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examples/distillation/README.md
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...@@ -2,25 +2,34 @@ ...@@ -2,25 +2,34 @@
This folder contains the original code used to train DistilBERT as well as examples showcasing how to use DistilBERT. This folder contains the original code used to train DistilBERT as well as examples showcasing how to use DistilBERT.
**2019, September 19th - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 97% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!
## What is DistilBERT ## What is DistilBERT
DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
For more information on DistilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5 For more information on DistilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
). ). *Please note that we will publish a formal write-up with updated and more complete results in the near future (September 19th).*
Here's the updated results on the dev sets of GLUE:
| Model | Macro-score | CoLA | MNLI | MRPC | QNLI | QQP | RTE | SST-2 | STS-B | WNLI |
| :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:|
| BERT-base | **77.6** | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7 |
| DistilBERT | **75.2** | 49.1 | 81.8 | 90.2 | 87.0 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4 |
## Setup ## Setup
This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`.
**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0). It is important to note that there is a small internal bug in the current version of PyTorch available on pip that causes a memory leak in our training/distillation. It has been recently fixed and will likely be integrated into the next release. For the moment, we recommend to [compile PyTorch from source](https://github.com/pytorch/pytorch#from-source). Please refer to [issue 1179](https://github.com/huggingface/pytorch-transformers/issues/1179) for more details. **Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0). It is important to note that there is a small internal bug in the current version of PyTorch available on pip that causes a memory leak in our training/distillation. It has been recently fixed and will likely be integrated into the next release. For the moment, we recommend to [compile PyTorch from source](https://github.com/pytorch/pytorch#from-source). Please refer to [issue 1179](https://github.com/huggingface/transformers/issues/1179) for more details.
## How to use DistilBERT ## How to use DistilBERT
PyTorch-Transformers includes two pre-trained DistilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT): Transformers includes two pre-trained DistilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):
- `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters. - `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.2 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score). - `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models. Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
......
examples/distillation/dataset.py
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...@@ -92,11 +92,11 @@ class Dataset: ...@@ -92,11 +92,11 @@ class Dataset:
Too short sequences are simply removed. This could be tunedd. Too short sequences are simply removed. This could be tunedd.
""" """
init_size = len(self) init_size = len(self)
indices = self.lengths > 5 indices = self.lengths > 11
self.token_ids = self.token_ids[indices] self.token_ids = self.token_ids[indices]
self.lengths = self.lengths[indices] self.lengths = self.lengths[indices]
new_size = len(self) new_size = len(self)
logger.info(f'Remove {init_size - new_size} too short (<=5 tokens) sequences.') logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')
def print_statistics(self): def print_statistics(self):
""" """
......
examples/distillation/distiller.py
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...@@ -18,15 +18,18 @@ ...@@ -18,15 +18,18 @@
import os import os
import math import math
import psutil import psutil
import time
from tensorboardX import SummaryWriter from tensorboardX import SummaryWriter
from tqdm import trange, tqdm from tqdm import trange, tqdm
import numpy as np import numpy as np
import psutil
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
from torch.optim import AdamW
from pytorch_transformers import AdamW, WarmupLinearSchedule from transformers import WarmupLinearSchedule
from utils import logger from utils import logger
from dataset import Dataset from dataset import Dataset
...@@ -58,10 +61,12 @@ class Distiller: ...@@ -58,10 +61,12 @@ class Distiller:
self.alpha_ce = params.alpha_ce self.alpha_ce = params.alpha_ce
self.alpha_mlm = params.alpha_mlm self.alpha_mlm = params.alpha_mlm
self.alpha_mse = params.alpha_mse self.alpha_mse = params.alpha_mse
self.alpha_cos = params.alpha_cos
assert self.alpha_ce >= 0. assert self.alpha_ce >= 0.
assert self.alpha_mlm >= 0. assert self.alpha_mlm >= 0.
assert self.alpha_mse >= 0. assert self.alpha_mse >= 0.
assert self.alpha_ce + self.alpha_mlm + self.alpha_mse > 0. assert self.alpha_cos >= 0.
assert self.alpha_ce + self.alpha_mlm + self.alpha_mse + self.alpha_cos > 0.
self.mlm_mask_prop = params.mlm_mask_prop self.mlm_mask_prop = params.mlm_mask_prop
assert 0.0 <= self.mlm_mask_prop <= 1.0 assert 0.0 <= self.mlm_mask_prop <= 1.0
...@@ -81,17 +86,21 @@ class Distiller: ...@@ -81,17 +86,21 @@ class Distiller:
self.last_loss = 0 self.last_loss = 0
self.last_loss_ce = 0 self.last_loss_ce = 0
self.last_loss_mlm = 0 self.last_loss_mlm = 0
self.last_loss_mse = 0 if self.alpha_mse > 0.: self.last_loss_mse = 0
if self.alpha_cos > 0.: self.last_loss_cos = 0
self.last_log = 0
self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean') self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean')
self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1) self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
if self.alpha_mse > 0.:
self.mse_loss_fct = nn.MSELoss(reduction='sum') self.mse_loss_fct = nn.MSELoss(reduction='sum')
if self.alpha_cos > 0.:
self.cosine_loss_fct = nn.CosineEmbeddingLoss(reduction='mean')
logger.info('--- Initializing model optimizer') logger.info('--- Initializing model optimizer')
assert params.gradient_accumulation_steps >= 1 assert params.gradient_accumulation_steps >= 1
self.num_steps_epoch = int(len(self.dataloader) / params.batch_size) + 1 self.num_steps_epoch = int(len(self.dataloader) / params.batch_size) + 1
num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1 num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
no_decay = ['bias', 'LayerNorm.weight'] no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [ optimizer_grouped_parameters = [
...@@ -104,6 +113,8 @@ class Distiller: ...@@ -104,6 +113,8 @@ class Distiller:
lr=params.learning_rate, lr=params.learning_rate,
eps=params.adam_epsilon, eps=params.adam_epsilon,
betas=(0.9, 0.98)) betas=(0.9, 0.98))
warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
self.scheduler = WarmupLinearSchedule(self.optimizer, self.scheduler = WarmupLinearSchedule(self.optimizer,
warmup_steps=warmup_steps, warmup_steps=warmup_steps,
t_total=num_train_optimization_steps) t_total=num_train_optimization_steps)
...@@ -272,11 +283,14 @@ class Distiller: ...@@ -272,11 +283,14 @@ class Distiller:
The real training loop. The real training loop.
""" """
if self.is_master: logger.info('Starting training') if self.is_master: logger.info('Starting training')
self.last_log = time.time()
self.student.train() self.student.train()
self.teacher.eval() self.teacher.eval()
for _ in range(self.params.n_epoch): for _ in range(self.params.n_epoch):
if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}') if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}')
if self.multi_gpu:
torch.distributed.barrier()
iter_bar = trange(self.num_steps_epoch, desc="-Iter", disable=self.params.local_rank not in [-1, 0]) iter_bar = trange(self.num_steps_epoch, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
for __ in range(self.num_steps_epoch): for __ in range(self.num_steps_epoch):
...@@ -314,9 +328,9 @@ class Distiller: ...@@ -314,9 +328,9 @@ class Distiller:
attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention. attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention.
mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels. mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels.
""" """
s_logits = self.student(input_ids=input_ids, attention_mask=attention_mask)[0] # (bs, seq_length, voc_size) s_logits, s_hidden_states = self.student(input_ids=input_ids, attention_mask=attention_mask) # (bs, seq_length, voc_size)
with torch.no_grad(): with torch.no_grad():
t_logits = self.teacher(input_ids=input_ids, attention_mask=attention_mask)[0] # (bs, seq_length, voc_size) t_logits, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=attention_mask) # (bs, seq_length, voc_size)
assert s_logits.size() == t_logits.size() assert s_logits.size() == t_logits.size()
#https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100 #https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
...@@ -341,6 +355,22 @@ class Distiller: ...@@ -341,6 +355,22 @@ class Distiller:
loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction
loss += self.alpha_mse * loss_mse loss += self.alpha_mse * loss_mse
if self.alpha_cos > 0.:
s_hidden_states = s_hidden_states[-1] # (bs, seq_length, dim)
t_hidden_states = t_hidden_states[-1] # (bs, seq_length, dim)
mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states) # (bs, seq_length, dim)
assert s_hidden_states.size() == t_hidden_states.size()
dim = s_hidden_states.size(-1)
s_hidden_states_slct = torch.masked_select(s_hidden_states, mask) # (bs * seq_length * dim)
s_hidden_states_slct = s_hidden_states_slct.view(-1, dim) # (bs * seq_length, dim)
t_hidden_states_slct = torch.masked_select(t_hidden_states, mask) # (bs * seq_length * dim)
t_hidden_states_slct = t_hidden_states_slct.view(-1, dim) # (bs * seq_length, dim)
target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1) # (bs * seq_length,)
loss_cos = self.cosine_loss_fct(s_hidden_states_slct, t_hidden_states_slct, target)
loss += self.alpha_cos * loss_cos
self.total_loss_epoch += loss.item() self.total_loss_epoch += loss.item()
self.last_loss = loss.item() self.last_loss = loss.item()
self.last_loss_ce = loss_ce.item() self.last_loss_ce = loss_ce.item()
...@@ -348,6 +378,8 @@ class Distiller: ...@@ -348,6 +378,8 @@ class Distiller:
self.last_loss_mlm = loss_mlm.item() self.last_loss_mlm = loss_mlm.item()
if self.alpha_mse > 0.: if self.alpha_mse > 0.:
self.last_loss_mse = loss_mse.item() self.last_loss_mse = loss_mse.item()
if self.alpha_cos > 0.:
self.last_loss_cos = loss_cos.item()
self.optimize(loss) self.optimize(loss)
...@@ -396,6 +428,7 @@ class Distiller: ...@@ -396,6 +428,7 @@ class Distiller:
if self.n_total_iter % self.params.log_interval == 0: if self.n_total_iter % self.params.log_interval == 0:
self.log_tensorboard() self.log_tensorboard()
self.last_log = time.time()
if self.n_total_iter % self.params.checkpoint_interval == 0: if self.n_total_iter % self.params.checkpoint_interval == 0:
self.save_checkpoint() self.save_checkpoint()
...@@ -421,9 +454,12 @@ class Distiller: ...@@ -421,9 +454,12 @@ class Distiller:
self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter)
if self.alpha_mse > 0.: if self.alpha_mse > 0.:
self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter)
if self.alpha_cos > 0.:
self.tensorboard.add_scalar(tag="losses/loss_cos", scalar_value=self.last_loss_cos, global_step=self.n_total_iter)
self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter)
self.tensorboard.add_scalar(tag="global/memory_usage", scalar_value=psutil.virtual_memory()._asdict()['used']/1_000_000, global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="global/memory_usage", scalar_value=psutil.virtual_memory()._asdict()['used']/1_000_000, global_step=self.n_total_iter)
self.tensorboard.add_scalar(tag="global/speed", scalar_value=time.time()-self.last_log, global_step=self.n_total_iter)
def end_epoch(self): def end_epoch(self):
""" """
......
examples/distillation/requirements.txt
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...@@ -2,3 +2,5 @@ gitpython==3.0.2 ...@@ -2,3 +2,5 @@ gitpython==3.0.2
tensorboard>=1.14.0 tensorboard>=1.14.0
tensorboardX==1.8 tensorboardX==1.8
psutil==5.6.3 psutil==5.6.3
scipy==1.3.1
pytorch_transformers==1.2.0
examples/distillation/scripts/binarized_data.py
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...@@ -20,7 +20,7 @@ import pickle ...@@ -20,7 +20,7 @@ import pickle
import random import random
import time import time
import numpy as np import numpy as np
from pytorch_transformers import BertTokenizer from transformers import BertTokenizer, RobertaTokenizer
import logging import logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
...@@ -32,16 +32,21 @@ def main(): ...@@ -32,16 +32,21 @@ def main():
parser = argparse.ArgumentParser(description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).") parser = argparse.ArgumentParser(description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).")
parser.add_argument('--file_path', type=str, default='data/dump.txt', parser.add_argument('--file_path', type=str, default='data/dump.txt',
help='The path to the data.') help='The path to the data.')
parser.add_argument('--bert_tokenizer', type=str, default='bert-base-uncased', parser.add_argument('--tokenizer_type', type=str, default='bert', choices=['bert', 'roberta'])
parser.add_argument('--tokenizer_name', type=str, default='bert-base-uncased',
help="The tokenizer to use.") help="The tokenizer to use.")
parser.add_argument('--dump_file', type=str, default='data/dump', parser.add_argument('--dump_file', type=str, default='data/dump',
help='The dump file prefix.') help='The dump file prefix.')
args = parser.parse_args() args = parser.parse_args()
logger.info(f'Loading Tokenizer ({args.bert_tokenizer})') logger.info(f'Loading Tokenizer ({args.tokenizer_name})')
bert_tokenizer = BertTokenizer.from_pretrained(args.bert_tokenizer) if args.tokenizer_type == 'bert':
tokenizer = BertTokenizer.from_pretrained(args.tokenizer_name)
elif args.tokenizer_type == 'roberta':
tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name)
bos = tokenizer.special_tokens_map['bos_token'] # `[CLS]` for bert, `<s>` for roberta
sep = tokenizer.special_tokens_map['sep_token'] # `[SEP]` for bert, `</s>` for roberta
logger.info(f'Loading text from {args.file_path}') logger.info(f'Loading text from {args.file_path}')
with open(args.file_path, 'r', encoding='utf8') as fp: with open(args.file_path, 'r', encoding='utf8') as fp:
...@@ -56,8 +61,8 @@ def main(): ...@@ -56,8 +61,8 @@ def main():
interval = 10000 interval = 10000
start = time.time() start = time.time()
for text in data: for text in data:
text = f'[CLS] {text.strip()} [SEP]' text = f'{bos} {text.strip()} {sep}'
token_ids = bert_tokenizer.encode(text) token_ids = tokenizer.encode(text)
rslt.append(token_ids) rslt.append(token_ids)
iter += 1 iter += 1
...@@ -69,7 +74,7 @@ def main(): ...@@ -69,7 +74,7 @@ def main():
logger.info(f'{len(data)} examples processed.') logger.info(f'{len(data)} examples processed.')
dp_file = f'{args.dump_file}.{args.bert_tokenizer}.pickle' dp_file = f'{args.dump_file}.{args.tokenizer_name}.pickle'
rslt_ = [np.uint16(d) for d in rslt] rslt_ = [np.uint16(d) for d in rslt]
random.shuffle(rslt_) random.shuffle(rslt_)
logger.info(f'Dump to {dp_file}') logger.info(f'Dump to {dp_file}')
......
examples/distillation/scripts/extract_for_distil.py
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...@@ -15,59 +15,73 @@ ...@@ -15,59 +15,73 @@
""" """
Preprocessing script before training DistilBERT. Preprocessing script before training DistilBERT.
""" """
from pytorch_transformers import BertForPreTraining from transformers import BertForMaskedLM, RobertaForMaskedLM
import torch import torch
import argparse import argparse
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForPreTraining for Transfer Learned Distillation") parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation")
parser.add_argument("--bert_model", default='bert-base-uncased', type=str) parser.add_argument("--model_type", default="bert", choices=["bert", "roberta"])
parser.add_argument("--dump_checkpoint", default='serialization_dir/transfer_learning_checkpoint_0247911.pth', type=str) parser.add_argument("--model_name", default='bert-base-uncased', type=str)
parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument("--vocab_transform", action='store_true') parser.add_argument("--vocab_transform", action='store_true')
args = parser.parse_args() args = parser.parse_args()
model = BertForPreTraining.from_pretrained(args.bert_model) if args.model_type == 'bert':
model = BertForMaskedLM.from_pretrained(args.model_name)
prefix = 'bert'
elif args.model_type == 'roberta':
model = RobertaForMaskedLM.from_pretrained(args.model_name)
prefix = 'roberta'
state_dict = model.state_dict() state_dict = model.state_dict()
compressed_sd = {} compressed_sd = {}
for w in ['word_embeddings', 'position_embeddings']: for w in ['word_embeddings', 'position_embeddings']:
compressed_sd[f'distilbert.embeddings.{w}.weight'] = \ compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
state_dict[f'bert.embeddings.{w}.weight'] state_dict[f'{prefix}.embeddings.{w}.weight']
for w in ['weight', 'bias']: for w in ['weight', 'bias']:
compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \ compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
state_dict[f'bert.embeddings.LayerNorm.{w}'] state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
std_idx = 0 std_idx = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]: for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ['weight', 'bias']: for w in ['weight', 'bias']:
compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.query.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.key.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.value.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.dense.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.intermediate.dense.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.output.dense.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}']
compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \ compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
state_dict[f'bert.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'] state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
std_idx += 1 std_idx += 1
if args.model_type == 'bert':
compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight'] compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight']
compressed_sd[f'vocab_projector.bias'] = state_dict[f'cls.predictions.bias'] compressed_sd[f'vocab_projector.bias'] = state_dict[f'cls.predictions.bias']
if args.vocab_transform: if args.vocab_transform:
for w in ['weight', 'bias']: for w in ['weight', 'bias']:
compressed_sd[f'vocab_transform.{w}'] = state_dict[f'cls.predictions.transform.dense.{w}'] compressed_sd[f'vocab_transform.{w}'] = state_dict[f'cls.predictions.transform.dense.{w}']
compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'cls.predictions.transform.LayerNorm.{w}'] compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
elif args.model_type == 'roberta':
compressed_sd[f'vocab_projector.weight'] = state_dict[f'lm_head.decoder.weight']
compressed_sd[f'vocab_projector.bias'] = state_dict[f'lm_head.bias']
if args.vocab_transform:
for w in ['weight', 'bias']:
compressed_sd[f'vocab_transform.{w}'] = state_dict[f'lm_head.dense.{w}']
compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'lm_head.layer_norm.{w}']
print(f'N layers selected for distillation: {std_idx}') print(f'N layers selected for distillation: {std_idx}')
print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}') print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
......
examples/distillation/train.py
View file @ d7395789
...@@ -23,8 +23,8 @@ import shutil ...@@ -23,8 +23,8 @@ import shutil
import numpy as np import numpy as np
import torch import torch
from pytorch_transformers import BertTokenizer, BertForMaskedLM from transformers import BertTokenizer, BertForMaskedLM, RobertaTokenizer, RobertaForMaskedLM
from pytorch_transformers import DistilBertForMaskedLM, DistilBertConfig from transformers import DistilBertForMaskedLM, DistilBertConfig
from distiller import Distiller from distiller import Distiller
from utils import git_log, logger, init_gpu_params, set_seed from utils import git_log, logger, init_gpu_params, set_seed
...@@ -70,8 +70,10 @@ def main(): ...@@ -70,8 +70,10 @@ def main():
help="Load student initialization checkpoint.") help="Load student initialization checkpoint.")
parser.add_argument("--from_pretrained_config", default=None, type=str, parser.add_argument("--from_pretrained_config", default=None, type=str,
help="Load student initialization architecture config.") help="Load student initialization architecture config.")
parser.add_argument("--bert_model", default='bert-base-uncased', type=str, parser.add_argument("--teacher_type", default="bert", choices=["bert", "roberta"],
help="The teacher BERT model.") help="Teacher type (BERT, RoBERTa).")
parser.add_argument("--teacher_name", default="bert-base-uncased", type=str,
help="The teacher model.")
parser.add_argument("--temperature", default=2., type=float, parser.add_argument("--temperature", default=2., type=float,
help="Temperature for the softmax temperature.") help="Temperature for the softmax temperature.")
...@@ -81,6 +83,8 @@ def main(): ...@@ -81,6 +83,8 @@ def main():
help="Linear weight for the MLM loss. Must be >=0.") help="Linear weight for the MLM loss. Must be >=0.")
parser.add_argument("--alpha_mse", default=0.0, type=float, parser.add_argument("--alpha_mse", default=0.0, type=float,
help="Linear weight of the MSE loss. Must be >=0.") help="Linear weight of the MSE loss. Must be >=0.")
parser.add_argument("--alpha_cos", default=0.0, type=float,
help="Linear weight of the cosine embedding loss. Must be >=0.")
parser.add_argument("--mlm_mask_prop", default=0.15, type=float, parser.add_argument("--mlm_mask_prop", default=0.15, type=float,
help="Proportion of tokens for which we need to make a prediction.") help="Proportion of tokens for which we need to make a prediction.")
parser.add_argument("--word_mask", default=0.8, type=float, parser.add_argument("--word_mask", default=0.8, type=float,
...@@ -165,11 +169,14 @@ def main(): ...@@ -165,11 +169,14 @@ def main():
### TOKENIZER ### ### TOKENIZER ###
bert_tokenizer = BertTokenizer.from_pretrained(args.bert_model) if args.teacher_type == 'bert':
tokenizer = BertTokenizer.from_pretrained(args.teacher_name)
elif args.teacher_type == 'roberta':
tokenizer = RobertaTokenizer.from_pretrained(args.teacher_name)
special_tok_ids = {} special_tok_ids = {}
for tok_name, tok_symbol in bert_tokenizer.special_tokens_map.items(): for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
idx = bert_tokenizer.all_special_tokens.index(tok_symbol) idx = tokenizer.all_special_tokens.index(tok_symbol)
special_tok_ids[tok_name] = bert_tokenizer.all_special_ids[idx] special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
logger.info(f'Special tokens {special_tok_ids}') logger.info(f'Special tokens {special_tok_ids}')
args.special_tok_ids = special_tok_ids args.special_tok_ids = special_tok_ids
...@@ -197,16 +204,17 @@ def main(): ...@@ -197,16 +204,17 @@ def main():
## STUDENT ## ## STUDENT ##
if args.from_pretrained_weights is not None: if args.from_pretrained_weights is not None:
assert os.path.isfile(os.path.join(args.from_pretrained_weights)) assert os.path.isfile(args.from_pretrained_weights)
assert os.path.isfile(os.path.join(args.from_pretrained_config)) assert os.path.isfile(args.from_pretrained_config)
logger.info(f'Loading pretrained weights from {args.from_pretrained_weights}') logger.info(f'Loading pretrained weights from {args.from_pretrained_weights}')
logger.info(f'Loading pretrained config from {args.from_pretrained_config}') logger.info(f'Loading pretrained config from {args.from_pretrained_config}')
stu_architecture_config = DistilBertConfig.from_json_file(args.from_pretrained_config) stu_architecture_config = DistilBertConfig.from_json_file(args.from_pretrained_config)
stu_architecture_config.output_hidden_states = True
student = DistilBertForMaskedLM.from_pretrained(args.from_pretrained_weights, student = DistilBertForMaskedLM.from_pretrained(args.from_pretrained_weights,
config=stu_architecture_config) config=stu_architecture_config)
else: else:
args.vocab_size_or_config_json_file = args.vocab_size args.vocab_size_or_config_json_file = args.vocab_size
stu_architecture_config = DistilBertConfig(**vars(args)) stu_architecture_config = DistilBertConfig(**vars(args), output_hidden_states=True)
student = DistilBertForMaskedLM(stu_architecture_config) student = DistilBertForMaskedLM(stu_architecture_config)
...@@ -216,10 +224,13 @@ def main(): ...@@ -216,10 +224,13 @@ def main():
## TEACHER ## ## TEACHER ##
teacher = BertForMaskedLM.from_pretrained(args.bert_model) if args.teacher_type == 'bert':
teacher = BertForMaskedLM.from_pretrained(args.teacher_name, output_hidden_states=True)
elif args.teacher_type == 'roberta':
teacher = RobertaForMaskedLM.from_pretrained(args.teacher_name, output_hidden_states=True)
if args.n_gpu > 0: if args.n_gpu > 0:
teacher.to(f'cuda:{args.local_rank}') teacher.to(f'cuda:{args.local_rank}')
logger.info(f'Teacher loaded from {args.bert_model}.') logger.info(f'Teacher loaded from {args.teacher_name}.')
## DISTILLER ## ## DISTILLER ##
torch.cuda.empty_cache() torch.cuda.empty_cache()
......
examples/run_bertology.py
View file @ d7395789
...@@ -32,7 +32,7 @@ from torch.utils.data import DataLoader, SequentialSampler, TensorDataset, Subse ...@@ -32,7 +32,7 @@ from torch.utils.data import DataLoader, SequentialSampler, TensorDataset, Subse
from torch.utils.data.distributed import DistributedSampler from torch.utils.data.distributed import DistributedSampler
from torch.nn import CrossEntropyLoss, MSELoss from torch.nn import CrossEntropyLoss, MSELoss
from pytorch_transformers import (WEIGHTS_NAME, from transformers import (WEIGHTS_NAME,
BertConfig, BertForSequenceClassification, BertTokenizer, BertConfig, BertForSequenceClassification, BertTokenizer,
XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer,
XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer) XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer)
......
examples/run_generation.py
View file @ d7395789
...@@ -26,12 +26,12 @@ import torch ...@@ -26,12 +26,12 @@ import torch
import torch.nn.functional as F import torch.nn.functional as F
import numpy as np import numpy as np
from pytorch_transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
from pytorch_transformers import GPT2LMHeadModel, GPT2Tokenizer from transformers import GPT2LMHeadModel, GPT2Tokenizer
from pytorch_transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
from pytorch_transformers import XLNetLMHeadModel, XLNetTokenizer from transformers import XLNetLMHeadModel, XLNetTokenizer
from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLTokenizer from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
......
examples/run_glue.py
View file @ d7395789
...@@ -31,7 +31,7 @@ from torch.utils.data.distributed import DistributedSampler ...@@ -31,7 +31,7 @@ from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter from tensorboardX import SummaryWriter
from tqdm import tqdm, trange from tqdm import tqdm, trange
from pytorch_transformers import (WEIGHTS_NAME, BertConfig, from transformers import (WEIGHTS_NAME, BertConfig,
BertForSequenceClassification, BertTokenizer, BertForSequenceClassification, BertTokenizer,
RobertaConfig, RobertaConfig,
RobertaForSequenceClassification, RobertaForSequenceClassification,
...@@ -39,12 +39,17 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig, ...@@ -39,12 +39,17 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
XLMConfig, XLMForSequenceClassification, XLMConfig, XLMForSequenceClassification,
XLMTokenizer, XLNetConfig, XLMTokenizer, XLNetConfig,
XLNetForSequenceClassification, XLNetForSequenceClassification,
XLNetTokenizer) XLNetTokenizer,
DistilBertConfig,
DistilBertForSequenceClassification,
DistilBertTokenizer)
from pytorch_transformers import AdamW, WarmupLinearSchedule from transformers import AdamW, WarmupLinearSchedule
from utils_glue import (compute_metrics, convert_examples_to_features, from transformers import glue_compute_metrics as compute_metrics
output_modes, processors) from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
from transformers import glue_convert_examples_to_features as convert_examples_to_features
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
...@@ -55,6 +60,7 @@ MODEL_CLASSES = { ...@@ -55,6 +60,7 @@ MODEL_CLASSES = {
'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer), 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer), 'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
} }
...@@ -128,10 +134,11 @@ def train(args, train_dataset, model, tokenizer): ...@@ -128,10 +134,11 @@ def train(args, train_dataset, model, tokenizer):
batch = tuple(t.to(args.device) for t in batch) batch = tuple(t.to(args.device) for t in batch)
inputs = {'input_ids': batch[0], inputs = {'input_ids': batch[0],
'attention_mask': batch[1], 'attention_mask': batch[1],
'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM and RoBERTa don't use segment_ids
'labels': batch[3]} 'labels': batch[3]}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None # XLM, DistilBERT and RoBERTa don't use segment_ids
outputs = model(**inputs) outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1: if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training loss = loss.mean() # mean() to average on multi-gpu parallel training
...@@ -148,8 +155,8 @@ def train(args, train_dataset, model, tokenizer): ...@@ -148,8 +155,8 @@ def train(args, train_dataset, model, tokenizer):
tr_loss += loss.item() tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0: if (step + 1) % args.gradient_accumulation_steps == 0:
scheduler.step() # Update learning rate schedule
optimizer.step() optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad() model.zero_grad()
global_step += 1 global_step += 1
...@@ -218,8 +225,9 @@ def evaluate(args, model, tokenizer, prefix=""): ...@@ -218,8 +225,9 @@ def evaluate(args, model, tokenizer, prefix=""):
with torch.no_grad(): with torch.no_grad():
inputs = {'input_ids': batch[0], inputs = {'input_ids': batch[0],
'attention_mask': batch[1], 'attention_mask': batch[1],
'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM and RoBERTa don't use segment_ids
'labels': batch[3]} 'labels': batch[3]}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None # XLM, DistilBERT and RoBERTa don't use segment_ids
outputs = model(**inputs) outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2] tmp_eval_loss, logits = outputs[:2]
...@@ -272,12 +280,11 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): ...@@ -272,12 +280,11 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
# HACK(label indices are swapped in RoBERTa pretrained model) # HACK(label indices are swapped in RoBERTa pretrained model)
label_list[1], label_list[2] = label_list[2], label_list[1] label_list[1], label_list[2] = label_list[2], label_list[1]
examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer, output_mode, features = convert_examples_to_features(examples,
cls_token_at_end=bool(args.model_type in ['xlnet']), # xlnet has a cls token at the end tokenizer,
cls_token=tokenizer.cls_token, label_list=label_list,
cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0, max_length=args.max_seq_length,
sep_token=tokenizer.sep_token, output_mode=output_mode,
sep_token_extra=bool(args.model_type in ['roberta']), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=bool(args.model_type in ['xlnet']), # pad on the left for xlnet pad_on_left=bool(args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0, pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
...@@ -291,14 +298,14 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): ...@@ -291,14 +298,14 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
# Convert to Tensors and build dataset # Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
if output_mode == "classification": if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long) all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
elif output_mode == "regression": elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float) all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
return dataset return dataset
...@@ -478,7 +485,7 @@ def main(): ...@@ -478,7 +485,7 @@ def main():
checkpoints = [args.output_dir] checkpoints = [args.output_dir]
if args.eval_all_checkpoints: if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints) logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints: for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
......
examples/run_lm_finetuning.py
View file @ d7395789
...@@ -35,11 +35,12 @@ from torch.utils.data.distributed import DistributedSampler ...@@ -35,11 +35,12 @@ from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter from tensorboardX import SummaryWriter
from tqdm import tqdm, trange from tqdm import tqdm, trange
from pytorch_transformers import (WEIGHTS_NAME, AdamW, WarmupLinearSchedule, from transformers import (WEIGHTS_NAME, AdamW, WarmupLinearSchedule,
BertConfig, BertForMaskedLM, BertTokenizer, BertConfig, BertForMaskedLM, BertTokenizer,
GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
RobertaConfig, RobertaForMaskedLM, RobertaTokenizer) RobertaConfig, RobertaForMaskedLM, RobertaTokenizer,
DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
...@@ -49,7 +50,8 @@ MODEL_CLASSES = { ...@@ -49,7 +50,8 @@ MODEL_CLASSES = {
'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer), 'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
'bert': (BertConfig, BertForMaskedLM, BertTokenizer), 'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer) 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
} }
...@@ -57,7 +59,7 @@ class TextDataset(Dataset): ...@@ -57,7 +59,7 @@ class TextDataset(Dataset):
def __init__(self, tokenizer, file_path='train', block_size=512): def __init__(self, tokenizer, file_path='train', block_size=512):
assert os.path.isfile(file_path) assert os.path.isfile(file_path)
directory, filename = os.path.split(file_path) directory, filename = os.path.split(file_path)
cached_features_file = os.path.join(directory, f'cached_lm_{block_size}_{filename}') cached_features_file = os.path.join(directory, 'cached_lm_{}_{}'.format(block_size, filename))
if os.path.exists(cached_features_file): if os.path.exists(cached_features_file):
logger.info("Loading features from cached file %s", cached_features_file) logger.info("Loading features from cached file %s", cached_features_file)
...@@ -72,9 +74,8 @@ class TextDataset(Dataset): ...@@ -72,9 +74,8 @@ class TextDataset(Dataset):
tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
while len(tokenized_text) >= block_size: # Truncate in block of block_size for i in range(0, len(tokenized_text)-block_size+1, block_size): # Truncate in block of block_size
self.examples.append(tokenizer.add_special_tokens_single_sentence(tokenized_text[:block_size])) self.examples.append(tokenizer.add_special_tokens_single_sequence(tokenized_text[i:i+block_size]))
tokenized_text = tokenized_text[block_size:]
# Note that we are loosing the last truncated example here for the sake of simplicity (no padding) # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
# If your dataset is small, first you should loook for a bigger one :-) and second you # If your dataset is small, first you should loook for a bigger one :-) and second you
# can change this behavior by adding (model specific) padding. # can change this behavior by adding (model specific) padding.
...@@ -186,7 +187,7 @@ def train(args, train_dataset, model, tokenizer): ...@@ -186,7 +187,7 @@ def train(args, train_dataset, model, tokenizer):
labels = labels.to(args.device) labels = labels.to(args.device)
model.train() model.train()
outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels) outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1: if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training loss = loss.mean() # mean() to average on multi-gpu parallel training
...@@ -380,7 +381,7 @@ def main(): ...@@ -380,7 +381,7 @@ def main():
parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
args = parser.parse_args() args = parser.parse_args()
if args.model_type in ["bert", "roberta"] and not args.mlm: if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm:
raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm " raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm "
"flag (masked language modeling).") "flag (masked language modeling).")
if args.eval_data_file is None and args.do_eval: if args.eval_data_file is None and args.do_eval:
...@@ -479,7 +480,7 @@ def main(): ...@@ -479,7 +480,7 @@ def main():
checkpoints = [args.output_dir] checkpoints = [args.output_dir]
if args.eval_all_checkpoints: if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints) logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints: for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
......
examples/run_multiple_choice.py
View file @ d7395789
...@@ -32,13 +32,13 @@ from torch.utils.data.distributed import DistributedSampler ...@@ -32,13 +32,13 @@ from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter from tensorboardX import SummaryWriter
from tqdm import tqdm, trange from tqdm import tqdm, trange
from pytorch_transformers import (WEIGHTS_NAME, BertConfig, from transformers import (WEIGHTS_NAME, BertConfig,
BertForMultipleChoice, BertTokenizer, BertForMultipleChoice, BertTokenizer,
XLNetConfig, XLNetForMultipleChoice, XLNetConfig, XLNetForMultipleChoice,
XLNetTokenizer, RobertaConfig, XLNetTokenizer, RobertaConfig,
RobertaForMultipleChoice, RobertaTokenizer) RobertaForMultipleChoice, RobertaTokenizer)
from pytorch_transformers import AdamW, WarmupLinearSchedule from transformers import AdamW, WarmupLinearSchedule
from utils_multiple_choice import (convert_examples_to_features, processors) from utils_multiple_choice import (convert_examples_to_features, processors)
...@@ -141,7 +141,7 @@ def train(args, train_dataset, model, tokenizer): ...@@ -141,7 +141,7 @@ def train(args, train_dataset, model, tokenizer):
'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids
'labels': batch[3]} 'labels': batch[3]}
outputs = model(**inputs) outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1: if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training loss = loss.mean() # mean() to average on multi-gpu parallel training
...@@ -508,7 +508,7 @@ def main(): ...@@ -508,7 +508,7 @@ def main():
checkpoints = [args.output_dir] checkpoints = [args.output_dir]
if args.eval_all_checkpoints: if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints) logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints: for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
...@@ -524,7 +524,7 @@ def main(): ...@@ -524,7 +524,7 @@ def main():
checkpoints = [args.output_dir] checkpoints = [args.output_dir]
# if args.eval_all_checkpoints: # can not use this to do test!! # if args.eval_all_checkpoints: # can not use this to do test!!
# checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) # checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
# logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging # logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints) logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints: for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
......
examples/run_squad.py
View file @ d7395789
...@@ -32,14 +32,15 @@ from tqdm import tqdm, trange ...@@ -32,14 +32,15 @@ from tqdm import tqdm, trange
from tensorboardX import SummaryWriter from tensorboardX import SummaryWriter
from pytorch_transformers import (WEIGHTS_NAME, BertConfig, from transformers import (WEIGHTS_NAME, BertConfig,
BertForQuestionAnswering, BertTokenizer, BertForQuestionAnswering, BertTokenizer,
XLMConfig, XLMForQuestionAnswering, XLMConfig, XLMForQuestionAnswering,
XLMTokenizer, XLNetConfig, XLMTokenizer, XLNetConfig,
XLNetForQuestionAnswering, XLNetForQuestionAnswering,
XLNetTokenizer) XLNetTokenizer,
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
from pytorch_transformers import AdamW, WarmupLinearSchedule from transformers import AdamW, WarmupLinearSchedule
from utils_squad import (read_squad_examples, convert_examples_to_features, from utils_squad import (read_squad_examples, convert_examples_to_features,
RawResult, write_predictions, RawResult, write_predictions,
...@@ -59,6 +60,7 @@ MODEL_CLASSES = { ...@@ -59,6 +60,7 @@ MODEL_CLASSES = {
'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer), 'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer), 'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer), 'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
} }
def set_seed(args): def set_seed(args):
...@@ -140,7 +142,7 @@ def train(args, train_dataset, model, tokenizer): ...@@ -140,7 +142,7 @@ def train(args, train_dataset, model, tokenizer):
inputs.update({'cls_index': batch[5], inputs.update({'cls_index': batch[5],
'p_mask': batch[6]}) 'p_mask': batch[6]})
outputs = model(**inputs) outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1: if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
...@@ -508,7 +510,7 @@ def main(): ...@@ -508,7 +510,7 @@ def main():
checkpoints = [args.output_dir] checkpoints = [args.output_dir]
if args.eval_all_checkpoints: if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs
logger.info("Evaluate the following checkpoints: %s", checkpoints) logger.info("Evaluate the following checkpoints: %s", checkpoints)
......
examples/run_tf_glue.py 0 → 100644
View file @ d7395789
import tensorflow as tf
import tensorflow_datasets
from transformers import BertTokenizer, TFBertForSequenceClassification, glue_convert_examples_to_features, BertForSequenceClassification
# Load dataset, tokenizer, model from pretrained model/vocabulary
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
data = tensorflow_datasets.load('glue/mrpc')
# Prepare dataset for GLUE as a tf.data.Dataset instance
train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, 128, 'mrpc')
valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, 128, 'mrpc')
train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
valid_dataset = valid_dataset.batch(64)
# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
# Train and evaluate using tf.keras.Model.fit()
history = model.fit(train_dataset, epochs=2, steps_per_epoch=115,
validation_data=valid_dataset, validation_steps=7)
# Load the TensorFlow model in PyTorch for inspection
model.save_pretrained('./save/')
pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
sentence_0 = "This research was consistent with his findings."
sentence_1 = "His findings were compatible with this research."
sentence_2 = "His findings were not compatible with this research."
inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sentence_0")
examples/utils_squad.py
View file @ d7395789
...@@ -24,7 +24,7 @@ import math ...@@ -24,7 +24,7 @@ import math
import collections import collections
from io import open from io import open
from pytorch_transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
# Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method) # Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
from utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get_raw_scores from utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get_raw_scores
......
hubconf.py
View file @ d7395789
from pytorch_transformers import ( from transformers import (
AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering
) )
from pytorch_transformers.file_utils import add_start_docstrings from transformers.file_utils import add_start_docstrings
dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses'] dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses']
...@@ -11,12 +11,12 @@ def config(*args, **kwargs): ...@@ -11,12 +11,12 @@ def config(*args, **kwargs):
# Using torch.hub ! # Using torch.hub !
import torch import torch
config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased') # Download configuration from S3 and cache. config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased') # Download configuration from S3 and cache.
config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')` config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/my_configuration.json') config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json')
config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False) config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True assert config.output_attention == True
config, unused_kwargs = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True) config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
assert config.output_attention == True assert config.output_attention == True
assert unused_kwargs == {'foo': False} assert unused_kwargs == {'foo': False}
...@@ -31,8 +31,8 @@ def tokenizer(*args, **kwargs): ...@@ -31,8 +31,8 @@ def tokenizer(*args, **kwargs):
# Using torch.hub ! # Using torch.hub !
import torch import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-uncased') # Download vocabulary from S3 and cache. tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased') # Download vocabulary from S3 and cache.
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', './test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')` tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
""" """
...@@ -45,13 +45,13 @@ def model(*args, **kwargs): ...@@ -45,13 +45,13 @@ def model(*args, **kwargs):
# Using torch.hub ! # Using torch.hub !
import torch import torch
model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache. model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/pytorch-transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased', output_attention=True) # Update configuration during loading model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower) # Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/pytorch-transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
""" """
...@@ -63,13 +63,13 @@ def modelWithLMHead(*args, **kwargs): ...@@ -63,13 +63,13 @@ def modelWithLMHead(*args, **kwargs):
# Using torch.hub ! # Using torch.hub !
import torch import torch
model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased') # Download model and configuration from S3 and cache. model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True) # Update configuration during loading model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower) # Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
""" """
return AutoModelWithLMHead.from_pretrained(*args, **kwargs) return AutoModelWithLMHead.from_pretrained(*args, **kwargs)
...@@ -81,13 +81,13 @@ def modelForSequenceClassification(*args, **kwargs): ...@@ -81,13 +81,13 @@ def modelForSequenceClassification(*args, **kwargs):
# Using torch.hub ! # Using torch.hub !
import torch import torch
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased') # Download model and configuration from S3 and cache. model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True) # Update configuration during loading model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower) # Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
""" """
...@@ -100,13 +100,13 @@ def modelForQuestionAnswering(*args, **kwargs): ...@@ -100,13 +100,13 @@ def modelForQuestionAnswering(*args, **kwargs):
# Using torch.hub ! # Using torch.hub !
import torch import torch
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased') # Download model and configuration from S3 and cache. model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True) # Update configuration during loading model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower) # Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
""" """
return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs) return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)
pytorch_transformers/__init__.py deleted 100644 → 0
View file @ 2e6797cc
__version__ = "1.2.0"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging
absl.logging.set_verbosity('info')
absl.logging.set_stderrthreshold('info')
absl.logging._warn_preinit_stderr = False
except:
pass
# Tokenizer
from .tokenization_utils import (PreTrainedTokenizer)
from .tokenization_auto import AutoTokenizer
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
# Configurations
from .configuration_utils import PretrainedConfig
from .configuration_auto import AutoConfig
from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
# Modeling
from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
AutoModelWithLMHead)
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForQuestionAnswering, XLNetForMultipleChoice,
load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering, XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
RobertaForMultipleChoice, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
# Optimization
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
# Files and general utilities
from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
cached_path, add_start_docstrings, add_end_docstrings,
WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME)
requirements.txt
View file @ d7395789
# PyTorch
torch>=1.0.0
# progress bars in model download and training scripts # progress bars in model download and training scripts
tqdm tqdm
# Accessing files from S3 directly. # Accessing files from S3 directly.
......
setup.py
View file @ d7395789
...@@ -13,11 +13,11 @@ To create the package for pypi. ...@@ -13,11 +13,11 @@ To create the package for pypi.
4. Build both the sources and the wheel. Do not change anything in setup.py between 4. Build both the sources and the wheel. Do not change anything in setup.py between
creating the wheel and the source distribution (obviously). creating the wheel and the source distribution (obviously).
For the wheel, run: "python setup.py bdist_wheel" in the top level allennlp directory. For the wheel, run: "python setup.py bdist_wheel" in the top level directory.
(this will build a wheel for the python version you use to build it - make sure you use python 3.x). (this will build a wheel for the python version you use to build it - make sure you use python 3.x).
For the sources, run: "python setup.py sdist" For the sources, run: "python setup.py sdist"
You should now have a /dist directory with both .whl and .tar.gz source versions of allennlp. You should now have a /dist directory with both .whl and .tar.gz source versions.
5. Check that everything looks correct by uploading the package to the pypi test server: 5. Check that everything looks correct by uploading the package to the pypi test server:
...@@ -25,7 +25,7 @@ To create the package for pypi. ...@@ -25,7 +25,7 @@ To create the package for pypi.
(pypi suggest using twine as other methods upload files via plaintext.) (pypi suggest using twine as other methods upload files via plaintext.)
Check that you can install it in a virtualenv by running: Check that you can install it in a virtualenv by running:
pip install -i https://testpypi.python.org/pypi pytorch-transformers pip install -i https://testpypi.python.org/pypi transformers
6. Upload the final version to actual pypi: 6. Upload the final version to actual pypi:
twine upload dist/* -r pypi twine upload dist/* -r pypi
...@@ -37,20 +37,19 @@ from io import open ...@@ -37,20 +37,19 @@ from io import open
from setuptools import find_packages, setup from setuptools import find_packages, setup
setup( setup(
name="pytorch_transformers", name="transformers",
version="1.2.0", version="2.0.0",
author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors", author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
author_email="thomas@huggingface.co", author_email="thomas@huggingface.co",
description="Repository of pre-trained NLP Transformer models: BERT & RoBERTa, GPT & GPT-2, Transformer-XL, XLNet and XLM", description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
long_description=open("README.md", "r", encoding='utf-8').read(), long_description=open("README.md", "r", encoding='utf-8').read(),
long_description_content_type="text/markdown", long_description_content_type="text/markdown",
keywords='NLP deep learning transformer pytorch BERT GPT GPT-2 google openai CMU', keywords='NLP deep learning transformer pytorch tensorflow BERT GPT GPT-2 google openai CMU',
license='Apache', license='Apache',
url="https://github.com/huggingface/pytorch-transformers", url="https://github.com/huggingface/transformers",
packages=find_packages(exclude=["*.tests", "*.tests.*", packages=find_packages(exclude=["*.tests", "*.tests.*",
"tests.*", "tests"]), "tests.*", "tests"]),
install_requires=['torch>=1.0.0', install_requires=['numpy',
'numpy',
'boto3', 'boto3',
'requests', 'requests',
'tqdm', 'tqdm',
...@@ -59,7 +58,7 @@ setup( ...@@ -59,7 +58,7 @@ setup(
'sacremoses'], 'sacremoses'],
entry_points={ entry_points={
'console_scripts': [ 'console_scripts': [
"pytorch_transformers=pytorch_transformers.__main__:main", "transformers=transformers.__main__:main",
] ]
}, },
# python_requires='>=3.5.0', # python_requires='>=3.5.0',
......
transformers/__init__.py 0 → 100644
View file @ d7395789
__version__ = "2.0.0"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging
absl.logging.set_verbosity('info')
absl.logging.set_stderrthreshold('info')
absl.logging._warn_preinit_stderr = False
except:
pass
import logging
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
# Files and general utilities
from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
cached_path, add_start_docstrings, add_end_docstrings,
WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
is_tf_available, is_torch_available)
from .data import (is_sklearn_available,
InputExample, InputFeatures, DataProcessor,
glue_output_modes, glue_convert_examples_to_features,
glue_processors, glue_tasks_num_labels)
if is_sklearn_available():
from .data import glue_compute_metrics
# Tokenizers
from .tokenization_utils import (PreTrainedTokenizer)
from .tokenization_auto import AutoTokenizer
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
# Configurations
from .configuration_utils import PretrainedConfig
from .configuration_auto import AutoConfig
from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
# Modeling
if is_torch_available():
from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
AutoModelWithLMHead)
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForMultipleChoice,
XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
RobertaForSequenceClassification, RobertaForMultipleChoice,
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
# Optimization
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
# TensorFlow
if is_tf_available():
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
TFAutoModelWithLMHead)
from .modeling_tf_bert import (TFBertPreTrainedModel, TFBertMainLayer, TFBertEmbeddings,
TFBertModel, TFBertForPreTraining,
TFBertForMaskedLM, TFBertForNextSentencePrediction,
TFBertForSequenceClassification, TFBertForMultipleChoice,
TFBertForTokenClassification, TFBertForQuestionAnswering,
load_bert_pt_weights_in_tf2,
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_gpt2 import (TFGPT2PreTrainedModel, TFGPT2MainLayer,
TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel,
load_gpt2_pt_weights_in_tf2,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_openai import (TFOpenAIGPTPreTrainedModel, TFOpenAIGPTMainLayer,
TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel,
load_openai_gpt_pt_weights_in_tf2,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_transfo_xl import (TFTransfoXLPreTrainedModel, TFTransfoXLMainLayer,
TFTransfoXLModel, TFTransfoXLLMHeadModel,
load_transfo_xl_pt_weights_in_tf2,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForQuestionAnsweringSimple,
load_xlnet_pt_weights_in_tf2,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_xlm import (TFXLMPreTrainedModel, TFXLMMainLayer,
TFXLMModel, TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
load_xlm_pt_weights_in_tf2,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_roberta import (TFRobertaPreTrainedModel, TFRobertaMainLayer,
TFRobertaModel, TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
load_roberta_pt_weights_in_tf2,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer,
TFDistilBertModel, TFDistilBertForMaskedLM,
TFDistilBertForSequenceClassification,
TFDistilBertForQuestionAnswering,
load_distilbert_pt_weights_in_tf2,
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
# TF 2.0 <=> PyTorch conversion utilities
if is_tf_available() and is_torch_available():
from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
load_pytorch_checkpoint_in_tf2_model,
load_pytorch_weights_in_tf2_model,
load_pytorch_model_in_tf2_model,
load_tf2_checkpoint_in_pytorch_model,
load_tf2_weights_in_pytorch_model,
load_tf2_model_in_pytorch_model)
if not is_tf_available() and not is_torch_available():
logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
"Models won't be available and only tokenizers, configuration"
"and file/data utilities can be used.")
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