From 4440801dbfc9bea20a86be6ceeb1431f5d020681 Mon Sep 17 00:00:00 2001 From: Yige Xu Date: Sun, 1 Sep 2019 01:19:10 +0800 Subject: [PATCH] 1. update bert.py and fix a bug in bert_embedding to adapt torch 1.2.0; 2. update models/bert.py and add BertForSentenceMatching model, now a BertEmbedding param should be passed to these five models; 3. create a small bert version for testing and modify test/models/test_bert.py; 4. move small glove and word2vec files to data_for_tests/embedding/small_static_embedding dir and fix relevant test codes; 5. delete some __init__.py files in test dir. --- fastNLP/embeddings/bert_embedding.py | 2 +- fastNLP/models/bert.py | 373 ++++++------------ fastNLP/modules/encoder/bert.py | 4 +- test/__init__.py | 3 - .../embedding/small_bert/config.json | 13 + .../small_bert/small_pytorch_model.bin | Bin 0 -> 37965 bytes .../embedding/small_bert/vocab.txt | 20 + .../glove.6B.50d_test.txt | 0 .../small_static_embedding}/word2vec_test.txt | 0 test/embeddings/__init__.py | 0 test/embeddings/test_bert_embedding.py | 11 +- test/embeddings/test_static_embedding.py | 6 +- test/io/test_embed_loader.py | 8 +- test/models/__init__.py | 0 test/models/test_bert.py | 86 ++-- test/modules/__init__.py | 0 test/modules/decoder/__init__.py | 0 17 files changed, 225 insertions(+), 301 deletions(-) delete mode 100644 test/__init__.py create mode 100644 test/data_for_tests/embedding/small_bert/config.json create mode 100644 test/data_for_tests/embedding/small_bert/small_pytorch_model.bin create mode 100644 test/data_for_tests/embedding/small_bert/vocab.txt rename test/data_for_tests/{ => embedding/small_static_embedding}/glove.6B.50d_test.txt (100%) rename test/data_for_tests/{ => embedding/small_static_embedding}/word2vec_test.txt (100%) delete mode 100644 test/embeddings/__init__.py delete mode 100644 test/models/__init__.py delete mode 100644 test/modules/__init__.py delete mode 100644 test/modules/decoder/__init__.py diff --git a/fastNLP/embeddings/bert_embedding.py b/fastNLP/embeddings/bert_embedding.py index d1a5514a..f6c36623 100644 --- a/fastNLP/embeddings/bert_embedding.py +++ b/fastNLP/embeddings/bert_embedding.py @@ -393,7 +393,7 @@ class _WordBertModel(nn.Module): batch_indexes = torch.arange(batch_size).to(words) word_pieces[batch_indexes, word_pieces_lengths + 1] = self._sep_index if self._has_sep_in_vocab: # 但[SEP]在vocab中出现应该才会需要token_ids - sep_mask = word_pieces.eq(self._sep_index) # batch_size x max_len + sep_mask = word_pieces.eq(self._sep_index).long() # batch_size x max_len sep_mask_cumsum = sep_mask.flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1]) token_type_ids = sep_mask_cumsum.fmod(2) if token_type_ids[0, 0].item(): # 如果开头是奇数，则需要flip一下结果，因为需要保证开头为0 diff --git a/fastNLP/models/bert.py b/fastNLP/models/bert.py index 0a89b765..08f16db2 100644 --- a/fastNLP/models/bert.py +++ b/fastNLP/models/bert.py @@ -5,253 +5,145 @@ bert.py is modified from huggingface/pytorch-pretrained-BERT, which is licensed __all__ = [] -import os +import warnings import torch from torch import nn from .base_model import BaseModel from ..core.const import Const -from ..core.utils import seq_len_to_mask +from ..core._logger import logger from ..modules.encoder import BertModel from ..modules.encoder.bert import BertConfig, CONFIG_FILE +from ..embeddings.bert_embedding import BertEmbedding class BertForSequenceClassification(BaseModel): """BERT model for classification. - This module is composed of the BERT model with a linear layer on top of - the pooled output. - Params: - `config`: a BertConfig class instance with the configuration to build a new model. - `num_labels`: the number of classes for the classifier. Default = 2. - Inputs: - `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] - with the word token indices in the vocabulary. Items in the batch should begin with the special "CLS" token. (see the tokens preprocessing logic in the scripts - `extract_features.py`, `run_classifier.py` and `run_squad.py`) - `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token - types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to - a `sentence B` token (see BERT paper for more details). - `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices - selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max - input sequence length in the current batch. It's the mask that we typically use for attention when - a batch has varying length sentences. - `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] - with indices selected in [0, ..., num_labels]. - Outputs: - if `labels` is not `None`: - Outputs the CrossEntropy classification loss of the output with the labels. - if `labels` is `None`: - Outputs the classification logits of shape [batch_size, num_labels]. - Example usage: - ```python - # Already been converted into WordPiece token ids - input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) - input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, - num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) - num_labels = 2 - model = BertForSequenceClassification(num_labels, config) - logits = model(input_ids, token_type_ids, input_mask) - ``` """ - def __init__(self, num_labels, config=None, bert_dir=None): + def __init__(self, init_embed: BertEmbedding, num_labels: int=2): super(BertForSequenceClassification, self).__init__() + self.num_labels = num_labels - if bert_dir is not None: - self.bert = BertModel.from_pretrained(bert_dir) - config = BertConfig(os.path.join(bert_dir, CONFIG_FILE)) - else: - if config is None: - config = BertConfig(30522) - self.bert = BertModel(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, num_labels) - - @classmethod - def from_pretrained(cls, num_labels, pretrained_model_dir): - config = BertConfig(pretrained_model_dir) - model = cls(num_labels=num_labels, config=config, bert_dir=pretrained_model_dir) - return model - - def forward(self, words, seq_len=None, target=None): - if seq_len is None: - seq_len = torch.ones_like(words, dtype=words.dtype, device=words.device) - if len(seq_len.size()) + 1 == len(words.size()): - seq_len = seq_len_to_mask(seq_len, max_len=words.size(-1)) - _, pooled_output = self.bert(words, attention_mask=seq_len, output_all_encoded_layers=False) - pooled_output = self.dropout(pooled_output) - logits = self.classifier(pooled_output) + self.bert = init_embed + self.dropout = nn.Dropout(0.1) + self.classifier = nn.Linear(self.bert.embedding_dim, num_labels) + + if not self.bert.model.include_cls_sep: + warn_msg = "Bert for sequence classification excepts BertEmbedding `include_cls_sep` True, but got False." + logger.warn(warn_msg) + warnings.warn(warn_msg) + + def forward(self, words): + hidden = self.dropout(self.bert(words)) + cls_hidden = hidden[:, 0] + logits = self.classifier(cls_hidden) + + return {Const.OUTPUT: logits} + + def predict(self, words): + logits = self.forward(words)[Const.OUTPUT] + return {Const.OUTPUT: torch.argmax(logits, dim=-1)} + + +class BertForSentenceMatching(BaseModel): + + """BERT model for matching. + """ + def __init__(self, init_embed: BertEmbedding, num_labels: int=2): + super(BertForSentenceMatching, self).__init__() + self.num_labels = num_labels + self.bert = init_embed + self.dropout = nn.Dropout(0.1) + self.classifier = nn.Linear(self.bert.embedding_dim, num_labels) + + if not self.bert.model.include_cls_sep: + error_msg = "Bert for sentence matching excepts BertEmbedding `include_cls_sep` True, but got False." + logger.error(error_msg) + raise RuntimeError(error_msg) - if target is not None: - loss_fct = nn.CrossEntropyLoss() - loss = loss_fct(logits, target) - return {Const.OUTPUT: logits, Const.LOSS: loss} - else: - return {Const.OUTPUT: logits} + def forward(self, words): + hidden = self.dropout(self.bert(words)) + cls_hidden = hidden[:, 0] + logits = self.classifier(cls_hidden) - def predict(self, words, seq_len=None): - logits = self.forward(words, seq_len=seq_len)[Const.OUTPUT] + return {Const.OUTPUT: logits} + + def predict(self, words): + logits = self.forward(words)[Const.OUTPUT] return {Const.OUTPUT: torch.argmax(logits, dim=-1)} class BertForMultipleChoice(BaseModel): """BERT model for multiple choice tasks. - This module is composed of the BERT model with a linear layer on top of - the pooled output. - Params: - `config`: a BertConfig class instance with the configuration to build a new model. - `num_choices`: the number of classes for the classifier. Default = 2. - Inputs: - `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] - with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts - `extract_features.py`, `run_classifier.py` and `run_squad.py`) - `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] - with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` - and type 1 corresponds to a `sentence B` token (see BERT paper for more details). - `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices - selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max - input sequence length in the current batch. It's the mask that we typically use for attention when - a batch has varying length sentences. - `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] - with indices selected in [0, ..., num_choices]. - Outputs: - if `labels` is not `None`: - Outputs the CrossEntropy classification loss of the output with the labels. - if `labels` is `None`: - Outputs the classification logits of shape [batch_size, num_labels]. - Example usage: - ```python - # Already been converted into WordPiece token ids - input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]]) - input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]]) - token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]]) - config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, - num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) - num_choices = 2 - model = BertForMultipleChoice(num_choices, config, bert_dir) - logits = model(input_ids, token_type_ids, input_mask) - ``` """ - def __init__(self, num_choices, config=None, bert_dir=None): + def __init__(self, init_embed: BertEmbedding, num_choices=2): super(BertForMultipleChoice, self).__init__() + self.num_choices = num_choices - if bert_dir is not None: - self.bert = BertModel.from_pretrained(bert_dir) - else: - if config is None: - config = BertConfig(30522) - self.bert = BertModel(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, 1) - - @classmethod - def from_pretrained(cls, num_choices, pretrained_model_dir): - config = BertConfig(pretrained_model_dir) - model = cls(num_choices=num_choices, config=config, bert_dir=pretrained_model_dir) - return model - - def forward(self, words, seq_len1=None, seq_len2=None, target=None): - input_ids, token_type_ids, attention_mask = words, seq_len1, seq_len2 - flat_input_ids = input_ids.view(-1, input_ids.size(-1)) - flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) - flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) - _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False) + self.bert = init_embed + self.dropout = nn.Dropout(0.1) + self.classifier = nn.Linear(self.bert.embedding_dim, 1) + self.include_cls_sep = init_embed.model.include_cls_sep + + if not self.bert.model.include_cls_sep: + error_msg = "Bert for multiple choice excepts BertEmbedding `include_cls_sep` True, but got False." + logger.error(error_msg) + raise RuntimeError(error_msg) + + def forward(self, words): + """ + :param torch.Tensor words: [batch_size, num_choices, seq_len] + :return: [batch_size, num_labels] + """ + batch_size, num_choices, seq_len = words.size() + + input_ids = words.view(batch_size * num_choices, seq_len) + hidden = self.bert(input_ids) + pooled_output = hidden[:, 0] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, self.num_choices) - if target is not None: - loss_fct = nn.CrossEntropyLoss() - loss = loss_fct(reshaped_logits, target) - return {Const.OUTPUT: reshaped_logits, Const.LOSS: loss} - else: - return {Const.OUTPUT: reshaped_logits} + return {Const.OUTPUT: reshaped_logits} - def predict(self, words, seq_len1=None, seq_len2=None,): - logits = self.forward(words, seq_len1=seq_len1, seq_len2=seq_len2)[Const.OUTPUT] + def predict(self, words): + logits = self.forward(words)[Const.OUTPUT] return {Const.OUTPUT: torch.argmax(logits, dim=-1)} class BertForTokenClassification(BaseModel): """BERT model for token-level classification. - This module is composed of the BERT model with a linear layer on top of - the full hidden state of the last layer. - Params: - `config`: a BertConfig class instance with the configuration to build a new model. - `num_labels`: the number of classes for the classifier. Default = 2. - `bert_dir`: a dir which contains the bert parameters within file `pytorch_model.bin` - Inputs: - `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] - with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts - `extract_features.py`, `run_classifier.py` and `run_squad.py`) - `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token - types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to - a `sentence B` token (see BERT paper for more details). - `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices - selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max - input sequence length in the current batch. It's the mask that we typically use for attention when - a batch has varying length sentences. - `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length] - with indices selected in [0, ..., num_labels]. - Outputs: - if `labels` is not `None`: - Outputs the CrossEntropy classification loss of the output with the labels. - if `labels` is `None`: - Outputs the classification logits of shape [batch_size, sequence_length, num_labels]. - Example usage: - ```python - # Already been converted into WordPiece token ids - input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) - input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, - num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) - num_labels = 2 - bert_dir = 'your-bert-file-dir' - model = BertForTokenClassification(num_labels, config, bert_dir) - logits = model(input_ids, token_type_ids, input_mask) - ``` """ - def __init__(self, num_labels, config=None, bert_dir=None): + def __init__(self, init_embed: BertEmbedding, num_labels): super(BertForTokenClassification, self).__init__() + self.num_labels = num_labels - if bert_dir is not None: - self.bert = BertModel.from_pretrained(bert_dir) - else: - if config is None: - config = BertConfig(30522) - self.bert = BertModel(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, num_labels) - - @classmethod - def from_pretrained(cls, num_labels, pretrained_model_dir): - config = BertConfig(pretrained_model_dir) - model = cls(num_labels=num_labels, config=config, bert_dir=pretrained_model_dir) - return model - - def forward(self, words, seq_len1=None, seq_len2=None, target=None): - sequence_output, _ = self.bert(words, seq_len1, seq_len2, output_all_encoded_layers=False) + self.bert = init_embed + self.dropout = nn.Dropout(0.1) + self.classifier = nn.Linear(self.bert.embedding_dim, num_labels) + self.include_cls_sep = init_embed.model.include_cls_sep + + if self.include_cls_sep: + warn_msg = "Bert for token classification excepts BertEmbedding `include_cls_sep` False, but got True." + warnings.warn(warn_msg) + logger.warn(warn_msg) + + def forward(self, words): + """ + :param torch.Tensor words: [batch_size, seq_len] + :return: [batch_size, seq_len, num_labels] + """ + sequence_output = self.bert(words) + if self.include_cls_sep: + sequence_output = sequence_output[:, 1: -1] # [batch_size, seq_len, embed_dim] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) - if target is not None: - loss_fct = nn.CrossEntropyLoss() - # Only keep active parts of the loss - if seq_len2 is not None: - active_loss = seq_len2.view(-1) == 1 - active_logits = logits.view(-1, self.num_labels)[active_loss] - active_labels = target.view(-1)[active_loss] - loss = loss_fct(active_logits, active_labels) - else: - loss = loss_fct(logits.view(-1, self.num_labels), target.view(-1)) - return {Const.OUTPUT: logits, Const.LOSS: loss} - else: - return {Const.OUTPUT: logits} - - def predict(self, words, seq_len1=None, seq_len2=None): - logits = self.forward(words, seq_len1, seq_len2)[Const.OUTPUT] + return {Const.OUTPUT: logits} + + def predict(self, words): + logits = self.forward(words)[Const.OUTPUT] return {Const.OUTPUT: torch.argmax(logits, dim=-1)} @@ -298,53 +190,24 @@ class BertForQuestionAnswering(BaseModel): start_logits, end_logits = model(input_ids, token_type_ids, input_mask) ``` """ - def __init__(self, config=None, bert_dir=None): + def __init__(self, init_embed: BertEmbedding, num_labels=2): super(BertForQuestionAnswering, self).__init__() - if bert_dir is not None: - self.bert = BertModel.from_pretrained(bert_dir) - else: - if config is None: - config = BertConfig(30522) - self.bert = BertModel(config) - # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version - # self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.qa_outputs = nn.Linear(config.hidden_size, 2) - - @classmethod - def from_pretrained(cls, pretrained_model_dir): - config = BertConfig(pretrained_model_dir) - model = cls(config=config, bert_dir=pretrained_model_dir) - return model - - def forward(self, words, seq_len1=None, seq_len2=None, target1=None, target2=None): - sequence_output, _ = self.bert(words, seq_len1, seq_len2, output_all_encoded_layers=False) - logits = self.qa_outputs(sequence_output) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1) - end_logits = end_logits.squeeze(-1) - - if target1 is not None and target2 is not None: - # If we are on multi-GPU, split add a dimension - if len(target1.size()) > 1: - target1 = target1.squeeze(-1) - if len(target2.size()) > 1: - target2 = target2.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - target1.clamp_(0, ignored_index) - target2.clamp_(0, ignored_index) - - loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, target1) - end_loss = loss_fct(end_logits, target2) - total_loss = (start_loss + end_loss) / 2 - return {Const.OUTPUTS(0): start_logits, Const.OUTPUTS(1): end_logits, Const.LOSS: total_loss} - else: - return {Const.OUTPUTS(0): start_logits, Const.OUTPUTS(1): end_logits} - - def predict(self, words, seq_len1=None, seq_len2=None): - logits = self.forward(words, seq_len1, seq_len2) - start_logits = logits[Const.OUTPUTS(0)] - end_logits = logits[Const.OUTPUTS(1)] - return {Const.OUTPUTS(0): torch.argmax(start_logits, dim=-1), - Const.OUTPUTS(1): torch.argmax(end_logits, dim=-1)} + + self.bert = init_embed + self.num_labels = num_labels + self.qa_outputs = nn.Linear(self.bert.embedding_dim, self.num_labels) + + if not self.bert.model.include_cls_sep: + error_msg = "Bert for multiple choice excepts BertEmbedding `include_cls_sep` True, but got False." + logger.error(error_msg) + raise RuntimeError(error_msg) + + def forward(self, words): + sequence_output = self.bert(words) + logits = self.qa_outputs(sequence_output) # [batch_size, seq_len, num_labels] + + return {Const.OUTPUTS(i): logits[:, :, i] for i in range(self.num_labels)} + + def predict(self, words): + logits = self.forward(words) + return {Const.OUTPUTS(i): torch.argmax(logits[Const.OUTPUTS(i)], dim=-1) for i in range(self.num_labels)} diff --git a/fastNLP/modules/encoder/bert.py b/fastNLP/modules/encoder/bert.py index e73a8172..6f6c4291 100644 --- a/fastNLP/modules/encoder/bert.py +++ b/fastNLP/modules/encoder/bert.py @@ -435,14 +435,14 @@ class BertModel(nn.Module): return encoded_layers, pooled_output @classmethod - def from_pretrained(cls, pretrained_model_dir_or_name, *inputs, **kwargs): + def from_pretrained(cls, model_dir_or_name, *inputs, **kwargs): state_dict = kwargs.get('state_dict', None) kwargs.pop('state_dict', None) kwargs.pop('cache_dir', None) kwargs.pop('from_tf', None) # get model dir from name or dir - pretrained_model_dir = _get_bert_dir(pretrained_model_dir_or_name) + pretrained_model_dir = _get_bert_dir(model_dir_or_name) # Load config config_file = _get_file_name_base_on_postfix(pretrained_model_dir, '.json') diff --git a/test/__init__.py b/test/__init__.py deleted file mode 100644 index c7a5f082..00000000 --- a/test/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -import fastNLP - -__all__ = ["fastNLP"] diff --git a/test/data_for_tests/embedding/small_bert/config.json b/test/data_for_tests/embedding/small_bert/config.json new file mode 100644 index 00000000..3e516872 --- /dev/null +++ b/test/data_for_tests/embedding/small_bert/config.json @@ -0,0 +1,13 @@ +{ + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 16, + "initializer_range": 0.02, + 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