5y9wXPvuR6dmQAO7BLWI07rm8urGz7N8+bde1ex1jZhr27cX1ladFp5egZp9/jowOnF8uK830a3hvkRBTGcvT6zg2rre21mw2fGth3VS0vv3fh98+by6tz17uzsA/jO3bne36+fF7x8/2bjasPavLm2du/J3N7inh828i3JmeubJ4+euko+Pz757o/fPXv2zJWpZ6B4cYnnAxhqlB5xGSW8kiO/9Nc/KpC26XSSjAYfQu0GQWuTYNL4+EjqMlORXeSeFTWkC18NXe9y6a7uJu4wRq2hIOG/U2g1olxIc6GVpOTAeoCXtxa4KKyfVDKLVOS2pcHfmdVBtWDefLAH0Ctrfant/dHB+tzM//1/7vuppc+/+OJv//Zv3SE+Ojl97X1rJ/pXPkht//faOlzxu4Cug6C6vuK4Paf3t695UzXx+N8xV41q4a37sPWQAFv3nO/42ohsPSFQqP6rxrQKtb3AXJwBqSr0N7TSpFAqgiat4a8ObZ3yjeHSDEBJY1l+TXoMcyjVRFpvJUyeFmbLtdKkbAObtKCaKIRPmgOQDswYph3w3STXy0urGAr02VosVcQhHLSkigaR4Ic83hlyhMcEajQpRhE5iiO2dKTJRkFKYgCnYWUjGX61BoTYxlyRSaCvL2q1HrCxdcxRC580gdXTaCwzSSnAJw5RgkOEsAZRYBljfIdY2dhiGBrc9OGIEkMt5reRI6cvVgqs4oiOYGQh9wlFzUfOLg13ykHAxJyJr94kywTFEUCXBbIYJlykvaRbG+uEKAFjYkIHTqCkowVNxCIBQl9rCIYJJlUbVrLkaY0gA2FFmDAMZY7oR5hS2UxeqU4UBiZbFE0gYPWiKwm+CPki0VaQqWmr+MVDCCAJBgWE66wrdEBp0hwGSsMU/lVt2XFHiBIwX+mUuCBESoMsBVhupkeRUlCE9FFwCFN3aRqKF2oUGEoHSBj6amqyko2kPUxOWRIAKEwo8SzMrwSKi3QKOUc0azRPL/eZUEurJrxYSYOmQSJRiuIaiHbAR5g0RZSjllDxmDRaq01Gl+uTuuXmvv7JybbvYGwuM/eU8snx+f3tTTdrnTgK+9Ku8s2M5db9YZEcv9s5ObuwRnoX1xNVJvT373ftYL88OVnzYHNf264u1S8Auhj1i4HHfaPk/n0eNhTCc15r19Y7wZZl930JxWa/1FPKjhR71hdXlxsbax9/+vH9h/dfv35lga+vJe/vc2rMP334aHVx6dWLH/Z3D7w5ag/3jUXm7Vuf0KoFbn3dFbZqcjS/WF+rsPoCJHGfsS/S6naJrO+NOCrSYknrWTNjyYjVYrZY3Uet5/aL8a9/WcGrhjXpX9ePHXtIrT7q6N6qF3H1vVqYHtzeYa83YUKrZXN61OfGhiIIzOOIr/zjSW/om4qhyQce03E2I+ti3TPMtSXtC2TexKtpzZJgc1R1vDy9vrpW3eMHl23j7+3uvX3zxVdfuUHgJ5bsVDPz0x1XZ36CqN5JspjW0BFpexTopCmEoI08uS1ALqsWk49vWFpF7tAXUg8vW9w1n6C66qVcH/FQGUz/7erjUtcMYDzzQaXX9XU4qrVfYhbPoBRJs/9BbszXeOwtgWQDhac20jAkIdlQd0dZkI8YFGlARSj64fmig5c6VONaVn11NAlDQunEjTm2T/IioakUYaJMDirtgEd4RRQg0CTBIxWMU6ViIydBfqbzJwiF0o9TiIphshiA+OqaRk4YbEmQ+VkqS4dyXFel5BNfzBKECJQFOgp0RsUwcaYoVkpRjdQOiKdkVcAKHscjxATBkM5PXNBxwJPT4UVKgkQvpGFIgqcG+cwPTXe1NZws2JCVJuA0yVPEJB1DGLXUBR9wJoOJglWKFY8YMbgmgKCmwangOpgoSxM8HHyKwjPnK/gpkoVjNhwe6ROiMFxAo+MAIOTdcnV1Vo0vy4oCwlOIXykX4VUZY5aiD4SaFKWUIUY2DUVHFo2Y8YmcDtuslAk1KROUh12DRp8mAmL8KA0a/ZhQQMm29eTgp6YKFdz06b+hZvanCZMCwneY9Vg/KyQbXyqCIJCoexBiG3NVKPfTSYqasGUHM4pEmPrCwQOJrzK+dfCTy47q0Ek2OpMwTOKmyq5oQbkIqt/3uWMP2ds0J6dnS/cWnzz91GevHZunFzfbK5sz5yfe3ZvzXq1fw104u3t8dHrHG0x7qm8Xc31zw/c0DJudvfow5Ns3r66vPEt8bcbsD0LdsWru7+5qE6HXd6eXl3why5bs8fGJXzm4uLy5WLpa8QWs9bWllSVfmXj39rVvUb58/UPdLp2tX1Z/+PiRinsG+ujk8I4PP8zM+Dqj+9F4z1E/+/aZSL7/7pmL2ocPHmsaK9LuyWF9E2Np5eSy7sJ4gbdWh64vxkWqaqO0mJaBmbbSX1Uwbbo0uFKYiujj0WBc8jr0dYLec2RnPxagrq+b7NUjtPWxl6l9kPJiod+qMADijibCJ6XfJpOjtXgPV9fwKRK5BcrD5y5inZGQWOwzMdpyqItjF8T9zXl4e2/evH7x/Nm33/zLf/n3X3z15b2lRYvuvB7wMpIt6HpQ+9I9e8938Y5USgyGNl4qKM5tEQvbJzb62K1QncEYMv5W2xn8edDrzod7vgfuqyx1IZwvVtcIozBtyWLKvpr6xzZsjElCoZqlTkVKOVJRddVnDQO9bAKXjtbjYopfnTJowCaGkQ6GAmVZFMZFGtj0/ihSmnmyRm8TQ0IHY5iY4CmoGpVqtSnBIYyv6FMYQpGjUZcoVANNJz0SvDmBVUqlCEIYfiCEN//EbYJv7CryRf/hJZpJ1ctUBpy+VpXG0eQCl42GlqoY0voCRTG+7cNDWOQ0FZFjEEbQGMIWVAIQqRT9NIS06zupsz+pUsxlWeHFihGuRz8ITfHO61Ph4Mc1TaWZQyELWzYxY+hL4cS7/taLTI4O9m8HGX0SVQ6gkDCILbkDWySgElUAtSDAKNAJySI4spjRPXgSjoAgAUchvJSvKJMPnt8UMTdMKYw60klHkii1Q24i/uijj/ChVAEaxhU2cwQtEjqCkZKMkQ1Tac1iTYoQlhzRB0WBVSSKKBAyIZFNR0jp0CSJX9mUxoQ+HbYJJpg0MdLgDxPI1vROJ96jQEI5faffIZMEKqnAELmUCWU1lVXaJYUWdxknEYoqvjAJMjFT5iseoxk1aWhgDoYJW2Q0abKMhlKur0SYNDmYPT24dN2zsLW0sr4sVt9Svrz68MZjVq6XFhYp2EbXwSs+lLG2ZgfYIv38+ffrBweWw9W1jSeP5jfX17777ruj3d2zw0M7yeSPHmzaQPTyrQ62EAvbgr1Wz1X5ISNtf+HlJXeRTi/OXQk7vgwbF757+9svn7/Yc7/45FD2vt8Qfrg1O3/Xqn+1V9+s8klna8m7N++84OtOsXHq0F68Z2Zc7A3QGgk6T6/4ZcZqhH5DSCPUhVr/s6CmcbStZtES9Qivi8DJOdvkhFIpoumcW8fh0014Qp1iTqqrwNrULjXrkxe0nJkUZn3PwjjgvL4s5stVnijXvxqhJ6Lq+vLb+Ekrwh4SI3VnkJpR44mvPhx7G2NRP24zsSSJpKbIvn1oTlilf3XjqWyPYi8sLs1cXv3+17/6w+9+8+Sjj3wu27NyzmaWF9cdIW5XXjhcnIv1h0EMBAeBKpbrilpDVi0dZHjdV8VmsKql8vrcqL+lXMPZDNCX03X1XAUTqvmjFAukCSLs1BpfpwzTQyAmOQOIf6aOAMeTdiZRWbd9tV6bTz3U72UXBTPNiCcJMh4jDf2knScofVRCjiadDAmldY4ynUxSCjljgBo+8QCXRZyxojlCEvzIKkoYJBEO8yDoRwzhZOdpurlIKAyDx5Y7w7jAkCfrUIqQBOERBiCFeBHeiEpRRduahHQQpqZLljLi5tI8heipc2NWEtUwipggQjyCy5AEQ8gwwdG3dKWD8XQQfTriu20eBEXUHHhxLR5MomocFj8SzfLdj31Dk1VGHw8NOeCl5B1sdWpMCEmGPIijSDawmKEJUxhWXEKwzElyYMc18+BIUzqqoCilEACmv+nwEomHsPAJNa7xcFLxhF0N1y2sYe3HUaDJddzBp0wCNhNNACmQeIoHg1gFE0/OSqvShAkn1anTtKW6TAxycGKlRoRsCQWTooBQQCQUUMLjItmg4ePXuYtGgEBetWomVcYP2wRM4qlLKXAuAoWBoCNgkqTWFPA1+m8KmXkiAYhH6b5EFS+Emgtal0+eFVJEJwQBAxkNSZyS0OQIM1I6SqMQQ0UubMTotmKr6TkfI6ol4ujkzAWu92Q9W3twdHZ9cT3n4sjT0e93fV15dXmxL5+u7/qNec/+3K17wHs79ZDUu3dvhf3gwblV2Rd4nz66/7Z3nt/d1C/gbm7dX11emnn40HF0en5J3w1dDxDZidYCNmttgOtxrWd5hmNx1xtmn+un166D93Z3zLlKvfjr3saDR48u+uJ7b2fvh5c/PH/2/Q8/vDES1N33s1wdHR3tWAIMHl/WVGWA7lF6SivNKJvWkLUMW3iqKZusvml2s32Y0vlTIkET2N78r5Hj+1d1OV3ANWB8+8J+s5M801dtxdb66y7qvAeynMfcuWccjH6Jax3BOOM5fGH1NTHGBSueifjM/AY7XhibW+ueX9Oq9aR1/+IQoSPofP9geXVle33NvSENa4vi2DbMHTtDhwtLfhTy9cN3jx89fmrPf2ll2adLvEYLHzGP33r3qS5DaxLrBSVLrmXYLfUrmb6HW4cAtratHYJ+C8vxmP3nKu6ayVYzT3YBR63VRvEkO7kj/uO4VSQSxHviaUc1DxsG6qMokmhqGFCCoKz9UVVmEkEdC+TRb3FNTSS3Kcq3xwYJzJDVByZ9rtUxgJRhgpIiIVGIPmeDj8eqTMc8UejOjU5cw6fQ7Vho8Z4iclaUZfHGm7UNH3NpvJOYx4ZJAk5WzAXak2T0gwaKAnBpeHIx1L3MeKrjsy/1SKGTw0XDjJosMyh0pCRKI3cdpu2Uajg9R+iwRLwkggYrc8gorUkt4QYKGrlIZLkwR9AH4rKSBD+ImmpIfZ5OJKotRVWlPpPA84hPhLyoFDVPrODhBDB+SaiFFA0GD4chNTEwz7RVHdNdJY1tAsPTSQUjrwgbJC0JGaAmSpxKKQMJTSLo0awvUhEpKASZYf0m2vQBpYDYFTcXQ6CT4EdTA6SDyCmMDgIFVi0EIwBqGplVgqFPHpMRWGoROf0hT73aSa1nGGGMUgwFKXOl/BobWjJCcmFEh0JAuEA0yVMUHULIdHjHZxDjGVJWRE0F/XhBQpWSJDw4dNgyjBq5bEJKd2Q4wYGpiAkEPEkoPGGyFDCEoQhjwgsEEowxWFc3A0fTtar7uJ6EdqXjJq99Rb8i5FSJvgPP/qvLw+UFTyjPe9/ksn6Vsd4G1NFCtduxu7uDMaeT+HIGX1rEyZMbunivAt+/X88we2TaxqkRawBbhn15w6AyVOiovs9Ouol7cLjvyxs6wd3i7oqZnffeCn5j18nltFV/88GyLzn//ne/s5arXS3VDsyd3XzxR1XqXd9Tr0fXG5Dwry+PHXVVd7Nn94Xaa6uMvV4ee6DWDnEdKfPLNTFBvk3aAVq30+S0kmQKUl1pfhKGS3CjeObeYr521e95VRffs2tca3HdTvZGF00UNF7iTpuEGV0DHw9c42TkTIdHHSP6wLY3OYJGQs2Y8UPHdo29lOW8StaZk2tcre1tjvOzUxv1Pje29eC5j4B+9PHHPrPtFx1EMh08FZTzMVHBJDSmwFpn+wSgDlskqnz9ytDT0WWS54P6CaxOMhTrBkc9a19opdbWhuuPzVvnJz0Uk9IZlQ2m6ocMMJRmV1QBtWEWPjokYmaeePQXBR7jVxpyMkZIGVEYpBnx03aoZk9rO43SjJQ1uNSISlYpwKEPU3isVICaSIJPQUhSVqm+ojiVxZBzHXMmiAIoKXy2Mac5SoMjS4inhqGG5wiaNOaKsliQKI1+zBOPgGUdC6zwlGf++//6P1NJax4p+3iNe8cqbQcwdLwmZlPnnx0BHTTcpPkoQKAQHaWjIShHLjgMtFSVPqth6OYNE0URClRslPUHPsqyhKIyGS0t1qcq0PAb16qTViDnmi1SZCsxymKjgKoa3dw8ck2nDrY2USlNJZjYMgma0kwomLhTlCo4NrPAaFyO0qqKOBI2/FSBoSqgNC98mtw5j6GA1ykYsPDZxjXA3DNO0414opC6AOGabVXYU2NLk4etKPOeZucdvtazvc9K54qZAltjUYpnyzWoHIfpFLBKyUVOUmp9441QHUUlckFqQCsDZcL4VQTHabXfqMcQioQLDDRQ+ICI7Se1AyjIgRzvQALOELFKc135VnKvtYTqyEsaBzgcalEevrz2yR1iTgE4Ryho9EWIFIElXFvdIgwUNZGDsvhpEAxwHlVTtyq1gJkTKcc7nPiS0hk8pzARBWNf62l207tF1JpGqku9Jeyb9yc+/HiwY1fAK0z2L3/x+aebG9scCc3q+/rteyAPHz/2oJbf53W7VBe/230HUHj2qD01NL9YB7KKWEQtwD4v4STAGcDHTz/SUA4GnwQxCE+989R3YX/9//x7tRO/aJWn4vgw1TQ90ZAIQ/XzEU01wvOi0fA8iiTNRV/fMaeg9bxBhKjRQUpHW8myHaOFeWCl9IU3Gpz3aILF8BsrLkbMeIDaR9siConZIMlIJgHoC1/igUAfGh6C1os+Hj65LBKGfWaaI2yYKBFS5k4pCWXx+4LHF//qP3Ro1La8iXhxwRrrF6vcifV64aXregOBmXb2vmefya/fW4MGpH8Iq569uvRwV+/AmXvq+TFH0AcrdB2tTkFOrs8wiGtUxU23eYLoSJfu1UyrvpymIkJNEw1DaviJ2lzd0qIfF2Equj7wZTlKkSCRiUXKJE55qbD6tQspzaptC1u9jlCaYnCAsNJ0wKN2O42tlEJ6E4/YQiPERDLSRCW+4NyWMyHkTgyYmBsMyCMU5Jg0kVIMNWMpQuFlXAnYmPSa2BhmXBha9MkZ4knighUeQn1BIhVWQCOUaBIWBRGQC4vEB+dG3TCIA6kmS+jxQUIf8UGClAZQkeyoUhfWMFVK/+ToMMrDI0MukFLdHEBZcgePvhM5inc6imSp4UNxoRZcezGSJnfwE09b1/qNoa8IYShgrGaK2A58QoaEgU20UkKpycpBK86A84jIpUyGFca4kY4Ix6A3ZLV5lsZgUmMuACGBQiRIwKnUiA1DLkXUpIaFpqAGCgk+FXdbnTA1ZSJmLgDGimECYxKnaRya5b6R8cism6mQGpM0lBU9hsZiMGW1CVgTsZScPu9K+UVAgow3llpW7cYw8jFgSJRCYC4A2UhAocAyEQw5iaqJKuOcYdCkwpDWmJse5NAoBE0p2+Cn0QQgsDRa2oQOBWkiwdORhaATMbIn5/XpD8QkyFwA0RoYJoicMhLP7rv3ftze3Vkdzp01YWW5fuvez98q9ViQPnQZaov14MB3JPdsOdr+1RqrG+tuj5oE9KwV9P72Qz8XiFxFezpa80oNqvuPHTB1fQnEKLcMW7nd63UN9+TRI8uwl339+sIfd/eeffedS+Gzw3oHT/zqKACUsEnCkIhcFTDqYq6STYtRiI52cKgyUUQiRaU8vWekNZhEGSM8CvQpRBMCfQqK+IqhouFXKRqS+AqU+JEi5iG8ohCJZq6K9Vm144Uyc6VSmOS88Nsqkyleb6Y0gIowUQCO1IitsCMUMN6GxA//9t/66WXf0/YTUoWQiau/k+pky89TWl6d+9C3R+JB6w+nk/eE7tb3wn33T2Q1/ilANmLKWXx0+6dBRjCl061keplqVXW4liIf3VQX0RZvuPoW2tERZUIgmkIaBCZl5ddR+gCUpoJSlB6hz4ui0uxRnVIgSFFSjOMxRVJWzHNwaWe2vDvpUYRP/ZTGVnaAwKEjFT9N8gSMx6BExSSY+tSRNsKLeXTYqj4Tyul6aphRSo6Y0ETi4RqT+IMpDMdj1EgApgoZPIkqgCpYwXDBZvgICnu4tGPsyBEWCTUK8Zo4widVCpSVIvpSOHwoxTAclPiiQw3RQVzgxRWFxBchcBVWDUISPPM0+uQksEcJ7xRGdWJLyDX81EJ1Eg85JnIm+juYhJETakE8OTUMQFAdct1Iw5Rq104RNSQAKUOtQZmcI5FbBoAgQjoY5tTwKoI3afJIOX2DVxR3bTdZqoMQd3RSxJwhYoJnlajw6kvIUXQYslIq/uEaiAlaSifBYEKgUhFWeOZSmhiYauq3XaWy6X2HLlj7mZYB8SQSthRk+fUiCkaDKCIXRjCBpOKpUQKODjlhwgbOPCYiDL6UJIbS1F1KiDCsoik76oVR5ApYUYga89F9lGW5y0jDMHGVghFSShODbAKmj5E1vwQndY/TpDE03mgiJuQwE3DGJyuVo+nUyd6vz2P4QqT3e+uBWI/kzty4F+qy2K8Hvnm/s9xfqlpdWX/8ePng4PCtl412dyH41di6FPYpru1t3eHUR+qXD6/W1y3MukDR5bJf9KsvZPm6kjePrbhuM1qqfTYaT9+1mGYRFf2KZzqnCDhVG82rFqqcOqqUUikJq1QwPCEiCWMAaHwuqBk8dLQ2X3EkC58EpX3SCzEh0dRSUN1iNR7gsEKJBNRoW2qRkwyiH1sxEObLdOEhYJgwVBRkPDlHJFznAyMRxiOepv5NC6RINdWO5MPSyq8OD304bPvBg//gb//F46dPbFZbdJfWfHf6yhezbZl4ext+jm3PXOlzMbhA88iZfndQeRha+9Tg7fvDdbRXI9eAr4+ITwe5eJQkHcIhwYRXEdUUpwgtwEJNzNIKuA95mqmvuo3qCzKawaEpi9IL0R9+0zgjq0b4ZGmmFMNcMAifziLBZPgNkBhK03fxGE2GzNVFNmpVz7F29sCTZYLoQFBlEgxHhKXdBKceJ5z2dfTFwEr7A6cVXhF5TQv9pjVeGACRIoNBPJQBZsTi6ZQkB4ADTEHqzyYVtmYQGvpUTdCEsragUyuOByUUamG4wUDjQCqCQXSiFhA8HaWg+JWubm5wlFJFmHiBg6ej5lKGFYylxUcKpkMkygGkkIowh68IKbI2kCNyEmpAgEOjhiFBiUHj5FMGlAPLhDCG0SQPFBOwHiLN1aRSRTThm45jRZMLNEIizxwhAAiKpDYwKSQMKZB4Ufe8hzCcUkNgRzCKSEaEeJiJUMpdNPV4wkjYImcSwyjDURQhnrJUFgOBfjqCsgqGKKgFgkNBFmHohMdoCqWywA1KKcBgEsKXJssLvqrc+gFUJEuY0tRFSg6cjnIg4ROteISHHwQhOAXV4THEIzpCQkx4H/VKVI6FuzN1rUaTjpScd1YQyMPLwo9QO9MUsFExAJXiEf3yemvg0QfVh2vVlI7d0joS7866J+wegkd+fPJOh3vnR9X2do/29w4vzq8+PJzxpJVnnu1SipNJLnnzKvDTR4890Pn26u0Pz1+crB/6DUFXV9QWZu/5IOX68or3rV0N//rb73549cq6aydZ6eb6hmezMUgkBk+6skOuRB3J01bVau7LzteeR6qAoZBSTBXngq+rr+7QFu7UlWWg6JBETYsFXB0dINLoE2KkiYF3+gITIYks16P3FdEUgAaPuSJM2v82ID7KQU5R0sgZKsKH4SgBeCgSr0hKf+gIAz8kDNWIztn5hbMfQfiV6Bcvv3/46JF3lj7/6kvfXrl7ZS4qVNe+6vChPnh9tji3ZDu6WuVygn937splFmSnTf1rFDVROG3SeER5b15DEBb1DWYPP2feqJD899iaJ790X1/Zy9dNBy/F9W6ZIA3C0pzOYGXRXaPIFpBoMOkROmmHCrvHfDntnmVCTb/IYqR0qqxbSZdFgRXSRzQhiyGA+JB2QzZsEoN0ECg6sumjdL3RAooJWEFm+EWTmsklsCSKEgwJp4JkIkVxodQCKJvSgFCOu1as4YcUJWxvanMtmxphqIGNDqEJkARI1coFmArbLqOBSFMHZsrwMZPqHk3G3i+EKBVf0jB47qkxSVjQMCTSaAJPrZLmjIaQAltdnl07X5zhCw6iGSs6bpzASTzANXEa9+S4hvU4PulDEyfNVIGyLNtYZdIffhUxR9wxkY05ZQyrAUiSAMDSNCBkUdRIUi/ZSISEAQuBiSwF+rKB4lR2xBnviVyQdh0TErUECUQXeGo0+PQDFRw6iVmaSJLaXeQFkad2MdeAw/XACRTl2wx9tnGXKkhHVqipKRCGCTX75ySIJjQg+OrTi+qsBBlkWVbUEIWESoiJO6V4ynAQISgpEPLwdCJfXlqOMK2HBxscjCwCJQVV9bqpM+XyfWtBVUSoNNVJafQ9SawoYwDOiDxdzEQfaZMEw9BPEeIFgyDwSwdjgYSTYBKPLPIBZ13jhp8wZRVtbG4+vHfv1asXvnW1ZditLHvQxlSrFXW5F311sTeCjTPj0mGCast6d9c6uruz49PN2utB/Ujwhmh//9tvDnf3z/zSnp/7feSLERsLs3PHM3d/88tf7bjwffkyvw4y7zdOZq58hUu0YlAR9XJgqkvOF9OAihAdWTpKZfGpsviHXIcSIgp4KZ6J84k0JuW0sJScTiRR1npRU4Vgxh0QRWJzkRHXSiFQi3ftLGBFo5RCdBgCIafMBL6Osx+glBwfprR7Pqzop2MPE/nS3brXwBfCQJOiOmfqcQ6KJgkvehbrnJ45BRsYxweHfrLxu2++/fzLLx49ffLko6eULIde2qazolJ+MdaRrr+tt/VkdH2/yle1XAy3n0Tx4wGrUumy1Jf3ELQQg9EUJLOLNd86YC0EbNMa0YQvi+IDjjr60mKlTRTwFHhkS6aOsoQQZKXDXLYDnhy/5PEy0DAowWMA6hEMvzQFGcAgB0qabLyUfeszJAnRwVCbpD2VydKMsDulepzrARucgrPn1Zopipyyzo13PCKPI8/IihyyprCowVSEeAlDnrFNjaSmLfYiRmlEqizTH0rh0msv1RB2mjUEinvpyA4dPiKUQhs6Q0himjDENSu/sg4SlCZISLHiGpOhLDykNJ2EUZosCSalvGhrEshCwiBCUSEmSgOYLmFFM3FS4I6cWqKFIEse5ZjT4U46aGQzUKAhJhRU01To9IIOgsAd8GQpMCFEvCRa7S9CCrxTIJSmdtQCIqsUToq0Hiawg0kVhAFhgCtFbE1bCFpKqdGHj0FpChKRqIvxwIRyzCmwUiQMDDmGMjnG5KK+8Q4wJlISIKzKQZPSRCJ+PFtigFLKJJRjKIaBkJhlY56oBlQCS4MAzDCgQ4FJCDgmKV+KKIgk3tUdnzAij21qigebiuCTDcOcfjwGP+OBPPFAIKdsVMRXqgmNIXKjjznyPowD0cHKhI6V12WrKH1R0oOHnn07Oz+1afzw0RPfZby+ee8t4rfv3h2fnGxu+O3ddSurFmNr5/nVq1fceQhrfXXjr7/8yv7K0cHhFfvjEzvMlmq//vurX/3KRxbVyw8cVZv71QB0cuohI1EJ2FBJm8BEeFGlCmpERxZRY66y2lAtGNJML1T1mkgix3g5R6ksWybS6OPpyiqigMhlMbzzyHVS7sgFSxJlhh1jBTkipIxoDliliJAOEq2sg5BKPefk41c9SGrP1wJzcT2949qfzJhOPqwgMAQLRDBSWcdLNWPTkGuWlcVlj7nVHsPMnScPHjqc3u2893kTG/7eyf7yr76yDC84y5uvw9mRf3lmrZ3U9INJqE6MJlNH1aV+xdCLd95tq+jEgNKYSjEiSdORk6CENBhxGqJaj7I2Fyp9WSc0dDRjNAMFbcGb391H0vY2SVixDXFBOhwFIekoAgUzUREGjYRHwrjDAIxEO9NBlCMkxwheqqmRGIzz2GpqDEk6SNUQkxpq7VdR8KXk4gkyNBKOWtE7YjXwSJRSS+rIgkYYokkfPzSTBSUMjshd+Aq1Jtw+jpgQUphsSZMSUSVKJUmCAhdDroZ0yAWBSKRRTio+DFzEij4JCoIi1KaV0EncAQGbJvZrRYpua0JQ4URIWc25hilaCJmXI0mj0FFKgpQaSekbyolqgGMIK9wOBo9YhVHKhSYnCR+PvKD0q9gSv7T8tS2n5EAI02ianm10pMKIx8QZ2JioJnMLcBoECB0mCRVPzkvikcXIUkjMFUFn44Uw2xuRBySYnDJP/MEXEknMoy+LEmrkspRZgRKn2vnNAFBagxANZV5kWSVU8ngZOHEHkHlWIzyTRE449GkC4YstebIkUeAIE0fMKSTIdBM5hZhQi6aUEMV7QgIYCWZUJL0cOWVkPaOPoZYqSDl1pIElZ0tfKTUx2EfEDCJHUaCpUrIQEIaaE9OaEH0Tq3aS66iu7NmZB6q8nmLP2Ps/Z8dHpl6f+j+fOfeLhr7UMTN3r99WOhUeKDOYA3593QPPdjbr93rfv33nRdPNzetHvh09v7jv3/7+q++fu9RSRIEL+I7Yuq72Fo+n9G38qst0gOlu4alRYlZl8ctKKadBKJATVkP3oUpOKIVPQlkqG2KYWUmDpIikLJui020zmU80svEffPJYMeHCQsI1IhShaGHoPkUYUCmKsjRdcNuRAFgFnEK8R4EyhhANJlkpzaSjiCQ4DFGaLmPpYG/f6ZFvmdXj3yentpettRsffbJ/cPD749/Z/3/85Mmnv/jM89J1yu77lVZdT945lDTdrO9y6dv6daauU627SMq1t5/xqisiQ7JPfuK/xrl2aF2aAiYBRHNmZ3dntKQ4C60plZKFXOC9EMpGOUJp1FhEQoFEFsMEDRwSJIviQoofUGG6vBLKJDoFyWrP20UBp0AOR1QNX0euImnACTHUpEiRNMjQWA1D46o8TedzpZQL39PpTQxlI7ythg8sk+hk7cfDvD1rMR/HO0hh1Hgbjc6eSEAOCQQUJfTBKzWOEwflKKQ++LRFwoUmMguJAy/IUWNLjnfMx8RMQQKWpomYeRolFQsO5MzRKkBOmHYshNnJpf3t6kFQeSaUQ2BJGrxOIyAkEsgUpOIhNOg1SNqRkMSEgREw20wisnRGOwQ/gOIxzqNPWbSWf8RXslFOAEzAhg8mXpDksmZGpSHVIUSywlDKjWyqnKx4lJIo4gUjS8e+hpQk5sN1hFk20lwQlJLEkBARApTqytsu4PNLaAG2BSoqhuqrmpBV2YQYcJgR0q8wenOPDvAgSBEE5oj39jw5IeWIXyk05tQACikpHJRgpHh7p8yV6n2GYfBCpcBQOiqoNO0JeQjTVtQSxmhPWYBeygU1qgaBnF9VlgJRFwjkMfRjOOSDKEBGhhCiPBBYyfpG9OLSYv3M0bU3bc5dnhhujhE/v+A7G9pn//VrH4/c2lj76MnT1Tk/kXQxO7ewtb3s7p9Xe72e5OD1WUiaEt/08Iln93Et23Y7r/20zkV95nd9ZdUd32fffvf69Ws9SHl5YfI2bcUzvYmlmvsW+z4joZPWS7OkgqnLqJ2sJlCkVRE5qxja5FQ7pNZqWppNXsIJVIRpnzSFYyFtK6UDhy1YpdQShlRUkHQKHqxU2A5kfA55thkJqQh9ZExSCBSFFMmmFCBH5AlyaMomwqhJozOUZZGsUBOMLDW8GMS27geN79Vsppn6/mQP6avL7a1NIfl1LC95v3r54pNPPrEM+zb4yupG3erlt1bOatw63b266XFVC7F1NQuwd5B4MT/yUp5unaoq8A6b4AWTKmTeoa9fUnddM+pOTpi6E6bKGqf0T+o2BD4VVBpAWWrwVRMFQRoQRYMicbyIETFRFOFoK4ahUaRPBzgrLgIIp9erSVRqQROlXjXgptuxXOgXBwbkVGp4B6VINgMgrsPT5zcDiU7qC2oc7yQIAjXE1kDFiEovywbHfC4SgUkzNUVz5n/67/5LcERQ0igZPQmOdk2y04/1s784KSCaePK4wfAqiA5msgB0PHX2p5QzRTA5khUTc0XakTA6zPny3IEsBeYBZMsEMSFkojThCcODCnEEn5yyIiRIdWaCVyQrVPj6WmtyEQVoidwrm3AoS/mKo8JpW8hpYikepXHNZVYaahyRgBIsnkfNKBg8Ex6ZYGiSIzxlMYzaYRB5qkPHxZA44RtMQjKnVAc5+KaTe+KEDCp84k99gSM/RceQXHiyiYGJwODwiFfq4OcRiNuHstTIZUWOME7btSeTRCgSdSd3H4sEQz+jQpNCkyZOze5ZXFWQ5cIrj1yPHg8aZRKEoSNOMaC0JB0kVPUK4XWlGgFnklpIqd1c1vBAeMoYCqogeOFxUVhdO6WYs/MjhpzKUkgA/DJUqQTANhVU6iEsVi40BcBEDJo3zUIHk3srWkBLKnUhq3cYIt75QhwJSTUhxJYw7XP3xicLff53rr8eXL+bo4uoGVdaWf18VKuOByVeDr6+fvrRlxrBYeELTZ7WIj8/OX6/884nSF1micF3G4XlC39i9pMBHq59/fKVGjltYqguIvGwlfBURKWAd9vXHIfuzt+jAEeNDEU6nMdKVFqpK1QJniT72KmUFKmmirOVAlF3muQaCv7h8VHagVMgGrwPorpxY8woIsHTjC+uh8cEQB/ZwpWSUEBxwYpTKXBhK2Wb2sWdUprw1VHVPB7lI51iY4IwoOKOvmCoYTgiV8rcw3FhlHKhSGqHOYbAOWWSAabU9hYQQlbuuwJ3jcWXVONc3tT1z9Ky77IsANFW/9F/8p8+evLY7QOj+VLX+wKnb5R+uHG7IPUFrnKeseIXZ7BwjUdchCHhEWlzVhpWlSnrFPUQA7WC6S6goC5MQkDIKchC8AWUNIhUdvBKc+SSgJUCIdGkbFWZR1AaBJGomlKEp8w8jnjHhA9DAZltEg8+paKKQrVYn/7CEZLa6RfnLtoTGbqGus5N7XQqhEANv3BIBEySkAQJityPlDHs4TAZRcM2ONQYSslJHGhpPSaycGBiKEhRzCFTgPxjQ6fg/zNVt8QN63bcMRwOko3vCJPGVlpjruPGY0Yl8SSJD4j+SB20y8/GppScCVJJKUfx9bP6Qxgd+pGkvVKjQE2Qh8EtZljdkhXb8kkT/6QIvtLhFA9fZSvWaa/chlVx8hzwNFM1jWBHEbIsikcMhUTOBAiKsJSayAPCJHzqm9Khr5Q8I1UReYKMnIvoh4FDmQSDCNOJgkzYhMylwaEJx4aaqmEoM1EUIvlnUWDHEAICDSV4UCRJI/9ngf8TyvwGdhJ3e0kwP2tFLY0mVBSdxPaz+pq0lLxs0J/Matv6qHE/5uqDwJpbo9Xngm/8LNGNXyGsT4+Ydd0dtHxS9GOGHtfxsQ73rzxWZHe6LoZn5k7u+Lj0xdsXr7z4q03MTe4gwq+He7oj1IL3nACKMIeer0P8bJx6OaMu3UofSPif1RcbwPQOTeZZ9rqClfA++NFokcSRFLIIlYbS7NDAalsK5LLUIiT/2WAIBZOBikm/sCX5S/p/Sc6RkETCFp/TsgT2syZuANNPXYSrP+3507SCCsNnvdwI8JkhUdn6cNj/7//r//b06dNPPvvUL2p4Q8Q74h6H9jVvjixnDNWxq1wHGkZDEuJRxqYciRObljkRd35c7xq12uRLQY1QjXbLdjIxktwmrXQ7G759/YxcaZqUAp6tiqOOpJIBpSg0hLqeRDp0/lmMkwCuM94CFaf/LBDKOkuzIObTGOvvXxpaGbEUYpIs/i/5/f+zAKeNRJCYjBvkcIqP1DOx4kccKY1cWFrHWs4QL42m2noAgQ4GEeotCtJ05J9XQykhL1FjK8vwzzUj+fMiJoRxR0cWVHCkDq+fQKWI8CdQQ/4T/WQzFGJFs4ZhL8BpH1CEiELStIlaCwyPFDlsFlZWW/HHoQknAccwUBGmTfCE4RMAPkegLGKYUgx5lHkkROYCOiKJXFZdyEVOGIaCUnLREgJRGmTy4EuL6W9ZU4ty4qT5z6XgJzawCUYauo0WCZ3bwvBi+3PhPy2BpspVkSYMEEIV+UuGdFJNTRrlnw0m5pqwSnnoZ34YGiYWYM0161O/H+r6oy53/LVqznmtpXrG24GuHH2mYWlhdvF6/sP1xW9fvXTB6/pscX5pY3XdN5N9efLl988Pd+qbGw49O5Z+bAlagtcjmPSLSDRsLdyO05Pjn60X5cjTv3AQSV53+XOTHOxagBpb1RE3yqdVCeGkSVVYtis+OfbJZaVg2SpF5asvLqWy0cekheNCdf48kkhUzihFzF1uMmcoHk39l0x+Vs4jXyh+oY1LlJ/VP/c7jR3VJGwt0gea717pkbSPr90p9ZydE637WxvHh/v/1797fWd27unHH3t1+OPPP9vcfPrm/Tud425V+fbKd07Q6hJ2MqVnuNco6rZaXqxNF6dcUg/fiaGq3J9kEafuEL80FcGnhX+2Cj8p+qeV08LRUan0o+xwlCKYiAJ5nCbb4qoKtZ8N5i8Js103aeQePHFqoPwlk5+Va6gYKk0wMJG2+ln9jENFGHWJMsO/pP//AsKIhZVioLldAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests\n",
"\n",
"url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/semantic-seg-image.png\"\n",
"image = Image.open(requests.get(url, stream=True).raw)\n",
"image"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kdK_bGhsLKKE"
},
"source": [
"Preprocess the image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G0z-3R-PBKc9",
"outputId": "56c91198-0116-4c2c-fc63-147dc7431b89"
},
"outputs": [],
"source": [
"# prepare image for the model\n",
"encoding = image_processor(image.convert(\"RGB\"), return_tensors=\"pt\")\n",
"print(encoding.pixel_values.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hJRijta4LLu9"
},
"source": [
"Run an inference. "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"id": "z1p-QDoiBP56"
},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" outputs = inference_model(pixel_values=encoding.pixel_values)\n",
" logits = outputs.logits\n",
"\n",
"upsampled_logits = nn.functional.interpolate(\n",
" logits,\n",
" size=image.size[::-1],\n",
" mode=\"bilinear\",\n",
" align_corners=False,\n",
")\n",
"\n",
"pred_seg = upsampled_logits.argmax(dim=1)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gmYIcfL4LNtj"
},
"source": [
"Visualize the results.\n",
"\n",
"We need a color palette to visualize the results. Here, we use [one provided by the TensorFlow Model Garden repository](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51)."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"id": "jy5c6vmzBqzC"
},
"outputs": [],
"source": [
"def ade_palette():\n",
" \"\"\"Creates a label colormap used in ADE20K segmentation benchmark.\n",
" Returns:\n",
" A colormap for visualizing segmentation results.\n",
" \"\"\"\n",
" return np.asarray(\n",
" [\n",
" [0, 0, 0],\n",
" [120, 120, 120],\n",
" [180, 120, 120],\n",
" [6, 230, 230],\n",
" [80, 50, 50],\n",
" [4, 200, 3],\n",
" [120, 120, 80],\n",
" [140, 140, 140],\n",
" [204, 5, 255],\n",
" [230, 230, 230],\n",
" [4, 250, 7],\n",
" [224, 5, 255],\n",
" [235, 255, 7],\n",
" [150, 5, 61],\n",
" [120, 120, 70],\n",
" [8, 255, 51],\n",
" [255, 6, 82],\n",
" [143, 255, 140],\n",
" [204, 255, 4],\n",
" [255, 51, 7],\n",
" [204, 70, 3],\n",
" [0, 102, 200],\n",
" [61, 230, 250],\n",
" [255, 6, 51],\n",
" [11, 102, 255],\n",
" [255, 7, 71],\n",
" [255, 9, 224],\n",
" [9, 7, 230],\n",
" [220, 220, 220],\n",
" [255, 9, 92],\n",
" [112, 9, 255],\n",
" [8, 255, 214],\n",
" [7, 255, 224],\n",
" [255, 184, 6],\n",
" [10, 255, 71],\n",
" [255, 41, 10],\n",
" [7, 255, 255],\n",
" [224, 255, 8],\n",
" [102, 8, 255],\n",
" [255, 61, 6],\n",
" [255, 194, 7],\n",
" [255, 122, 8],\n",
" [0, 255, 20],\n",
" [255, 8, 41],\n",
" [255, 5, 153],\n",
" [6, 51, 255],\n",
" [235, 12, 255],\n",
" [160, 150, 20],\n",
" [0, 163, 255],\n",
" [140, 140, 140],\n",
" [250, 10, 15],\n",
" [20, 255, 0],\n",
" [31, 255, 0],\n",
" [255, 31, 0],\n",
" [255, 224, 0],\n",
" [153, 255, 0],\n",
" [0, 0, 255],\n",
" [255, 71, 0],\n",
" [0, 235, 255],\n",
" [0, 173, 255],\n",
" [31, 0, 255],\n",
" [11, 200, 200],\n",
" [255, 82, 0],\n",
" [0, 255, 245],\n",
" [0, 61, 255],\n",
" [0, 255, 112],\n",
" [0, 255, 133],\n",
" [255, 0, 0],\n",
" [255, 163, 0],\n",
" [255, 102, 0],\n",
" [194, 255, 0],\n",
" [0, 143, 255],\n",
" [51, 255, 0],\n",
" [0, 82, 255],\n",
" [0, 255, 41],\n",
" [0, 255, 173],\n",
" [10, 0, 255],\n",
" [173, 255, 0],\n",
" [0, 255, 153],\n",
" [255, 92, 0],\n",
" [255, 0, 255],\n",
" [255, 0, 245],\n",
" [255, 0, 102],\n",
" [255, 173, 0],\n",
" [255, 0, 20],\n",
" [255, 184, 184],\n",
" [0, 31, 255],\n",
" [0, 255, 61],\n",
" [0, 71, 255],\n",
" [255, 0, 204],\n",
" [0, 255, 194],\n",
" [0, 255, 82],\n",
" [0, 10, 255],\n",
" [0, 112, 255],\n",
" [51, 0, 255],\n",
" [0, 194, 255],\n",
" [0, 122, 255],\n",
" [0, 255, 163],\n",
" [255, 153, 0],\n",
" [0, 255, 10],\n",
" [255, 112, 0],\n",
" [143, 255, 0],\n",
" [82, 0, 255],\n",
" [163, 255, 0],\n",
" [255, 235, 0],\n",
" [8, 184, 170],\n",
" [133, 0, 255],\n",
" [0, 255, 92],\n",
" [184, 0, 255],\n",
" [255, 0, 31],\n",
" [0, 184, 255],\n",
" [0, 214, 255],\n",
" [255, 0, 112],\n",
" [92, 255, 0],\n",
" [0, 224, 255],\n",
" [112, 224, 255],\n",
" [70, 184, 160],\n",
" [163, 0, 255],\n",
" [153, 0, 255],\n",
" [71, 255, 0],\n",
" [255, 0, 163],\n",
" [255, 204, 0],\n",
" [255, 0, 143],\n",
" [0, 255, 235],\n",
" [133, 255, 0],\n",
" [255, 0, 235],\n",
" [245, 0, 255],\n",
" [255, 0, 122],\n",
" [255, 245, 0],\n",
" [10, 190, 212],\n",
" [214, 255, 0],\n",
" [0, 204, 255],\n",
" [20, 0, 255],\n",
" [255, 255, 0],\n",
" [0, 153, 255],\n",
" [0, 41, 255],\n",
" [0, 255, 204],\n",
" [41, 0, 255],\n",
" [41, 255, 0],\n",
" [173, 0, 255],\n",
" [0, 245, 255],\n",
" [71, 0, 255],\n",
" [122, 0, 255],\n",
" [0, 255, 184],\n",
" [0, 92, 255],\n",
" [184, 255, 0],\n",
" [0, 133, 255],\n",
" [255, 214, 0],\n",
" [25, 194, 194],\n",
" [102, 255, 0],\n",
" [92, 0, 255],\n",
" ]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 595
},
"id": "3KJFvgENBih0",
"outputId": "63d42e4f-3867-4d33-8ac0-83bebf8819ca"
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8)\n",
"palette = np.array(ade_palette())\n",
"\n",
"for label, color in enumerate(palette):\n",
" color_seg[pred_seg == label, :] = color\n",
"color_seg = color_seg[..., ::-1] # convert to BGR\n",
"\n",
"img = np.array(image) * 0.5 + color_seg * 0.5 # plot the image with the segmentation map\n",
"img = img.astype(np.uint8)\n",
"\n",
"plt.figure(figsize=(15, 10))\n",
"plt.imshow(img)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q1aGuHYFLP7i"
},
"source": [
"The results are definitely not as expected and as mentioned above, this example is not meant to provide a state-of-the-art model. It exists to familiarize you with the end-to-end workflow. \n",
"\n",
"On the other hand, if you perform full fine-tuning on the same setup (same model variant, same dataset, same training schedule, etc.), the results would not have been any different. This is a crucial aspect of parameter-efficient fine-tuning -- to be able to match up to the results of the full fine-tuning but with a fraction of total trainable parameters. \n",
"\n",
"Here are some things that you can try to get better results:\n",
"\n",
"* Increase the number of training samples. \n",
"* Try a larger SegFormer model variant (know about the available model variants [here](https://huggingface.co/models?search=segformer)). \n",
"* Try different values for the arguments available in `LoraConfig`. \n",
"* Tune the learning rate and batch size. "
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"machine_shape": "hm",
"provenance": []
},
"gpuClass": "premium",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
},
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"style": "IPY_MODEL_9746875e74b845daab06619393b4d46b",
"value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. "
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================================================
FILE: examples/sequence_classification/C3A.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "d36e1e93-ae93-4a4e-93c6-68fd868d2882",
"metadata": {},
"source": [
"# Using C3A for sequence classification"
]
},
{
"cell_type": "markdown",
"id": "ddfc0610-55f6-4343-a950-125ccf0f45ac",
"metadata": {},
"source": [
"In this example, we fine-tune Roberta (base) on a sequence classification task using C3A."
]
},
{
"cell_type": "markdown",
"id": "45addd81-d4f3-4dfd-960d-3920d347f0a6",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9935ae2",
"metadata": {},
"outputs": [],
"source": [
"# To run this notebook, please run `pip install evaluate` to install additional dependencies not covered by PEFT.\n",
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
"from peft import (\n",
" get_peft_model,\n",
" C3AConfig,\n",
" PeftType,\n",
")\n",
"from peft.utils import infer_device\n",
"\n",
"import evaluate\n",
"from datasets import load_dataset\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed, AutoConfig\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "markdown",
"id": "62c959bf-7cc2-49e0-b97e-4c10ec3b9bf3",
"metadata": {},
"source": [
"## Parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3b13308",
"metadata": {},
"outputs": [],
"source": [
"batch_size = 32\n",
"model_name_or_path = \"roberta-base\"\n",
"task = \"mrpc\"\n",
"peft_type = PeftType.C3A\n",
"device = infer_device()\n",
"num_epochs = 5 # for better results, increase this number\n",
"block_size = 768 # for better results, increase this number\n",
"max_length = 512\n",
"torch.manual_seed(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0526f571",
"metadata": {},
"outputs": [],
"source": [
"peft_config = C3AConfig(\n",
" task_type=\"SEQ_CLS\", \n",
" block_size=block_size,\n",
" target_modules=[\"query\", \"value\"],\n",
")\n",
"head_lr = 4e-6 # the learning rate for the classification head for NLU tasks\n",
"ft_lr = 3e-1 # the learning rate for C3A parameters, a much larger LR than that is usually used, at least 1e-1"
]
},
{
"cell_type": "markdown",
"id": "c075c5d2-a457-4f37-a7f1-94fd0d277972",
"metadata": {},
"source": [
"## Loading data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7bb52cb4-d1c3-4b04-8bf0-f39ca88af139",
"metadata": {},
"outputs": [],
"source": [
"if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e69c5e1f-d27b-4264-a41e-fc9b99d025e6",
"metadata": {},
"outputs": [],
"source": [
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0209f778-c93b-40eb-a4e0-24c25db03980",
"metadata": {},
"outputs": [],
"source": [
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=max_length)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7453954e-982c-46f0-b09c-589776e6d6cb",
"metadata": {},
"outputs": [],
"source": [
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f3b9b2e8-f415-4d0f-9fb4-436f1a3585ea",
"metadata": {},
"source": [
"## Preparing the C3A model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ed5ac74",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 610,562 || all params: 125,257,732 || trainable%: 0.4874\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, max_length=None)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0d2d0381",
"metadata": {},
"outputs": [],
"source": [
"head_param = list(map(id, model.classifier.parameters()))\n",
"\n",
"others_param = filter(lambda p: id(p) not in head_param, model.parameters()) \n",
"\n",
"optimizer = AdamW([\n",
" {\"params\": model.classifier.parameters(), \"lr\": head_lr},\n",
" {\"params\": others_param, \"lr\": ft_lr}\n",
"],weight_decay=0.)\n",
"\n",
"\n",
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c0dd5aa8-977b-4ac0-8b96-884b17bcdd00",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fa0e73be",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/115 [00:00"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch_size = 32\n",
"model_name_or_path = \"roberta-base\"\n",
"task = \"mrpc\"\n",
"peft_type = PeftType.FOURIERFT\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"num_epochs = 5 # for better results, increase this number\n",
"n_frequency = 1000 # for better results, increase this number\n",
"scaling = 150.0\n",
"max_length = 512\n",
"torch.manual_seed(0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0526f571",
"metadata": {},
"outputs": [],
"source": [
"peft_config = FourierFTConfig(\n",
" task_type=\"SEQ_CLS\", \n",
" n_frequency=n_frequency,\n",
" target_modules=[\"query\", \"value\"],\n",
" scaling = scaling,\n",
")\n",
"head_lr = 6e-3 # the learning rate for the classification head for NLU tasks\n",
"fft_lr = 6e-2 # the learning rate for the parameters other than the classification head (q,v in this case)"
]
},
{
"cell_type": "markdown",
"id": "c075c5d2-a457-4f37-a7f1-94fd0d277972",
"metadata": {},
"source": [
"## Loading data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7bb52cb4-d1c3-4b04-8bf0-f39ca88af139",
"metadata": {},
"outputs": [],
"source": [
"if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e69c5e1f-d27b-4264-a41e-fc9b99d025e6",
"metadata": {},
"outputs": [],
"source": [
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0209f778-c93b-40eb-a4e0-24c25db03980",
"metadata": {},
"outputs": [],
"source": [
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=max_length)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7453954e-982c-46f0-b09c-589776e6d6cb",
"metadata": {},
"outputs": [],
"source": [
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f3b9b2e8-f415-4d0f-9fb4-436f1a3585ea",
"metadata": {},
"source": [
"## Preparing the FourierFT model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ed5ac74",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 616,130 || all params: 125,263,300 || trainable%: 0.4919\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, max_length=None)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0d2d0381",
"metadata": {},
"outputs": [],
"source": [
"head_param = list(map(id, model.classifier.parameters()))\n",
"\n",
"others_param = filter(lambda p: id(p) not in head_param, model.parameters()) \n",
"\n",
"optimizer = AdamW([\n",
" {\"params\": model.classifier.parameters(), \"lr\": head_lr},\n",
" {\"params\": others_param, \"lr\": fft_lr}\n",
"],weight_decay=0.)\n",
"\n",
"\n",
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c0dd5aa8-977b-4ac0-8b96-884b17bcdd00",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fa0e73be",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/115 [00:00 use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
"\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")\n",
"test_dataloader = DataLoader(tokenized_datasets[\"test\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2ed5ac74",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"0cecb897c86c4892b94a1990ab08a926",
"b8af0294819e4280ad41fa1c11006adf",
"c7530d63b2f745e799713284abacbd2c",
"12a1e302a69543c5bc0e0a66be008ca0",
"9c372e9e9b20433faed8530ca0f4424c",
"b07f26a21325493cac19113f1aa1ee96",
"fbdf6c544fb54294903524a69384e773",
"6c6f2223243b4a7485aef7fbbfe07668",
"a93509d61ac94628a74bc0f98c0eec06",
"3a8de0eb7db44647a734590b6b351b44",
"64d8affd2e854a1c9043fec7ca8a2796"
]
},
"id": "2ed5ac74",
"outputId": "18ea15ac-ed8d-4d80-b166-706681ee49ab"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0cecb897c86c4892b94a1990ab08a926",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading model.safetensors: 0%| | 0.00/1.42G [00:00 use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cf5ef289-f42f-4582-bd5e-9852ad8beff2",
"metadata": {},
"outputs": [],
"source": [
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "739b3655-9db0-48bc-8542-308c6d5e0b8b",
"metadata": {},
"outputs": [],
"source": [
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0288f311-8475-4a0e-99af-e4b909d10e01",
"metadata": {},
"outputs": [],
"source": [
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(\n",
" tokenized_datasets[\"train\"],\n",
" shuffle=True,\n",
" collate_fn=collate_fn,\n",
" batch_size=batch_size,\n",
")\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"],\n",
" shuffle=False,\n",
" collate_fn=collate_fn,\n",
" batch_size=batch_size,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "fcaf6f9e-c9d1-445a-9f08-18ef462f67ce",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e5dfff56-ea80-4561-aeaf-43216bbb9af7",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ac42f98e60d412496fe77ed7eb5c6df",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00, weight=AffineQuantizedTensor(shape=torch.Size([2048, 2304]), block_size=(1, 2304), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (lora_dropout): ModuleDict(\n",
" (default): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (lora_A): ModuleDict(\n",
" (default): Linear(in_features=2304, out_features=16, bias=False)\n",
" )\n",
" (lora_B): ModuleDict(\n",
" (default): Linear(in_features=16, out_features=2048, bias=False)\n",
" )\n",
" (lora_embedding_A): ParameterDict()\n",
" (lora_embedding_B): ParameterDict()\n",
" (lora_magnitude_vector): ModuleDict()\n",
" )\n",
" (k_proj): Linear(in_features=2304, out_features=1024, weight=LinearActivationQuantizedTensor(activation=, weight=AffineQuantizedTensor(shape=torch.Size([1024, 2304]), block_size=(1, 2304), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (v_proj): lora.TorchaoLoraLinear(\n",
" (base_layer): Linear(in_features=2304, out_features=1024, weight=LinearActivationQuantizedTensor(activation=, weight=AffineQuantizedTensor(shape=torch.Size([1024, 2304]), block_size=(1, 2304), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (lora_dropout): ModuleDict(\n",
" (default): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (lora_A): ModuleDict(\n",
" (default): Linear(in_features=2304, out_features=16, bias=False)\n",
" )\n",
" (lora_B): ModuleDict(\n",
" (default): Linear(in_features=16, out_features=1024, bias=False)\n",
" )\n",
" (lora_embedding_A): ParameterDict()\n",
" (lora_embedding_B): ParameterDict()\n",
" (lora_magnitude_vector): ModuleDict()\n",
" )\n",
" (o_proj): Linear(in_features=2048, out_features=2304, weight=LinearActivationQuantizedTensor(activation=, weight=AffineQuantizedTensor(shape=torch.Size([2304, 2048]), block_size=(1, 2048), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (rotary_emb): Gemma2RotaryEmbedding()\n",
" )\n",
" (mlp): Gemma2MLP(\n",
" (gate_proj): Linear(in_features=2304, out_features=9216, weight=LinearActivationQuantizedTensor(activation=, weight=AffineQuantizedTensor(shape=torch.Size([9216, 2304]), block_size=(1, 2304), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (up_proj): Linear(in_features=2304, out_features=9216, weight=LinearActivationQuantizedTensor(activation=, weight=AffineQuantizedTensor(shape=torch.Size([9216, 2304]), block_size=(1, 2304), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (down_proj): Linear(in_features=9216, out_features=2304, weight=LinearActivationQuantizedTensor(activation=, weight=AffineQuantizedTensor(shape=torch.Size([2304, 9216]), block_size=(1, 9216), device=cuda:0, layout_type=PlainLayoutType(), layout_tensor_dtype=torch.int8, quant_min=None, quant_max=None)))\n",
" (act_fn): PytorchGELUTanh()\n",
" )\n",
" (input_layernorm): Gemma2RMSNorm((2304,), eps=1e-06)\n",
" (post_attention_layernorm): Gemma2RMSNorm((2304,), eps=1e-06)\n",
" (pre_feedforward_layernorm): Gemma2RMSNorm((2304,), eps=1e-06)\n",
" (post_feedforward_layernorm): Gemma2RMSNorm((2304,), eps=1e-06)\n",
" )\n",
" )\n",
" (norm): Gemma2RMSNorm((2304,), eps=1e-06)\n",
" )\n",
" (score): ModulesToSaveWrapper(\n",
" (original_module): Linear(in_features=2304, out_features=2, bias=False)\n",
" (modules_to_save): ModuleDict(\n",
" (default): Linear(in_features=2304, out_features=2, bias=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.config.use_cache = False\n",
"model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "fa0e73be",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/230 [00:00 use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cf5ef289-f42f-4582-bd5e-9852ad8beff2",
"metadata": {},
"outputs": [],
"source": [
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "739b3655-9db0-48bc-8542-308c6d5e0b8b",
"metadata": {},
"outputs": [],
"source": [
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0288f311-8475-4a0e-99af-e4b909d10e01",
"metadata": {},
"outputs": [],
"source": [
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(\n",
" tokenized_datasets[\"train\"],\n",
" shuffle=True,\n",
" collate_fn=collate_fn,\n",
" batch_size=batch_size,\n",
")\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"],\n",
" shuffle=False,\n",
" collate_fn=collate_fn,\n",
" batch_size=batch_size,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "fcaf6f9e-c9d1-445a-9f08-18ef462f67ce",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e5dfff56-ea80-4561-aeaf-43216bbb9af7",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "512d9dc10a4d4ecc88b9440575b0973a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00 use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
"\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ed5ac74",
"metadata": {},
"outputs": [],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0d2d0381",
"metadata": {},
"outputs": [],
"source": [
"optimizer = AdamW(params=model.parameters(), lr=lr)\n",
"\n",
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fa0e73be",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/115 [00:00 use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
"\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3c15af0",
"metadata": {},
"outputs": [],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6d3c5edb",
"metadata": {},
"outputs": [],
"source": [
"optimizer = AdamW(params=model.parameters(), lr=lr)\n",
"\n",
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4d279225",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/115 [00:00"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch_size = 32\n",
"model_name_or_path = \"roberta-large\"\n",
"task = \"mrpc\"\n",
"peft_type = PeftType.VBLORA\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"num_epochs = 20\n",
"rank = 4\n",
"max_length = 128\n",
"num_vectors = 90\n",
"vector_length = 256\n",
"torch.manual_seed(0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0526f571",
"metadata": {},
"outputs": [],
"source": [
"peft_config = VBLoRAConfig(\n",
" task_type=\"SEQ_CLS\", \n",
" r=rank,\n",
" topk=2,\n",
" target_modules=['key', 'value', 'query', 'output.dense', 'intermediate.dense'],\n",
" num_vectors=num_vectors,\n",
" vector_length=vector_length,\n",
" save_only_topk_weights=True, # Set to True to reduce storage space. Note that the saved parameters cannot be used to resume training from checkpoints.\n",
" vblora_dropout=0.,\n",
")\n",
"head_lr = 4e-3\n",
"vector_bank_lr = 1e-3\n",
"logits_lr = 1e-2"
]
},
{
"cell_type": "markdown",
"id": "c075c5d2-a457-4f37-a7f1-94fd0d277972",
"metadata": {},
"source": [
"## Loading data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7bb52cb4-d1c3-4b04-8bf0-f39ca88af139",
"metadata": {},
"outputs": [],
"source": [
"if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e69c5e1f-d27b-4264-a41e-fc9b99d025e6",
"metadata": {},
"outputs": [],
"source": [
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0209f778-c93b-40eb-a4e0-24c25db03980",
"metadata": {},
"outputs": [],
"source": [
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=max_length)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7453954e-982c-46f0-b09c-589776e6d6cb",
"metadata": {},
"outputs": [],
"source": [
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f3b9b2e8-f415-4d0f-9fb4-436f1a3585ea",
"metadata": {},
"source": [
"## Preparing the VB-LoRA model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ed5ac74",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 1,696,770 || all params: 357,058,564 || trainable%: 0.4752\n",
"VB-LoRA params to-be-saved (float32-equivalent): 33,408 || total params to-be-saved: 1,085,058\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, max_length=None)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model.print_savable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0d2d0381",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS\n",
"from transformers.trainer_pt_utils import get_parameter_names\n",
"\n",
"decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)\n",
"decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n",
"vector_bank_parameters = [name for name, _ in model.named_parameters() if \"vector_bank\" in name]\n",
"logits_parameters = [name for name, _ in model.named_parameters() if \"logits\" in name ]\n",
"\n",
"optimizer_grouped_parameters = [\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n in decay_parameters and \\\n",
" n not in logits_parameters and n not in vector_bank_parameters],\n",
" \"weight_decay\": 0.1,\n",
" \"lr\": head_lr,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n not in decay_parameters and \\\n",
" n not in logits_parameters and n not in vector_bank_parameters],\n",
" \"weight_decay\": 0.0,\n",
" \"lr\": head_lr,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n in vector_bank_parameters],\n",
" \"lr\": vector_bank_lr,\n",
" \"weight_decay\": 0.0,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n in logits_parameters],\n",
" \"lr\": logits_lr,\n",
" \"weight_decay\": 0.0,\n",
" },\n",
"]\n",
"\n",
"optimizer = AdamW(optimizer_grouped_parameters)\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c0dd5aa8-977b-4ac0-8b96-884b17bcdd00",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fa0e73be",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/115 [00:00
![](\"bdlora-sharding.png\")
\n",
"![](\"bdlora-performance.png\")
\n",
"