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Repository: wenhui0206/NeuroGPT
Branch: main
Commit: 230571d45ca4
Files: 23
Total size: 194.8 KB
Directory structure:
gitextract_e8qms5uv/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
├── scripts/
│ ├── finetune.sh
│ └── train.sh
└── src/
├── batcher/
│ ├── base.py
│ ├── downstream_dataset.py
│ └── make.py
├── decoder/
│ ├── gpt.py
│ ├── make_decoder.py
│ └── unembedder.py
├── embedder/
│ ├── base.py
│ ├── csm.py
│ ├── csm_causal.py
│ └── make.py
├── encoder/
│ ├── base.py
│ └── conformer_braindecode.py
├── model.py
├── train_gpt.py
├── trainer/
│ ├── base.py
│ └── make.py
└── utils.py
================================================
FILE CONTENTS
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================================================
FILE: .gitignore
================================================
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# mypy
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# Pyre type checker
.pyre/
data/
result/
================================================
FILE: LICENSE
================================================
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IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# NeuroGPT
### Neuro-GPT: Towards a Foundation Model for EEG [paper](https://arxiv.org/abs/2311.03764)
#### Published on IEEE - ISBI 2024
We propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch.
### Pre-trained foundation model available [here](https://huggingface.co/wenhuic/Neuro-GPT/tree/main)
<!--
<picture>
<source> -->
<!-- </picture> -->
## Installation
```console
git clone git@github.com:wenhui0206/NeuroGPT.git
pip install -r requirements.txt
cd NeuroGPT/scripts
./train.sh
```
## Requirements
pip install -r requirements.txt
## Datasets
- [TUH EEG Corpus](https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg)
- [BCI Competition IV 2a Dataset](https://www.bbci.de/competition/iv/#datasets)
## Acknowledgments
This project is developed based on the following open-source repositories:
- [Self-supervised learning of brain dynamics from broad neuroimaging data](https://github.com/athms/learning-from-brains)
- [EEG-Conformer](https://github.com/eeyhsong/EEG-Conformer)
================================================
FILE: requirements.txt
================================================
einops==0.7.0
h5py==3.10.0
numpy==1.26.4
pandas==2.2.1
scikit_learn==1.4.0
scipy==1.12.0
torch==2.2.0
torchinfo==1.8.0
tqdm==4.66.2
transformers==4.38.1
================================================
FILE: scripts/finetune.sh
================================================
python3 ../src/train_gpt.py --training-style='decoding' --num-decoding-classes=4 --training-steps=10000 --eval_every_n_steps=500 --log-every-n-steps=1000 --num_chunks=2 --per-device-training-batch-size=32 --per-device-validation-batch-size=32 --chunk_len=500 --chunk_ovlp=0 --run-name='dst' --ft-only-encoder='True' --fold_i=0 --num-encoder-layers=6 --num-hidden-layers=6 --learning-rate=1e-4 --use-encoder='True' --embedding-dim=1024 --pretrained-model='../pretrained_model/pytorch_model.bin' --dst-data-path="../../bci2a_egg_npz/"
================================================
FILE: scripts/train.sh
================================================
python3 ../src/train_gpt.py --training-steps=50000 --eval_every_n_steps=1000 --log-every-n-steps=3000 --per-device-training-batch-size=32 --per-device-validation-batch-size=32 --num-workers=16 --num_chunks=32 --chunk_len=500 --chunk_ovlp=50 --num-hidden-layers=6 --num-encoder-layers=6 --run-name='32clen2_embed1024' --training-style='CSM_causal' --embedding-dim=1024 --train-data-path='../../tuh_tensors'
================================================
FILE: src/batcher/base.py
================================================
#!/usr/bin/env python3
from typing import Dict
import numpy as np
# import webdataset as wds
import torch
# import gzip
# import pickle
import h5py
import os
# import webdataset as wds
from torch.utils.data import Dataset
def _pad_seq_right_to_n(
seq: np.ndarray,
n: int,
pad_value: float = 0.
) -> np.ndarray:
if n == seq.shape[0]:
return seq
return np.concatenate(
[
seq,
np.ones(
(
n-seq.shape[0],
*seq.shape[1:]
)
) * pad_value,
],
axis=0,
)
class EEGDataset(Dataset):
def __init__(self, filenames, sample_keys, chunk_len=500, num_chunks=10, ovlp=50, root_path="", population_mean=0, population_std=1, gpt_only=False, normalization=True, start_samp_pnt=-1):
if root_path == "":
self.filenames = filenames
else:
self.filenames = [root_path + fn for fn in filenames if os.path.isfile(root_path+fn)]
self.root_path = root_path
print("Number of subjects loaded: ", len(self.filenames))
# self.data = data_all
self.chunk_len = chunk_len
self.num_chunks = num_chunks
self.ovlp = ovlp
self.sample_keys = sample_keys
self.mean = population_mean
self.std = population_std
self.do_normalization = normalization
self.gpt_only=gpt_only
self.start_samp_pnt = start_samp_pnt
def __len__(self):
return len(self.filenames)
def __getitem__(self, idx):
data = self.load_tensor(self.filenames[idx])
#===reorder channels====
data = self.reorder_channels(data)
return self.preprocess_sample(data, seq_len=self.num_chunks)
@staticmethod
def _pad_seq_right_to_n(
seq: np.ndarray,
n: int,
pad_value: float = 0
) -> np.ndarray:
return _pad_seq_right_to_n(
seq=seq,
n=n,
pad_value=pad_value
)
def load_single_file(self, filename):
with h5py.File(filename, 'r') as file:
data_dict = file['Result']
data = []
for i in range(data_dict['data'].shape[0]):
ref = data_dict['data'][i][0]
time_series = data_dict[ref]
if len(data) > 0 and time_series.shape[0] < data[0].shape[0]:
time_series = np.zeros_like(data[0])
data.append(np.array(time_series).squeeze())
return data
def load_tensor(self, filename):
# tensor_fn = filename[:-3] + 'pt'
tensor_data = torch.load(filename)
return tensor_data.numpy()
def reorder_channels(self, data):
chann_labels = {'FP1': 0, 'FP2': 1, 'F3': 2, 'F4': 3, 'C3': 4, 'C4': 5, 'P3': 6, 'P4': 7, 'O1': 8, 'O2': 9, 'F7': 10, 'F8': 11, 'T3': 12, 'T4': 13, 'T5': 14, 'T6': 15, 'FZ': 16, 'CZ': 17, 'PZ': 18, 'OZ': 19, 'T1': 20, 'T2': 21}
reorder_labels = {'FP1': 0, 'FP2': 1, 'F7': 2, 'F3': 3, 'FZ': 4, 'F4': 5, 'F8': 6, 'T1': 7, 'T3': 8, 'C3': 9, 'CZ': 10, 'C4': 11, 'T4': 12, 'T2': 13, 'T5': 14, 'P3': 15, 'PZ': 16, 'P4': 17, 'T6': 18, 'O1': 19, 'OZ': 20, 'O2': 21}
reordered = np.zeros_like(data)
for label, target_idx in reorder_labels.items():
mapped_idx = chann_labels[label]
reordered[target_idx, :] = data[mapped_idx, :]
return reordered
def split_chunks(self, data, length=500, ovlp=50, num_chunks=10, start_point=-1):
'''2 seconds, 0.2 seconds overlap'''
all_chunks = []
total_len = data.shape[1]
actual_num_chunks = num_chunks
if start_point == -1:
if num_chunks * length > total_len - 1:
start_point = 0
actual_num_chunks = total_len // length
else:
start_point = np.random.randint(0, total_len - num_chunks * length)
for i in range(actual_num_chunks):
chunk = data[:, start_point: start_point + length]
all_chunks.append(np.array(chunk))
start_point = start_point + length - ovlp
return np.array(all_chunks), start_point
def normalize(self, data):
mean = np.mean(data, axis=-1, keepdims=True)
std = np.std(data, axis=-1, keepdims=True)
# Ensure std is not zero to avoid division by zero.
# If std is zero, normalization doesn't make sense,
# so you might set std to a small positive value or handle it in another way.
# std = np.where(std == 0, 1e-23, std)
return (data - mean) / (std + 1e-25)
def preprocess_sample(
self,
sample,
seq_len,
labels=None
) -> Dict[str, torch.Tensor]:
out = {}
if self.do_normalization:
sample = self.normalize(sample)
chunks, seq_on = self.split_chunks(sample, self.chunk_len, self.ovlp, seq_len, self.start_samp_pnt)
attention_mask = np.ones(seq_len)
chunks = self._pad_seq_right_to_n(
seq=chunks,
n=seq_len,
pad_value=0
)
attention_mask = self._pad_seq_right_to_n(
seq=attention_mask,
n=seq_len,
pad_value=0
)
if self.gpt_only == True:
chunks = np.reshape(chunks, (seq_len, chunks.shape[1]*chunks.shape[2]))
out["inputs"] = torch.from_numpy(chunks).to(torch.float)
out["attention_mask"] = torch.from_numpy(attention_mask).to(torch.long)
out['seq_on'] = seq_on
out['seq_len'] = seq_len
if self.sample_keys is not None:
out = {
key: out[key]
for key in self.sample_keys
if key in out
}
if labels is not None:
out['labels'] = torch.from_numpy(np.array(labels)).to(torch.long)
return out
================================================
FILE: src/batcher/downstream_dataset.py
================================================
import os
import pdb
import numpy as np
from batcher.base import EEGDataset
from scipy.io import loadmat
from scipy.signal import butter, filtfilt
class MotorImageryDataset(EEGDataset):
def __init__(self, filenames, sample_keys, chunk_len=500, num_chunks=10, ovlp=50, root_path="", gpt_only=True):
super().__init__(filenames, sample_keys, chunk_len, num_chunks, ovlp, root_path=root_path, gpt_only=gpt_only)
self.data_all = []
for fn in self.filenames:
self.data_all.append(np.load(fn))
self.mi_types = {769: 'left', 770: 'right',
771: 'foot', 772: 'tongue', 1023: 'rejected'} # , 783: 'unknown', 1023: 'rejected'
# Types of motor imagery
self.labels_string2int = {'left': 0, 'right': 1,
'foot': 2, 'tongue':3 } #, 'unknown': -1
self.Fs = 250 # 250Hz from original paper
self.P = np.load("../inputs/tMatrix_value.npy")
self.trials, self.labels, self.num_trials_per_sub = self.get_trials_all()
# keys of data ['s', 'etyp', 'epos', 'edur', 'artifacts']
def __len__(self):
return sum(self.num_trials_per_sub)
def __getitem__(self, idx):
return self.preprocess_sample(self.trials[idx], self.num_chunks, self.labels[idx])
def map2pret(self, data):
return np.matmul(self.P, data) # 22x22, 22xTime
def get_trials_from_single_subj(self, sub_id):
raw = self.data_all[sub_id]['s'].T
events_type = self.data_all[sub_id]['etyp'].T
events_position = self.data_all[sub_id]['epos'].T
events_duration = self.data_all[sub_id]['edur'].T
artifacts = self.data_all[sub_id]['artifacts'].T
# Channel default is C3
startrial_code = 768
starttrial_events = events_type == startrial_code
idxs = [i for i, x in enumerate(starttrial_events[0]) if x]
trial_labels = self.get_labels(sub_id)
trials = []
classes = []
for j, index in enumerate(idxs):
try:
# print(index)
# type_e = events_type[0, index+1]
# class_e = self.mi_types[type_e]
# if type_e == 1023:
# continue
# classes.append(self.labels_string2int[class_e])
classes.append(trial_labels[j])
start = events_position[0, index]
stop = start + events_duration[0, index]
trial = raw[:22, start+500 : stop-375]
#add band-pass filter
# self.bandpass_filter(trial, lowcut=4, highcut=40, fs=250, order=5)
trials.append(trial)
except:
# print("Cannot load trial")
continue
return trials, classes
def get_labels(self, sub_id):
label_path = self.root_path + "true_labels/"
base_name = os.path.basename(self.filenames[sub_id])
sub_name = os.path.splitext(base_name)[0]
labels = loadmat(label_path + sub_name +".mat")["classlabel"]
return labels.squeeze() - 1
def get_trials_all(self):
trials_all = []
labels_all = []
total_num = []
for sub_id in range(len(self.data_all)):
trials, labels = self.get_trials_from_single_subj(sub_id)
total_num.append(len(trials))
trials_all.append(np.array(trials))
labels_all.append(np.array(labels))
# reordered_data = self.reorder_channels(np.vstack(trials_all))
trials_all_arr = np.vstack(trials_all)
# map to same channel configuration as pretraining
trials_all_arr = self.map2pret(trials_all_arr)
return self.normalize(trials_all_arr), np.array(labels_all).flatten(), total_num
# def normalize(self, data):
# return (data - np.mean(data)) / np.std(data)
def bandpass_filter(self, data, lowcut, highcut, fs, order=5):
"""
Apply a bandpass filter to the data.
Parameters:
- data: The EEG signal
- lowcut: Low cut-off frequency
- highcut: High cut-off frequency
- fs: Sampling rate (frequency)
- order: Order of the filter
Returns:
- Filtered data
"""
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
filtered_data = filtfilt(b, a, data)
return filtered_data
================================================
FILE: src/batcher/make.py
================================================
#!/usr/bin/env python3
from batcher.base import BaseBatcher
def make_batcher(
training_style: str='CSM',
tr: float=2.0,
chunk_len:int=500,
num_chunks: int=10,
seq_min: int=10,
seq_max: int=50,
bert_seq_gap_min: int=1,
bert_seq_gap_max: int=5,
decoding_target: str=None,
sample_random_seq: bool=True,
seed: int=None,
bold_dummy_mode: bool=False,
) -> BaseBatcher:
"""
Make a batcher object.
The batcher is used to generate batches of
input data for training and evaluation.
Args:
-----
training_style: str
The used training style (ie., framework).
One of: 'BERT', 'CSM', 'NetBERT', 'autoencoder',
'decoding'.
seq_min: int
The minimum sequence length (in sequence elements)
used for the random sampling of input sequences.
seq_max: int
The maximum sequence length (in sequence elements)
used for the random sampling of input sequences.
bert_seq_gap_min: int
The minimum gap (in sequence elements) between
two consecutive sequences for BERT-style training,
if they are sampled from the same data run file.
bert_seq_gap_max: int
The maximum gap (in sequence elements) between
two consecutive sequences for BERT-style training,
if they are sampled from the same data run file.
decoding_target: str
Key of decoding target variable in data
run files.
sample_random_seq: bool
If True, the sequences are sampled randomly from
the data run files, given the spefied
sequence length (seq_min and seq_max) and the
specified gap consecutive sequences (bert_seq_gap_min,
bert_seq_gap_max) for BERT-style training.
seed: int
The seed for the random number generator.
bold_dummy_mode: bool
If True, the BOLD data are replaced with simple
dummy data (for internal testing purposed only).
Core methods:
-----
dataset(tarfiles: list)
Returns a Pytorch dataset that can be used for training,
given the specified list of data run file paths (tarfiles).
"""
kwargs = {
"tr": tr,
"chunk_len": chunk_len,
"num_chunks": num_chunks,
"seq_min": seq_min,
"seq_max": seq_max,
"gap_min": bert_seq_gap_min,
"gap_max": bert_seq_gap_max,
"decoding_target": decoding_target,
"sample_random_seq": sample_random_seq,
"seed": seed,
"bold_dummy_mode": bold_dummy_mode
}
sample_keys = [
'inputs',
'attention_mask',
't_rs'
]
if training_style in {'CSM', 'MSM', 'MNM', 'autoencoder'}:
from batcher.base import BaseBatcher
return BaseBatcher(**{**kwargs, **{'sample_keys': sample_keys}})
elif training_style == 'decoding':
sample_keys.append('labels')
from batcher.base import BaseBatcher
return BaseBatcher(**{**kwargs, **{'sample_keys': sample_keys}})
else:
raise ValueError('unknown training style.')
================================================
FILE: src/decoder/gpt.py
================================================
#!/usr/bin/env python3
from typing import Dict
import warnings
import torch
from transformers import GPT2Config, GPT2Model
import torch.nn as nn
class GPTModel(torch.nn.Module):
def __init__(
self,
num_hidden_layers: int = 6,
num_attention_heads: int = 12,
embed_dim: int = 768,
intermediate_dim_factor: int = 4,
n_positions: int = 512,
hidden_activation: str = 'gelu',
dropout: float = 0.1,
**kwargs
) -> None:
super().__init__()
self.name = 'GPT'
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.embed_dim = embed_dim
self.intermediate_dim_factor = intermediate_dim_factor
self.n_positions = n_positions
self.hidden_activation = hidden_activation
self.dropout_resid = dropout
self.dropout_attn = dropout
self.dropout_embd = dropout
self.mse_loss = torch.nn.MSELoss()
self.bxe_loss = torch.nn.BCEWithLogitsLoss()
self.config = GPT2Config(
vocab_size=1,
n_positions=self.n_positions,
n_embd=self.embed_dim,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
n_inner=self.embed_dim * self.intermediate_dim_factor,
resid_pdrop=self.dropout_resid,
attn_pdrop=self.dropout_attn,
embd_pdrop=self.dropout_embd,
activation_function=self.hidden_activation
)
self.transformer = GPT2Model(config=self.config)
self.is_decoding_mode = False
self.decoding_head = None
self.num_decoding_classes = None
self.pooler_layer = None
self.add_pooler_layer()
def switch_decoding_mode(
self,
is_decoding_mode: bool=False,
num_decoding_classes: int=None
) -> None:
self.is_decoding_mode = is_decoding_mode
if self.is_decoding_mode:
if self.pooler_layer is None:
self.add_pooler_layer()
self.add_decoding_head(num_decoding_classes=num_decoding_classes)
else:
self.decoding_head = None
def add_pooler_layer(self):
if self.pooler_layer is not None:
warnings.warn(
'Warning: overwriting existing pooler layer'
)
self.pooler_layer = torch.nn.Sequential(
torch.nn.Linear(
in_features=self.embed_dim,
out_features=self.embed_dim
),
torch.nn.Tanh(),
torch.nn.Dropout(self.dropout_resid)
)
def add_decoding_head(
self,
num_decoding_classes: int
) -> None:
if self.decoding_head is not None:
if self.num_decoding_classes == num_decoding_classes:
warnings.warn(
'Warning: not overwriting decoding head, as '
f'{num_decoding_classes}-class decoding head exists.'
)
return None
else:
warnings.warn(
f'Warning: overwriting existing {num_decoding_classes}-class decoding head.'
)
self.num_decoding_classes = num_decoding_classes
# self.decoding_head = torch.nn.Sequential(
# torch.nn.Linear(
# in_features=self.embed_dim,
# out_features=self.num_decoding_classes
# )
# )
self.decoding_head = nn.Sequential(
nn.Linear(self.embed_dim, 256),
nn.ELU(),
nn.Dropout(0.5),
nn.Linear(256, 32),
nn.ELU(),
nn.Dropout(0.3),
nn.Linear(32, self.num_decoding_classes)
)
return None
def decode(
self,
outputs: torch.tensor,
attention_mask: torch.tensor,
) -> Dict[str, torch.tensor]:
assert self.is_decoding_mode, 'GPTModel must be in decoding_mode.'
assert self.pooler_layer is not None, 'pooler_layer head must be added.'
assert self.decoding_head is not None, 'decoding head must be added.'
batch_size = outputs.size()[0]
sequence_lengths = attention_mask.sum(dim=1)-1
decoding_outputs = {
'pooler_outputs': self.pooler_layer(
outputs[torch.arange(batch_size, device=outputs.device), sequence_lengths]
)
}
decoding_outputs['decoding_logits'] = self.decoding_head(decoding_outputs['pooler_outputs'])
return decoding_outputs
def forward(
self,
batch: Dict[str, torch.tensor]
) -> Dict[str, torch.tensor]:
transformer_outputs = self.transformer.forward(
inputs_embeds=batch['inputs_embeds'],
attention_mask=batch['attention_mask'],
token_type_ids=batch.get('token_type_ids', None),
return_dict=True
)
outputs = {'outputs': transformer_outputs['last_hidden_state']}
if not self.is_decoding_mode:
return outputs
outputs.update(
self.decode(
outputs=outputs['outputs'],
attention_mask=batch['attention_mask']
)
)
return outputs
class PretrainedGPT2(GPTModel):
def __init__(
self,
**kwargs
):
super().__init__(**kwargs)
self.name = 'PretrainedGPT2'
self.config = GPT2Config()
self.n_positions = self.config.n_positions
self.embed_dim = self.config.n_embd
self.num_hidden_layers = self.config.n_layer
self.num_attention_heads = self.config.n_head
self.intermediate_dim_factor = 4
self.dropout_resid = self.config.resid_pdrop
self.dropout_attn = self.config.attn_pdrop
self.dropout_embd = self.config.embd_pdrop
self.hidden_activation = self.config.activation_function
self.transformer = GPT2Model.from_pretrained("gpt2")
================================================
FILE: src/decoder/make_decoder.py
================================================
#!/usr/bin/env python3
import torch
def make_decoder(
architecture: str='GPT',
num_hidden_layers: int = 4,
embed_dim: int = 768,
output_dim: int = 1024,
num_attention_heads: int = 12,
intermediate_dim_factor: int=4,
n_positions: int = 512,
hidden_activation: str='gelu_new',
dropout: float = 0.1
) -> torch.nn.Module:
"""
Make a decoder object.
The decoder contains the core
model architecture used for learning.
Args:
-----
architecture: str
The model architecture to use.
One of: 'GPT', 'BERT', 'NetBERT', autoencoder',
'PretrainedGPT', 'PretrainedBERT', 'LinearBaseline'.
num_hidden_layers: int
The number of hidden layers of the model.
Does not apply to 'PretrainedGPT', 'PretrainedBERT',
'LinearBaseline'.
For 'autoencoder', num_hidden_layers represents
the number of hidden layers of the encoder and decoder
model.
embed_dim: int
The dimension of the used embedding space (see src.embedder).
output_dim: int
The dimension of the output projection (needs to match
in_dim of src.embedder for upstream learning).
num_attention_heads: int
The number of attention heads of transformer models. Does
not apply to any other model architecture as well as the
'PretrainedGPT' and 'PretrainedBERT' architectures.
intermediate_dim_factor: int
Scales feed-forward transformer layer dimension relative to '
embed_dim: intermediate_dim_factor * embed_dim
n_positions: int
The maximum number of sequence elements that
the model can handle (in sequence elements).
hidden_activation: str
Type of hidden activation of transformer layers
One of 'gelu', 'gelu_new', 'relu', 'silu'.
Does not apply to non-transformer models.
dropout: float
Dropout ratio for attendion heads and residual layers
of transofmer models and between LSTM layers of
encoder / decoder parts of autoencoder models.
Core methods:
-----
forward(batch: Dict):
Forward pass of the model, generates Dict containing
predicted output seqeuences, given input batch
(as generated by src.embedder.prep_batch).
decode(outputs: Dict):
Make decoding prediction, given outputs generated by
caling forward().
switch_decoding_mode(is_decoding_mode: bool):
Switch model to decoding mode (is_decoding_mode=True).
Relevant for adaptation of pre-trained models
to downstream decoding tasks.
"""
kwargs = {
"num_hidden_layers": num_hidden_layers,
"embed_dim": embed_dim,
"output_dim": output_dim,
"num_attention_heads": num_attention_heads,
"intermediate_dim_factor": intermediate_dim_factor,
"n_positions": n_positions,
"hidden_activation": hidden_activation,
"dropout": dropout
}
if architecture == 'GPT':
from decoder.gpt import GPTModel
return GPTModel(**kwargs)
elif architecture == 'PretrainedGPT2':
from decoder.gpt import PretrainedGPT2
return PretrainedGPT2(**kwargs)
else:
raise ValueError(f'{architecture}-architecture unkown.')
================================================
FILE: src/decoder/unembedder.py
================================================
#!/usr/bin/env python3
import torch
from einops import rearrange
import torch.nn as nn
from einops.layers.torch import Rearrange
class DeconvNet(nn.Module):
def __init__(self, n_filters_time=40, n_channels=22, filter_time_length=25, stride_avg_pool=15, pool_time_length=75):
super(DeconvNet, self).__init__()
# To reverse AvgPool2d
self.depool = nn.Sequential(Rearrange("b seq d_model -> b d_model 1 seq"),
nn.Upsample(size=(1, 476), mode='nearest'))
#nn.ConvTranspose2d(n_filters_time, n_filters_time, kernel_size=(1, pool_time_length), stride=(1, stride_avg_pool))
self.deconv1 = nn.ConvTranspose2d(n_filters_time, n_filters_time, (n_channels, 1), (1, 1))
self.deconv2 = nn.ConvTranspose2d(n_filters_time, 1, (1, filter_time_length), (1, 1))
def forward(self, x):
x = self.depool(x)
x = self.deconv1(x)
x = nn.ELU()(x) # We're keeping ELU activation.
x = self.deconv2(x)
return {'outputs': x.squeeze()}
class UnEmbedder(torch.nn.Module):
"""
Unmebedding model; used to project predicted
output sequences of src.decoder back to input
space during upstream learning.
Args
----
embed_dim: int
Dimension of the embedding space.
out_dim: int
Dimension of the output space.
num_hidden_layers: int
Number of hidden layers of projection model.
If >1, all hidden layers except for the last
are activated with Gelu activation.
dropout: float
Dropout ratio for the projection model.
Core methods
----
forward(inputs, **kwargs)
Projection of input to output space.
"""
def __init__(
self,
embed_dim: int = 768,
out_dim: int = 1024,
num_hidden_layers: int = 1,
dropout: int = 0.1,
) -> None:
super().__init__()
self.embed_dim = embed_dim
self.out_dim = out_dim
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
layer_stack = []
for _ in range(self.num_hidden_layers-1):
layer_stack.extend(
[
torch.nn.Linear(
in_features=self.embed_dim,
out_features=self.embed_dim
),
torch.nn.LayerNorm(self.embed_dim),
torch.nn.GELU(),
torch.nn.Dropout(p=self.dropout)
]
)
layer_stack.extend(
[
torch.nn.Linear(
in_features=self.embed_dim,
out_features=self.out_dim
)
]
)
self.model = torch.nn.Sequential(*layer_stack)
def stack_inputs(
self,
tensor
) -> torch.tensor:
return rearrange(
tensor=tensor,
pattern='b s e -> (b s) e'
)
def unstack_inputs(
self,
tensor,
b
) -> torch.tensor:
return rearrange(
tensor=tensor,
pattern='(b s) e -> b s e',
b=b
)
def forward(
self,
inputs,
**kwargs
) -> torch.tensor:
inputs_stacked = self.stack_inputs(tensor=inputs)
return {
'outputs': self.unstack_inputs(
tensor=self.model(inputs_stacked),
b=inputs.size()[0]
)
}
def make_unembedder(
embed_dim: int = 768,
out_dim: int = 1024,
num_hidden_layers: int = 1,
dropout: int = 0.1
) -> torch.nn.Module:
"""
Creates a UnEmbedder object.
Args
----
embed_dim: int
Dimension of the embedding space.
out_dim: int
Dimension of the output space.
num_hidden_layers: int
Number of hidden layers of projection model.
If >1, all hidden layers except for the last
are activated with Gelu activation.
dropout: float
Dropout ratio for the projection model.
Core methods
----
forward(inputs, **kwargs)
Projection of input to output space.
"""
return UnEmbedder(
embed_dim=embed_dim,
out_dim=out_dim,
num_hidden_layers=num_hidden_layers,
dropout=dropout
)
================================================
FILE: src/embedder/base.py
================================================
#/usr/bin/env python3
import pdb
import torch
from typing import Dict
from einops import rearrange
class EmbeddingModel(torch.nn.Module):
def __init__(
self,
in_dim: int = 1024,
embed_dim: int = 768,
num_hidden_layers: int = 1,
dropout: int = 0.1,
) -> None:
super().__init__()
self.in_dim = in_dim
self.embed_dim = embed_dim
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
layer_stack = []
for _ in range(self.num_hidden_layers-1):
layer_stack.extend(
[
torch.nn.Linear(
in_features=self.in_dim,
out_features=self.embed_dim
),
torch.nn.LayerNorm(self.embed_dim),
torch.nn.GELU(),
torch.nn.Dropout(p=self.dropout)
]
)
layer_stack.extend(
[
torch.nn.Linear(
in_features=self.embed_dim if self.num_hidden_layers>1 else self.in_dim,
out_features=self.embed_dim
),
torch.nn.LayerNorm(self.embed_dim),
torch.nn.Dropout(p=self.dropout)
]
)
self.model = torch.nn.Sequential(*layer_stack)
def _stack_inputs(
self,
tensor
) -> torch.tensor:
return rearrange(
tensor=tensor,
pattern='b s e -> (b s) e'
)
def _unstack_inputs(
self,
tensor,
b
) -> torch.tensor:
return rearrange(
tensor=tensor,
pattern='(b s) e -> b s e',
b=b
)
def forward(
self,
inputs,
**kwargs
) -> torch.tensor:
inputs_stacked = self._stack_inputs(tensor=inputs)
return self._unstack_inputs(
tensor=self.model(inputs_stacked),
b=inputs.size()[0]
)
class BaseEmbedder(torch.nn.Module):
def __init__(self,
in_dim: int = 1024,
embed_dim: int = 768,
num_hidden_layers: int = 1,
dropout: float = 0.1,
**kwargs
) -> None:
super().__init__()
self.name = 'BaseEmbedder'
self.training_style = 'base'
self._root_training_style = 'base'
self.in_dim = in_dim
self.embed_dim = embed_dim
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.xe_loss = torch.nn.CrossEntropyLoss(reduction='mean')
self.bxe_loss = torch.nn.BCEWithLogitsLoss(reduction='mean')
self.l1_loss = torch.nn.L1Loss(reduction='mean')
self.l2_loss = torch.nn.MSELoss(reduction='mean') # for L2 loss
# self.huber_loss = torch.nn.HuberLoss(reduction='mean', delta=1.0) # for Huber loss
self.embed_model = EmbeddingModel(
in_dim=self.in_dim,
embed_dim=self.embed_dim,
num_hidden_layers=self.num_hidden_layers,
dropout=self.dropout
)
self.is_decoding_mode = False
def switch_decoding_mode(self, is_decoding_mode: bool=False) -> None:
self.is_decoding_mode = is_decoding_mode
if self.is_decoding_mode:
self.training_style = 'decoding'
else:
self.training_style = self._root_training_style
@staticmethod
def _pad_tensor_left_by_n(
tensor,
n,
pad_value
) -> torch.tensor:
filling = torch.ones(
(
tensor.size()[0],
n,
*tensor.size()[2:]
),
device=tensor.device
) * pad_value
return torch.cat(
[
filling,
tensor
],
dim=1
).to(torch.long)
@staticmethod
def _round_to_precision(
x: torch.tensor,
precision: float,
) -> torch.tensor:
return torch.round(x / precision) * precision
def embed_inputs(
self,
inputs: torch.tensor
) -> torch.tensor:
return self.embed_model(inputs)
def forward(
self,
batch: Dict[str, torch.tensor]
) -> torch.tensor:
inputs_key = 'inputs' if 'inputs_embeds' not in batch else 'inputs_embeds'
if self.in_dim == self.embed_dim:
inputs_embeds = batch[inputs_key]
else:
inputs_embeds = self.embed_inputs(inputs=batch[inputs_key])
return inputs_embeds
def decoding_loss(
self,
decoding_logits,
labels,
**kwargs
) -> Dict[str, torch.tensor]:
# pdb.set_trace()
return {
'decoding_loss': self.xe_loss(
input=decoding_logits,
target=labels.to(dtype=torch.long)
)
}
def reconstruction_loss(
self,
input,
target,
**kwargs
) -> Dict[str, torch.tensor]:
return {
'reconstruction_loss': self.l2_loss(
input=input,
target=target
)
}
def prep_batch(
self,
batch: Dict[str, torch.tensor]
) -> Dict:
batch_out = {}
for key in batch:
if (
torch.is_tensor(batch[key])
and key != 'labels'
):
batch_out[key] = batch[key].to(torch.float)
elif key == 'labels':
batch_out[key] = batch['labels'].to(torch.int)
else:
batch_out[key] = torch.clone(batch[key])
# dummy copy of inputs to be used in forward pass
batch_out['inputs_embeds'] = torch.clone(batch_out['inputs'])
return batch_out
def _root_loss(
self,
inputs,
outputs,
attention_mask,
**kwargs
) -> Dict[str, torch.tensor]:
attention_mask = torch.unsqueeze(attention_mask, -1).repeat(1,1,self.in_dim)
return self.reconstruction_loss(
input=torch.masked_select(outputs, attention_mask.to(torch.bool)),
target=torch.masked_select(inputs, attention_mask.to(torch.bool))
)
def loss(
self,
batch,
outputs
) -> Dict[str, torch.tensor]:
if self.is_decoding_mode:
losses = self.decoding_loss(
**batch,
**outputs
)
else:
losses = self._root_loss(
**batch,
**outputs
)
if 'loss' not in losses:
losses['loss'] = sum(losses.values())
return losses
================================================
FILE: src/embedder/csm.py
================================================
#/usr/bin/env python3
import pdb
from typing import Dict
import torch
from embedder.base import BaseEmbedder
class CSMEmbedder(BaseEmbedder):
def __init__(
self,
**kwargs
) -> None:
super().__init__(**kwargs)
self.name = 'CSMEmbedder'
self.training_style = 'CSM'
assert self.training_style in {'CSM', 'decoding'}, f'{self.training_style} not supported'
self._root_training_style = 'CSM'
##=========
self.in_dim_for_mask = self.in_dim
self.msk_embed = torch.nn.Parameter(
torch.empty(
size=(1, 1, self.in_dim_for_mask)
)
)
self.cls_embed = torch.nn.Parameter(
torch.empty(
size=(1, 1, self.in_dim_for_mask)
)
)
self._embeds = [
self.msk_embed,
self.cls_embed
]
self._init_embeds()
def _init_embeds(self):
for embed in self._embeds:
torch.nn.init.normal_(
tensor=embed,
mean=0.0,
std=1.0,
)
def prep_batch(
self,
batch: Dict[str, torch.tensor],
) -> Dict[str, torch.tensor]:
batch_out = dict(batch)
labels = torch.clone(batch['labels']) if 'labels' in batch else None
if self.training_style != 'decoding':
return self.mask_inputs(batch=batch_out)
batch_out = self.add_cls_embed(batch=batch_out)
if labels is not None:
batch_out['labels'] = labels
return batch_out
def mask_inputs(
self,
batch: Dict[str, torch.tensor],
) -> Dict[str, torch.tensor]:
inputs_key = 'inputs' if 'inputs_embeds' not in batch else 'inputs_embeds'
assert inputs_key in batch, f'{inputs_key} not found in batch'
input_shape = batch[inputs_key].size()
device = batch[inputs_key].device
masking_i = torch.cat(
[
torch.randint(
low=1, # at least one seq value before mask!
high=sum(batch['attention_mask'][i]==1), # high is exclusive, so this accounts for 0-indexing
size=(1,),
device=device
)
for i in range(input_shape[0])
],
dim=0
)
print("masking id", masking_i)
modelling_mask = torch.zeros_like(
batch[inputs_key],
device=device
)
modelling_mask[torch.arange(input_shape[0]), masking_i] = 1
batch['modelling_mask'] = modelling_mask.to(torch.long)
batch['masked_inputs'] = torch.masked_select(
input=batch[inputs_key],
mask=batch['modelling_mask'].to(torch.bool)
).detach().clone()
batch['inputs_embeds'] = torch.where(
batch['modelling_mask']==1,
self.msk_embed.repeat(
input_shape[0],
input_shape[1],
1
),
batch[inputs_key].to(torch.float)
)
batch['attention_mask'] = torch.cat(
[
torch.cat(
(
torch.ones(
(
1,
i+1 # to account for 0-indexing in python
),
device=device
),
torch.zeros(
(
1,
input_shape[1]-i-1 # to account for 0-indexing in python
),
device=device
)
),
dim = 1
)
for i in masking_i
],
dim = 0
).to(torch.long)
# re-mask inputs
attention_mask_expanded = torch.unsqueeze(
batch['attention_mask'],
dim=2
).repeat(
1,
1,
self.in_dim_for_mask
)
batch["inputs_embeds"] = torch.where(
attention_mask_expanded == 1,
batch['inputs_embeds'],
torch.zeros_like(batch['inputs_embeds'])
)
return batch
def add_cls_embed(
self,
batch: Dict[str, torch.tensor]
) -> Dict[str, torch.tensor]:
inputs_key = 'inputs' if 'inputs_embeds' not in batch else 'inputs_embeds'
assert inputs_key in batch, f'{inputs_key} not found in batch'
batch_size = batch[inputs_key].size()[0]
sequence_lengths = batch['attention_mask'].sum(dim=1)
inputs_embeds = []
if 't_rs' in batch:
t_rs = []
for i in range(len(sequence_lengths)):
inputs_embeds.append(
torch.cat(
[
batch[inputs_key][i, :sequence_lengths[i], :],
self.cls_embed[0],
batch[inputs_key][i, sequence_lengths[i]:, :]
],
dim=0
)
)
if 't_rs' in batch:
t_rs.append(
torch.cat(
[
batch['t_rs'][i, :sequence_lengths[i]],
torch.ones(1, device=batch['t_rs'].device) * -1,
batch['t_rs'][i, sequence_lengths[i]:]
],
dim=0
)
)
batch['inputs_embeds'] = torch.stack(
inputs_embeds,
dim=0
)
if 't_rs' in batch:
batch['t_rs'] = torch.stack(
t_rs,
dim=0
)
if 'token_type_ids' in batch:
batch['token_type_ids'] = self._pad_tensor_left_by_n(
tensor=batch['token_type_ids'],
n=1,
pad_value=0
)
if 'modelling_mask' in batch:
batch['modelling_mask'] = self._pad_tensor_left_by_n(
tensor=batch['modelling_mask'],
n=1,
pad_value=0
)
if 'attention_mask' in batch:
batch['attention_mask'] = self._pad_tensor_left_by_n(
tensor=batch['attention_mask'],
n=1,
pad_value=1
)
return batch
def masking_loss(
self,
masked_inputs,
outputs,
modelling_mask
) -> Dict[str, torch.tensor]:
return {
'masking_loss': self.reconstruction_loss(
input=torch.masked_select(outputs, modelling_mask.to(torch.bool)),
target=masked_inputs
)['reconstruction_loss']
}
def _root_loss(
self,
masked_inputs,
outputs,
modelling_mask,
**kwargs
) -> Dict[str, torch.tensor]:
return self.masking_loss(
masked_inputs=masked_inputs,
outputs=outputs,
modelling_mask=modelling_mask
)
================================================
FILE: src/embedder/csm_causal.py
================================================
#/usr/bin/env python3
import pdb
from typing import Dict, Tuple
import torch
from embedder.base import BaseEmbedder
import numpy as np
class CSMEmbedder(BaseEmbedder):
def __init__(
self,
**kwargs
) -> None:
super().__init__(**kwargs)
self.name = 'CSMEmbedder'
self.training_style = 'CSM'
assert self.training_style in {'CSM', 'decoding'}, f'{self.training_style} not supported'
self._root_training_style = 'CSM'
##=========
self.in_dim_for_mask = self.in_dim
self.msk_embed = torch.nn.Parameter(
torch.empty(
size=(1, 1, self.in_dim_for_mask)
)
)
self.cls_embed = torch.nn.Parameter(
torch.empty(
size=(1, 1, self.in_dim_for_mask)
)
)
self._embeds = [
self.msk_embed,
self.cls_embed
]
self._init_embeds()
def _init_embeds(self):
for embed in self._embeds:
torch.nn.init.normal_(
tensor=embed,
mean=0.0,
std=1.0,
)
def duplicate_batch(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
duplicated_batch = {}
batch_size = batch['inputs'].size()[0]
times = [sum(batch['attention_mask'][i]==1) - 1 for i in range(batch_size)]
for key, tensor in batch.items():
new_tensors = []
for idx in range(batch_size):
rest_dims = tensor[idx].size()
duplicated_tensor = tensor[idx].unsqueeze(0).expand(times[idx], *rest_dims)
new_tensors.append(duplicated_tensor)
duplicated_batch[key] = torch.cat(new_tensors, dim=0)
return duplicated_batch, times
def prep_batch(
self,
batch: Dict[str, torch.tensor],
) -> Dict[str, torch.tensor]:
batch_out = dict(batch)
labels = torch.clone(batch['labels']) if 'labels' in batch else None
if self.training_style != 'decoding':
duplicated_batch, duplicate_times = self.duplicate_batch(batch_out)
masking_pos = [torch.arange(1, max_mask_pos_in_seq + 1) for max_mask_pos_in_seq in duplicate_times]
batch_out = self.mask_inputs(batch=duplicated_batch, masking_pos=masking_pos)
return batch_out
batch_out = self.add_cls_embed(batch=batch_out)
if labels is not None:
batch_out['labels'] = labels
return batch_out
def mask_inputs(
self,
batch: Dict[str, torch.tensor],
masking_pos = None
) -> Dict[str, torch.tensor]:
inputs_key = 'inputs' if 'inputs_embeds' not in batch else 'inputs_embeds'
assert inputs_key in batch, f'{inputs_key} not found in batch'
input_shape = batch[inputs_key].size()
device = batch[inputs_key].device
if masking_pos is not None:
masking_i = torch.cat(masking_pos, dim=0)
# pdb.set_trace()
else:
masking_i = torch.cat(
[
torch.randint(
low=1, # at least one seq value before mask!
high=sum(batch['attention_mask'][i]==1), # high is exclusive, so this accounts for 0-indexing
size=(1,),
device=device
)
for i in range(input_shape[0])
],
dim=0
)
# print("masking id", masking_i)
modelling_mask = torch.zeros_like(
batch[inputs_key],
device=device
)
modelling_mask[torch.arange(input_shape[0]), masking_i] = 1
batch['modelling_mask'] = modelling_mask.to(torch.long)
batch['masked_inputs'] = torch.masked_select(
input=batch[inputs_key],
mask=batch['modelling_mask'].to(torch.bool)
).detach().clone() # this is the actual label, masked_inputs
batch['inputs_embeds'] = torch.where(
batch['modelling_mask']==1,
self.msk_embed.repeat(
input_shape[0],
input_shape[1],
1
),
batch[inputs_key].to(torch.float)
)
batch['attention_mask'] = torch.cat(
[
torch.cat(
(
torch.ones(
(
1,
i+1 # to account for 0-indexing in python
),
device=device
),
torch.zeros(
(
1,
input_shape[1]-i-1 # to account for 0-indexing in python
),
device=device
)
),
dim = 1
)
for i in masking_i
],
dim = 0
).to(torch.long)
# re-mask inputs
attention_mask_expanded = torch.unsqueeze(
batch['attention_mask'],
dim=2
).repeat(
1,
1,
self.in_dim_for_mask
)
batch["inputs_embeds"] = torch.where(
attention_mask_expanded == 1,
batch['inputs_embeds'],
torch.zeros_like(batch['inputs_embeds'])
)
return batch
def add_cls_embed(
self,
batch: Dict[str, torch.tensor]
) -> Dict[str, torch.tensor]:
inputs_key = 'inputs' if 'inputs_embeds' not in batch else 'inputs_embeds'
assert inputs_key in batch, f'{inputs_key} not found in batch'
batch_size = batch[inputs_key].size()[0]
sequence_lengths = batch['attention_mask'].sum(dim=1)
inputs_embeds = []
if 't_rs' in batch:
t_rs = []
for i in range(len(sequence_lengths)):
inputs_embeds.append(
torch.cat(
[
batch[inputs_key][i, :sequence_lengths[i], :],
self.cls_embed[0],
batch[inputs_key][i, sequence_lengths[i]:, :]
],
dim=0
)
)
if 't_rs' in batch:
t_rs.append(
torch.cat(
[
batch['t_rs'][i, :sequence_lengths[i]],
torch.ones(1, device=batch['t_rs'].device) * -1,
batch['t_rs'][i, sequence_lengths[i]:]
],
dim=0
)
)
batch['inputs_embeds'] = torch.stack(
inputs_embeds,
dim=0
)
if 't_rs' in batch:
batch['t_rs'] = torch.stack(
t_rs,
dim=0
)
if 'token_type_ids' in batch:
batch['token_type_ids'] = self._pad_tensor_left_by_n(
tensor=batch['token_type_ids'],
n=1,
pad_value=0
)
if 'modelling_mask' in batch:
batch['modelling_mask'] = self._pad_tensor_left_by_n(
tensor=batch['modelling_mask'],
n=1,
pad_value=0
)
if 'attention_mask' in batch:
batch['attention_mask'] = self._pad_tensor_left_by_n(
tensor=batch['attention_mask'],
n=1,
pad_value=1
)
return batch
def masking_loss(
self,
masked_inputs,
outputs,
modelling_mask
) -> Dict[str, torch.tensor]:
return {
'masking_loss': self.reconstruction_loss(
input=torch.masked_select(outputs, modelling_mask.to(torch.bool)),
target=masked_inputs
)['reconstruction_loss']
}
def _root_loss(
self,
masked_inputs,
outputs,
modelling_mask,
**kwargs
) -> Dict[str, torch.tensor]:
return self.masking_loss(
masked_inputs=masked_inputs,
outputs=outputs,
modelling_mask=modelling_mask
)
================================================
FILE: src/embedder/make.py
================================================
#!/usr/bin/env python3
import torch
def make_embedder(
architecture: str='GPT',
training_style: str='CSM',
in_dim: int=1024,
embed_dim: int=768,
num_hidden_layers: int=1,
dropout: float=0.1,
n_positions: int=512
) -> torch.nn.Module:
"""
Make an embedder object.
The embedder is used to prepare an input batch
(as generated by src.batcher) for training and
compute the model's training loss, given the
specified training style.
Args:
-----
architecture: str
The model architecture to use.
One of: 'GPT', 'BERT', 'NetBERT', autoencoder',
'PretrainedGPT', 'PretrainedBERT', 'LinearBaseline'.
training_style: str
The used training style (ie., framework).
One of: 'BERT', 'CSM', 'NetBERT', 'autoencoder',
'decoding'.
in_dim: int
The input dimension (ie., # networks) of the
parcelated BOLD data.
embed_dim: int
The dimension of the used embedding space.
num_hidden_layers: int
The number of hidden layers of the embedding
model. If more than one layers are used, all
layers except the last one are activated through
Gelu activation (see src.base.EmbeddingModel).
dropout: float
Dropout rate used emebdding model.
n_positions: int
The maximum number of sequence elements that
the model can handle (in sequence elements).
Core methods:
-----
prep_batch(batch):
Makes all training-style specific edits of input batch
(as generated by src.batcher);
i.e., projection of input BOLD sequences into an
embedding space (as defined by embed_dim)
and addition of all training-style specific tokens to
the input data
loss(batch, outputs):
Compute the training-style specific loss,
given batch (as generated by prep_batch) and
the the full model's (see src.model) output
(as generated by model.forward)
switch_decoding_mode(is_decoding_mode):
Switch the embedder to decoding mode (is_decoding_mode=True).
This function is needed to adapt a pre-trained model
to a downstream decoding task.
"""
kwargs = {
"in_dim": in_dim,
"embed_dim": embed_dim,
"num_hidden_layers": num_hidden_layers,
"dropout": dropout,
"n_positions": n_positions
}
if training_style == 'CSM_causal':
from embedder.csm_causal import CSMEmbedder
embedder = CSMEmbedder(**kwargs)
elif training_style == 'CSM':
from embedder.csm import CSMEmbedder
embedder = CSMEmbedder(**kwargs)
elif training_style == 'decoding':
if architecture in {'GPT', 'PretrainedGPT2'}:
from embedder.csm import CSMEmbedder
embedder = CSMEmbedder(**kwargs)
else:
raise ValueError('unkown architecture')
else:
raise ValueError('unknown training style.')
return embedder
================================================
FILE: src/encoder/base.py
================================================
# Authors: Pierre Guetschel
# Maciej Sliwowski
#
# License: BSD-3
import warnings
from typing import Dict, Iterable, List, Optional, Tuple
from collections import OrderedDict
import numpy as np
import torch
from torchinfo import ModelStatistics, summary
def deprecated_args(obj, *old_new_args):
out_args = []
for old_name, new_name, old_val, new_val in old_new_args:
if old_val is None:
out_args.append(new_val)
else:
warnings.warn(
f'{obj.__class__.__name__}: {old_name!r} is depreciated. Use {new_name!r} instead.'
)
if new_val is not None:
raise ValueError(
f'{obj.__class__.__name__}: Both {old_name!r} and {new_name!r} were specified.'
)
out_args.append(old_val)
return out_args
class EEGModuleMixin():
"""
Mixin class for all EEG models in braindecode.
Parameters
----------
n_outputs : int
Number of outputs of the model. This is the number of classes
in the case of classification.
n_chans : int
Number of EEG channels.
chs_info : list of dict
Information about each individual EEG channel. This should be filled with
``info["chs"]``. Refer to :class:`mne.Info` for more details.
n_times : int
Number of time samples of the input window.
input_window_seconds : float
Length of the input window in seconds.
sfreq : float
Sampling frequency of the EEG recordings.
add_log_softmax: bool
Whether to use log-softmax non-linearity as the output function.
LogSoftmax final layer will be removed in the future.
Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
Check the documentation of the torch.nn loss functions:
https://pytorch.org/docs/stable/nn.html#loss-functions.
Raises
------
ValueError: If some input signal-related parameters are not specified
and can not be inferred.
FutureWarning: If add_log_softmax is True, since LogSoftmax final layer
will be removed in the future.
Notes
-----
If some input signal-related parameters are not specified,
there will be an attempt to infer them from the other parameters.
"""
def __init__(
self,
n_outputs: Optional[int] = None,
n_chans: Optional[int] = None,
chs_info: Optional[List[Dict]] = None,
n_times: Optional[int] = None,
input_window_seconds: Optional[float] = None,
sfreq: Optional[float] = None,
add_log_softmax: Optional[bool] = False,
):
if (
n_chans is not None and
chs_info is not None and
len(chs_info) != n_chans
):
raise ValueError(f'{n_chans} different from {chs_info} length')
if (
n_times is not None and
input_window_seconds is not None and
sfreq is not None and
n_times != int(input_window_seconds * sfreq)
):
raise ValueError(
f'{n_times} different from '
f'{input_window_seconds} * {sfreq}'
)
self._n_outputs = n_outputs
self._n_chans = n_chans
self._chs_info = chs_info
self._n_times = n_times
self._input_window_seconds = input_window_seconds
self._sfreq = sfreq
self._add_log_softmax = add_log_softmax
super().__init__()
@property
def n_outputs(self):
if self._n_outputs is None:
raise ValueError('n_outputs not specified.')
return self._n_outputs
@property
def n_chans(self):
if self._n_chans is None and self._chs_info is not None:
return len(self._chs_info)
elif self._n_chans is None:
raise ValueError(
'n_chans could not be inferred. Either specify n_chans or chs_info.'
)
return self._n_chans
@property
def chs_info(self):
if self._chs_info is None:
raise ValueError('chs_info not specified.')
return self._chs_info
@property
def n_times(self):
if (
self._n_times is None and
self._input_window_seconds is not None and
self._sfreq is not None
):
return int(self._input_window_seconds * self._sfreq)
elif self._n_times is None:
raise ValueError(
'n_times could not be inferred. '
'Either specify n_times or input_window_seconds and sfreq.'
)
return self._n_times
@property
def input_window_seconds(self):
if (
self._input_window_seconds is None and
self._n_times is not None and
self._sfreq is not None
):
return self._n_times / self._sfreq
elif self._input_window_seconds is None:
raise ValueError(
'input_window_seconds could not be inferred. '
'Either specify input_window_seconds or n_times and sfreq.'
)
return self._input_window_seconds
@property
def sfreq(self):
if (
self._sfreq is None and
self._input_window_seconds is not None and
self._n_times is not None
):
return self._n_times / self._input_window_seconds
elif self._sfreq is None:
raise ValueError(
'sfreq could not be inferred. '
'Either specify sfreq or input_window_seconds and n_times.'
)
return self._sfreq
@property
def add_log_softmax(self):
if self._add_log_softmax:
warnings.warn("LogSoftmax final layer will be removed! " +
"Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!")
return self._add_log_softmax
@property
def input_shape(self) -> Tuple[int]:
"""Input data shape."""
return (1, self.n_chans, self.n_times)
def get_output_shape(self) -> Tuple[int]:
"""Returns shape of neural network output for batch size equal 1.
Returns
-------
output_shape: Tuple[int]
shape of the network output for `batch_size==1` (1, ...)
"""
with torch.inference_mode():
try:
return tuple(self.forward(
torch.zeros(
self.input_shape,
dtype=next(self.parameters()).dtype,
device=next(self.parameters()).device
)).shape)
except RuntimeError as exc:
if str(exc).endswith(
("Output size is too small",
"Kernel size can't be greater than actual input size")
):
msg = (
"During model prediction RuntimeError was thrown showing that at some "
f"layer `{str(exc).split('.')[-1]}` (see above in the stacktrace). This "
"could be caused by providing too small `n_times`/`input_window_seconds`. "
"Model may require longer chunks of signal in the input than "
f"{self.input_shape}."
)
raise ValueError(msg) from exc
raise exc
mapping = None
def load_state_dict(self, state_dict, *args, **kwargs):
mapping = self.mapping if self.mapping else {}
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if k in mapping:
new_state_dict[mapping[k]] = v
else:
new_state_dict[k] = v
return super().load_state_dict(new_state_dict, *args, **kwargs)
def to_dense_prediction_model(self, axis: Tuple[int] = (2, 3)) -> None:
"""
Transform a sequential model with strides to a model that outputs
dense predictions by removing the strides and instead inserting dilations.
Modifies model in-place.
Parameters
----------
axis: int or (int,int)
Axis to transform (in terms of intermediate output axes)
can either be 2, 3, or (2,3).
Notes
-----
Does not yet work correctly for average pooling.
Prior to version 0.1.7, there had been a bug that could move strides
backwards one layer.
"""
if not hasattr(axis, "__len__"):
axis = [axis]
assert all([ax in [2, 3] for ax in axis]), "Only 2 and 3 allowed for axis"
axis = np.array(axis) - 2
stride_so_far = np.array([1, 1])
for module in self.modules():
if hasattr(module, "dilation"):
assert module.dilation == 1 or (module.dilation == (1, 1)), (
"Dilation should equal 1 before conversion, maybe the model is "
"already converted?"
)
new_dilation = [1, 1]
for ax in axis:
new_dilation[ax] = int(stride_so_far[ax])
module.dilation = tuple(new_dilation)
if hasattr(module, "stride"):
if not hasattr(module.stride, "__len__"):
module.stride = (module.stride, module.stride)
stride_so_far *= np.array(module.stride)
new_stride = list(module.stride)
for ax in axis:
new_stride[ax] = 1
module.stride = tuple(new_stride)
def get_torchinfo_statistics(
self,
col_names: Optional[Iterable[str]] = (
"input_size",
"output_size",
"num_params",
"kernel_size",
),
row_settings: Optional[Iterable[str]] = ("var_names", "depth"),
) -> ModelStatistics:
"""Generate table describing the model using torchinfo.summary.
Parameters
----------
col_names : tuple, optional
Specify which columns to show in the output, see torchinfo for details, by default
("input_size", "output_size", "num_params", "kernel_size")
row_settings : tuple, optional
Specify which features to show in a row, see torchinfo for details, by default
("var_names", "depth")
Returns
-------
torchinfo.ModelStatistics
ModelStatistics generated by torchinfo.summary.
"""
return summary(
self,
input_size=(1, self.n_chans, self.n_times),
col_names=col_names,
row_settings=row_settings,
verbose=0,
)
def __str__(self) -> str:
return str(self.get_torchinfo_statistics())
================================================
FILE: src/encoder/conformer_braindecode.py
================================================
# Authors: Yonghao Song <eeyhsong@gmail.com>
#
# License: BSD (3-clause)
import torch
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch import Rearrange
from torch import nn, Tensor
import warnings
from encoder.base import EEGModuleMixin, deprecated_args
class EEGConformer(EEGModuleMixin, nn.Module):
"""EEG Conformer.
This neural network architecture recieves a traditional braindecode input.
The input shape should be three-dimensional matrix representing the EEG
signals.
`(batch_size, n_channels, n_timesteps)`.
The EEG Conformer architecture is composed of three modules:
- PatchEmbedding
- TransformerEncoder
- ClassificationHead
Notes
-----
The authors recommend using data augmentation before using Conformer,
e.g. sementation and recombination,
Please refer to the original paper and code for more details.
The model was initially tuned on 4 seconds of 250 Hz data.
Please adjust the scale of the temporal convolutional layer,
and the pooling layer for better performance.
.. versionadded:: 0.8
We aggregate the parameters based on the parts of the models, or
when the parameters were used first, e.g. n_filters_time.
Parameters
----------
n_filters_time: int
Number of temporal filters, defines also embedding size.
filter_time_length: int
Length of the temporal filter.
pool_time_length: int
Length of temporal pooling filter.
pool_time_stride: int
Length of stride between temporal pooling filters.
drop_prob: float
Dropout rate of the convolutional layer.
att_depth: int
Number of self-attention layers.
att_heads: int
Number of attention heads.
att_drop_prob: float
Dropout rate of the self-attention layer.
final_fc_length: int | str
The dimension of the fully connected layer.
return_features: bool
If True, the forward method returns the features before the
last classification layer. Defaults to False.
n_classes :
Alias for n_outputs.
n_channels :
Alias for n_chans.
input_window_samples :
Alias for n_times.
References
---------------
.. [ConformerCode] Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
conformer: Convolutional transformer for EEG decoding and visualization.
https://github.com/eeyhsong/EEG-Conformer.
"""
def __init__(
self,
n_outputs=4,
n_chans=None,
n_filters_time=40,
filter_time_length=25,
pool_time_length=75,
pool_time_stride=15,
drop_prob=0.5,
att_depth=6,
att_heads=10,
att_drop_prob=0.5,
final_fc_length="auto",
return_features=False,
n_times=None,
chs_info=None,
input_window_seconds=None,
sfreq=None,
n_classes=None,
n_channels=None,
input_window_samples=None,
add_log_softmax=True,
ch_pos=None,
is_decoding_mode=False,
):
n_outputs, n_chans, n_times = deprecated_args(
self,
('n_classes', 'n_outputs', n_classes, n_outputs),
('n_channels', 'n_chans', n_channels, n_chans),
('input_window_samples', 'n_times', input_window_samples, n_times)
)
super().__init__(
n_outputs=n_outputs,
n_chans=n_chans,
chs_info=chs_info,
n_times=n_times,
input_window_seconds=input_window_seconds,
sfreq=sfreq,
add_log_softmax=add_log_softmax,
)
self.mapping = {
'classification_head.fc.6.weight': 'final_layer.final_layer.0.weight',
'classification_head.fc.6.bias': 'final_layer.final_layer.0.bias'
}
del n_outputs, n_chans, chs_info, n_times, input_window_seconds, sfreq
del n_classes, n_channels, input_window_samples
if not (self.n_chans <= 64):
warnings.warn("This model has only been tested on no more " +
"than 64 channels. no guarantee to work with " +
"more channels.", UserWarning)
self.patch_embedding = _PatchEmbedding(
n_filters_time=n_filters_time,
filter_time_length=filter_time_length,
n_channels=self.n_chans,
pool_time_length=pool_time_length,
stride_avg_pool=pool_time_stride,
drop_prob=drop_prob)
if final_fc_length == "auto":
assert self.n_times is not None
final_fc_length = self.get_fc_size()
self.transformer = _TransformerEncoder(
att_depth=att_depth,
emb_size=n_filters_time,
att_heads=att_heads,
att_drop=att_drop_prob)
self.ch_pos = ch_pos
self.is_decoding_mode = is_decoding_mode
if self.is_decoding_mode:
print("FC Layer for Classification created.")
self.fc = _FullyConnected(
final_fc_length=final_fc_length)
self.final_layer = _FinalLayer(n_classes=self.n_outputs,
return_features=return_features,
add_log_softmax=self.add_log_softmax)
def forward(self, x: Tensor) -> Tensor:
batch, chunks, chann, time = x.size()
x = x.contiguous().view(batch*chunks, chann, time)
# x = x.permute(0, 2, 1, 3).contiguous().view(batch, chann, -1)
x = torch.unsqueeze(x, dim=1) # add one extra dimension
x = self.patch_embedding(x)
x = self.transformer(x)
if self.is_decoding_mode:
# pdb.set_trace()
x = self.fc(x)
x = self.final_layer(x)
return x
def get_fc_size(self):
out = self.patch_embedding(torch.ones((1, 1,
self.n_chans,
self.n_times)))
size_embedding_1 = out.cpu().data.numpy().shape[1]
size_embedding_2 = out.cpu().data.numpy().shape[2]
return size_embedding_1 * size_embedding_2
class _PatchEmbedding(nn.Module):
"""Patch Embedding.
The authors used a convolution module to capture local features,
instead of position embedding.
Parameters
----------
n_filters_time: int
Number of temporal filters, defines also embedding size.
filter_time_length: int
Length of the temporal filter.
n_channels: int
Number of channels to be used as number of spatial filters.
pool_time_length: int
Length of temporal pooling filter.
stride_avg_pool: int
Length of stride between temporal pooling filters.
drop_prob: float
Dropout rate of the convolutional layer.
Returns
-------
x: torch.Tensor
The output tensor of the patch embedding layer.
"""
def __init__(
self,
n_filters_time,
filter_time_length,
n_channels,
pool_time_length,
stride_avg_pool,
drop_prob,
):
super().__init__()
self.shallownet = nn.Sequential(
nn.Conv2d(1, n_filters_time,
(1, filter_time_length), (1, 1)),
nn.Conv2d(n_filters_time, n_filters_time,
(n_channels, 1), (1, 1)),
nn.BatchNorm2d(num_features=n_filters_time),
nn.ELU(),
nn.AvgPool2d(
kernel_size=(1, pool_time_length),
stride=(1, stride_avg_pool)
),
# pooling acts as slicing to obtain 'patch' along the
# time dimension as in ViT
nn.Dropout(p=drop_prob),
)
self.projection = nn.Sequential(
nn.Conv2d(
n_filters_time, n_filters_time, (1, 1), stride=(1, 1)
), # transpose, conv could enhance fiting ability slightly
Rearrange("b d_model 1 seq -> b seq d_model"), # no need, because it will be flattened
)
def forward(self, x: Tensor) -> Tensor:
x = self.shallownet(x)
x = self.projection(x)
return x
class _MultiHeadAttention(nn.Module):
def __init__(self, emb_size, num_heads, dropout):
super().__init__()
self.emb_size = emb_size
self.num_heads = num_heads
self.keys = nn.Linear(emb_size, emb_size)
self.queries = nn.Linear(emb_size, emb_size)
self.values = nn.Linear(emb_size, emb_size)
self.att_drop = nn.Dropout(dropout)
self.projection = nn.Linear(emb_size, emb_size)
def forward(self, x: Tensor, mask: Tensor = None) -> Tensor:
queries = rearrange(
self.queries(x), "b n (h d) -> b h n d", h=self.num_heads
)
keys = rearrange(
self.keys(x), "b n (h d) -> b h n d", h=self.num_heads
)
values = rearrange(
self.values(x), "b n (h d) -> b h n d", h=self.num_heads
)
energy = torch.einsum("bhqd, bhkd -> bhqk", queries, keys)
if mask is not None:
fill_value = torch.finfo(torch.float32).min
energy.mask_fill(~mask, fill_value)
scaling = self.emb_size ** (1 / 2)
att = F.softmax(energy / scaling, dim=-1)
att = self.att_drop(att)
out = torch.einsum("bhal, bhlv -> bhav ", att, values)
out = rearrange(out, "b h n d -> b n (h d)")
out = self.projection(out)
return out
class _ResidualAdd(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
res = x
x = self.fn(x, **kwargs)
x += res
return x
class _FeedForwardBlock(nn.Sequential):
def __init__(self, emb_size, expansion, drop_p):
super().__init__(
nn.Linear(emb_size, expansion * emb_size),
nn.GELU(),
nn.Dropout(drop_p),
nn.Linear(expansion * emb_size, emb_size),
)
class _TransformerEncoderBlock(nn.Sequential):
def __init__(self, emb_size, att_heads, att_drop, forward_expansion=4):
super().__init__(
_ResidualAdd(
nn.Sequential(
nn.LayerNorm(emb_size),
_MultiHeadAttention(emb_size, att_heads, att_drop),
nn.Dropout(att_drop),
)
),
_ResidualAdd(
nn.Sequential(
nn.LayerNorm(emb_size),
_FeedForwardBlock(
emb_size, expansion=forward_expansion,
drop_p=att_drop
),
nn.Dropout(att_drop),
)
),
)
class _TransformerEncoder(nn.Sequential):
"""Transformer encoder module for the transformer encoder.
Similar to the layers used in ViT.
Parameters
----------
att_depth : int
Number of transformer encoder blocks.
emb_size : int
Embedding size of the transformer encoder.
att_heads : int
Number of attention heads.
att_drop : float
Dropout probability for the attention layers.
"""
def __init__(self, att_depth, emb_size, att_heads, att_drop):
super().__init__(
*[
_TransformerEncoderBlock(emb_size, att_heads, att_drop)
for _ in range(att_depth)
]
)
class _FullyConnected(nn.Module):
def __init__(self, final_fc_length,
drop_prob_1=0.5, drop_prob_2=0.3, out_channels=256,
hidden_channels=32):
"""Fully-connected layer for the transformer encoder.
Parameters
----------
final_fc_length : int
Length of the final fully connected layer.
n_classes : int
Number of classes for classification.
drop_prob_1 : float
Dropout probability for the first dropout layer.
drop_prob_2 : float
Dropout probability for the second dropout layer.
out_channels : int
Number of output channels for the first linear layer.
hidden_channels : int
Number of output channels for the second linear layer.
return_features : bool
Whether to return input features.
add_log_softmax: bool
Whether to add LogSoftmax non-linearity as the final layer.
"""
super().__init__()
self.fc = nn.Sequential(
nn.Linear(final_fc_length*2, out_channels),
nn.ELU(),
nn.Dropout(drop_prob_1),
nn.Linear(out_channels, hidden_channels),
nn.ELU(),
# nn.Dropout(drop_prob_2),
)
def forward(self, x):
x = x.contiguous().view(x.size(0)//2, -1)
out = self.fc(x)
return out
class _FinalLayer(nn.Module):
def __init__(self, n_classes, hidden_channels=32, return_features=False, add_log_softmax=True):
"""Classification head for the transformer encoder.
Parameters
----------
n_classes : int
Number of classes for classification.
hidden_channels : int
Number of output channels for the second linear layer.
return_features : bool
Whether to return input features.
add_log_softmax : bool
Adding LogSoftmax or not.
"""
super().__init__()
self.final_layer = nn.Sequential(
nn.Linear(hidden_channels, n_classes),
)
self.return_features = return_features
if add_log_softmax:
classification = nn.LogSoftmax(dim=1)
else:
classification = nn.Identity()
if not self.return_features:
self.final_layer.add_module("classification", classification)
def forward(self, x):
if self.return_features:
out = self.final_layer(x)
return out, x
else:
out = self.final_layer(x)
return out
================================================
FILE: src/model.py
================================================
#!/usr/bin/env python3
import torch
from typing import Dict
import warnings
class Model(torch.nn.Module):
"""
Create Model object from embedder, decoder,
and unembedder (if not None).
Args
----
embedder: src.embedder.make_embedder
Instance of embedder class.
decoder: src.decoder.make_decoder
Instance of decoder class.
unembedder: src.unembedder.make_unembedder
Instance of unembedder class.
Only added to model if not None.
Methods
----
forward(batch: Dict[str, torch.tensor])
Forward pass of model.
prep_batch(batch: Dict[str, torch.tensor])
Prepare batch for forward pass.
compute_loss(batch: Dict[str, torch.tensor])
Compute training loss.
from_pretrained(pretrained_path: str)
Load pretrained model from pretrained_path.
Needs to point to pytorch_model.bin file
"""
def __init__(
self,
encoder: torch.nn.Module,
embedder: torch.nn.Module,
decoder: torch.nn.Module,
unembedder: torch.nn.Module = None
) -> torch.nn.Module:
super().__init__()
self.name = f'Embedder-{embedder.name}_Decoder-{decoder.name}'
self.encoder = encoder
self.embedder = embedder
self.decoder = decoder
self.unembedder = unembedder
self.is_decoding_mode = False
self.ft_only_encoder = False
def from_pretrained(
self,
pretrained_path: str
) -> None:
"""Load pretrained model from pretrained_path.
Needs to point to pytorch_model.bin file.
"""
print(
f'Loading pretrained model from {pretrained_path}'
)
if next(self.parameters()).is_cuda:
pretrained = torch.load(pretrained_path)
else:
pretrained = torch.load(pretrained_path, map_location=torch.device('cpu'))
for k in self.state_dict():
if k in pretrained:
assert pretrained[k].shape == self.state_dict()[k].shape,\
f'{k} shape mismatch between pretrained model and current model '+\
f'{pretrained[k].shape} vs {self.state_dict()[k].shape}'
for k in pretrained:
if k not in self.state_dict():
warnings.warn(
f'Warning: /!\ Skipping {k} from {pretrained_path} '\
'because it is not part of the current model'
)
# we set strict=False, because we can be sure
# that all relevant keys are in pretrained
self.load_state_dict(pretrained, strict=False)
def switch_ft_mode(self, ft_encoder_only=False):
self.ft_only_encoder = ft_encoder_only
def switch_decoding_mode(
self,
is_decoding_mode: bool = False,
num_decoding_classes: int = None
) -> None:
"""Switch model to decoding model or back to training mode.
Necessary to adapt pre-trained models to downstream
decoding tasks.
Args
----
is_decoding_mode: bool
Whether to switch to decoding mode or not.
num_decoding_classes: int
Number of classes to use for decoding.
"""
self.is_decoding_mode = is_decoding_mode
self.embedder.switch_decoding_mode(is_decoding_mode=is_decoding_mode)
self.decoder.switch_decoding_mode(
is_decoding_mode=is_decoding_mode,
num_decoding_classes=num_decoding_classes
)
def compute_loss(
self,
batch: Dict[str, torch.tensor],
return_outputs: bool = False
) -> Dict[str, torch.tensor]:
"""
Compute training loss, based on
embedder's training-style.
Args
----
batch: Dict[str, torch.tensor]
Input batch (as generated by src.batcher)
return_outputs: bool
Whether to return outputs of forward pass
or not. If False, only loss is returned.
Returns
----
losses: Dict[str, torch.tensor]
Training losses.
outputs: torch.tensor
Outputs of forward pass.
"""
(outputs, batch) = self.forward(
batch=batch,
return_batch=True
)
losses = self.embedder.loss(
batch=batch,
outputs=outputs
)
return (losses, outputs) if return_outputs else losses
def prep_batch(
self,
batch: Dict[str, torch.tensor]
) -> Dict[str, torch.tensor]:
"""Prepare input batch for forward pass.
Calls src.embedder.prep_batch.
Args
----
batch: Dict[str, torch.tensor]
Input batch (as generated by src.batcher)
"""
return self.embedder.prep_batch(batch=dict(batch))
def forward(
self,
batch: Dict[str, torch.tensor],
prep_batch: bool = True,
return_batch: bool = False
) -> torch.tensor:
"""
Forward pass of model.
Args
----
batch: Dict[str, torch.tensor]
Input batch (as generated by src.batcher)
prep_batch: bool
Whether to prep batch for forward pass
by calling self.embedder.prep_batch
return_batch: bool
Whether to return batch after forward pass
or not. If False, only outputs of forward pass
are returned.
Returns
----
outputs: torch.tensor
Outputs of forward pass.
batch: Dict[str, torch.tensor]
Input batch (as returned by prep_batch,
if prep_batch is True)
"""
if self.encoder is not None:
#before prep_batch masking and things, we need to first let the splitted chunks of raw input through the encoder
features = self.encoder(batch['inputs'])
#attempt for trying fine-tune only the encoder, but the encoder cannot combine information across chunks.
if self.is_decoding_mode and self.ft_only_encoder:
outputs={'outputs': features, 'decoding_logits': features}
return (outputs, batch) if return_batch else outputs
b, f1, f2 = features.size()
nchunks = batch['inputs'].size()[1]
batch['inputs'] = features.view(b//nchunks, nchunks, f1*f2)
if prep_batch:
if len(batch['inputs'].size()) > 3:
bsize, chunk, chann, time = batch['inputs'].size()
batch['inputs'] = batch['inputs'].view(bsize, chunk, chann*time)
batch = self.prep_batch(batch=batch)
# batch['inputs_embeds'] = batch['inputs_embeds'].view(bsize, chunk, chann, time)
# print("preparing batch")
else:
assert 'inputs_embeds' in batch, 'inputs_embeds not in batch'
# pdb.set_trace()
batch['inputs_embeds'] = self.embedder(batch=batch)
outputs = self.decoder(batch=batch)
if self.unembedder is not None and not self.is_decoding_mode:
outputs['outputs'] = self.unembedder(inputs=outputs['outputs'])['outputs']
return (outputs, batch) if return_batch else outputs
================================================
FILE: src/train_gpt.py
================================================
#!/usr/bin/env python3
"""
train.py
Training of models on given data. See get_args() for
details on command line arguments.
To train a model, multiple core components from ..src/
are invoked:
src/batcher: Building PyTorch dataloaders for given data.
src/embedder: Embedding of inputs into embedding space,
training-style specific addition of training tokens
and masking, and computation of training-style specific
losses.
Valid training styles:
- CSM (Causal Sequence Modeling)
- decoding
src/decoder: Model architecture used for decoding / sequence modeling.
One of the following:
- GPT
- PretrainedBERT (as provided by HuggingFace)
src/unembedder: Projecting sequence output of decoder back
to input space.
src/trainer: Trainer for model; invokes instance of
Hugging Face's Trainer object.
src/model: Build full model from components (ie., embedder,
decoder, unembedder). See make_model() below for details.
"""
from batcher.downstream_dataset import MotorImageryDataset
import torch
import os
import argparse
import pdb
from typing import Dict
import json
from datetime import datetime
from numpy import random
import pandas as pd
import numpy as np
from encoder.conformer_braindecode import EEGConformer
from torch import manual_seed
import sys
from utils import cv_split_bci, read_threshold_sub
script_path = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, os.path.join(script_path, '../'))
# from batcher.make import make_batcher
from batcher.base import EEGDataset
from decoder.make_decoder import make_decoder
from embedder.make import make_embedder
from trainer.make import make_trainer
from trainer.base import Trainer
from decoder.unembedder import make_unembedder
os.environ["WANDB_DISABLED"] = "true"
def train(config: Dict=None) -> Trainer:
"""Model training according to config.
-> see get_args() below for all command
line arguments.
"""
if config is None:
config = get_config()
if config['do_train']:
os.makedirs(
config["log_dir"],
exist_ok=True
)
resume_path = str(config["resume_from"]) if config["resume_from"] is not None else None
if resume_path is not None:
config_filepath = os.path.join(
config["resume_from"],
'train_config.json'
)
if os.path.isfile(config_filepath):
print(
f'Loading training config from {config_filepath}'
)
with open(config_filepath, 'r') as f:
config = json.load(f)
else:
with open(config_filepath, 'w') as f:
json.dump(config, f, indent=2)
checkpoints = [
int(p.split('checkpoint-')[1])
for p in os.listdir(resume_path)
if 'checkpoint-' in p
and os.path.isdir(os.path.join(resume_path, p))
]
last_checkpoint = max(checkpoints)
print(
f'Resuming training from checkpoint-{last_checkpoint} in {resume_path}'
)
config["resume_from"] = os.path.join(
resume_path,
f'checkpoint-{last_checkpoint}'
)
else:
config_filepath = os.path.join(
config["log_dir"],
'train_config.json'
)
with open(config_filepath, 'w') as f:
json.dump(config, f, indent=2)
config["resume_from"] = None
assert config["training_style"] in {
'CSM',
'CSM_causal',
'decoding'
}, f'{config["training_style"]} is not supported.'
assert config["architecture"] in {
'GPT',
'PretrainedGPT2'
}, f'{config["architecture"]} is not supported.'
if config['set_seed']:
random.seed(config["seed"])
manual_seed(config["seed"])
#handles the input part, which are the output from encoder.
if config["training_style"] == 'decoding':
downstream_path = config["dst_data_path"]
train_folds, test_folds = cv_split_bci(sorted(os.listdir(downstream_path))[:18])
train_files = train_folds[config['fold_i']]
test_files = test_folds[config['fold_i']]
train_dataset = MotorImageryDataset(train_files, sample_keys=[
'inputs',
'attention_mask'
], chunk_len=config["chunk_len"], num_chunks=config["num_chunks"], ovlp=config["chunk_ovlp"], root_path=downstream_path, gpt_only= not config["use_encoder"])
# pdb.set_trace()
test_dataset = MotorImageryDataset(test_files, sample_keys=[
'inputs',
'attention_mask'
], chunk_len=config["chunk_len"], num_chunks=config["num_chunks"], ovlp=config["chunk_ovlp"], root_path=downstream_path, gpt_only= not config["use_encoder"])
validation_dataset = test_dataset
test_dataset = train_dataset
else:
root_path = config["train_data_path"]
files = read_threshold_sub('../inputs/sub_list2.csv', lower_bound=1000, upper_bound=1000000)# time len
random.shuffle(files)
train_dataset = EEGDataset(files[1000:], sample_keys=[
'inputs',
'attention_mask'
], chunk_len=config["chunk_len"], num_chunks=config["num_chunks"], ovlp=config["chunk_ovlp"], root_path=root_path, gpt_only= not config["use_encoder"], normalization=config["do_normalization"])
validation_dataset = EEGDataset(files[:1000], sample_keys=[
'inputs',
'attention_mask'
], chunk_len=config["chunk_len"], num_chunks=config["num_chunks"], ovlp=config["chunk_ovlp"], root_path=root_path, gpt_only= not config["use_encoder"], normalization=config["do_normalization"])
test_dataset = None
def model_init(params: Dict=None):
model_config = dict(config)
if params is not None:
model_config |= params
return make_model(model_config)
if config["training_style"] == "decoding":
model_save_steps = config["training_steps"]*2
else:
model_save_steps = config["log_every_n_steps"]
trainer = make_trainer(
model_init=model_init,
training_style=config["training_style"],
run_name=config["run_name"],
output_dir=config["log_dir"],
train_dataset=train_dataset,
validation_dataset=validation_dataset,
per_device_train_batch_size=config["per_device_training_batch_size"],
per_device_eval_batch_size=config["per_device_validation_batch_size"],
dataloader_num_workers=config["num_workers"],
optim=config["optim"],
learning_rate=config["learning_rate"],
weight_decay=config["weight_decay"],
adam_beta1=config["adam_beta_1"],
adam_beta2=config["adam_beta_1"],
adam_epsilon=config["adam_epsilon"],
max_grad_norm=config["max_grad_norm"],
lr_scheduler_type=config["lr_scheduler"],
warmup_ratio=config["warmup_ratio"],
max_steps=config["training_steps"],
# num_train_epochs=5,
save_steps=model_save_steps,
logging_steps=config["log_every_n_steps"],
eval_steps=config["eval_every_n_steps"],
seed=config["seed"] if config['set_seed'] else np.random.choice(range(1, 100000)),
fp16=config["fp16"],
deepspeed=config["deepspeed"],
)
if config['do_train']:
trainer.train(resume_from_checkpoint=config["resume_from"])
trainer.save_model(
os.path.join(
config["log_dir"],
'model_final'
)
)
if test_dataset is not None:
test_prediction = trainer.predict(test_dataset)
pd.DataFrame(
test_prediction.metrics,
index=[0]
).to_csv(
os.path.join(
config["log_dir"],
'test_metrics.csv'
),
index=False
)
np.save(
os.path.join(
config["log_dir"],
'test_predictions.npy'
),
test_prediction.predictions
)
np.save(
os.path.join(
config["log_dir"],
'test_label_ids.npy'
),
test_prediction.label_ids
)
return trainer
def make_model(model_config: Dict=None):
"""Make model from model_config
(as generated by get_config()).
"""
if model_config["use_encoder"] == True:
chann_coords = None
encoder = EEGConformer(n_outputs=model_config["num_decoding_classes"], n_chans=22, n_times=model_config['chunk_len'], ch_pos=chann_coords, is_decoding_mode=model_config["ft_only_encoder"])
#calculates the output dimension of the encoder, which is the output of transformer layer.
model_config["parcellation_dim"] = ((model_config['chunk_len'] - model_config['filter_time_length'] + 1 - model_config['pool_time_length']) // model_config['stride_avg_pool'] + 1) * model_config['n_filters_time']
else:
encoder = None
model_config["parcellation_dim"] = model_config["chunk_len"] * 22
embedder = make_embedder(
training_style=model_config["training_style"],
architecture=model_config["architecture"],
in_dim=model_config["parcellation_dim"], # flattened, channel x chunk length
embed_dim=model_config["embedding_dim"],
num_hidden_layers=model_config["num_hidden_layers_embedding_model"],
dropout=model_config["dropout"],
n_positions=model_config["n_positions"]
)
decoder = make_decoder(
architecture=model_config["architecture"],
num_hidden_layers=model_config["num_hidden_layers"],
embed_dim=model_config["embedding_dim"],
num_attention_heads=model_config["num_attention_heads"],
n_positions=model_config["n_positions"],
intermediate_dim_factor=model_config["intermediate_dim_factor"],
hidden_activation=model_config["hidden_activation"],
dropout=model_config["dropout"]
)
if model_config["embedding_dim"] != model_config["parcellation_dim"]:
unembedder = make_unembedder(
embed_dim=model_config["embedding_dim"],
num_hidden_layers=model_config["num_hidden_layers_unembedding_model"],
out_dim=model_config["parcellation_dim"],
dropout=model_config["dropout"],
)
else:
print("No Embedder and Unembedder!")
unembedder = None
from model import Model
model = Model(
encoder=encoder,
embedder=embedder,
decoder=decoder,
unembedder=unembedder
)
if model_config["ft_only_encoder"]:
model.switch_ft_mode(ft_encoder_only=True)
if model_config["training_style"] == 'decoding':
model.switch_decoding_mode(
is_decoding_mode=True,
num_decoding_classes=model_config["num_decoding_classes"]
)
if model_config["pretrained_model"] is not None:
model.from_pretrained(model_config["pretrained_model"])
if model_config["freeze_embedder"]:
for param in model.embedder.parameters():
param.requires_grad = False
if model_config["freeze_decoder"]:
for param in model.decoder.parameters():
param.requires_grad = False
if model_config["freeze_encoder"]:
for name, param in model.encoder.named_parameters():
if 'fc.' in name \
or 'final_layer' in name:
continue
else:
param.requires_grad = False
if 'freeze_decoder_without_pooler_heads' in model_config \
and model_config["freeze_decoder_without_pooler_heads"]:
for name, param in model.decoder.named_parameters():
if 'pooler_layer' in name \
or 'decoding_head' in name \
or 'is_next_head' in name:
continue
else:
param.requires_grad = False
if model_config["freeze_unembedder"] and unembedder is not None:
for param in model.unembedder.parameters():
param.requires_grad = False
return model
def get_config(args: argparse.Namespace=None) -> Dict:
"""
Make config from command line arguments (as created by get_args()).
Performs additional formating of args required for calling train().
"""
if args is None:
args = get_args().parse_args()
if args.smoke_test == "True":
args.per_device_training_batch_size = 2
args.per_device_validation_batch_size = 2
args.training_steps = 2
args.validation_steps = 2
args.test_steps = 2
args.log_every_n_steps = 1
if args.num_attention_heads == -1:
assert (
args.embedding_dim%64
) == 0, f'embedding-dim needs be be multiple of 64 (currently: {args.embedding_dim})'
args.num_attention_heads = args.embedding_dim//64
if args.run_name == 'none':
args.run_name = f'{args.architecture}'
if args.architecture != 'LinearBaseline':
if 'Pretrained' not in args.architecture:
args.run_name += f'_lrs-{args.num_hidden_layers}'
args.run_name += f'_hds-{args.num_attention_heads}'
# args.run_name += f'_embd-{args.embedding_dim}'
# args.run_name += f'_train-{args.training_style}'
# args.run_name += f'_lr-{str(args.learning_rate).replace(".", "")[1:]}'
# args.run_name += f'_bs-{args.per_device_training_batch_size}'
# args.run_name += f'_drp-{str(args.dropout).replace(".", "")}'
args.run_name += f'_ChunkLen-{args.chunk_len}'
args.run_name += f'_NumChunks-{args.num_chunks}'
args.run_name += f'_ovlp-{args.chunk_ovlp}'
else:
args.run_name += f'_train-{args.training_style}'
args.run_name += f"_{datetime.now().strftime('%Y-%m-%d_%H')}"
if args.training_style == "decoding":
args.run_name += '-' + str(args.fold_i)
if args.smoke_test == "True":
args.run_name = f'smoke-test_{args.run_name}'
args.log_dir = os.path.join(args.log_dir, args.run_name)
args.wandb_mode = args.wandb_mode if args.wandb_mode in {'online', 'offline'} and args.local_rank in {-1, 0} else "disabled"
config = vars(args)
for arg in config:
if config[arg] in {'True', 'False'}:
config[arg] = config[arg] == 'True'
elif config[arg] == 'none':
config[arg] = None
elif 'subjects_per_dataset' in arg:
config[arg] = None if config[arg] == -1 else config[arg]
return config
def get_args() -> argparse.ArgumentParser:
"""Get command line arguments"""
parser = argparse.ArgumentParser(
description='run model training'
)
# Data pipeline settings:
parser.add_argument(
'--train-data-path',
metavar='DIR',
default='../../tuh_tensors/',
type=str,
help='path to training data directory '
'(default: data/upstream)'
)
parser.add_argument(
'--dst-data-path',
metavar='DIR',
default="../../bci2a_egg_npz/",
type=str,
help='path to training data directory '
'(default: data/upstream)'
)
parser.add_argument(
'--parcellation-dim',
metavar='INT',
default=1024,
type=int,
help='dimension of input data parcellation (default: 1024). '
'! This is fixed for the current up-/downstream data.'
)
parser.add_argument(
'--pretrained-model',
metavar='DIR',
type=str,
default='none',
help='checkpoint used to initialize model weights '
'(default: none)'
)
# Embedder settings:
parser.add_argument(
'--embedding-dim',
metavar='INT',
default=1024,
type=int,
help='dimension of input embedding '
'(default: 1024)'
)
parser.add_argument(
'--num-hidden-layers-embedding-model',
metavar='INT',
default=1,
type=int,
help='numer of layers of linear embedding model '
'(default: 1)'
)
parser.add_argument(
'--freeze-embedder',
metavar='BOOL',
default='False',
choices=('True', 'False'),
type=str,
help='whether or not to freeze embedder weights during training '
'(default: False) '
)
# UnEmbedder settings:
parser.add_argument(
'--num-hidden-layers-unembedding-model',
metavar='INT',
default=1,
type=int,
help='numer of hidden layers for linear unembedding model '
'(default: 1)'
)
parser.add_argument(
'--freeze-unembedder',
metavar='BOOL',
default='False',
choices=('True', 'False'),
type=str,
help='whether or not to freeze unembedder weights during training '
'(default: False) '
)
# Decoder settings:
parser.add_argument(
'--architecture',
metavar='STR',
default='GPT',
choices=(
'GPT',
'PretrainedGPT2'
),
type=str,
help='Model architecture used for sequence modeling / decoding. '
'(default: GPT) '
)
parser.add_argument(
'--num-hidden-layers',
metavar='INT',
default=4,
type=int,
help='number of hidden model layers in --architecture '
'(default: 4). '
'! Does not apply to LinearBaseline; '
'! Same number of hidden layers is used for decoder / encoder '
'parts of autoencoder (ie., default creates encoder and decoder '
'with 4 hidden layers each)'
)
parser.add_argument(
'--num-attention-heads',
metavar='INT',
default=-1,
type=int,
help='number of attention heads per transformer layer '
'(default: embedding-dim // 64). '
'! Does not apply to non-transformer models'
)
parser.add_argument(
'--intermediate-dim-factor',
metavar='INT',
default=4,
type=int,
help='scales feed-forward transformer layer dimension relative to '
'embedding-dim: intermediate-dim-factor * embedding-dim '
'(default: 4)'
)
parser.add_argument(
'--hidden-activation',
metavar='STR',
default='gelu_new',
choices=(
'gelu',
'gelu_new',
'relu',
'silu'
),
type=str,
help='type of hidden activation of transformer layers '
'(default: gelu_new); '
'one of {"gelu", "gelu_new", "relu", "silu"}. '
'! Does not apply to non-transformer models'
)
parser.add_argument(
'--freeze-decoder',
metavar='BOOL',
default='False',
choices=('True', 'False'),
type=str,
help='whether or not to freeze decoder model weights during training '
'as specified by --architecture '
'(default: False) '
)
parser.add_argument(
'--freeze-decoder-without-pooler-heads',
metavar='BOOL',
default='False',
choices=('True', 'False'),
type=str,
help='whether or not to freeze decoder model weights during training '
'as specified by --architecture, without pooler layer and '
' is-next-pred / decoding heads '
'(default: False) '
)
# Trainer settings:
parser.add_argument(
'--resume-from',
metavar='DIR',
type=str,
default='none',
help='continue training from specified checkpoint '
'(default: none)'
)
parser.add_argument(
'--training-style',
metavar='STR',
default='CSM_causal',
choices=(
'CSM',
'CSM_causal',
'decoding'
),
type=str,
help='training framework / style (default: CSM); '
'one of CSM, decoding'
)
parser.add_argument(
'--decoding-target',
metavar='STR',
default='none',
type=str,
help='key for decoding target variable in .tar-files in --data'
'(default: none). '
'! Must be specified when setting --training-style to "decoding"'
)
parser.add_argument(
'--num-decoding-classes',
metavar='INT',
default=4,
type=int,
help='number of decoding classes (ie., mental states) in --data '
'(default: 0). '
'! Must be specified when setting --training-style to "decoding"'
)
parser.add_argument(
'--training-steps',
metavar='INT',
default=60000,
type=int,
help='number of training steps to perform '
'(default: 400000)'
)
parser.add_argument(
'--validation-steps',
metavar='INT',
default=1000,
type=int,
help='number of validation steps to perform at evaluation time '
'(default: 1000)'
)
parser.add_argument(
'--test-steps',
metavar='INT',
default=1000,
type=int,
help='number of test steps to perform at test time'
'(default: 2000). '
'! Test evaluation only performed if test set created by '
'setting --n-test-subjects-per-dataset != -1'
)
parser.add_argument(
'--per-device-training-batch-size',
metavar='INT',
default=16,
type=int,
help='batch size during training per training device '
'(default: 64)'
)
parser.add_argument(
'--per-device-validation-batch-size',
metavar='INT',
default=16,
type=int,
help='batch size during evaluation per training device '
'(default: 64)'
)
parser.add_argument(
'--optim',
metavar='STR',
default='adamw_hf',
type=str,
help='optimizer to use for training '
'(default: adamw_hf) -> adamw from HuggingFrace transformer library. '
'For other options see Huggingface TrainerArgs.'
)
parser.add_argument(
'--learning-rate',
metavar='FLOAT',
default=1e-4,
type=float,
help='maximum learning rate during training '
'(default: 1e-4)'
)
parser.add_argument(
'--warmup-ratio',
metavar='FLOAT',
default=0.01,
type=float,
help='warm-up steps for linear learning rate scheduler '
'specified as fraction of --training-steps '
'(default: 0.01)'
)
parser.add_argument(
'--weight-decay',
metavar='FLOAT',
default=0.1,
type=float,
help='weight decay strength (indicating l2-regularisation strength) '
'(default: 0.1)'
)
parser.add_argument(
'--adam-beta-1',
metavar='FLOAT',
default=0.9,
type=float,
help='adam beta 1 (default: 0.9)'
)
parser.add_argument(
'--adam-beta-2',
metavar='FLOAT',
default=0.999,
type=float,
help='adam beta 2 (default: 0.999)'
)
parser.add_argument(
'--adam-epsilon',
metavar='FLOAT',
default=1e-8,
type=float,
help='adam beta 2 (default: 1e-8)'
)
parser.add_argument(
'--max-grad-norm',
metavar='FLOAT',
default=1.0,
type=float,
help='maximum gradient clipping norm (default: 1.0)'
)
parser.add_argument(
'--lr-scheduler',
metavar='STR',
default='linear',
choices=(
'linear',
'constant_with_warmup',
'none'
),
type=str,
help='learning rate scheduler; '
'one of {linear, constant_with_warmup, none} '
'(default: linear)'
)
parser.add_argument(
'--dropout',
metavar='FLOAT',
default=0.1,
type=float,
help='dropout ratio for hidden layers of embedder and decoder model parts '
'(default: 0.1)'
)
# Logging settings:
parser.add_argument(
'--log-dir',
metavar='DIR',
type=str,
default='results/models/upstream',
help='path where training is logged '
'(default: results/models/upstream)'
)
parser.add_argument(
'--log-every-n-steps',
metavar='INT',
default=1000,
type=int,
help='frequence of logging in training steps '
'(default: 10000)'
)
parser.add_argument(
'--run-name',
metavar='STR',
type=str,
default='none',
help='descriptor of the training run used for logging and wandb; '
'! if set to "none", a unique identifier is automatically created'
)
parser.add_argument(
'--wandb-mode',
metavar='STR',
choices=(
'online',
'offline',
'disabled'
),
default='disabled',
help='track training w/ wandb online or offline or not at all '
'(default: disabled) '
'! requires setting up weights-and-bias for this machine; '
'see: https://docs.wandb.ai/'
)
parser.add_argument(
'--wandb-project-name',
metavar='STR',
type=str,
default='learning-from-brains',
help='name of wandb project where data is logged '
'(default: learning-from-brains)'
)
# Other settings:
parser.add_argument(
'--seed',
metavar='INT',
default=1234,
type=int,
help='random seed (default: 1234)'
)
parser.add_argument(
'--set-seed',
metavar='BOOL',
choices=('True', 'False'),
default='True',
type=str,
help='whether or not to set random seed (default: True)'
)
parser.add_argument(
'--fp16',
metavar='BOOL',
choices=('True', 'False'),
default='True',
help='whether or not to use 16-bit precision GPU training '
'(default: True)'
)
parser.add_argument(
'--deepspeed',
metavar='DIR',
default="none",
type=str,
help='location of deepspeed configuration file; '
'automatically adds deepspeed functionality to training if specified '
'(default: none)'
)
parser.add_argument(
'--local_rank',
metavar='INT',
default=-1,
type=int,
help='Rank of the process during distributed training '
'(default: -1)'
)
parser.add_argument(
'--num-workers',
metavar='INT',
default=8,
type=int,
help='number of data loading workers '
'(default: 0 -> load in main process)'
)
parser.add_argument(
'--plot-model-graph',
metavar='BOOL',
default="False",
type=str,
choices=('True', 'False'),
help='whether or not to save an image of the model graph to log-dir '
'(default: False)'
)
parser.add_argument(
'--smoke-test',
metavar='BOOL',
default="False",
type=str,
choices=("True", "False"),
help='whetehr or not to run training in smoke test-mode '
'(default: False)'
'If set to "True", training is restricted by setting: '
'--per-device-training_batch_size 2 '
'--per-device-validation_batch_size 2 '
'--training-steps 2 '
'--validation-steps 2 '
'--test-steps 2 '
'--log-every-n-steps 1'
)
parser.add_argument(
'--bold-dummy-mode',
metavar='BOOL',
default='False',
type=str,
choices=('True', 'False'),
help='whether or not to replace BOLD with dummy during training; '
'for internal testing purposes only! '
'(default: False)'
)
parser.add_argument(
'--do-train',
metavar='BOOL',
default='True',
type=str,
choices=('True', 'False'),
help='whether or not to run training '
'(default: True). '
'If "False", train() still returns trainer'
)
parser.add_argument(
'--n-positions',
metavar='INT',
default=512,
type=int,
help='maximum sequence length that transformer model might ever be used with '
'(default: 512)'
)
## EEG settings
parser.add_argument(
'--chunk_len',
default=500,
type=int)
parser.add_argument(
'--num_chunks',
default=8,
type=int)
parser.add_argument(
'--chunk_ovlp',
default=50,
type=int)
parser.add_argument(
'--sampling_rate',
default=250,
type=int)
parser.add_argument(
'--fold_i',
default=0,
type=int)
parser.add_argument(
'--use-encoder',
metavar='BOOL',
default='True',
type=str,
choices=('True', 'False'),
help='whether to use encoder or not'
)
parser.add_argument(
'--do-normalization',
metavar='BOOL',
default='True',
type=str,
choices=('True', 'False'),
help='whether to use encoder or not'
)
parser.add_argument('--filter-time-length', metavar='INT', default=25, type=int, help='length of the temporal filter (default: 25)')
parser.add_argument('--pool-time-length', metavar='INT', default=75, type=int, help='length of temporal pooling filter (default: 75)')
parser.add_argument('--stride-avg-pool', metavar='INT', default=15, type=int, help='length of stride between temporal pooling filters (default: 15)')
parser.add_argument('--n-filters-time', metavar='INT', default=40, type=int, help='number of temporal filters (default: 40)')
parser.add_argument('--num-encoder-layers', metavar='INT', default=6, type=int, help='number of transformer layers in encoder')
parser.add_argument('--eval_every_n_steps', default=200, type=int)
parser.add_argument('--freeze-encoder', metavar='BOOL', default='False',
choices=('True', 'False'),
type=str,
help='whether or not to freeze encoder weights during training '
'(default: False) '
)
parser.add_argument('--ft-only-encoder', metavar='BOOL', default='False',
choices=('True', 'False'),
type=str,
help='finetune with only encoder or not '
'(default: False) '
)
return parser
if __name__ == '__main__':
trainer = train()
================================================
FILE: src/trainer/base.py
================================================
#!/usr/bin/env python3
from typing import Dict, List, Optional, Tuple
from collections.abc import Mapping
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
# from apex import amp
from tqdm.auto import tqdm
import torch
from torch import nn
from transformers import Trainer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
from transformers.integrations import ( # isort: split
hp_params,
)
from transformers import PretrainedConfig
from transformers.data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
from transformers.deepspeed import deepspeed_init, is_deepspeed_zero3_enabled
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_callback import (
TrainerState,
)
from transformers.trainer_pt_utils import (
IterableDatasetShard,
)
from transformers.trainer_utils import (
seed_worker
)
from transformers.training_args import OptimizerNames, ParallelMode, TrainingArguments
from transformers.utils import (
is_sagemaker_mp_enabled,
is_torch_tensorrt_fx_available,
is_datasets_available,
is_torch_tpu_available,
is_torchdynamo_available,
logging,
)
from transformers.utils.generic import ContextManagers
logger = logging.get_logger(__name__)
TRAINING_ARGS_NAME = "training_args.bin"
TRAINER_STATE_NAME = "trainer_state.json"
OPTIMIZER_NAME = "optimizer.pt"
SCHEDULER_NAME = "scheduler.pt"
SCALER_NAME = "scaler.pt"
class Trainer(Trainer):
def __init__(
self,
is_deepspeed: bool = False,
**kwargs
) -> None:
super().__init__(**kwargs)
self.name = "Trainer"
self.is_deepspeed = is_deepspeed
def get_train_dataloader(self) -> DataLoader:
"""
Returns the training [`~torch.utils.data.DataLoader`].
Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this method if you want to inject some custom behavior.
"""
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
train_dataset = self.train_dataset
data_collator = self.data_collator
# if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
# train_dataset = self._remove_unused_columns(train_dataset, description="training")
# else:
# data_collator = self._get_collator_with_removed_columns(data_collator, description="training")
# pdb.set_trace()
if isinstance(train_dataset, torch.utils.data.IterableDataset):
# if self.args.world_size > 1:
# train_dataset = IterableDatasetShard(
# train_dataset,
# batch_size=self._train_batch_size,
# drop_last=self.args.dataloader_drop_last,
# num_processes=self.args.world_size,
# process_index=self.args.process_index,
# )
print("iterable dataset")
# pdb.set_trace()
return DataLoader(
train_dataset,
batch_size=self.args.per_device_train_batch_size,
# collate_fn=data_collator,
num_workers=self.args.dataloader_num_workers,
pin_memory=True,
)
train_sampler = self._get_train_sampler()
train_loader = DataLoader(
train_dataset,
batch_size=self._train_batch_size,
sampler=train_sampler,
# collate_fn=data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
pin_memory=True,
worker_init_fn=seed_worker,
)
return train_loader
def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
"""
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`torch.utils.data.Dataset`, *optional*):
If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
by the `model.forward()` method are automatically removed. It must implement `__len__`.
"""
if eval_dataset is None and self.eval_dataset is None:
raise ValueError("Trainer: evaluation requires an eval_dataset.")
eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
data_collator = self.data_collator
# if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
# eval_dataset = self._remove_unused_columns(eval_dataset, description="evaluation")
# else:
# data_collator = self._get_collator_with_removed_columns(data_collator, description="evaluation")
if isinstance(eval_dataset, torch.utils.data.IterableDataset):
if self.args.world_size > 1:
eval_dataset = IterableDatasetShard(
eval_dataset,
batch_size=self.args.per_device_eval_batch_size,
drop_last=self.args.dataloader_drop_last,
num_processes=self.args.world_size,
process_index=self.args.process_index,
)
return DataLoader(
eval_dataset,
batch_size=self.args.eval_batch_size,
# collate_fn=data_collator,
num_workers=self.args.dataloader_num_workers,
pin_memory=self.args.dataloader_pin_memory,
)
eval_sampler = self._get_eval_sampler(eval_dataset)
return DataLoader(
eval_dataset,
sampler=eval_sampler,
batch_size=self.args.eval_batch_size,
# collate_fn=data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
pin_memory=self.args.dataloader_pin_memory,
)
def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
"""
Returns the test [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
test_dataset (`torch.utils.data.Dataset`, *optional*):
The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the
`model.forward()` method are automatically removed. It must implement `__len__`.
"""
# data_collator = self.data_collator
if isinstance(test_dataset, torch.utils.data.IterableDataset):
if self.args.world_size > 1:
test_dataset = IterableDatasetShard(
test_dataset,
batch_size=self.args.eval_batch_size,
drop_last=self.args.dataloader_drop_last,
num_processes=self.args.world_size,
process_index=self.args.process_index,
)
return DataLoader(
test_dataset,
batch_size=self.args.eval_batch_size,
# collate_fn=data_collator,
num_workers=self.args.dataloader_num_workers,
pin_memory=self.args.dataloader_pin_memory,
)
test_sampler = self._get_eval_sampler(test_dataset)
# We use the same batch_size as for eval.
return DataLoader(
test_dataset,
sampler=test_sampler,
batch_size=self.args.eval_batch_size,
# collate_fn=data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
pin_memory=self.args.dataloader_pin_memory,
)
# def _inner_training_loop(
# self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
# ):
# self._train_batch_size = batch_size
# # Data loader and number of training steps
# train_dataloader = self.get_train_dataloader()
# # Setting up training control variables:
# # number of training epochs: num_train_epochs
# # number of training steps per epoch: num_update_steps_per_epoch
# # total number of training steps to execute: max_steps
# total_train_batch_size = args.train_batch_size * args.gradient_accumulation_steps * args.world_size
# len_dataloader = None
# if len(train_dataloader) > 0:
# len_dataloader = len(train_dataloader)
# num_update_steps_per_epoch = len_dataloader // args.gradient_accumulation_steps
# num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
# num_examples = self.num_examples(train_dataloader)
# if args.max_steps > 0:
# max_steps = args.max_steps
# num_train_epochs = args.max_steps // num_update_steps_per_epoch + int(
# args.max_steps % num_update_steps_per_epoch > 0
# )
# # May be slightly incorrect if the last batch in the training dataloader has a smaller size but it's
# # the best we can do.
# num_train_samples = args.max_steps * total_train_batch_size
# else:
# max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch)
# num_train_epochs = math.ceil(args.num_train_epochs)
# num_train_samples = self.num_examples(train_dataloader) * args.num_train_epochs
# elif args.max_steps > 0: # Rely on max_steps when dataloader does not have a working size
# max_steps = args.max_steps
# # Setting a very large number of epochs so we go as many times as necessary over the iterator.
# num_train_epochs = sys.maxsize
# num_update_steps_per_epoch = max_steps
# num_examples = total_train_batch_size * args.max_steps
# num_train_samples = args.max_steps * total_train_batch_size
# else:
# raise ValueError(
# "args.max_steps must be set to a positive value if dataloader does not have a length, was"
# f" {args.max_steps}"
# )
# delay_optimizer_creation = (
# self.sharded_ddp is not None
# and self.sharded_ddp != ShardedDDPOption.SIMPLE
# or is_sagemaker_mp_enabled()
# or self.fsdp is not None
# )
# if args.deepspeed:
# deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(
# self, num_training_steps=max_steps, resume_from_checkpoint=resume_from_checkpoint
# )
# self.model = deepspeed_engine.module
# self.model_wrapped = deepspeed_engine
# self.deepspeed = deepspeed_engine
# self.optimizer = optimizer
# self.lr_scheduler = lr_scheduler
# elif not delay_optimizer_creation:
# self.create_optimizer_and_scheduler(num_training_steps=max_steps)
# self.state = TrainerState()
# self.state.is_hyper_param_search = trial is not None
# # Activate gradient checkpointing if needed
# if args.gradient_checkpointing:
# self.model.gradient_checkpointing_enable()
# model = self._wrap_model(self.model_wrapped)
# if is_sagemaker_mp_enabled() and resume_from_checkpoint is not None:
# self._load_from_checkpoint(resume_from_checkpoint, model)
# # for the rest of this function `model` is the outside model, whether it was wrapped or not
# if model is not self.model:
# self.model_wrapped = model
# if delay_optimizer_creation:
# self.create_optimizer_and_scheduler(num_training_steps=max_steps)
# # Check if saved optimizer or scheduler states exist
# self._load_optimizer_and_scheduler(resume_from_checkpoint)
# # important: at this point:
# # self.model is the Transformers Model
# # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), etc.
# # Train!
# logger.info("***** Running training *****")
# logger.info(f" Num examples = {num_examples}")
# logger.info(f" Num Epochs = {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_train_batch_size}")
# logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
# logger.info(f" Total optimization steps = {max_steps}")
# logger.info(
# f" Number of trainable parameters = {sum(p.numel() for p in model.parameters() if p.requires_grad)}"
# )
# self.state.epoch = 0
# start_time = time.time()
# epochs_trained = 0
# steps_trained_in_current_epoch = 0
# steps_trained_progress_bar = None
# # Check if continuing training from a checkpoint
# if resume_from_checkpoint is not None and os.path.isfile(
# os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
# ):
# self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
# epochs_trained = self.state.global_step // num_update_steps_per_epoch
# if not args.ignore_data_skip:
# steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
# steps_trained_in_current_epoch *= args.gradient_accumulation_steps
# else:
# steps_trained_in_current_epoch = 0
# logger.info(" Continuing training from checkpoint, will skip to saved global_step")
# logger.info(f" Continuing training from epoch {epochs_trained}")
# logger.info(f" Continuing training from global step {self.state.global_step}")
# if not args.ignore_data_skip:
# logger.info(
# f" Will skip the first {epochs_trained} epochs then the first {steps_trained_in_current_epoch} "
# "batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` "
# "flag to your launch command, but you will resume the training on data already seen by your model."
# )
# if self.is_local_process_zero() and not args.disable_tqdm:
# steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)
# steps_trained_progress_bar.set_description("Skipping the first batches")
# # Update the references
# self.callback_handler.model = self.model
# self.callback_handler.optimizer = self.optimizer
# self.callback_handler.lr_scheduler = self.lr_scheduler
# self.callback_handler.train_dataloader = train_dataloader
# if self.hp_name is not None and self._trial is not None:
# # use self._trial because the SigOpt/Optuna hpo only call `_hp_search_setup(trial)` instead of passing trial
# # parameter to Train when using DDP.
# self.state.trial_name = self.hp_name(self._trial)
# if trial is not None:
# assignments = trial.assignments if self.hp_search_backend == HPSearchBackend.SIGOPT else trial
# self.state.trial_params = hp_params(assignments)
# else:
# self.state.trial_params = None
# # This should be the same if the state has been saved but in case the training arguments changed, it's safer
# # to set this after the load.
# self.state.max_steps = max_steps
# self.state.num_train_epochs = num_train_epochs
# self.state.is_local_process_zero = self.is_local_process_zero()
# self.state.is_world_process_zero = self.is_world_process_zero()
# # tr_loss is a tensor to avoid synchronization of TPUs through .item()
# tr_loss = torch.tensor(0.0).to(args.device)
# # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
# self._total_loss_scalar = 0.0
# self._globalstep_last_logged = self.state.global_step
# model.zero_grad()
# self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
# # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
# if not args.ignore_data_skip:
# for epoch in range(epochs_trained):
# is_random_sampler = hasattr(train_dataloader, "sampler") and isinstance(
# train_dataloader.sampler, RandomSampler
# )
# if is_torch_less_than_1_11 or not is_random_sampler:
# # We just need to begin an iteration to create the randomization of the sampler.
# # That was before PyTorch 1.11 however...
# for _ in train_dataloader:
# break
# else:
# # Otherwise we need to call the whooooole sampler cause there is some random operation added
# # AT THE VERY END!
# _ = list(train_dataloader.sampler)
# for epoch in range(epochs_trained, num_train_epochs):
# if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
# train_dataloader.sampler.set_epoch(epoch)
# elif hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDatasetShard):
# train_dataloader.dataset.set_epoch(epoch)
# epoch_iterator = train_dataloader
# # Reset the past mems state at the beginning of each epoch if necessary.
# if args.past_index >= 0:
# self._past = None
# steps_in_epoch = (
# len(epoch_iterator)
# if len_dataloader is not None
# else args.max_steps * args.gradient_accumulation_steps
# )
# self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)
# if epoch == epochs_trained and resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0:
# self._load_rng_state(resume_from_checkpoint)
# step = -1
# for step, inputs in enumerate(epoch_iterator):
# # Skip past any already trained steps if resuming training
# pdb.set_trace()
# if steps_trained_in_current_epoch > 0:
# steps_trained_in_current_epoch -= 1
# if steps_trained_progress_bar is not None:
# steps_trained_progress_bar.update(1)
# if steps_trained_in_current_epoch == 0:
# self._load_rng_state(resume_from_checkpoint)
# continue
# elif steps_trained_progress_bar is not None:
# steps_trained_progress_bar.close()
# steps_trained_progress_bar = None
# if step % args.gradient_accumulation_steps == 0:
# self.control = self.callback_handler.on_step_begin(args, self.state, self.control)
# if (
# ((step + 1) % args.gradient_accumulation_steps != 0)
# and args.local_rank != -1
# and args._no_sync_in_gradient_accumulation
# ):
# # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
# with model.no_sync():
# tr_loss_step = self.training_step(model, inputs)
# else:
# tr_loss_step = self.training_step(model, inputs)
# if (
# args.logging_nan_inf_filter
# and not is_torch_tpu_available()
# and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
# ):
# # if loss is nan or inf simply add the average of previous logged losses
# tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
# else:
# tr_loss += tr_loss_step
# self.current_flos += float(self.floating_point_ops(inputs))
# # Optimizer step for deepspeed must be called on every step regardless of the value of gradient_accumulation_steps
# if self.deepspeed:
# self.deepspeed.step()
# if (step + 1) % args.gradient_accumulation_steps == 0 or (
# # last step in epoch but step is always smaller than gradient_accumulation_steps
# steps_in_epoch <= args.gradient_accumulation_steps
# and (step + 1) == steps_in_epoch
# ):
# # Gradient clipping
# if args.max_grad_norm is not None and args.max_grad_norm > 0 and not self.deepspeed:
# # deepspeed does its own clipping
# if self.do_grad_scaling:
# # Reduce gradients first for XLA
# # if is_torch_tpu_available():
# # gradients = xm._fetch_gradients(self.optimizer)
# # xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
# # AMP: gradients need unscaling
# self.scaler.unscale_(self.optimizer)
# if is_sagemaker_mp_enabled() and args.fp16:
# self.optimizer.clip_master_grads(args.max_grad_norm)
# elif hasattr(self.optimizer, "clip_grad_norm"):
# # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
# self.optimizer.clip_grad_norm(args.max_grad_norm)
# elif hasattr(model, "clip_grad_norm_"):
# # Some models (like FullyShardedDDP) have a specific way to do gradient clipping
# model.clip_grad_norm_(args.max_grad_norm)
# else:
# # Revert to normal clipping otherwise, handling Apex or full precision
# # if is_apex_available():
# # nn.utils.clip_grad_norm_(
# # amp.master_params(self.optimizer) if self.use_apex else model.parameters(),
# # args.max_grad_norm,
# # )
# continue
# # Optimizer step
# optimizer_was_run = True
# if self.deepspeed:
# pass # called outside the loop
# elif self.do_grad_scaling:
# scale_before = self.scaler.get_scale()
# self.scaler.step(self.optimizer)
# self.scaler.update()
# scale_after = self.scaler.get_scale()
# optimizer_was_run = scale_before <= scale_after
# else:
# self.optimizer.step()
# if optimizer_was_run and not self.deepspeed:
# self.lr_scheduler.step()
# model.zero_grad()
# self.state.global_step += 1
# self.state.epoch = epoch + (step + 1) / steps_in_epoch
# self.control = self.callback_handler.on_step_end(args, self.state, self.control)
# print(self.state.epoch)
# pdb.set_trace()
# self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
# else:
# self.control = self.callback_handler.on_substep_end(args, self.state, self.control)
# pdb.set_trace()
# if self.control.should_epoch_stop or self.control.should_training_stop:
# print('should stop')
# pdb.set_trace()
# break
# if step < 0:
# logger.warning(
# "There seems to be not a single sample in your epoch_iterator, stopping training at step"
# f" {self.state.global_step}! This is expected if you're using an IterableDataset and set"
# f" num_steps ({max_steps}) higher than the number of available samples."
# )
# self.control.should_training_stop = True
# self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
# self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
# if self.control.should_training_stop:
# break
# if args.past_index and hasattr(self, "_past"):
# # Clean the state at the end of training
# delattr(self, "_past")
# logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
# if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
# # Wait for everyone to get here so we are sur the model has been saved by process 0.
# # if is_torch_tpu_available():
# # xm.rendezvous("load_best_model_at_end")
# if args.local_rank != -1:
# dist.barrier()
# # elif is_sagemaker_mp_enabled():
# # smp.barrier()
# self._load_best_model()
# # add remaining tr_loss
# self._total_loss_scalar += tr_loss.item()
# train_loss = self._total_loss_scalar / self.state.global_step
# metrics = speed_metrics("train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)
# self.store_flos()
# metrics["total_flos"] = self.state.total_flos
# metrics["train_loss"] = train_loss
# self.is_in_train = False
# self._memory_tracker.stop_and_update_metrics(metrics)
# self.log(metrics)
# run_dir = self._get_output_dir(trial)
# checkpoints_sorted = self._sorted_checkpoints(use_mtime=False, output_dir=run_dir)
# # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint.
# if self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1:
# for checkpoint in checkpoints_sorted:
# if checkpoint != self.state.best_model_checkpoint:
# logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
# shutil.rmtree(checkpoint)
# self.control = self.callback_handler.on_train_end(args, self.state, self.control)
# return TrainOutput(self.state.global_step, train_loss, metrics)
# def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
# """
# Perform a training step on a batch of inputs.
# Subclass and override to inject custom behavior.
# Args:
# model (`nn.Module`):
# The model to train.
# inputs (`Dict[str, Union[torch.Tensor, Any]]`):
# The inputs and targets of the model.
# The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
# argument `labels`. Check your model's documentation for all accepted arguments.
# Return:
# `torch.Tensor`: The tensor with training loss on this batch.
# """
# model.train()
# inputs = self._prepare_inputs(inputs)
# # if is_sagemaker_mp_enabled():
# # loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps)
# # return loss_mb.reduce_mean().detach().to(self.args.device)
# with self.compute_loss_context_manager():
# loss = self.compute_loss(model, inputs)
# if self.args.n_gpu > 1:
# loss = loss.mean() # mean() to average on multi-gpu parallel training
# if self.args.gradient_accumulation_steps > 1 and not self.deepspeed:
# # deepspeed handles loss scaling by gradient_accumulation_steps in its `backward`
# loss = loss / self.args.gradient_accumulation_steps
# if self.do_grad_scaling:
# self.scaler.scale(loss).backward()
# # elif self.use_apex:
# # with amp.scale_loss(loss, self.optimizer) as scaled_loss:
# # scaled_loss.backward()
# elif self.deepspeed:
# # loss gets scaled under gradient_accumulation_steps in deepspeed
# loss = self.deepspeed.backward(loss)
# else:
# loss.backward()
# return loss.detach()
def prediction_step(
self,
model,
batch,
prediction_loss_only: bool = False,
ignore_keys: Optional[List[str]] = None
) -> Tuple[torch.tensor, torch.tensor, torch.tensor]:
batch = self._move_batch_to_device(batch=batch)
with torch.no_grad():
(loss, outputs) = self.compute_loss(
model=model,
batch=batch,
return_outputs=True
)
if not prediction_loss_only and 'labels' in batch:
return (loss, outputs['decoding_logits'], batch['labels'])
else:
return (loss, outputs, None)
def compute_loss(
self,
model,
batch,
return_outputs=False,
**kwargs
):
batch = self._move_batch_to_device(batch=batch)
if isinstance(
model,
(
torch.nn.DataParallel,
torch.nn.parallel.DistributedDataParallel
)
) or self.is_deepspeed:
(losses, outputs) = model.module.compute_loss(
batch=batch,
return_outputs=True
)
else:
(losses, outputs) = model.compute_loss(
batch=batch,
return_outputs=True
)
loss = losses['loss'] if 'loss' in losses.keys() else sum(losses.values())
return (loss, outputs) if return_outputs else loss
def _move_batch_to_device(
self,
batch
) -> Dict[str, torch.tensor]:
batch = self._prepare_inputs(batch)
if "labels" in batch:
batch["labels"] = batch["labels"].to(torch.long).to(batch["inputs"].device)
return self._prepare_inputs(batch)
================================================
FILE: src/trainer/make.py
================================================
#!/usr/bin/env python3
import os
from typing import Dict, List, Tuple
import numpy as np
from sklearn.metrics import accuracy_score
import torch
from transformers import TrainingArguments,TrainerCallback
from trainer.base import Trainer
class CSVLogCallback(TrainerCallback):
def __init__(self):
super().__init__()
self.train_log_filepath = None
self.eval_log_filepath = None
def on_log(
self,
args,
state,
control,
model,
**kwargs
) -> None:
if args.local_rank not in {-1, 0}:
return
if self.train_log_filepath is None:
self.train_log_filepath = os.path.join(
args.output_dir,
'train_history.csv'
)
with open(self.train_log_filepath, 'a') as f:
f.write('step,loss,lr\n')
if self.eval_log_filepath is None:
self.eval_log_filepath = os.path.join(
args.output_dir,
'eval_history.csv'
)
with open(self.eval_log_filepath, 'a') as f:
f.write('step,loss,accuracy\n')
is_eval = any('eval' in k for k in state.log_history[-1].keys())
if is_eval:
with open(self.eval_log_filepath, 'a') as f:
f.write('{},{},{}\n'.format(
state.global_step,
state.log_history[-1]['eval_loss'],
state.log_history[-1]['eval_accuracy'] if 'eval_accuracy' in state.log_history[-1] else np.nan
)
)
else:
with open(self.train_log_filepath, 'a') as f:
f.write('{},{},{}\n'.format(
state.global_step,
state.log_history[-1]['loss'] if 'loss' in state.log_history[-1] else state.log_history[-1]['train_loss'],
state.log_history[-1]['learning_rate'] if 'learning_rate' in state.log_history[-1] else None
)
)
def _cat_data_collator(features: List) -> Dict[str, torch.tensor]:
if not isinstance(features[0], dict):
features = [vars(f) for f in features]
return {
k: torch.cat(
[
f[k]
for f in features
]
)
for k in features[0].keys()
if not k.startswith('__')
}
def decoding_accuracy_metrics(eval_preds):
preds, labels = eval_preds
preds = preds.argmax(axis=-1)
accuracy = accuracy_score(labels, preds)
return {
"accuracy": round(accuracy, 3)
}
def make_trainer(
model_init,
training_style,
train_dataset,
validation_dataset,
do_train: bool = True,
do_eval: bool = True,
run_name: str = None,
output_dir: str = None,
overwrite_output_dir: bool = True,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
optim: str='adamw_hf',
learning_rate: float = 1e-4,
weight_decay: float = 0.1,
adam_beta1: float=0.9,
adam_beta2: float=0.999,
adam_epsilon: float=1e-8,
max_grad_norm: float=1.0,
per_device_train_batch_size: int = 64,
per_device_eval_batch_size: int = 64,
dataloader_num_workers: int = 0,
max_steps: int = 400000,
num_train_epochs: int = 1,
lr_scheduler_type: str = 'linear',
warmup_ratio: float = 0.01,
evaluation_strategy: str = 'steps',
prediction_loss_only: bool = False,
logging_strategy: str = 'steps',
save_strategy: str = 'steps',
save_total_limit: int = 5,
save_steps: int = 10000,
logging_steps: int = 10000,
eval_steps: int = None,
logging_first_step: bool = True,
greater_is_better: bool = True,
seed: int = 1,
fp16: bool = True,
deepspeed: str = None,
compute_metrics = None,
**kwargs
) -> Trainer:
"""
Make a Trainer object for training a model.
Returns an instance of transformers.Trainer.
See the HuggingFace transformers documentation for more details
on input arguments:
https://huggingface.co/transformers/main_classes/trainer.html
Custom arguments:
---
model_init: callable
A callable that does not require any arguments and
returns model that is to be trained (see scripts.train.model_init)
training_style: str
The training style (ie., framework) to use.
One of: 'BERT', 'CSM', 'NetBERT', 'autoencoder',
'decoding'.
train_dataset: src.batcher.dataset
The training dataset, as generated by src.batcher.dataset
validation_dataset: src.batcher.dataset
The validation dataset, as generated by src.batcher.dataset
Returns
----
trainer: transformers.Trainer
"""
trainer_args = TrainingArguments(
output_dir=output_dir,
run_name=run_name,
do_train=do_train,
do_eval=do_eval,
overwrite_output_dir=overwrite_output_dir,
prediction_loss_only=prediction_loss_only,
per_device_train_batch_size=per_device_train_batch_size,
per_device_eval_batch_size=per_device_eval_batch_size,
dataloader_num_workers=dataloader_num_workers,
optim=optim,
learning_rate=learning_rate,
warmup_ratio=warmup_ratio,
max_steps=max_steps,
num_train_epochs=num_train_epochs,
weight_decay=weight_decay,
adam_beta1=adam_beta1,
adam_beta2=adam_beta2,
adam_epsilon=adam_epsilon,
lr_scheduler_type=lr_scheduler_type,
save_strategy=save_strategy,
save_total_limit=save_total_limit,
greater_is_better=greater_is_better,
save_steps=save_steps,
logging_strategy=logging_strategy,
logging_first_step=logging_first_step,
logging_steps=logging_steps,
evaluation_strategy=evaluation_strategy,
eval_steps=eval_steps if eval_steps is not None else logging_steps,
seed=seed,
fp16=fp16,
max_grad_norm=max_grad_norm,
deepspeed=deepspeed,
**kwargs
)
data_collator = _cat_data_collator
is_deepspeed = deepspeed is not None
# TODO: custom compute_metrics so far not working in multi-gpu setting
compute_metrics = decoding_accuracy_metrics if training_style=='decoding' and compute_metrics is None else compute_metrics
trainer = Trainer(
args=trainer_args,
model_init=model_init,
train_dataset=train_dataset,
eval_dataset=validation_dataset,
data_collator=data_collator,
compute_metrics=compute_metrics,
optimizers=optimizers,
is_deepspeed=is_deepspeed
)
trainer.add_callback(CSVLogCallback)
return trainer
================================================
FILE: src/utils.py
================================================
import os
import pdb
import shutil
import h5py
import numpy as np
import gzip
import pickle
import time
import pandas as pd
def load_tuh_all(path):
# files = os.listdir(path)
filepath = []
file=""
# for file in files:
groups = os.listdir(path)
for group in groups:
if os.path.isdir(os.path.join(path, group)):
subs = os.listdir(os.path.join(path, file, group))
else:
continue
for sub in subs:
sessions = os.listdir(os.path.join(path, file, group, sub))
for sess in sessions:
montages = os.listdir(os.path.join(path, file, group, sub, sess))
for mont in montages:
edf_files = os.listdir(os.path.join(path, file, group, sub, sess, mont))
for edf in edf_files:
full_path = os.path.join(path, file, group, sub, sess, mont, edf)
filepath.append(full_path)
# pdb.set_trace()
shutil.move(full_path, os.path.join(path, group, sess + "_" + mont + "_" + edf))
# pdb.set_trace()
# load_eeg(filepath[-1])
return filepath
def load_pickle(filename):
start_time = time.time()
with gzip.open(filename, "rb") as file:
data = pickle.load(file)
print(data)
end_time = time.time()
print("Compressed Elapsed time:", end_time - start_time, "seconds")
return data['data'], np.array(data['channel'])
def read_threshold_sub(csv_file, lower_bound=2599, upper_bound=10000
gitextract_e8qms5uv/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
├── scripts/
│ ├── finetune.sh
│ └── train.sh
└── src/
├── batcher/
│ ├── base.py
│ ├── downstream_dataset.py
│ └── make.py
├── decoder/
│ ├── gpt.py
│ ├── make_decoder.py
│ └── unembedder.py
├── embedder/
│ ├── base.py
│ ├── csm.py
│ ├── csm_causal.py
│ └── make.py
├── encoder/
│ ├── base.py
│ └── conformer_braindecode.py
├── model.py
├── train_gpt.py
├── trainer/
│ ├── base.py
│ └── make.py
└── utils.py
SYMBOL INDEX (151 symbols across 17 files)
FILE: src/batcher/base.py
function _pad_seq_right_to_n (line 14) | def _pad_seq_right_to_n(
class EEGDataset (line 34) | class EEGDataset(Dataset):
method __init__ (line 35) | def __init__(self, filenames, sample_keys, chunk_len=500, num_chunks=1...
method __len__ (line 54) | def __len__(self):
method __getitem__ (line 57) | def __getitem__(self, idx):
method _pad_seq_right_to_n (line 64) | def _pad_seq_right_to_n(
method load_single_file (line 75) | def load_single_file(self, filename):
method load_tensor (line 87) | def load_tensor(self, filename):
method reorder_channels (line 92) | def reorder_channels(self, data):
method split_chunks (line 103) | def split_chunks(self, data, length=500, ovlp=50, num_chunks=10, start...
method normalize (line 122) | def normalize(self, data):
method preprocess_sample (line 131) | def preprocess_sample(
FILE: src/batcher/downstream_dataset.py
class MotorImageryDataset (line 8) | class MotorImageryDataset(EEGDataset):
method __init__ (line 9) | def __init__(self, filenames, sample_keys, chunk_len=500, num_chunks=1...
method __len__ (line 27) | def __len__(self):
method __getitem__ (line 30) | def __getitem__(self, idx):
method map2pret (line 33) | def map2pret(self, data):
method get_trials_from_single_subj (line 36) | def get_trials_from_single_subj(self, sub_id):
method get_labels (line 72) | def get_labels(self, sub_id):
method get_trials_all (line 79) | def get_trials_all(self):
method bandpass_filter (line 98) | def bandpass_filter(self, data, lowcut, highcut, fs, order=5):
FILE: src/batcher/make.py
function make_batcher (line 6) | def make_batcher(
FILE: src/decoder/gpt.py
class GPTModel (line 9) | class GPTModel(torch.nn.Module):
method __init__ (line 10) | def __init__(
method switch_decoding_mode (line 53) | def switch_decoding_mode(
method add_pooler_layer (line 66) | def add_pooler_layer(self):
method add_decoding_head (line 80) | def add_decoding_head(
method decode (line 113) | def decode(
method forward (line 131) | def forward(
class PretrainedGPT2 (line 155) | class PretrainedGPT2(GPTModel):
method __init__ (line 157) | def __init__(
FILE: src/decoder/make_decoder.py
function make_decoder (line 4) | def make_decoder(
FILE: src/decoder/unembedder.py
class DeconvNet (line 8) | class DeconvNet(nn.Module):
method __init__ (line 9) | def __init__(self, n_filters_time=40, n_channels=22, filter_time_lengt...
method forward (line 18) | def forward(self, x):
class UnEmbedder (line 26) | class UnEmbedder(torch.nn.Module):
method __init__ (line 50) | def __init__(
method stack_inputs (line 85) | def stack_inputs(
method unstack_inputs (line 95) | def unstack_inputs(
method forward (line 107) | def forward(
function make_unembedder (line 122) | def make_unembedder(
FILE: src/embedder/base.py
class EmbeddingModel (line 8) | class EmbeddingModel(torch.nn.Module):
method __init__ (line 10) | def __init__(
method _stack_inputs (line 47) | def _stack_inputs(
method _unstack_inputs (line 57) | def _unstack_inputs(
method forward (line 69) | def forward(
class BaseEmbedder (line 82) | class BaseEmbedder(torch.nn.Module):
method __init__ (line 83) | def __init__(self,
method switch_decoding_mode (line 112) | def switch_decoding_mode(self, is_decoding_mode: bool=False) -> None:
method _pad_tensor_left_by_n (line 121) | def _pad_tensor_left_by_n(
method _round_to_precision (line 144) | def _round_to_precision(
method embed_inputs (line 151) | def embed_inputs(
method forward (line 157) | def forward(
method decoding_loss (line 170) | def decoding_loss(
method reconstruction_loss (line 184) | def reconstruction_loss(
method prep_batch (line 198) | def prep_batch(
method _root_loss (line 223) | def _root_loss(
method loss (line 237) | def loss(
FILE: src/embedder/csm.py
class CSMEmbedder (line 10) | class CSMEmbedder(BaseEmbedder):
method __init__ (line 12) | def __init__(
method _init_embeds (line 39) | def _init_embeds(self):
method prep_batch (line 48) | def prep_batch(
method mask_inputs (line 65) | def mask_inputs(
method add_cls_embed (line 147) | def add_cls_embed(
method masking_loss (line 217) | def masking_loss(
method _root_loss (line 231) | def _root_loss(
FILE: src/embedder/csm_causal.py
class CSMEmbedder (line 10) | class CSMEmbedder(BaseEmbedder):
method __init__ (line 12) | def __init__(
method _init_embeds (line 39) | def _init_embeds(self):
method duplicate_batch (line 48) | def duplicate_batch(self, batch: Dict[str, torch.Tensor]) -> Dict[str,...
method prep_batch (line 64) | def prep_batch(
method mask_inputs (line 84) | def mask_inputs(
method add_cls_embed (line 172) | def add_cls_embed(
method masking_loss (line 242) | def masking_loss(
method _root_loss (line 256) | def _root_loss(
FILE: src/embedder/make.py
function make_embedder (line 6) | def make_embedder(
FILE: src/encoder/base.py
function deprecated_args (line 15) | def deprecated_args(obj, *old_new_args):
class EEGModuleMixin (line 32) | class EEGModuleMixin():
method __init__ (line 73) | def __init__(
method n_outputs (line 109) | def n_outputs(self):
method n_chans (line 115) | def n_chans(self):
method chs_info (line 125) | def chs_info(self):
method n_times (line 131) | def n_times(self):
method input_window_seconds (line 146) | def input_window_seconds(self):
method sfreq (line 161) | def sfreq(self):
method add_log_softmax (line 176) | def add_log_softmax(self):
method input_shape (line 183) | def input_shape(self) -> Tuple[int]:
method get_output_shape (line 187) | def get_output_shape(self) -> Tuple[int]:
method load_state_dict (line 220) | def load_state_dict(self, state_dict, *args, **kwargs):
method to_dense_prediction_model (line 232) | def to_dense_prediction_model(self, axis: Tuple[int] = (2, 3)) -> None:
method get_torchinfo_statistics (line 275) | def get_torchinfo_statistics(
method __str__ (line 309) | def __str__(self) -> str:
FILE: src/encoder/conformer_braindecode.py
class EEGConformer (line 13) | class EEGConformer(EEGModuleMixin, nn.Module):
method __init__ (line 77) | def __init__(
method forward (line 158) | def forward(self, x: Tensor) -> Tensor:
method get_fc_size (line 173) | def get_fc_size(self):
class _PatchEmbedding (line 184) | class _PatchEmbedding(nn.Module):
method __init__ (line 211) | def __init__(
method forward (line 245) | def forward(self, x: Tensor) -> Tensor:
class _MultiHeadAttention (line 252) | class _MultiHeadAttention(nn.Module):
method __init__ (line 253) | def __init__(self, emb_size, num_heads, dropout):
method forward (line 263) | def forward(self, x: Tensor, mask: Tensor = None) -> Tensor:
class _ResidualAdd (line 287) | class _ResidualAdd(nn.Module):
method __init__ (line 288) | def __init__(self, fn):
method forward (line 292) | def forward(self, x, **kwargs):
class _FeedForwardBlock (line 299) | class _FeedForwardBlock(nn.Sequential):
method __init__ (line 300) | def __init__(self, emb_size, expansion, drop_p):
class _TransformerEncoderBlock (line 309) | class _TransformerEncoderBlock(nn.Sequential):
method __init__ (line 310) | def __init__(self, emb_size, att_heads, att_drop, forward_expansion=4):
class _TransformerEncoder (line 332) | class _TransformerEncoder(nn.Sequential):
method __init__ (line 350) | def __init__(self, att_depth, emb_size, att_heads, att_drop):
class _FullyConnected (line 359) | class _FullyConnected(nn.Module):
method __init__ (line 360) | def __init__(self, final_fc_length,
method forward (line 395) | def forward(self, x):
class _FinalLayer (line 401) | class _FinalLayer(nn.Module):
method __init__ (line 402) | def __init__(self, n_classes, hidden_channels=32, return_features=Fals...
method forward (line 429) | def forward(self, x):
FILE: src/model.py
class Model (line 7) | class Model(torch.nn.Module):
method __init__ (line 34) | def __init__(
method from_pretrained (line 51) | def from_pretrained(
method switch_ft_mode (line 86) | def switch_ft_mode(self, ft_encoder_only=False):
method switch_decoding_mode (line 89) | def switch_decoding_mode(
method compute_loss (line 113) | def compute_loss(
method prep_batch (line 148) | def prep_batch(
method forward (line 162) | def forward(
FILE: src/train_gpt.py
function train (line 59) | def train(config: Dict=None) -> Trainer:
function make_model (line 255) | def make_model(model_config: Dict=None):
function get_config (line 356) | def get_config(args: argparse.Namespace=None) -> Dict:
function get_args (line 428) | def get_args() -> argparse.ArgumentParser:
FILE: src/trainer/base.py
class Trainer (line 52) | class Trainer(Trainer):
method __init__ (line 53) | def __init__(
method get_train_dataloader (line 62) | def get_train_dataloader(self) -> DataLoader:
method get_eval_dataloader (line 113) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
method get_test_dataloader (line 163) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
method prediction_step (line 622) | def prediction_step(
method compute_loss (line 644) | def compute_loss(
method _move_batch_to_device (line 675) | def _move_batch_to_device(
FILE: src/trainer/make.py
class CSVLogCallback (line 12) | class CSVLogCallback(TrainerCallback):
method __init__ (line 14) | def __init__(self):
method on_log (line 19) | def on_log(
function _cat_data_collator (line 71) | def _cat_data_collator(features: List) -> Dict[str, torch.tensor]:
function decoding_accuracy_metrics (line 88) | def decoding_accuracy_metrics(eval_preds):
function make_trainer (line 97) | def make_trainer(
FILE: src/utils.py
function load_tuh_all (line 12) | def load_tuh_all(path):
function load_pickle (line 39) | def load_pickle(filename):
function read_threshold_sub (line 50) | def read_threshold_sub(csv_file, lower_bound=2599, upper_bound=1000000):
function get_epi_files (line 61) | def get_epi_files(path, epi_csv, nonepi_csv, lower_bound=2599, upper_bou...
function read_sub_list (line 73) | def read_sub_list(epi_list):
function exclude_epi_subs (line 80) | def exclude_epi_subs(csv_file, epi_list, lower_bound=2599, upper_bound=1...
function exclude_sz_subs (line 91) | def exclude_sz_subs(csv_file, lower_bound=2599, upper_bound=1000000, fil...
function cv_split_bci (line 102) | def cv_split_bci(filenames):
Condensed preview — 23 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (207K chars).
[
{
"path": ".gitignore",
"chars": 1863,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\nslurm*\n*.npy\n*.out\n*.csv\n*.pt\n*.bin\n*.json\n*.p"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 1512,
"preview": "# NeuroGPT\n### Neuro-GPT: Towards a Foundation Model for EEG [paper](https://arxiv.org/abs/2311.03764)\n\n#### Published "
},
{
"path": "requirements.txt",
"chars": 153,
"preview": "einops==0.7.0\nh5py==3.10.0\nnumpy==1.26.4\npandas==2.2.1\nscikit_learn==1.4.0\nscipy==1.12.0\ntorch==2.2.0\ntorchinfo==1.8.0\nt"
},
{
"path": "scripts/finetune.sh",
"chars": 534,
"preview": "python3 ../src/train_gpt.py --training-style='decoding' --num-decoding-classes=4 --training-steps=10000 --eval_every_n_"
},
{
"path": "scripts/train.sh",
"chars": 405,
"preview": "python3 ../src/train_gpt.py --training-steps=50000 --eval_every_n_steps=1000 --log-every-n-steps=3000 --per-device-train"
},
{
"path": "src/batcher/base.py",
"chars": 5986,
"preview": "#!/usr/bin/env python3\nfrom typing import Dict\nimport numpy as np\n# import webdataset as wds\nimport torch\n# import gzip\n"
},
{
"path": "src/batcher/downstream_dataset.py",
"chars": 4524,
"preview": "import os\nimport pdb\nimport numpy as np\nfrom batcher.base import EEGDataset\nfrom scipy.io import loadmat\nfrom scipy.sign"
},
{
"path": "src/batcher/make.py",
"chars": 3098,
"preview": "#!/usr/bin/env python3\n\nfrom batcher.base import BaseBatcher\n\n\ndef make_batcher(\n training_style: str='CSM',\n tr: "
},
{
"path": "src/decoder/gpt.py",
"chars": 6050,
"preview": "#!/usr/bin/env python3\n\nfrom typing import Dict\nimport warnings\nimport torch\nfrom transformers import GPT2Config, GPT2Mo"
},
{
"path": "src/decoder/make_decoder.py",
"chars": 3309,
"preview": "#!/usr/bin/env python3\nimport torch\n\ndef make_decoder(\n architecture: str='GPT',\n num_hidden_layers: int = 4,\n "
},
{
"path": "src/decoder/unembedder.py",
"chars": 4384,
"preview": "#!/usr/bin/env python3\n\nimport torch\nfrom einops import rearrange\nimport torch.nn as nn\nfrom einops.layers.torch import "
},
{
"path": "src/embedder/base.py",
"chars": 6917,
"preview": "#/usr/bin/env python3\n\nimport pdb\nimport torch\nfrom typing import Dict\nfrom einops import rearrange\n\nclass EmbeddingMode"
},
{
"path": "src/embedder/csm.py",
"chars": 7334,
"preview": "\n#/usr/bin/env python3\n\nimport pdb\nfrom typing import Dict\nimport torch\nfrom embedder.base import BaseEmbedder\n\n\nclass C"
},
{
"path": "src/embedder/csm_causal.py",
"chars": 8554,
"preview": "\n#/usr/bin/env python3\n\nimport pdb\nfrom typing import Dict, Tuple\nimport torch\nfrom embedder.base import BaseEmbedder\nim"
},
{
"path": "src/embedder/make.py",
"chars": 3055,
"preview": "#!/usr/bin/env python3\n\nimport torch\n\n\ndef make_embedder(\n architecture: str='GPT',\n training_style: str='CSM',\n "
},
{
"path": "src/encoder/base.py",
"chars": 11040,
"preview": "# Authors: Pierre Guetschel\n# Maciej Sliwowski\n#\n# License: BSD-3\nimport warnings\nfrom typing import Dict, Iter"
},
{
"path": "src/encoder/conformer_braindecode.py",
"chars": 14322,
"preview": "# Authors: Yonghao Song <eeyhsong@gmail.com>\n#\n# License: BSD (3-clause)\nimport torch\nimport torch.nn.functional as F\nfr"
},
{
"path": "src/model.py",
"chars": 7384,
"preview": "#!/usr/bin/env python3 \nimport torch\nfrom typing import Dict\nimport warnings\n\n\nclass Model(torch.nn.Module):\n \"\"\"\n "
},
{
"path": "src/train_gpt.py",
"chars": 31256,
"preview": "#!/usr/bin/env python3\n\n\"\"\"\ntrain.py\n\nTraining of models on given data. See get_args() for \ndetails on command line argu"
},
{
"path": "src/trainer/base.py",
"chars": 31892,
"preview": "#!/usr/bin/env python3\nfrom typing import Dict, List, Optional, Tuple\n\nfrom collections.abc import Mapping\nfrom pathlib "
},
{
"path": "src/trainer/make.py",
"chars": 6787,
"preview": "#!/usr/bin/env python3\n\nimport os\nfrom typing import Dict, List, Tuple\nimport numpy as np\nfrom sklearn.metrics import ac"
},
{
"path": "src/utils.py",
"chars": 3976,
"preview": "import os\nimport pdb\nimport shutil\n\nimport h5py\nimport numpy as np\nimport gzip\nimport pickle\nimport time\nimport pandas a"
}
]
About this extraction
This page contains the full source code of the wenhui0206/NeuroGPT GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 23 files (194.8 KB), approximately 44.4k tokens, and a symbol index with 151 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.