#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Author: Xiaoy LI # Last update: 2019.03.23 # First create: 2019.03.23 # Description: # bert_basic_model.py import os import sys import copy import json import math import logging import tarfile import tempfile import shutil root_path = "/".join(os.path.realpath(__file__).split("/")[:-2]) if root_path not in sys.path: sys.path.insert(0, root_path) import torch from torch import nn from torch.nn import CrossEntropyLoss from utils.file_utils import cached_path PRETRAINED_MODEL_ARCHIVE_MAP = { 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", } logger = logging.getLogger(__name__) CONFIG_NAME = "bert_config.json" WEIGHTS_NAME = "pytorch_model.bin" def gelu(x): return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) def swish(x): return x * torch.sigmoid(x) ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish} class BertConfig(object): """Configuration class to store the configuration of a `BertModel`. """ def __init__(self, vocab_size_or_config_json_file, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02): """Constructs BertConfig. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. hidden_size: Size of the encoder layers and the pooler layer. num_hidden_layers: Number of hidden layers in the Transformer encoder. num_attention_heads: Number of attention heads for each attention layer in the Transformer encoder. intermediate_size: The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act: The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu" and "swish" are supported. hidden_dropout_prob: The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob: The dropout ratio for the attention probabilities. max_position_embeddings: The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size: The vocabulary size of the `token_type_ids` passed into `BertModel`. initializer_range: The sttdev of the truncated_normal_initializer for initializing all weight matrices. """ if isinstance(vocab_size_or_config_json_file, str): with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: json_config = json.loads(reader.read()) for key, value in json_config.items(): self.__dict__[key] = value elif isinstance(vocab_size_or_config_json_file, int): self.vocab_size = vocab_size_or_config_json_file self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range else: raise ValueError("First argument must be either a vocabulary size (int)" "or the path to a pretrained model config file (str)") @classmethod def from_dict(cls, json_object): """Constructs a `BertConfig` from a Python dictionary of parameters.""" config = BertConfig(vocab_size_or_config_json_file=-1) for key, value in json_object.items(): config.__dict__[key] = value return config @classmethod def from_json_file(cls, json_file): """Constructs a `BertConfig` from a json file of parameters.""" with open(json_file, "r", encoding='utf-8') as reader: text = reader.read() return cls.from_dict(json.loads(text)) def __repr__(self): return str(self.to_json_string()) def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" try: from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm except ImportError: print("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.") class BertLayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-12): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(BertLayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = eps def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias class BertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings. """ def __init__(self, config): super(BertEmbeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None): seq_length = input_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = words_embeddings + position_embeddings + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertSelfAttention(nn.Module): def __init__(self, config): super(BertSelfAttention, self).__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads)) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask): mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer, attention_probs class BertSelfOutput(nn.Module): def __init__(self, config): super(BertSelfOutput, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertAttention(nn.Module): def __init__(self, config): super(BertAttention, self).__init__() self.self = BertSelfAttention(config) self.output = BertSelfOutput(config) def forward(self, input_tensor, attention_mask): self_output, attn = self.self(input_tensor, attention_mask) attention_output = self.output(self_output, input_tensor) return attention_output, attn class BertIntermediate(nn.Module): def __init__(self, config): super(BertIntermediate, self).__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) self.intermediate_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class BertOutput(nn.Module): def __init__(self, config): super(BertOutput, self).__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertLayer(nn.Module): def __init__(self, config): super(BertLayer, self).__init__() self.attention = BertAttention(config) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) def forward(self, hidden_states, attention_mask): attention_output, attention = self.attention(hidden_states, attention_mask) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output, attention class BertEncoder(nn.Module): def __init__(self, config): super(BertEncoder, self).__init__() layer = BertLayer(config) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): all_encoder_layers = [] all_attns = [] for idx, layer_module in enumerate(self.layer): hidden_states, attn = layer_module(hidden_states, attention_mask) if output_all_encoded_layers: all_encoder_layers.append(hidden_states) all_attns.append(attn) if not output_all_encoded_layers: all_encoder_layers.append(hidden_states) all_attns.append(attn) return all_encoder_layers, all_attns class BertPooler(nn.Module): def __init__(self, config): super(BertPooler, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class BertPredictionHeadTransform(nn.Module): def __init__(self, config): super(BertPredictionHeadTransform, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.transform_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertLMPredictionHead(nn.Module): def __init__(self, config, bert_model_embedding_weights): super(BertLMPredictionHead, self).__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(bert_model_embedding_weights.size(1), bert_model_embedding_weights.size(0), bias=False) self.decoder.weight = bert_model_embedding_weights self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) + self.bias return hidden_states class BertOnlyMLMHead(nn.Module): def __init__(self, config, bert_model_embedding_weights): super(BertOnlyMLMHead, self).__init__() self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class BertOnlyNSPHead(nn.Module): def __init__(self, config): super(BertOnlyNSPHead, self).__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score class BertPreTrainingHeads(nn.Module): def __init__(self, config, bert_model_embedding_weights): super(BertPreTrainingHeads, self).__init__() self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class PreTrainedBertModel(nn.Module): """ An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models. """ def __init__(self, config, *inputs, **kwargs): super(PreTrainedBertModel, self).__init__() if not isinstance(config, BertConfig): raise ValueError( "Parameter config in `{}(config)` should be an instance of class `BertConfig`. " "To create a model from a Google pretrained model use " "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( self.__class__.__name__, self.__class__.__name__ )) self.config = config def init_bert_weights(self, module): """ Initialize the weights. """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, BertLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() @classmethod def from_pretrained(cls, pretrained_model_name, input_configs=None, state_dict=None, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: pretrained_model_name: either: - a str with the name of a pre-trained model to load selected in the list of: . `bert-base-uncased` . `bert-large-uncased` . `bert-base-cased` . `bert-large-cased` . `bert-base-multilingual-uncased` . `bert-base-multilingual-cased` . `bert-base-chinese` - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance 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 Google pre-trained models *inputs, **kwargs: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification) """ if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP: archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name] else: archive_file = pretrained_model_name # redirect to the cache, if necessary try: resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir) except FileNotFoundError: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find any file " "associated to this path or url.".format( pretrained_model_name, ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), archive_file)) return None if resolved_archive_file == archive_file: logger.info("loading archive file {}".format(archive_file)) else: logger.info("loading archive file {} from cache at {}".format( archive_file, resolved_archive_file)) tempdir = None if os.path.isdir(resolved_archive_file): serialization_dir = resolved_archive_file else: # Extract archive to temp dir tempdir = tempfile.mkdtemp() logger.info("extracting archive file {} to temp dir {}".format( resolved_archive_file, tempdir)) with tarfile.open(resolved_archive_file, 'r:gz') as archive: archive.extractall(tempdir) serialization_dir = tempdir # Load config config_file = os.path.join(serialization_dir, CONFIG_NAME) config = BertConfig.from_json_file(config_file) logger.info("Model config {}".format(config)) # Instantiate model. model = cls(config, *inputs, **kwargs) # model = cls(input_configs, *inputs, **kwargs) if state_dict is None: weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) state_dict = torch.load(weights_path) old_keys = [] new_keys = [] for key in state_dict.keys(): new_key = None if 'gamma' in key: new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): state_dict[new_key] = state_dict.pop(old_key) missing_keys = [] unexpected_keys = [] error_msgs = [] # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, '_metadata', None) state_dict = state_dict.copy() if metadata is not None: state_dict._metadata = metadata def load(module, prefix=''): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(model, prefix='' if hasattr(model, 'bert') else 'bert.') if len(missing_keys) > 0: logger.info("Weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, missing_keys)) if len(unexpected_keys) > 0: logger.info("Weights from pretrained model not used in {}: {}".format( model.__class__.__name__, unexpected_keys)) if tempdir: # Clean up temp dir shutil.rmtree(tempdir) return model class BertModel(PreTrainedBertModel): """BERT model ("Bidirectional Embedding Representations from a Transformer"). Params: config: a BertConfig class instance with the configuration to build a new model 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. `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. Outputs: Tuple of (encoded_layers, pooled_output) `encoded_layers`: controled by `output_all_encoded_layers` argument: - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding to the last attention block of shape [batch_size, sequence_length, hidden_size], `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper). 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 = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = modeling.BertModel(config=config) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertModel, self).__init__(config) self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True): if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 embedding_output = self.embeddings(input_ids, token_type_ids) encoded_layers, attns = self.encoder(embedding_output, extended_attention_mask, output_all_encoded_layers=output_all_encoded_layers) sequence_output = encoded_layers[-1] pooled_output = self.pooler(sequence_output) attn = attns[-1] if not output_all_encoded_layers: encoded_layers = encoded_layers[-1] return encoded_layers, pooled_output, attn class BertForPreTraining(PreTrainedBertModel): """BERT model with pre-training heads. This module comprises the BERT model followed by the two pre-training heads: - the masked language modeling head, and - the next sentence classification head. Params: config: a BertConfig class instance with the configuration to build a new model. 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. `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size] `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] with indices selected in [0, 1]. 0 => next sentence is the continuation, 1 => next sentence is a random sentence. Outputs: if `masked_lm_labels` and `next_sentence_label` are not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss. if `masked_lm_labels` or `next_sentence_label` is `None`: Outputs a tuple comprising - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and - the next sentence classification logits of shape [batch_size, 2]. 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) model = BertForPreTraining(config) masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertForPreTraining, self).__init__(config) self.bert = BertModel(config) self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None): sequence_output, pooled_output, attn = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) if masked_lm_labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss(ignore_index=-1) masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss return total_loss, attn else: return prediction_scores, seq_relationship_score, attn