Repository: bigcode-project/starcoder
Branch: main
Commit: d72c7fe3dda8
Files: 15
Total size: 80.2 KB
Directory structure:
gitextract_483pvkp7/
├── .gitignore
├── LICENSE
├── README.md
├── chat/
│ ├── README.md
│ ├── config.py
│ ├── config.yaml
│ ├── deepspeed_z3_config_bf16.json
│ ├── dialogues.py
│ ├── generate.py
│ ├── requirements.txt
│ ├── train.py
│ └── utils.py
├── finetune/
│ ├── finetune.py
│ └── merge_peft_adapters.py
└── requirements.txt
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FILE: README.md
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# 💫 StarCoder
[Paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) | [Model](https://huggingface.co/bigcode/starcoder) | [Playground](https://huggingface.co/spaces/bigcode/bigcode-playground) | [VSCode](https://marketplace.visualstudio.com/items?itemName=HuggingFace.huggingface-vscode) | [Chat](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground)
# What is this about?
💫 StarCoder is a language model (LM) trained on source code and natural language text. Its training data incorporates more that 80 different programming languages as well as text extracted from GitHub issues and commits and from notebooks. This repository showcases how we get an overview of this LM's capabilities.
# News
* **May 9, 2023:** We've fine-tuned StarCoder to act as a helpful coding assistant 💬! Check out the `chat/` directory for the training code and play with the model [here](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground).
# Disclaimer
Before you can use the model go to `hf.co/bigcode/starcoder` and accept the agreement. And make sure you are logged into the Hugging Face hub with:
```bash
huggingface-cli login
```
# Table of Contents
1. [Quickstart](#quickstart)
- [Installation](#installation)
- [Code generation with StarCoder](#code-generation)
- [Text-generation-inference code](#text-generation-inference)
2. [Fine-tuning](#fine-tuning)
- [Step by step installation with conda](#step-by-step-installation-with-conda)
- [Datasets](#datasets)
- [Stack Exchange](#stack-exchange-se)
- [Merging PEFT adapter layers](#merging-peft-adapter-layers)
3. [Evaluation](#evaluation)
4. [Inference hardware requirements](#inference-hardware-requirements)
# Quickstart
StarCoder was trained on GitHub code, thus it can be used to perform code generation. More precisely, the model can complete the implementation of a function or infer the following characters in a line of code. This can be done with the help of the 🤗's [transformers](https://github.com/huggingface/transformers) library.
## Installation
First, we have to install all the libraries listed in `requirements.txt`
```bash
pip install -r requirements.txt
```
## Code generation
The code generation pipeline is as follows
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/starcoder"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# to save memory consider using fp16 or bf16 by specifying torch_dtype=torch.float16 for example
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
# clean_up_tokenization_spaces=False prevents a tokenizer edge case which can result in spaces being removed around punctuation
print(tokenizer.decode(outputs[0], clean_up_tokenization_spaces=False))
```
or
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
checkpoint = "bigcode/starcoder"
model = AutoModelForCausalLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
print( pipe("def hello():") )
```
For hardware requirements, check the section [Inference hardware requirements](#inference-hardware-requirements).
## Text-generation-inference
```bash
docker run -p 8080:80 -v $PWD/data:/data -e HUGGING_FACE_HUB_TOKEN=<YOUR BIGCODE ENABLED TOKEN> -d ghcr.io/huggingface/text-generation-inference:latest --model-id bigcode/starcoder --max-total-tokens 8192
```
For more details, see [here](https://github.com/huggingface/text-generation-inference).
# Fine-tuning
Here, we showcase how we can fine-tune this LM on a specific downstream task.
## Step by step installation with conda
Create a new conda environment and activate it
```bash
conda create -n env
conda activate env
```
Install the `pytorch` version compatible with your version of cuda [here](https://pytorch.org/get-started/previous-versions/), for example the following command works with cuda 11.6
```bash
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
```
Install `transformers` and `peft`
```bash
conda install -c huggingface transformers
pip install git+https://github.com/huggingface/peft.git
```
Note that you can install the latest stable version of transformers by using
```bash
pip install git+https://github.com/huggingface/transformers
```
Install `datasets`, `accelerate` and `huggingface_hub`
```bash
conda install -c huggingface -c conda-forge datasets
conda install -c conda-forge accelerate
conda install -c conda-forge huggingface_hub
```
Finally, install `bitsandbytes` and `wandb`
```bash
pip install bitsandbytes
pip install wandb
```
To get the full list of arguments with descriptions you can run the following command on any script:
```
python scripts/some_script.py --help
```
Before you run any of the scripts make sure you are logged in and can push to the hub:
```bash
huggingface-cli login
```
Make sure you are logged in `wandb`:
```bash
wandb login
```
Now that everything is done, you can clone the repository and get into the corresponding directory.
## Datasets
💫 StarCoder can be fine-tuned to achieve multiple downstream tasks. Our interest here is to fine-tune StarCoder in order to make it follow instructions. [Instruction fine-tuning](https://arxiv.org/pdf/2109.01652.pdf) has gained a lot of attention recently as it proposes a simple framework that teaches language models to align their outputs with human needs. That procedure requires the availability of quality instruction datasets, which contain multiple `instruction - answer` pairs. Unfortunately such datasets are not ubiquitous but thanks to Hugging Face 🤗's [datasets](https://github.com/huggingface/datasets) library we can have access to some good proxies. To fine-tune cheaply and efficiently, we use Hugging Face 🤗's [PEFT](https://github.com/huggingface/peft) as well as Tim Dettmers' [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
### Stack Exchange SE
[Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange) is a well-known network of Q&A websites on topics in diverse fields. It is a place where a user can ask a question and obtain answers from other users. Those answers are scored and ranked based on their quality. [Stack exchange instruction](https://huggingface.co/datasets/ArmelR/stack-exchange-instruction) is a dataset that was obtained by scrapping the site in order to build a collection of Q&A pairs. A language model can then be fine-tuned on that dataset to make it elicit strong and diverse question-answering skills.
To execute the fine-tuning script run the following command:
```bash
python finetune/finetune.py \
--model_path="bigcode/starcoder"\
--dataset_name="ArmelR/stack-exchange-instruction"\
--subset="data/finetune"\
--split="train"\
--size_valid_set 10000\
--streaming\
--seq_length 2048\
--max_steps 1000\
--batch_size 1\
--input_column_name="question"\
--output_column_name="response"\
--gradient_accumulation_steps 16\
--learning_rate 1e-4\
--lr_scheduler_type="cosine"\
--num_warmup_steps 100\
--weight_decay 0.05\
--output_dir="./checkpoints" \
```
The size of the SE dataset is better manageable when using streaming. We also have to precise the split of the dataset that is used. For more details, check the [dataset's page](https://huggingface.co/datasets/ArmelR/stack-exchange-instruction) on 🤗. Similarly we can modify the command to account for the availability of GPUs
```bash
python -m torch.distributed.launch \
--nproc_per_node number_of_gpus finetune/finetune.py \
--model_path="bigcode/starcoder"\
--dataset_name="ArmelR/stack-exchange-instruction"\
--subset="data/finetune"\
--split="train"\
--size_valid_set 10000\
--streaming \
--seq_length 2048\
--max_steps 1000\
--batch_size 1\
--input_column_name="question"\
--output_column_name="response"\
--gradient_accumulation_steps 16\
--learning_rate 1e-4\
--lr_scheduler_type="cosine"\
--num_warmup_steps 100\
--weight_decay 0.05\
--output_dir="./checkpoints" \
```
## Merging PEFT adapter layers
If you train a model with PEFT, you'll need to merge the adapter layers with the base model if you want to run inference / evaluation. To do so, run:
```bash
python finetune/merge_peft_adapters.py --base_model_name_or_path model_to_merge --peft_model_path model_checkpoint
# Push merged model to the Hub
python finetune/merge_peft_adapters.py --base_model_name_or_path model_to_merge --peft_model_path model_checkpoint --push_to_hub
```
For example
```bash
python finetune/merge_peft_adapters.py --model_name_or_path bigcode/starcoder --peft_model_path checkpoints/checkpoint-1000 --push_to_hub
```
# Evaluation
To evaluate StarCoder and its derivatives, you can use the [BigCode-Evaluation-Harness](https://github.com/bigcode-project/bigcode-evaluation-harness) for evaluating Code LLMs.
# Inference hardware requirements
In FP32 the model requires more than 60GB of RAM, you can load it in FP16 or BF16 in ~30GB, or in 8bit under 20GB of RAM with
```python
# make sure you have accelerate and bitsandbytes installed
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder")
# for fp16 replace with `load_in_8bit=True` with `torch_dtype=torch.float16`
model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder", device_map="auto", load_in_8bit=True)
print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
````
```
Memory footprint: 15939.61 MB
```
You can also try [starcoder.cpp](https://github.com/bigcode-project/starcoder.cpp), a C++ implementation with [ggml](https://github.com/ggerganov/ggml) library.
================================================
FILE: chat/README.md
================================================
# Fine-tuning StarCoder for chat-based applications
This is a fully-working example to fine-tune `StarCoder` on a corpus of multi-turn dialogues and thus create a coding assistant that is chatty and helpful. The resulting model is quite good at generating code for plots and other programming tasks. For example, given the prompt:
```
Draw me a map of the world using geopandas. Make it so that only Germany and Spain are colored red.
```
the model will produce:
```python
import pandas as pd
from matplotlib import pyplot as plt
import geopandas as gpd
from shapely.geometry import Point, Polygon
%matplotlib inline
# Load data from URL or local file
url = 'https://raw.githubusercontent.com/datasets/geo-countries/master/data/countries.geojson'
df = gpd.read_file(url)
# Create plot
fig, ax = plt.subplots()
ax.set_aspect('equal')
world = gpd.GeoDataFrame({'name': ['World'],
'geometry': [Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])]})
germany = df[df['ADMIN'] == "Germany"]
spain = df[df['ADMIN'] == "Spain"]
other = df[(df['ADMIN']!= "Germany") & (df['ADMIN']!= "Spain")]
world.plot(color='lightgrey', edgecolor='white', ax=ax)
germany.plot(color="red", ax=ax)
spain.plot(color="red", ax=ax)
other.plot(color="skyblue", ax=ax)
plt.title("European Countries")
plt.show()
```
Check out our [blog post](https://huggingface.co/blog/starchat-alpha) for more details.
## Getting started
To run the `train.py` script, first create a Python virtual environment using e.g. Conda:
```shell
conda create -n chat python=3.10 && conda activate chat
```
Next, install PyTorch v1.13.1. Since this is hardware-dependent, we direct you to the [PyTorch Installation Page](https://pytorch.org/get-started/previous-versions/#v1131) for this step. Next, install the rest of the project dependencies:
```shell
pip install -r requirements.txt
```
You'll also need to be logged into both your Hugging Face account. To do so, run:
```shell
huggingface-cli login
```
Finally, install Git LFS with:
```shell
sudo apt-get install git-lfs
```
## Prepare your dataset
For training and inference, we use _dialogue templates_ to format each message in a conversation. For example, a typical dialogue between a human user and AI assistant takes the form:
```json
{
"messages": [
{
"content": "Is it possible to imagine a society without law?",
"role": "user"},
{
"content": "It is difficult to imagine a society that is able to be maintained without any semblance of Law.",
"role": "assistant",
},
{
"content": "It seems like you consider the absence of law equal to the absence of anything that could guide the behaviour of the individual.",
"role": "user",
},
{
"content": "You are correct that there are other factors that can guide behavior in a society and play a role in shaping individuals' behavior and interactions with each other. However, even in societies where these factors are present, laws still serve an important role in maintaining social order and resolving conflicts.",
"role": "assistant",
}
]
}
```
Make sure you convert your dataset according to this schema, in particular you need to include a `messages` column like the above. You can adjust the model, dataset, and hyperparamters in the `config.yaml` file.
## Launch training
We use DeepSpeed ZeRO-3 to shard the model and optimizer across 8 x A100 (80GB) GPUs. To fine-tune run:
```
TRANSFORMERS_VERBOSITY=info torchrun --nproc_per_node=8 train.py config.yaml --deepspeed=deepspeed_z3_config_bf16.json
```
By default, this will save the model checkpoint in the `data/` directory and also push it to the Hugging Face Hub.
## Generate samples
To generate a few coding examples from your model, run:
```shell
python generate.py --model_id path/to/your/model
```
================================================
FILE: chat/config.py
================================================
# coding=utf-8
# 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, field
from typing import List, Optional
import transformers
from transformers import MODEL_FOR_CAUSAL_LM_MAPPING
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
@dataclass
class DataArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
dialogue_template: Optional[str] = field(
default="no_system",
metadata={
"help": "The name of the dialogue template to use for conditioning the model. See h4.training.dialogues for choices."
},
)
@dataclass
class TrainingArguments(transformers.TrainingArguments):
"""
Arguments related to the training process itself. For all parameters, see: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
"""
logging_first_step: Optional[bool] = field(
default=True,
metadata={"help": ("Whether to log and evaluate the first global_step or not.")},
)
optim: Optional[str] = field(default="adamw_torch")
================================================
FILE: chat/config.yaml
================================================
# Model arguments
model_name_or_path: bigcode/starcoderbase
# Data training arguments
block_size: 1024
dataset_name: HuggingFaceH4/oasst1_en
dialogue_template: no_system
preprocessing_num_workers: 12
# Training arguments with sensible defaults
# Add other options from here: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
bf16: true # Gives ~2x speed up in training time, but disable if you start seeing NaNs
do_eval: true
do_train: true
evaluation_strategy: epoch # One of ["no", "steps", "epoch"]
gradient_accumulation_steps: 8
gradient_checkpointing: true
hub_model_id: lewtun/starchat-alpha
hub_private_repo: true
hub_strategy: every_save
learning_rate: 2.0e-05
log_level: passive
logging_steps: 8
logging_strategy: steps
lr_scheduler_type: cosine
max_steps: -1
num_train_epochs: 3
output_dir: data/starchat-alpha
overwrite_output_dir: true
per_device_eval_batch_size: 4
per_device_train_batch_size: 4
push_to_hub: true
remove_unused_columns: true
report_to:
- tensorboard
save_steps: 500
save_strategy: steps
save_total_limit: null
seed: 42
tf32: true
warmup_ratio: 0.03
weight_decay: 0.
================================================
FILE: chat/deepspeed_z3_config_bf16.json
================================================
{
"bf16": {
"enabled": "auto"
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
================================================
FILE: chat/dialogues.py
================================================
# coding=utf-8
# 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.
import json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, TypeVar, Union
from huggingface_hub import ModelHubMixin, hf_hub_download
# Generic variable that is either ModelHubMixin or a subclass thereof
T = TypeVar("T", bound="ModelHubMixin")
TEMPLATE_FILENAME = "dialogue_template.json"
IGNORE_INDEX = -100
@dataclass
class DialogueTemplate(ModelHubMixin):
"""Converts all turns of a dialogue between a user and assistant to a standardized format.
Adapted from OpenAI's ChatML (https://github.com/openai/openai-python/blob/main/chatml.md) and Vicuna (https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py)
"""
system: str
messages: List[Dict[str, str]] = None
system_token: str = "<|system|>"
user_token: str = "<|user|>"
assistant_token: str = "<|assistant|>"
end_token: str = "<|end|>"
def get_training_prompt(self) -> str:
prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
if self.messages is None:
raise ValueError("Dialogue template must have at least one message.")
for message in self.messages:
if message["role"] == "user":
prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
else:
prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
return prompt
def get_inference_prompt(self) -> str:
prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
if self.messages is None:
raise ValueError("Dialogue template must have at least one message.")
for message in self.messages:
if message["role"] == "user":
prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
else:
prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
prompt += self.assistant_token
return prompt
def get_dialogue(self):
"""Helper function to format the messages as an easy-to-read dialogue."""
prompt = ""
if self.messages is None:
raise ValueError("Dialogue template must have at least one message.")
for message in self.messages:
if message["role"] == "user":
prompt += "\n\nHuman: " + message["content"]
else:
prompt += "\n\nAssistant: " + message["content"]
return prompt
def get_special_tokens(self) -> List[str]:
return [self.system_token, self.user_token, self.assistant_token, self.end_token]
def copy(self):
return DialogueTemplate(
system=self.system,
messages=self.messages,
system_token=self.system_token,
user_token=self.user_token,
assistant_token=self.assistant_token,
end_token=self.end_token,
)
def to_dict(self) -> Dict[str, Any]:
return {k: v for k, v in asdict(self).items()}
@classmethod
def from_dict(cls, data):
return DialogueTemplate(
system=data["system"] if "system" in data else "",
messages=data["messages"] if "messages" in data else None,
system_token=data["system_token"] if "system_token" in data else "<|system|>",
user_token=data["user_token"] if "user_token" in data else "<|user|>",
assistant_token=data["assistant_token"] if "assistant_token" in data else "<|assistant|>",
end_token=data["end_token"] if "end_token" in data else "<|end|>",
)
def _save_pretrained(self, save_directory: Union[str, Path]) -> None:
save_directory = Path(save_directory)
save_directory.mkdir(exist_ok=True)
with open(save_directory / "dialogue_template.json", "w") as f:
json.dump(self.to_dict(), f, indent=2)
@classmethod
def _from_pretrained(
cls: Type[T],
*,
model_id: str,
revision: Optional[str],
cache_dir: Optional[Union[str, Path]],
force_download: bool,
proxies: Optional[Dict],
resume_download: bool,
local_files_only: bool,
token: Optional[Union[str, bool]],
**model_kwargs,
) -> T:
"""Loads the dialogue template from a local directory or the Huggingface Hub.
Args:
model_id (`str`):
ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
revision (`str`, *optional*):
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
latest commit on `main` branch.
force_download (`bool`, *optional*, defaults to `False`):
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.
resume_download (`bool`, *optional*, defaults to `False`):
Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`).
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running `huggingface-cli login`.
cache_dir (`str`, `Path`, *optional*):
Path to the folder where cached files are stored.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, avoid downloading the file and return the path to the local cached file if it exists.
model_kwargs:
Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
"""
if os.path.isdir(model_id): # Can either be a local directory
print("Loading dialogue template from local directory")
template_file = os.path.join(model_id, TEMPLATE_FILENAME)
else: # Or a template on the Hub
template_file = hf_hub_download( # Download from the hub, passing same input args
repo_id=model_id,
filename=TEMPLATE_FILENAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
)
# Load template
with open(template_file, "r") as f:
data = json.load(f)
return cls.from_dict(data=data)
# A shortened version of the system message in Anthropic's HHH prompt: https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt
default_template = DialogueTemplate(
system="Below is a dialogue between a human user and an AI assistant. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed.",
)
# OpenAI and OpenAssistant train on few to no system messages.
# TODO: consider defining this as the `default` template
no_system_template = DialogueTemplate(
system="",
)
alpaca_template = DialogueTemplate(
system="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
user_token="### Instruction:",
assistant_token="### Response:",
)
SUPPORTED_DIALOGUE_TEMPLATES = {
"default": default_template,
"no_system": no_system_template,
"alpaca": alpaca_template,
}
def get_dialogue_template(template: str) -> DialogueTemplate:
if template not in SUPPORTED_DIALOGUE_TEMPLATES.keys():
raise ValueError(f"Template {template} is not supported!")
return SUPPORTED_DIALOGUE_TEMPLATES[template].copy()
def prepare_dialogue(example, dialogue_template, is_train=True):
"""Format example to single- or multi-turn dialogue."""
# TODO: make this simpler by just ensuring every dataset has a messages column
if "messages" in example.keys() and example["messages"] is not None:
dialogue_template.messages = example["messages"]
elif all(k in example.keys() for k in ("prompt", "completion")):
# Construct single-turn dialogue from prompt and completion
dialogue_template.messages = [
{"role": "user", "content": example["prompt"]},
{"role": "assistant", "content": example["completion"]},
]
elif "prompt" in example.keys():
# Construct single-turn dialogue from prompt (inference only)
dialogue_template.messages = [
{"role": "user", "content": example["prompt"]},
]
else:
raise ValueError(
f"Could not format example as dialogue! Require either `messages` or `[prompt, completion]` or `[prompt]` keys but found {list(example.keys())}"
)
if is_train:
example["text"] = dialogue_template.get_training_prompt()
else:
example["text"] = dialogue_template.get_inference_prompt()
return example
def mask_user_labels(tokenizer, dialogue_template, labels):
"""Masks the user turns of a dialogue from the loss"""
user_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.user_token)
assistant_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.assistant_token)
for idx, label_id in enumerate(labels):
if label_id == user_token_id:
current_idx = idx
while labels[current_idx] != assistant_token_id and current_idx < len(labels):
labels[current_idx] = IGNORE_INDEX
current_idx += 1
================================================
FILE: chat/generate.py
================================================
# coding=utf-8
# Copyright 2023 The BigCode and HuggingFace teams. 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.
#
"""A simple script to quickly check the model outputs of a generative model"""
import argparse
import torch
from dialogues import DialogueTemplate, get_dialogue_template
from transformers import (AutoModelForCausalLM, AutoTokenizer,
GenerationConfig, set_seed)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_id",
type=str,
help="Name of model to generate samples with",
)
parser.add_argument(
"--revision",
type=str,
default=None,
help="The model repo's revision to use",
)
parser.add_argument(
"--system_prompt", type=str, default=None, help="Overrides the dialogue template's system prompt"
)
args = parser.parse_args()
# Set seed for reproducibility
set_seed(42)
prompts = [
[
{
"role": "user",
"content": "Develop a C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file.",
}
],
[
{
"role": "user",
"content": "Implement a Python function to find the longest common subsequence of two input strings using dynamic programming.",
}
],
[{"role": "user", "content": "Implement a regular expression in Python to validate an email address."}],
[
{
"role": "user",
"content": "Write a program to find the nth Fibonacci number using dynamic programming.",
}
],
[
{
"role": "user",
"content": "Implement a binary search algorithm to find a specific element in a sorted array.",
}
],
[{"role": "user", "content": "Implement a queue data structure using two stacks in Python."}],
[
{
"role": "user",
"content": "Implement a program to find the common elements in two arrays without using any extra data structures.",
}
],
]
try:
dialogue_template = DialogueTemplate.from_pretrained(args.model_id, revision=args.revision)
except Exception:
print("No dialogue template found in model repo. Defaulting to the `no_system` template.")
dialogue_template = get_dialogue_template("no_system")
if args.system_prompt is not None:
dialogue_template.system = args.system_prompt
formatted_prompts = []
for prompt in prompts:
dialogue_template.messages = [prompt] if isinstance(prompt, dict) else prompt
formatted_prompts.append(dialogue_template.get_inference_prompt())
print("=== SAMPLE PROMPT ===")
print(formatted_prompts[0])
print("=====================")
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(args.model_id, revision=args.revision)
print(f"Special tokens: {tokenizer.special_tokens_map}")
print(f"EOS token ID for generation: {tokenizer.convert_tokens_to_ids(dialogue_template.end_token)}")
generation_config = GenerationConfig(
temperature=0.2,
top_k=50,
top_p=0.95,
repetition_penalty=1.2,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids(dialogue_template.end_token),
min_new_tokens=32,
max_new_tokens=256,
)
model = AutoModelForCausalLM.from_pretrained(
args.model_id, revision=args.revision, load_in_8bit=True, device_map="auto", torch_dtype=torch.float16
)
outputs = ""
for idx, prompt in enumerate(formatted_prompts):
batch = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False).to(device)
generated_ids = model.generate(**batch, generation_config=generation_config)
generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=False).lstrip()
outputs += generated_text + "\n\n"
print(f"=== EXAMPLE {idx} ===")
print()
print(generated_text)
print()
print("======================")
print()
raw_model_name = args.model_id.split("/")[-1]
model_name = f"{raw_model_name}"
if args.revision is not None:
model_name += f"-{args.revision}"
with open(f"data/samples-{model_name}.txt", "w", encoding="utf-8") as f:
f.write(outputs)
if __name__ == "__main__":
main()
================================================
FILE: chat/requirements.txt
================================================
transformers>=4.28.1
tokenizers>=0.13.3
deepspeed==0.9.1
datasets>=2.12.0
accelerate>=0.18.0
tensorboard
================================================
FILE: chat/train.py
================================================
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The BigCode & HuggingFace Inc. teams. 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.
"""
Script to instruction fine-tune causal language models on a Hub dataset
Adapted from huggingface/transformers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py
"""
import logging
import math
import os
import random
import sys
from itertools import chain
import datasets
import torch
import transformers
from config import DataArguments, ModelArguments, TrainingArguments
from datasets import load_dataset
from dialogues import get_dialogue_template, mask_user_labels, prepare_dialogue
from transformers import (AutoModelForCausalLM, AutoTokenizer, Trainer,
default_data_collator, set_seed)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from utils import StarChatArgumentParser, hf_login
logger = logging.getLogger(__name__)
def main():
parser = StarChatArgumentParser((ModelArguments, DataArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
# If we pass only one argument to the script and it's the path to a YAML file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
# parse command line args and yaml file
elif len(sys.argv) > 2 and sys.argv[1].endswith(".yaml"):
model_args, data_args, training_args = parser.parse_yaml_and_args(os.path.abspath(sys.argv[1]), sys.argv[2:])
# parse command line args only
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Set seed for reproducibility
set_seed(training_args.seed)
###############
# Setup logging
###############
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process a small summary
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Model parameters {model_args}")
logger.info(f"Data parameters {data_args}")
logger.info(f"Training/evaluation parameters {training_args}")
# Login to HuggingFace Hub if needed
hf_login()
###########################
# Detecting last checkpoint
###########################
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
###############
# Load datasets
###############
raw_datasets = load_dataset(data_args.dataset_name)
logger.info(
f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
)
with training_args.main_process_first(desc="Log a few random samples from the raw training set"):
for index in random.sample(range(len(raw_datasets["train"])), 3):
logger.info(f"Sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['messages']}")
#########################
# Apply dialogue template
#########################
dialogue_template = get_dialogue_template(data_args.dialogue_template)
logger.info(f"System prompt for dialogue template: {dialogue_template.system}")
raw_datasets = raw_datasets.map(prepare_dialogue, fn_kwargs={"dialogue_template": dialogue_template})
#####################################
# Load tokenizer and process datasets
#####################################
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
revision=model_args.model_revision,
)
# Note that we must call `add_tokens` before adding any special tokens
dialogue_tokens = dialogue_template.get_special_tokens()
num_added_tokens = tokenizer.add_special_tokens({"additional_special_tokens": dialogue_tokens})
logger.info(f"Added {num_added_tokens} new tokens: {dialogue_tokens}")
if training_args.do_train:
column_names = list(raw_datasets["train"].features)
else:
column_names = list(raw_datasets["test"].features)
text_column_name = "text" if "text" in column_names else column_names[0]
with training_args.main_process_first(desc="Log a few random samples from the training set"):
for index in random.sample(range(len(raw_datasets["train"])), 3):
logger.info(f"Sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['text']}")
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples[text_column_name], return_token_type_ids=False)
# clm input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
" before being passed to the model."
)
return output
with training_args.main_process_first(desc="dataset map tokenization"):
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset",
)
##############################
# Concatenate and chunk corpus
##############################
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`."
)
block_size = 1024
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= block_size:
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
labels = result["input_ids"].copy()
mask_user_labels(tokenizer, dialogue_template, labels)
result["labels"] = labels
return result
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
# to preprocess.
with training_args.main_process_first(desc="grouping texts together"):
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc=f"Grouping texts in chunks of {block_size}",
)
if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = lm_datasets["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
if training_args.do_eval:
if "test" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = lm_datasets["test"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
#######################
# Load pretrained model
#######################
logger.info("*** Load pretrained model ***")
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
revision=model_args.model_revision,
torch_dtype=torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
)
model.resize_token_embeddings(len(tokenizer))
########################
# Initialize the Trainer
########################
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
# Data collator defaults to DataCollatorWithPadding, so we change it
# since we've already chunked our corpus
data_collator=default_data_collator,
)
###############
# Training loop
###############
if training_args.do_train:
logger.info("*** Train ***")
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
#################################
# Create model card & push to Hub
#################################
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
kwargs["dataset_args"] = data_args.dataset_config_name
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
kwargs["dataset"] = data_args.dataset_name
kwargs["dataset_args"] = "default"
# Store dialogue template so we can load it at deployment time
dialogue_template.save_pretrained(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.save_model(training_args.output_dir)
trainer.create_model_card(**kwargs)
with training_args.main_process_first(desc="Generate a sample from the model"):
inputs = tokenizer(
"<|system|>\n<|end|>\n<|user|>\nHow many helicopters can a human eat in one sitting?<|end|>\n<|assistant|>",
return_tensors="pt",
return_token_type_ids=False,
).to(training_args.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids(dialogue_template.end_token),
)
logger.info(f"=== SAMPLE OUTPUT ==\n\n{tokenizer.decode(outputs[0], skip_special_tokens=False)}")
if __name__ == "__main__":
main()
================================================
FILE: chat/utils.py
================================================
# coding=utf-8
# 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.
import dataclasses
import os
from dataclasses import dataclass
from typing import List, Optional
from huggingface_hub import login
from transformers import HfArgumentParser
class StarChatArgumentParser(HfArgumentParser):
def parse_yaml_and_args(self, yaml_arg: str, other_args: Optional[List[str]] = None) -> List[dataclass]:
arg_list = self.parse_yaml_file(os.path.abspath(yaml_arg))
outputs = []
# strip other args list into dict of key-value pairs
other_args = {arg.split("=")[0].strip("-"): arg.split("=")[1] for arg in other_args}
used_args = {}
# overwrite the default/loaded value with the value provided to the command line
# adapted from https://github.com/huggingface/transformers/blob/d0b5002378daabf62769159add3e7d66d3f83c3b/src/transformers/hf_argparser.py#L327
for data_yaml, data_class in zip(arg_list, self.dataclass_types):
keys = {f.name for f in dataclasses.fields(data_yaml) if f.init}
inputs = {k: v for k, v in vars(data_yaml).items() if k in keys}
for arg, val in other_args.items():
# add only if in keys
if arg in keys:
base_type = data_yaml.__dataclass_fields__[arg].type
inputs[arg] = val
# cast type for ints, floats, and bools (default to strings)
if base_type in [int, float, bool]:
inputs[arg] = base_type(val)
# add to used-args so we can check if double add
if arg not in used_args:
used_args[arg] = val
else:
raise ValueError(f"Duplicate argument provided: {arg}, may cause unexpected behavior")
obj = data_class(**inputs)
outputs.append(obj)
return outputs
def hf_login():
"""Login to HuggingFace Hub if HF_TOKEN is defined in the environment"""
hf_token = os.getenv("HF_TOKEN")
if hf_token is not None:
login(token=hf_token)
================================================
FILE: finetune/finetune.py
================================================
import argparse
import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, set_peft_model_state_dict
from torch.utils.data import IterableDataset
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments, logging, set_seed
from transformers import TrainerCallback, TrainingArguments, TrainerState, TrainerControl
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
"""
Fine-Tune StarCoder on Code Alpaca/SE
"""
class SavePeftModelCallback(TrainerCallback):
def on_save(
self,
args: TrainingArguments,
state: TrainerState,
control: TrainerControl,
**kwargs,
):
checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
kwargs["model"].save_pretrained(checkpoint_folder)
pytorch_model_path = os.path.join(checkpoint_folder, "pytorch_model.bin")
torch.save({}, pytorch_model_path)
return control
class LoadBestPeftModelCallback(TrainerCallback):
def on_train_end(
self,
args: TrainingArguments,
state: TrainerState,
control: TrainerControl,
**kwargs,
):
print(f"Loading best peft model from {state.best_model_checkpoint} (score: {state.best_metric}).")
best_model_path = os.path.join(state.best_model_checkpoint, "adapter_model.bin")
adapters_weights = torch.load(best_model_path)
model = kwargs["model"]
set_peft_model_state_dict(model, adapters_weights)
return control
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="bigcode/large-model")
parser.add_argument("--dataset_name", type=str, default="HuggingFaceH4/CodeAlpaca_20K")
parser.add_argument("--subset", type=str)
parser.add_argument("--split", type=str)
parser.add_argument("--size_valid_set", type=int, default=10000)
parser.add_argument("--streaming", action="store_true")
parser.add_argument("--shuffle_buffer", type=int, default=5000)
parser.add_argument("--input_column_name", type=str, default="prompt")
parser.add_argument("--output_column_name", type=str, default="completion")
parser.add_argument("--seq_length", type=int, default=2048)
parser.add_argument("--max_steps", type=int, default=10000)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--eos_token_id", type=int, default=49152)
parser.add_argument("--lora_r", type=int, default=16)
parser.add_argument("--lora_alpha", type=int, default=32)
parser.add_argument("--lora_dropout", type=float, default=0.05)
parser.add_argument("--learning_rate", type=float, default=5e-6)
parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
parser.add_argument("--num_warmup_steps", type=int, default=100)
parser.add_argument("--weight_decay", type=float, default=0.05)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--no_fp16", action="store_false")
parser.add_argument("--bf16", action="store_true", default=True)
parser.add_argument("--no_gradient_checkpointing", action="store_false", default=False)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=None)
parser.add_argument("--output_dir", type=str, default="./checkpoints")
parser.add_argument("--log_freq", default=100, type=int)
parser.add_argument("--eval_freq", default=100, type=int)
parser.add_argument("--save_freq", default=1000, type=int)
return parser.parse_args()
def chars_token_ratio(dataset, tokenizer, input_column_name="prompt", output_column_name="completion", nb_examples=400):
"""
Estimate the average number of characters per token in the dataset.
"""
total_characters, total_tokens = 0, 0
for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
text = prepare_sample_text(example, input_column_name, output_column_name)
total_characters += len(text)
if tokenizer.is_fast:
total_tokens += len(tokenizer(text).tokens())
else:
total_tokens += len(tokenizer.tokenize(text))
return total_characters / total_tokens
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}"
)
def prepare_sample_text(example, input_column_name="prompt", output_column_name="completion"):
"""Prepare the text from a sample of the dataset."""
text = f"Question: {example[input_column_name]}\n\nAnswer: {example[output_column_name]}"
return text
class ConstantLengthDataset(IterableDataset):
"""
Iterable dataset that returns constant length chunks of tokens from stream of text files.
Args:
tokenizer (Tokenizer): The processor used for proccessing the data.
dataset (dataset.Dataset): Dataset with text files.
infinite (bool): If True the iterator is reset after dataset reaches end else stops.
seq_length (int): Length of token sequences to return.
num_of_sequences (int): Number of token sequences to keep in buffer.
chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
"""
def __init__(
self,
tokenizer,
dataset,
infinite=False,
seq_length=1024,
num_of_sequences=1024,
chars_per_token=3.6,
input_column_name="prompt",
output_column_name="completion"
):
self.tokenizer = tokenizer
self.concat_token_id = tokenizer.eos_token_id if tokenizer.eos_token_id is not None else args.eos_token_id
self.dataset = dataset
self.seq_length = seq_length
self.infinite = infinite
self.current_size = 0
self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
self.input_column_name = input_column_name
self.output_column_name = output_column_name
def __iter__(self):
iterator = iter(self.dataset)
more_examples = True
while more_examples:
buffer, buffer_len = [], 0
while True:
if buffer_len >= self.max_buffer_size:
break
try:
buffer.append(prepare_sample_text(next(iterator), self.input_column_name, self.output_column_name))
buffer_len += len(buffer[-1])
except StopIteration:
if self.infinite:
iterator = iter(self.dataset)
else:
more_examples = False
break
tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
all_token_ids = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id])
for i in range(0, len(all_token_ids), self.seq_length):
input_ids = all_token_ids[i : i + self.seq_length]
if len(input_ids) == self.seq_length:
self.current_size += 1
yield {
"input_ids": torch.LongTensor(input_ids),
"labels": torch.LongTensor(input_ids),
}
def create_datasets(tokenizer, args):
dataset = load_dataset(
args.dataset_name,
data_dir=args.subset,
split=args.split,
use_auth_token=True,
num_proc=args.num_workers if not args.streaming else None,
streaming=args.streaming,
)
if args.streaming:
print("Loading the dataset in streaming mode")
valid_data = dataset.take(args.size_valid_set)
train_data = dataset.skip(args.size_valid_set)
train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
else:
train_data = dataset["train"]
valid_data = dataset["test"]
print(f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}")
chars_per_token = chars_token_ratio(train_data, tokenizer, args.input_column_name, args.output_column_name)
print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
infinite=True,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
input_column_name=args.input_column_name,
output_column_name=args.output_column_name
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
infinite=False,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
input_column_name=args.input_column_name,
output_column_name=args.output_column_name
)
return train_dataset, valid_dataset
def run_training(args, train_data, val_data):
print("Loading the model")
# disable caching mechanism when using gradient checkpointing
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
use_auth_token=True,
use_cache=not args.no_gradient_checkpointing,
load_in_8bit=True,
device_map={"": Accelerator().process_index},
)
model = prepare_model_for_int8_training(model)
lora_config = LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
bias="none",
task_type="CAUSAL_LM",
target_modules = ["c_proj", "c_attn", "q_attn"]
)
model = get_peft_model(model, lora_config)
print_trainable_parameters(model)
train_data.start_iteration = 0
print("Starting main loop")
training_args = TrainingArguments(
output_dir=args.output_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
save_strategy="steps",
load_best_model_at_end=True,
max_steps=args.max_steps,
eval_steps=args.eval_freq,
save_steps=args.save_freq,
logging_steps=args.log_freq,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.learning_rate,
lr_scheduler_type=args.lr_scheduler_type,
warmup_steps=args.num_warmup_steps,
gradient_accumulation_steps=args.gradient_accumulation_steps,
gradient_checkpointing=not args.no_gradient_checkpointing,
fp16=not args.no_fp16,
bf16=args.bf16,
weight_decay=args.weight_decay,
run_name="StarCoder-finetuned",
report_to="wandb",
ddp_find_unused_parameters=False,
)
trainer = Trainer(model=model, args=training_args, train_dataset=train_data, eval_dataset=val_data, callbacks=[SavePeftModelCallback, LoadBestPeftModelCallback])
print("Training...")
trainer.train()
print("Saving last checkpoint of the model")
model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
def main(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_path, use_auth_token=True)
train_dataset, eval_dataset = create_datasets(tokenizer, args)
run_training(args, train_dataset, eval_dataset)
if __name__ == "__main__":
args = get_args()
set_seed(args.seed)
os.makedirs(args.output_dir, exist_ok=True)
logging.set_verbosity_error()
main(args)
================================================
FILE: finetune/merge_peft_adapters.py
================================================
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
import os
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--base_model_name_or_path", type=str, default="bigcode/large-model")
parser.add_argument("--peft_model_path", type=str, default="/")
parser.add_argument("--push_to_hub", action="store_true", default=True)
return parser.parse_args()
def main():
args = get_args()
base_model = AutoModelForCausalLM.from_pretrained(
args.base_model_name_or_path,
return_dict=True,
torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(base_model, args.peft_model_path)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(args.base_model_name_or_path)
if args.push_to_hub:
print(f"Saving to hub ...")
model.push_to_hub(f"{args.base_model_name_or_path}-merged", use_temp_dir=False, private=True)
tokenizer.push_to_hub(f"{args.base_model_name_or_path}-merged", use_temp_dir=False, private=True)
else:
model.save_pretrained(f"{args.base_model_name_or_path}-merged")
tokenizer.save_pretrained(f"{args.base_model_name_or_path}-merged")
print(f"Model saved to {args.base_model_name_or_path}-merged")
if __name__ == "__main__" :
main()
================================================
FILE: requirements.txt
================================================
tqdm==4.65.0
transformers==4.28.1
datasets==2.11.0
huggingface-hub==0.13.4
accelerate==0.18.0
gitextract_483pvkp7/ ├── .gitignore ├── LICENSE ├── README.md ├── chat/ │ ├── README.md │ ├── config.py │ ├── config.yaml │ ├── deepspeed_z3_config_bf16.json │ ├── dialogues.py │ ├── generate.py │ ├── requirements.txt │ ├── train.py │ └── utils.py ├── finetune/ │ ├── finetune.py │ └── merge_peft_adapters.py └── requirements.txt
SYMBOL INDEX (37 symbols across 7 files)
FILE: chat/config.py
class ModelArguments (line 26) | class ModelArguments:
class DataArguments (line 60) | class DataArguments:
class TrainingArguments (line 112) | class TrainingArguments(transformers.TrainingArguments):
FILE: chat/dialogues.py
class DialogueTemplate (line 32) | class DialogueTemplate(ModelHubMixin):
method get_training_prompt (line 45) | def get_training_prompt(self) -> str:
method get_inference_prompt (line 56) | def get_inference_prompt(self) -> str:
method get_dialogue (line 68) | def get_dialogue(self):
method get_special_tokens (line 80) | def get_special_tokens(self) -> List[str]:
method copy (line 83) | def copy(self):
method to_dict (line 93) | def to_dict(self) -> Dict[str, Any]:
method from_dict (line 97) | def from_dict(cls, data):
method _save_pretrained (line 107) | def _save_pretrained(self, save_directory: Union[str, Path]) -> None:
method _from_pretrained (line 114) | def _from_pretrained(
function get_dialogue_template (line 199) | def get_dialogue_template(template: str) -> DialogueTemplate:
function prepare_dialogue (line 205) | def prepare_dialogue(example, dialogue_template, is_train=True):
function mask_user_labels (line 232) | def mask_user_labels(tokenizer, dialogue_template, labels):
FILE: chat/generate.py
function main (line 25) | def main():
FILE: chat/train.py
function main (line 44) | def main():
FILE: chat/utils.py
class StarChatArgumentParser (line 25) | class StarChatArgumentParser(HfArgumentParser):
method parse_yaml_and_args (line 26) | def parse_yaml_and_args(self, yaml_arg: str, other_args: Optional[List...
function hf_login (line 61) | def hf_login():
FILE: finetune/finetune.py
class SavePeftModelCallback (line 18) | class SavePeftModelCallback(TrainerCallback):
method on_save (line 19) | def on_save(
class LoadBestPeftModelCallback (line 35) | class LoadBestPeftModelCallback(TrainerCallback):
method on_train_end (line 36) | def on_train_end(
function get_args (line 51) | def get_args():
function chars_token_ratio (line 93) | def chars_token_ratio(dataset, tokenizer, input_column_name="prompt", ou...
function print_trainable_parameters (line 109) | def print_trainable_parameters(model):
function prepare_sample_text (line 124) | def prepare_sample_text(example, input_column_name="prompt", output_colu...
class ConstantLengthDataset (line 130) | class ConstantLengthDataset(IterableDataset):
method __init__ (line 142) | def __init__(
method __iter__ (line 163) | def __iter__(self):
function create_datasets (line 194) | def create_datasets(tokenizer, args):
function run_training (line 237) | def run_training(args, train_data, val_data):
function main (line 300) | def main(args):
FILE: finetune/merge_peft_adapters.py
function get_args (line 8) | def get_args():
function main (line 16) | def main():
Condensed preview — 15 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (86K chars).
[
{
"path": ".gitignore",
"chars": 3091,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 9956,
"preview": "# 💫 StarCoder\n\n[Paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) | [Model](https://hugging"
},
{
"path": "chat/README.md",
"chars": 3922,
"preview": "# Fine-tuning StarCoder for chat-based applications\n\nThis is a fully-working example to fine-tune `StarCoder` on a corpu"
},
{
"path": "chat/config.py",
"chars": 4389,
"preview": "# coding=utf-8\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Versio"
},
{
"path": "chat/config.yaml",
"chars": 1151,
"preview": "# Model arguments\nmodel_name_or_path: bigcode/starcoderbase\n\n# Data training arguments\nblock_size: 1024\ndataset_name: Hu"
},
{
"path": "chat/deepspeed_z3_config_bf16.json",
"chars": 959,
"preview": "{\n \"bf16\": {\n \"enabled\": \"auto\"\n },\n \"optimizer\": {\n \"type\": \"AdamW\",\n \"params\": {\n \"lr\": \"auto\",\n "
},
{
"path": "chat/dialogues.py",
"chars": 10589,
"preview": "# coding=utf-8\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Versio"
},
{
"path": "chat/generate.py",
"chars": 5153,
"preview": "# coding=utf-8\n# Copyright 2023 The BigCode and HuggingFace teams. All rights reserved.\n#\n# Licensed under the Apache Li"
},
{
"path": "chat/requirements.txt",
"chars": 104,
"preview": "transformers>=4.28.1\ntokenizers>=0.13.3\ndeepspeed==0.9.1\ndatasets>=2.12.0\naccelerate>=0.18.0\ntensorboard"
},
{
"path": "chat/train.py",
"chars": 15161,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2023 The BigCode & HuggingFace Inc. teams. All rights reserved.\n#\n# Lic"
},
{
"path": "chat/utils.py",
"chars": 2694,
"preview": "# coding=utf-8\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Versio"
},
{
"path": "finetune/finetune.py",
"chars": 12100,
"preview": "import argparse\nimport os\n\nimport torch\nfrom accelerate import Accelerator\nfrom datasets import load_dataset\nfrom peft i"
},
{
"path": "finetune/merge_peft_adapters.py",
"chars": 1376,
"preview": "from transformers import AutoModelForCausalLM, AutoTokenizer\nfrom peft import PeftModel\nimport torch\n\nimport os\nimport a"
},
{
"path": "requirements.txt",
"chars": 94,
"preview": "tqdm==4.65.0\ntransformers==4.28.1\ndatasets==2.11.0\nhuggingface-hub==0.13.4\naccelerate==0.18.0\n"
}
]
About this extraction
This page contains the full source code of the bigcode-project/starcoder GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 15 files (80.2 KB), approximately 18.7k tokens, and a symbol index with 37 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.