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Unverified Commit 0a2fecdf authored by Thomas Wolf's avatar Thomas Wolf Committed by GitHub
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examples/single_model_scripts/run_openai_gpt.py
View file @ 0a2fecdf
......@@ -205,7 +205,7 @@ def main():
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
......
examples/test_examples.py
View file @ 0a2fecdf
......@@ -81,7 +81,7 @@ class ExamplesTests(unittest.TestCase):
"--do_train",
"--do_eval",
"--version_2_with_negative",
"--learning_rate=1e-4",
"--learning_rate=2e-4",
"--per_gpu_train_batch_size=2",
"--per_gpu_eval_batch_size=1",
"--overwrite_output_dir",
......
examples/utils_glue.py
View file @ 0a2fecdf
......@@ -390,10 +390,16 @@ class WnliProcessor(DataProcessor):
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer, output_mode,
cls_token_at_end=False, pad_on_left=False,
cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
sequence_a_segment_id=0, sequence_b_segment_id=1,
cls_token_segment_id=1, pad_token_segment_id=0,
cls_token_at_end=False,
cls_token='[CLS]',
cls_token_segment_id=1,
sep_token='[SEP]',
sep_token_extra=False,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
sequence_a_segment_id=0,
sequence_b_segment_id=1,
mask_padding_with_zero=True):
""" Loads a data file into a list of `InputBatch`s
`cls_token_at_end` define the location of the CLS token:
......@@ -416,12 +422,14 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
tokens_b = tokenizer.tokenize(example.text_b)
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
# Account for [CLS], [SEP], [SEP] with "- 3". " -4" for RoBERTa.
special_tokens_count = 4 if sep_token_extra else 3
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
special_tokens_count = 3 if sep_token_extra else 2
if len(tokens_a) > max_seq_length - special_tokens_count:
tokens_a = tokens_a[:(max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
......@@ -442,6 +450,9 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = tokens_a + [sep_token]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
segment_ids = [sequence_a_segment_id] * len(tokens)
if tokens_b:
......
hubconfs/bert_hubconf.py
View file @ 0a2fecdf
......@@ -37,7 +37,7 @@ bert_docstring = """
checkpoint
cache_dir: an optional path to a folder in which the pre-trained models
will be cached.
state_dict: an optional state dictionnary
state_dict: an optional state dictionary
(collections.OrderedDict object) to use instead of Google
pre-trained models
*inputs, **kwargs: additional input for the specific Bert class
......@@ -84,12 +84,12 @@ def bertTokenizer(*args, **kwargs):
Default: ["[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"]
Example:
>>> import torch
>>> sentence = 'Hello, World!'
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
>>> toks = tokenizer.tokenize(sentence)
import torch
sentence = 'Hello, World!'
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
toks = tokenizer.tokenize(sentence)
['Hello', '##,', 'World', '##!']
>>> ids = tokenizer.convert_tokens_to_ids(toks)
ids = tokenizer.convert_tokens_to_ids(toks)
[8667, 28136, 1291, 28125]
"""
tokenizer = BertTokenizer.from_pretrained(*args, **kwargs)
......@@ -105,20 +105,20 @@ def bertModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertModel', 'bert-base-cased')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertModel', 'bert-base-cased')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
encoded_layers, _ = model(tokens_tensor, segments_tensors)
"""
model = BertModel.from_pretrained(*args, **kwargs)
......@@ -134,20 +134,20 @@ def bertForNextSentencePrediction(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertForNextSentencePrediction
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForNextSentencePrediction', 'bert-base-cased')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForNextSentencePrediction', 'bert-base-cased')
model.eval()
# Predict the next sentence classification logits
>>> with torch.no_grad():
with torch.no_grad():
next_sent_classif_logits = model(tokens_tensor, segments_tensors)
"""
model = BertForNextSentencePrediction.from_pretrained(*args, **kwargs)
......@@ -164,17 +164,17 @@ def bertForPreTraining(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertForPreTraining
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForPreTraining', 'bert-base-cased')
>>> masked_lm_logits_scores, seq_relationship_logits = model(tokens_tensor, segments_tensors)
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForPreTraining', 'bert-base-cased')
masked_lm_logits_scores, seq_relationship_logits = model(tokens_tensor, segments_tensors)
"""
model = BertForPreTraining.from_pretrained(*args, **kwargs)
return model
......@@ -188,25 +188,25 @@ def bertForMaskedLM(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> masked_index = 8
>>> tokenized_text[masked_index] = '[MASK]'
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
masked_index = 8
tokenized_text[masked_index] = '[MASK]'
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertForMaskedLM
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMaskedLM', 'bert-base-cased')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMaskedLM', 'bert-base-cased')
model.eval()
# Predict all tokens
>>> with torch.no_grad():
with torch.no_grad():
predictions = model(tokens_tensor, segments_tensors)
>>> predicted_index = torch.argmax(predictions[0, masked_index]).item()
>>> predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
predicted_index = torch.argmax(predictions[0, masked_index]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
'henson'
"""
model = BertForMaskedLM.from_pretrained(*args, **kwargs)
......@@ -230,24 +230,24 @@ def bertForSequenceClassification(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertForSequenceClassification
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2)
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2)
model.eval()
# Predict the sequence classification logits
>>> with torch.no_grad():
with torch.no_grad():
seq_classif_logits = model(tokens_tensor, segments_tensors)
# Or get the sequence classification loss
>>> labels = torch.tensor([1])
>>> seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
labels = torch.tensor([1])
seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
"""
model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
return model
......@@ -265,24 +265,24 @@ def bertForMultipleChoice(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens, indexed_tokens]).unsqueeze(0)
>>> segments_tensors = torch.tensor([segments_ids, segments_ids]).unsqueeze(0)
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens, indexed_tokens]).unsqueeze(0)
segments_tensors = torch.tensor([segments_ids, segments_ids]).unsqueeze(0)
# Load bertForMultipleChoice
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMultipleChoice', 'bert-base-cased', num_choices=2)
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMultipleChoice', 'bert-base-cased', num_choices=2)
model.eval()
# Predict the multiple choice logits
>>> with torch.no_grad():
with torch.no_grad():
multiple_choice_logits = model(tokens_tensor, segments_tensors)
# Or get the multiple choice loss
>>> labels = torch.tensor([1])
>>> multiple_choice_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
labels = torch.tensor([1])
multiple_choice_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
"""
model = BertForMultipleChoice.from_pretrained(*args, **kwargs)
return model
......@@ -298,25 +298,25 @@ def bertForQuestionAnswering(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertForQuestionAnswering
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForQuestionAnswering', 'bert-base-cased')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForQuestionAnswering', 'bert-base-cased')
model.eval()
# Predict the start and end positions logits
>>> with torch.no_grad():
with torch.no_grad():
start_logits, end_logits = model(tokens_tensor, segments_tensors)
# Or get the total loss which is the sum of the CrossEntropy loss for the start and end token positions
>>> start_positions, end_positions = torch.tensor([12]), torch.tensor([14])
start_positions, end_positions = torch.tensor([12]), torch.tensor([14])
# set model.train() before if training this loss
>>> multiple_choice_loss = model(tokens_tensor, segments_tensors, start_positions=start_positions, end_positions=end_positions)
multiple_choice_loss = model(tokens_tensor, segments_tensors, start_positions=start_positions, end_positions=end_positions)
"""
model = BertForQuestionAnswering.from_pretrained(*args, **kwargs)
return model
......@@ -337,24 +337,24 @@ def bertForTokenClassification(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
# Load bertForTokenClassification
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForTokenClassification', 'bert-base-cased', num_labels=2)
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'bertForTokenClassification', 'bert-base-cased', num_labels=2)
model.eval()
# Predict the token classification logits
>>> with torch.no_grad():
with torch.no_grad():
classif_logits = model(tokens_tensor, segments_tensors)
# Or get the token classification loss
>>> labels = torch.tensor([[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0]])
>>> classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
labels = torch.tensor([[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0]])
classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
"""
model = BertForTokenClassification.from_pretrained(*args, **kwargs)
return model
hubconfs/gpt2_hubconf.py
View file @ 0a2fecdf
......@@ -52,11 +52,11 @@ def gpt2Tokenizer(*args, **kwargs):
Default: None
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
>>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text)
text = "Who was Jim Henson ?"
indexed_tokens = tokenizer.encode(tokenized_text)
"""
tokenizer = GPT2Tokenizer.from_pretrained(*args, **kwargs)
return tokenizer
......@@ -71,24 +71,24 @@ def gpt2Model(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> indexed_tokens_1 = tokenizer.encode(text_1)
>>> indexed_tokens_2 = tokenizer.encode(text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load gpt2Model
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Model', 'gpt2')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Model', 'gpt2')
model.eval()
# Predict hidden states features for each layer
# past can be used to reuse precomputed hidden state in a subsequent predictions
>>> with torch.no_grad():
with torch.no_grad():
hidden_states_1, past = model(tokens_tensor_1)
hidden_states_2, past = model(tokens_tensor_2, past=past)
"""
......@@ -104,31 +104,31 @@ def gpt2LMHeadModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> indexed_tokens_1 = tokenizer.encode(text_1)
>>> indexed_tokens_2 = tokenizer.encode(text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load gpt2LMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2LMHeadModel', 'gpt2')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2LMHeadModel', 'gpt2')
model.eval()
# Predict hidden states features for each layer
# past can be used to reuse precomputed hidden state in a subsequent predictions
>>> with torch.no_grad():
with torch.no_grad():
predictions_1, past = model(tokens_tensor_1)
predictions_2, past = model(tokens_tensor_2, past=past)
# Get the predicted last token
>>> predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
>>> predicted_token = tokenizer.decode([predicted_index])
>>> assert predicted_token == ' who'
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
assert predicted_token == ' who'
"""
model = GPT2LMHeadModel.from_pretrained(*args, **kwargs)
return model
......@@ -143,25 +143,25 @@ def gpt2DoubleHeadsModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input
>>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
>>> text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
>>> tokenized_text1 = tokenizer.tokenize(text1)
>>> tokenized_text2 = tokenizer.tokenize(text2)
>>> indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
>>> indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
>>> tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
>>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
tokenized_text1 = tokenizer.tokenize(text1)
tokenized_text2 = tokenizer.tokenize(text2)
indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# Load gpt2DoubleHeadsModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2DoubleHeadsModel', 'gpt2')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2DoubleHeadsModel', 'gpt2')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
lm_logits, multiple_choice_logits, presents = model(tokens_tensor, mc_token_ids)
"""
model = GPT2DoubleHeadsModel.from_pretrained(*args, **kwargs)
......
hubconfs/gpt_hubconf.py
View file @ 0a2fecdf
......@@ -40,7 +40,7 @@ gpt_docstring = """
. a series of NumPy files containing OpenAI TensorFlow trained weights
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object)
state_dict: an optional state dictionary (collections.OrderedDict object)
to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific OpenAI-GPT class
"""
......@@ -76,12 +76,12 @@ def openAIGPTTokenizer(*args, **kwargs):
Default: None
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
>>> text = "Who was Jim Henson ? Jim Henson was a puppeteer"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
[763, 509, 4265, 2298, 945, 257, 4265, 2298, 945, 509, 246, 10148, 39041, 483]
"""
tokenizer = OpenAIGPTTokenizer.from_pretrained(*args, **kwargs)
......@@ -97,21 +97,21 @@ def openAIGPTModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
# Prepare tokenized input
>>> text = "Who was Jim Henson ? Jim Henson was a puppeteer"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> tokens_tensor = torch.tensor([indexed_tokens])
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
# Load openAIGPTModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTModel', 'openai-gpt')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTModel', 'openai-gpt')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
hidden_states = model(tokens_tensor)
"""
model = OpenAIGPTModel.from_pretrained(*args, **kwargs)
......@@ -126,26 +126,26 @@ def openAIGPTLMHeadModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
# Prepare tokenized input
>>> text = "Who was Jim Henson ? Jim Henson was a puppeteer"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> tokens_tensor = torch.tensor([indexed_tokens])
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
# Load openAIGPTLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTLMHeadModel', 'openai-gpt')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTLMHeadModel', 'openai-gpt')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
predictions = model(tokens_tensor)
# Get the predicted last token
>>> predicted_index = torch.argmax(predictions[0, -1, :]).item()
>>> predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
predicted_index = torch.argmax(predictions[0, -1, :]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
'.</w>'
"""
model = OpenAIGPTLMHeadModel.from_pretrained(*args, **kwargs)
......@@ -161,25 +161,25 @@ def openAIGPTDoubleHeadsModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
# Prepare tokenized input
>>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
>>> text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
>>> tokenized_text1 = tokenizer.tokenize(text1)
>>> tokenized_text2 = tokenizer.tokenize(text2)
>>> indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
>>> indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
>>> tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
>>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
tokenized_text1 = tokenizer.tokenize(text1)
tokenized_text2 = tokenizer.tokenize(text2)
indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# Load openAIGPTDoubleHeadsModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTDoubleHeadsModel', 'openai-gpt')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTDoubleHeadsModel', 'openai-gpt')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
lm_logits, multiple_choice_logits = model(tokens_tensor, mc_token_ids)
"""
model = OpenAIGPTDoubleHeadsModel.from_pretrained(*args, **kwargs)
......
hubconfs/transformer_xl_hubconf.py
View file @ 0a2fecdf
......@@ -23,7 +23,7 @@ transformer_xl_docstring = """
. `model.chkpt` a TensorFlow checkpoint
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific TransformerXL class
"""
......@@ -45,12 +45,12 @@ def transformerXLTokenizer(*args, **kwargs):
* transfo-xl-wt103
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
>>> text = "Who was Jim Henson ?"
>>> tokenized_text = tokenizer.tokenize(tokenized_text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
text = "Who was Jim Henson ?"
tokenized_text = tokenizer.tokenize(tokenized_text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
"""
tokenizer = TransfoXLTokenizer.from_pretrained(*args, **kwargs)
return tokenizer
......@@ -63,26 +63,26 @@ def transformerXLModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> tokenized_text_1 = tokenizer.tokenize(text_1)
>>> tokenized_text_2 = tokenizer.tokenize(text_2)
>>> indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
>>> indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
tokenized_text_1 = tokenizer.tokenize(text_1)
tokenized_text_2 = tokenizer.tokenize(text_2)
indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load transformerXLModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLModel', 'transfo-xl-wt103')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLModel', 'transfo-xl-wt103')
model.eval()
# Predict hidden states features for each layer
# We can re-use the memory cells in a subsequent call to attend a longer context
>>> with torch.no_grad():
with torch.no_grad():
hidden_states_1, mems_1 = model(tokens_tensor_1)
hidden_states_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
"""
......@@ -98,33 +98,33 @@ def transformerXLLMHeadModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> tokenized_text_1 = tokenizer.tokenize(text_1)
>>> tokenized_text_2 = tokenizer.tokenize(text_2)
>>> indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
>>> indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
tokenized_text_1 = tokenizer.tokenize(text_1)
tokenized_text_2 = tokenizer.tokenize(text_2)
indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load transformerXLLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLLMHeadModel', 'transfo-xl-wt103')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLLMHeadModel', 'transfo-xl-wt103')
model.eval()
# Predict hidden states features for each layer
# We can re-use the memory cells in a subsequent call to attend a longer context
>>> with torch.no_grad():
with torch.no_grad():
predictions_1, mems_1 = model(tokens_tensor_1)
predictions_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
# Get the predicted last token
>>> predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
>>> predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
>>> assert predicted_token == 'who'
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
assert predicted_token == 'who'
"""
model = TransfoXLLMHeadModel.from_pretrained(*args, **kwargs)
return model
hubconfs/xlm_hubconf.py
View file @ 0a2fecdf
......@@ -17,16 +17,16 @@ xlm_start_docstring = """
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> indexed_tokens_1 = tokenizer.encode(text_1)
>>> indexed_tokens_2 = tokenizer.encode(text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
"""
# A lot of models share the same param doc. Use a decorator
......@@ -76,11 +76,11 @@ def xlmTokenizer(*args, **kwargs):
Default: None
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
>>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text)
text = "Who was Jim Henson ?"
indexed_tokens = tokenizer.encode(tokenized_text)
"""
tokenizer = XLMTokenizer.from_pretrained(*args, **kwargs)
return tokenizer
......@@ -91,11 +91,11 @@ def xlmTokenizer(*args, **kwargs):
def xlmModel(*args, **kwargs):
"""
# Load xlmModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlmModel', 'xlm-mlm-en-2048')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'xlmModel', 'xlm-mlm-en-2048')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
hidden_states_1, mems = model(tokens_tensor_1)
hidden_states_2, mems = model(tokens_tensor_2, past=mems)
"""
......@@ -108,26 +108,26 @@ def xlmModel(*args, **kwargs):
def xlmLMHeadModel(*args, **kwargs):
"""
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> indexed_tokens_1 = tokenizer.encode(text_1)
>>> indexed_tokens_2 = tokenizer.encode(text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlm-mlm-en-2048')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlm-mlm-en-2048')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
predictions_1, mems = model(tokens_tensor_1)
predictions_2, mems = model(tokens_tensor_2, mems=mems)
# Get the predicted last token
>>> predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
>>> predicted_token = tokenizer.decode([predicted_index])
>>> assert predicted_token == ' who'
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
assert predicted_token == ' who'
"""
model = XLMWithLMHeadModel.from_pretrained(*args, **kwargs)
return model
......@@ -142,25 +142,25 @@ def xlmLMHeadModel(*args, **kwargs):
# Example:
# # Load the tokenizer
# >>> import torch
# >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlm-mlm-en-2048')
# import torch
# tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlm-mlm-en-2048')
# # Prepare tokenized input
# >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
# >>> text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
# >>> tokenized_text1 = tokenizer.tokenize(text1)
# >>> tokenized_text2 = tokenizer.tokenize(text2)
# >>> indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
# >>> indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
# >>> tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
# >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
# text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
# tokenized_text1 = tokenizer.tokenize(text1)
# tokenized_text2 = tokenizer.tokenize(text2)
# indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
# indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
# tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
# mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# # Load xlnetForSequenceClassification
# >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlm-mlm-en-2048')
# >>> model.eval()
# model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlm-mlm-en-2048')
# model.eval()
# # Predict sequence classes logits
# >>> with torch.no_grad():
# with torch.no_grad():
# lm_logits, mems = model(tokens_tensor)
# """
# model = XLNetForSequenceClassification.from_pretrained(*args, **kwargs)
......
hubconfs/xlnet_hubconf.1.py
View file @ 0a2fecdf
......@@ -53,11 +53,11 @@ def xlnetTokenizer(*args, **kwargs):
Default: None
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
>>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text)
text = "Who was Jim Henson ?"
indexed_tokens = tokenizer.encode(tokenized_text)
"""
tokenizer = XLNetTokenizer.from_pretrained(*args, **kwargs)
return tokenizer
......@@ -72,23 +72,23 @@ def xlnetModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> indexed_tokens_1 = tokenizer.encode(text_1)
>>> indexed_tokens_2 = tokenizer.encode(text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetModel', 'xlnet-large-cased')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetModel', 'xlnet-large-cased')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
hidden_states_1, mems = model(tokens_tensor_1)
hidden_states_2, mems = model(tokens_tensor_2, past=mems)
"""
......@@ -106,30 +106,30 @@ def xlnetLMHeadModel(*args, **kwargs):
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?"
>>> text_2 = "Jim Henson was a puppeteer"
>>> indexed_tokens_1 = tokenizer.encode(text_1)
>>> indexed_tokens_2 = tokenizer.encode(text_2)
>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlnet-large-cased')
>>> model.eval()
model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlnet-large-cased')
model.eval()
# Predict hidden states features for each layer
>>> with torch.no_grad():
with torch.no_grad():
predictions_1, mems = model(tokens_tensor_1)
predictions_2, mems = model(tokens_tensor_2, mems=mems)
# Get the predicted last token
>>> predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
>>> predicted_token = tokenizer.decode([predicted_index])
>>> assert predicted_token == ' who'
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
assert predicted_token == ' who'
"""
model = XLNetLMHeadModel.from_pretrained(*args, **kwargs)
return model
......@@ -144,25 +144,25 @@ def xlnetLMHeadModel(*args, **kwargs):
# Example:
# # Load the tokenizer
# >>> import torch
# >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# import torch
# tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# # Prepare tokenized input
# >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
# >>> text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
# >>> tokenized_text1 = tokenizer.tokenize(text1)
# >>> tokenized_text2 = tokenizer.tokenize(text2)
# >>> indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
# >>> indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
# >>> tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
# >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
# text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
# tokenized_text1 = tokenizer.tokenize(text1)
# tokenized_text2 = tokenizer.tokenize(text2)
# indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
# indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
# tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
# mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# # Load xlnetForSequenceClassification
# >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlnet-large-cased')
# >>> model.eval()
# model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlnet-large-cased')
# model.eval()
# # Predict sequence classes logits
# >>> with torch.no_grad():
# with torch.no_grad():
# lm_logits, mems = model(tokens_tensor)
# """
# model = XLNetForSequenceClassification.from_pretrained(*args, **kwargs)
......
pytorch_transformers/__init__.py
View file @ 0a2fecdf
__version__ = "1.0.0"
__version__ = "1.1.0"
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_utils import (PreTrainedTokenizer, clean_up_tokenization)
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
from .modeling_bert import (BertConfig, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTConfig, OpenAIGPTModel,
from .tokenization_utils import (PreTrainedTokenizer)
from .modeling_auto import (AutoConfig, AutoModel)
from .modeling_bert import (BertConfig, BertPreTrainedModel, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTConfig, OpenAIGPTPreTrainedModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel,
from .modeling_transfo_xl import (TransfoXLConfig, TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2Config, GPT2Model,
from .modeling_gpt2 import (GPT2Config, GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
......@@ -29,14 +35,19 @@ from .modeling_xlnet import (XLNetConfig,
XLNetForSequenceClassification, XLNetForQuestionAnswering,
load_tf_weights_in_xlnet, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMConfig, XLMModel,
from .modeling_xlm import (XLMConfig, XLMPreTrainedModel , XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_roberta import (RobertaConfig, RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertConfig, DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path)
from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE, cached_path)
pytorch_transformers/convert_gpt2_checkpoint_to_pytorch.py
View file @ 0a2fecdf
......@@ -35,7 +35,7 @@ def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, p
if gpt2_config_file == "":
config = GPT2Config()
else:
config = GPT2Config(gpt2_config_file)
config = GPT2Config.from_json_file(gpt2_config_file)
model = GPT2Model(config)
# Load weights from numpy
......@@ -58,7 +58,7 @@ if __name__ == "__main__":
default = None,
type = str,
required = True,
help = "Path the TensorFlow checkpoint path.")
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
......
pytorch_transformers/convert_openai_checkpoint_to_pytorch.py
View file @ 0a2fecdf
......@@ -35,7 +35,7 @@ def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_c
if openai_config_file == "":
config = OpenAIGPTConfig()
else:
config = OpenAIGPTConfig(openai_config_file)
config = OpenAIGPTConfig.from_json_file(openai_config_file)
model = OpenAIGPTModel(config)
# Load weights from numpy
......@@ -58,7 +58,7 @@ if __name__ == "__main__":
default = None,
type = str,
required = True,
help = "Path the TensorFlow checkpoint path.")
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
......
pytorch_transformers/convert_pytorch_checkpoint_to_tf.py
View file @ 0a2fecdf
......@@ -20,7 +20,7 @@ import argparse
import torch
import numpy as np
import tensorflow as tf
from pytorch_pretrained_bert.modeling import BertModel
from pytorch_transformers.modeling import BertModel
def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:str):
......@@ -41,7 +41,7 @@ def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:s
N BertForQuestionAnswering
"""
tensors_to_transopse = (
tensors_to_transpose = (
"dense.weight",
"attention.self.query",
"attention.self.key",
......@@ -62,34 +62,34 @@ def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:s
if not os.path.isdir(ckpt_dir):
os.makedirs(ckpt_dir)
session = tf.Session()
state_dict = model.state_dict()
tf_vars = []
def to_tf_var_name(name:str):
for patt, repl in iter(var_map):
name = name.replace(patt, repl)
return 'bert/{}'.format(name)
def assign_tf_var(tensor:np.ndarray, name:str):
tmp_var = tf.Variable(initial_value=tensor)
tf_var = tf.get_variable(dtype=tmp_var.dtype, shape=tmp_var.shape, name=name)
op = tf.assign(ref=tf_var, value=tmp_var)
session.run(tf.variables_initializer([tmp_var, tf_var]))
session.run(fetches=[op, tf_var])
def create_tf_var(tensor:np.ndarray, name:str, session:tf.Session):
tf_dtype = tf.dtypes.as_dtype(tensor.dtype)
tf_var = tf.get_variable(dtype=tf_dtype, shape=tensor.shape, name=name, initializer=tf.zeros_initializer())
session.run(tf.variables_initializer([tf_var]))
session.run(tf_var)
return tf_var
for var_name in state_dict:
tf_name = to_tf_var_name(var_name)
torch_tensor = state_dict[var_name].numpy()
if any([x in var_name for x in tensors_to_transopse]):
torch_tensor = torch_tensor.T
tf_tensor = assign_tf_var(tensor=torch_tensor, name=tf_name)
tf_vars.append(tf_tensor)
print("{0}{1}initialized".format(tf_name, " " * (60 - len(tf_name))))
saver = tf.train.Saver(tf_vars)
saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
tf_name = to_tf_var_name(var_name)
torch_tensor = state_dict[var_name].numpy()
if any([x in var_name for x in tensors_to_transpose]):
torch_tensor = torch_tensor.T
tf_var = create_tf_var(tensor=torch_tensor, name=tf_name, session=session)
tf.keras.backend.set_value(tf_var, torch_tensor)
tf_weight = session.run(tf_var)
print("Successfully created {}: {}".format(tf_name, np.allclose(tf_weight, torch_tensor)))
saver = tf.train.Saver(tf.trainable_variables())
saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))
def main(raw_args=None):
......
pytorch_transformers/convert_roberta_checkpoint_to_pytorch.py 0 → 100644
View file @ 0a2fecdf
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoBERTa checkpoint."""
from __future__ import absolute_import, division, print_function
import argparse
import logging
import numpy as np
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from pytorch_transformers.modeling_bert import (BertConfig, BertEncoder,
BertIntermediate, BertLayer,
BertModel, BertOutput,
BertSelfAttention,
BertSelfOutput)
from pytorch_transformers.modeling_roberta import (RobertaEmbeddings,
RobertaForMaskedLM,
RobertaForSequenceClassification,
RobertaModel)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
SAMPLE_TEXT = 'Hello world! cécé herlolip'
def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path, pytorch_dump_folder_path, classification_head):
"""
Copy/paste/tweak roberta's weights to our BERT structure.
"""
roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
roberta.eval() # disable dropout
config = BertConfig(
vocab_size_or_config_json_file=50265,
hidden_size=roberta.args.encoder_embed_dim,
num_hidden_layers=roberta.args.encoder_layers,
num_attention_heads=roberta.args.encoder_attention_heads,
intermediate_size=roberta.args.encoder_ffn_embed_dim,
max_position_embeddings=514,
type_vocab_size=1,
)
if classification_head:
config.num_labels = roberta.args.num_classes
print("Our BERT config:", config)
model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
roberta_sent_encoder = roberta.model.decoder.sentence_encoder
model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(model.roberta.embeddings.token_type_embeddings.weight) # just zero them out b/c RoBERTa doesn't use them.
model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
model.roberta.embeddings.LayerNorm.variance_epsilon = roberta_sent_encoder.emb_layer_norm.eps
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: BertLayer = model.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
### self attention
self_attn: BertSelfAttention = layer.attention.self
assert(
roberta_layer.self_attn.in_proj_weight.shape == torch.Size((3 * config.hidden_size, config.hidden_size))
)
# we use three distinct linear layers so we split the source layer here.
self_attn.query.weight.data = roberta_layer.self_attn.in_proj_weight[:config.hidden_size, :]
self_attn.query.bias.data = roberta_layer.self_attn.in_proj_bias[:config.hidden_size]
self_attn.key.weight.data = roberta_layer.self_attn.in_proj_weight[config.hidden_size:2*config.hidden_size, :]
self_attn.key.bias.data = roberta_layer.self_attn.in_proj_bias[config.hidden_size:2*config.hidden_size]
self_attn.value.weight.data = roberta_layer.self_attn.in_proj_weight[2*config.hidden_size:, :]
self_attn.value.bias.data = roberta_layer.self_attn.in_proj_bias[2*config.hidden_size:]
### self-attention output
self_output: BertSelfOutput = layer.attention.output
assert(
self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
)
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
self_output.LayerNorm.variance_epsilon = roberta_layer.self_attn_layer_norm.eps
### intermediate
intermediate: BertIntermediate = layer.intermediate
assert(
intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
)
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
### output
bert_output: BertOutput = layer.output
assert(
bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
)
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
bert_output.LayerNorm.variance_epsilon = roberta_layer.final_layer_norm.eps
#### end of layer
if classification_head:
model.classifier.dense.weight = roberta.model.classification_heads['mnli'].dense.weight
model.classifier.dense.bias = roberta.model.classification_heads['mnli'].dense.bias
model.classifier.out_proj.weight = roberta.model.classification_heads['mnli'].out_proj.weight
model.classifier.out_proj.bias = roberta.model.classification_heads['mnli'].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = roberta.model.decoder.lm_head.dense.weight
model.lm_head.dense.bias = roberta.model.decoder.lm_head.dense.bias
model.lm_head.layer_norm.weight = roberta.model.decoder.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = roberta.model.decoder.lm_head.layer_norm.bias
model.lm_head.layer_norm.variance_epsilon = roberta.model.decoder.lm_head.layer_norm.eps
model.lm_head.decoder.weight = roberta.model.decoder.lm_head.weight
model.lm_head.bias = roberta.model.decoder.lm_head.bias
# Let's check that we get the same results.
input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = model(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads['mnli'](roberta.extract_features(input_ids))
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
success = torch.allclose(our_output, their_output, atol=1e-3)
print(
"Do both models output the same tensors?",
"🔥" if success else "💩"
)
if not success:
raise Exception("Something went wRoNg")
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--roberta_checkpoint_path",
default = None,
type = str,
required = True,
help = "Path the official PyTorch dump.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
required = True,
help = "Path to the output PyTorch model.")
parser.add_argument("--classification_head",
action = "store_true",
help = "Whether to convert a final classification head.")
args = parser.parse_args()
convert_roberta_checkpoint_to_pytorch(
args.roberta_checkpoint_path,
args.pytorch_dump_folder_path,
args.classification_head
)
pytorch_transformers/convert_tf_checkpoint_to_pytorch.py
View file @ 0a2fecdf
......@@ -47,7 +47,7 @@ if __name__ == "__main__":
default = None,
type = str,
required = True,
help = "Path the TensorFlow checkpoint path.")
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--bert_config_file",
default = None,
type = str,
......
pytorch_transformers/convert_transfo_xl_checkpoint_to_pytorch.py
View file @ 0a2fecdf
......@@ -24,11 +24,10 @@ from io import open
import torch
import pytorch_transformers.tokenization_transfo_xl as data_utils
from pytorch_transformers.modeling_transfo_xl import (CONFIG_NAME,
WEIGHTS_NAME,
TransfoXLConfig,
TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl)
from pytorch_transformers import CONFIG_NAME, WEIGHTS_NAME
from pytorch_transformers.modeling_transfo_xl import (TransfoXLConfig, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl)
from pytorch_transformers.tokenization_transfo_xl import (CORPUS_NAME, VOCAB_FILES_NAMES)
if sys.version_info[0] == 2:
......@@ -76,7 +75,7 @@ def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
if transfo_xl_config_file == "":
config = TransfoXLConfig()
else:
config = TransfoXLConfig(transfo_xl_config_file)
config = TransfoXLConfig.from_json_file(transfo_xl_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = TransfoXLLMHeadModel(config)
......
pytorch_transformers/convert_xlm_checkpoint_to_pytorch.py
View file @ 0a2fecdf
......@@ -36,7 +36,7 @@ def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_p
model = chkpt['model']
config = chkpt['params']
config = dict((n, v) for n, v in config.items() if not isinstance(v, (torch.Tensor, numpy.ndarray)))
config = dict((n, v) for n, v in config.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray)))
vocab = chkpt['dico_word2id']
vocab = dict((s + '</w>' if s.find('@@') == -1 and i > 13 else s.replace('@@', ''), i) for s, i in vocab.items())
......
pytorch_transformers/convert_xlnet_checkpoint_to_pytorch.py
View file @ 0a2fecdf
......@@ -79,7 +79,7 @@ if __name__ == "__main__":
default = None,
type = str,
required = True,
help = "Path the TensorFlow checkpoint path.")
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--xlnet_config_file",
default = None,
type = str,
......
pytorch_transformers/file_utils.py
View file @ 0a2fecdf
......@@ -17,8 +17,9 @@ from hashlib import sha256
from io import open
import boto3
import requests
from botocore.config import Config
from botocore.exceptions import ClientError
import requests
from tqdm import tqdm
try:
......@@ -38,10 +39,13 @@ except ImportError:
try:
from pathlib import Path
PYTORCH_PRETRAINED_BERT_CACHE = Path(
os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path))
os.getenv('PYTORCH_TRANSFORMERS_CACHE', os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)))
except (AttributeError, ImportError):
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
default_cache_path)
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_TRANSFORMERS_CACHE',
os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
default_cache_path))
PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE # Kept for backward compatibility
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
......@@ -70,7 +74,7 @@ def filename_to_url(filename, cache_dir=None):
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
cache_dir = PYTORCH_TRANSFORMERS_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
......@@ -90,15 +94,18 @@ def filename_to_url(filename, cache_dir=None):
return url, etag
def cached_path(url_or_filename, cache_dir=None):
def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None):
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
Args:
cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
force_download: if True, re-dowload the file even if it's already cached in the cache dir.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
cache_dir = PYTORCH_TRANSFORMERS_CACHE
if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
......@@ -108,7 +115,7 @@ def cached_path(url_or_filename, cache_dir=None):
if parsed.scheme in ('http', 'https', 's3'):
# URL, so get it from the cache (downloading if necessary)
return get_from_cache(url_or_filename, cache_dir)
return get_from_cache(url_or_filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
elif os.path.exists(url_or_filename):
# File, and it exists.
return url_or_filename
......@@ -153,24 +160,24 @@ def s3_request(func):
@s3_request
def s3_etag(url):
def s3_etag(url, proxies=None):
"""Check ETag on S3 object."""
s3_resource = boto3.resource("s3")
s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag
@s3_request
def s3_get(url, temp_file):
def s3_get(url, temp_file, proxies=None):
"""Pull a file directly from S3."""
s3_resource = boto3.resource("s3")
s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
def http_get(url, temp_file):
req = requests.get(url, stream=True)
def http_get(url, temp_file, proxies=None):
req = requests.get(url, stream=True, proxies=proxies)
content_length = req.headers.get('Content-Length')
total = int(content_length) if content_length is not None else None
progress = tqdm(unit="B", total=total)
......@@ -181,13 +188,13 @@ def http_get(url, temp_file):
progress.close()
def get_from_cache(url, cache_dir=None):
def get_from_cache(url, cache_dir=None, force_download=False, proxies=None):
"""
Given a URL, look for the corresponding dataset in the local cache.
If it's not there, download it. Then return the path to the cached file.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
cache_dir = PYTORCH_TRANSFORMERS_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if sys.version_info[0] == 2 and not isinstance(cache_dir, str):
......@@ -198,10 +205,10 @@ def get_from_cache(url, cache_dir=None):
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url)
etag = s3_etag(url, proxies=proxies)
else:
try:
response = requests.head(url, allow_redirects=True)
response = requests.head(url, allow_redirects=True, proxies=proxies)
if response.status_code != 200:
etag = None
else:
......@@ -224,17 +231,17 @@ def get_from_cache(url, cache_dir=None):
if matching_files:
cache_path = os.path.join(cache_dir, matching_files[-1])
if not os.path.exists(cache_path):
if not os.path.exists(cache_path) or force_download:
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with tempfile.NamedTemporaryFile() as temp_file:
logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
# GET file object
if url.startswith("s3://"):
s3_get(url, temp_file)
s3_get(url, temp_file, proxies=proxies)
else:
http_get(url, temp_file)
http_get(url, temp_file, proxies=proxies)
# we are copying the file before closing it, so flush to avoid truncation
temp_file.flush()
......
pytorch_transformers/modeling_auto.py 0 → 100644
View file @ 0a2fecdf
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Auto Model class. """
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from torch.nn.parameter import Parameter
from .modeling_bert import BertConfig, BertModel
from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel
from .modeling_gpt2 import GPT2Config, GPT2Model
from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel
from .modeling_xlnet import XLNetConfig, XLNetModel
from .modeling_xlm import XLMConfig, XLMModel
from .modeling_roberta import RobertaConfig, RobertaModel
from .modeling_distilbert import DistilBertConfig, DistilBertModel
from .modeling_utils import PreTrainedModel, SequenceSummary
logger = logging.getLogger(__name__)
class AutoConfig(object):
r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
that will be instantiated as one of the configuration classes of the library
when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method take care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `bert`: BertConfig (Bert model)
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
- contains `xlnet`: XLNetConfig (XLNet model)
- contains `xlm`: XLMConfig (XLM model)
- contains `roberta`: RobertaConfig (RoBERTa model)
This class cannot be instantiated using `__init__()` (throw an error).
"""
def __init__(self):
raise EnvironmentError("AutoConfig is designed to be instantiated "
"using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r""" Instantiate a one of the configuration classes of the library
from a pre-trained model configuration.
The configuration class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `bert`: BertConfig (Bert model)
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
- contains `xlnet`: XLNetConfig (XLNet model)
- contains `xlm`: XLMConfig (XLM model)
- contains `roberta`: RobertaConfig (RoBERTa model)
Params:
**pretrained_model_name_or_path**: either:
- a string with the `shortcut name` of a pre-trained model configuration to load from cache
or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
- a path to a `directory` containing a configuration file saved
using the `save_pretrained(save_directory)` method.
- a path or url to a saved configuration `file`.
**cache_dir**: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
**return_unused_kwargs**: (`optional`) bool:
- If False, then this function returns just the final configuration object.
- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs`
is a dictionary consisting of the key/value pairs whose keys are not configuration attributes:
ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
**kwargs**: (`optional`) dict:
Dictionary of key/value pairs with which to update the configuration object after loading.
- The values in kwargs of any keys which are configuration attributes will be used
to override the loaded values.
- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
by the `return_unused_kwargs` keyword parameter.
Examples::
config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True
config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
foo=False, return_unused_kwargs=True)
assert config.output_attention == True
assert unused_kwargs == {'foo': False}
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta'".format(pretrained_model_name_or_path))
class AutoModel(object):
r"""
:class:`~pytorch_transformers.AutoModel` is a generic model class
that will be instantiated as one of the base model classes of the library
when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method take care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
- contains `xlnet`: XLNetModel (XLNet model)
- contains `xlm`: XLMModel (XLM model)
- contains `roberta`: RobertaModel (RoBERTa model)
This class cannot be instantiated using `__init__()` (throw an error).
"""
def __init__(self):
raise EnvironmentError("AutoModel is designed to be instantiated "
"using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiate a one of the base model classes of the library
from a pre-trained model configuration.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
- contains `xlnet`: XLNetModel (XLNet model)
- contains `xlm`: XLMModel (XLM model)
- contains `roberta`: RobertaModel (RoBERTa model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
**pretrained_model_name_or_path**: either:
- a string with the `shortcut name` of a pre-trained model to load from cache
or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
- a path to a `directory` containing a configuration file saved
using the `save_pretrained(save_directory)` method.
- a path or url to a tensorflow index checkpoint `file` (e.g. `./tf_model/model.ckpt.index`).
In this case, ``from_tf`` should be set to True and a configuration object should be
provided as `config` argument. This loading option is slower than converting the TensorFlow
checkpoint in a PyTorch model using the provided conversion scripts and loading
the PyTorch model afterwards.
**model_args**: (`optional`) Sequence:
All remaning positional arguments will be passed to the underlying model's __init__ function
**config**: an optional configuration for the model to use instead of an automatically loaded configuation.
Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with a `shortcut name` of a pre-trained model), or
- the model was saved using the `save_pretrained(save_directory)` (loaded by suppling the save directory).
**state_dict**: an optional state dictionnary for the model to use instead of a state dictionary loaded
from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
a simpler option.
**cache_dir**: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
**output_loading_info**: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
**kwargs**: (`optional`) dict:
Dictionary of key, values to update the configuration object after loading.
Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
- If a configuration is provided with `config`, **kwargs will be directly passed
to the underlying model's __init__ method.
- If a configuration is not provided, **kwargs will be first passed to the pretrained
model configuration class loading function (`PretrainedConfig.from_pretrained`).
Each key of **kwargs that corresponds to a configuration attribute
will be used to override said attribute with the supplied **kwargs value.
Remaining keys that do not correspond to any configuration attribute will
be passed to the underlying model's __init__ function.
Examples::
model = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta'".format(pretrained_model_name_or_path))
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