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473 lines
20 KiB
Python
473 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch BERT model."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import json
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import math
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import six
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import torch
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import torch.nn as nn
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from torch.nn import CrossEntropyLoss
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def gelu(x):
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"""Implementation of the gelu activation function.
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For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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"""
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return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
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class BertConfig(object):
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"""Configuration class to store the configuration of a `BertModel`.
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"""
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def __init__(self,
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vocab_size,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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initializer_range=0.02):
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"""Constructs BertConfig.
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Args:
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vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
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hidden_size: Size of the encoder layers and the pooler layer.
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num_hidden_layers: Number of hidden layers in the Transformer encoder.
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num_attention_heads: Number of attention heads for each attention layer in
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the Transformer encoder.
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intermediate_size: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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hidden_act: The non-linear activation function (function or string) in the
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encoder and pooler.
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hidden_dropout_prob: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob: The dropout ratio for the attention
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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type_vocab_size: The vocabulary size of the `token_type_ids` passed into
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`BertModel`.
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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"""
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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@classmethod
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def from_dict(cls, json_object):
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
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config = BertConfig(vocab_size=None)
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for (key, value) in six.iteritems(json_object):
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config.__dict__[key] = value
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return config
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@classmethod
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def from_json_file(cls, json_file):
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"""Constructs a `BertConfig` from a json file of parameters."""
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with open(json_file, "r") as reader:
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text = reader.read()
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return cls.from_dict(json.loads(text))
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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class BERTLayerNorm(nn.Module):
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def __init__(self, config, variance_epsilon=1e-12):
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"""Construct a layernorm module in the TF style (epsilon inside the square root).
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"""
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super(BERTLayerNorm, self).__init__()
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self.gamma = nn.Parameter(torch.ones(config.hidden_size))
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self.beta = nn.Parameter(torch.zeros(config.hidden_size))
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self.variance_epsilon = variance_epsilon
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def forward(self, x):
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u = x.mean(-1, keepdim=True)
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s = (x - u).pow(2).mean(-1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.variance_epsilon)
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return self.gamma * x + self.beta
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class BERTEmbeddings(nn.Module):
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def __init__(self, config):
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super(BERTEmbeddings, self).__init__()
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"""Construct the embedding module from word, position and token_type embeddings.
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"""
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = BERTLayerNorm(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, input_ids, token_type_ids=None):
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seq_length = input_ids.size(1)
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position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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words_embeddings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = words_embeddings + position_embeddings + token_type_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class BERTSelfAttention(nn.Module):
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def __init__(self, config):
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super(BERTSelfAttention, self).__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads))
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(self, hidden_states, attention_mask):
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(hidden_states)
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mixed_value_layer = self.value(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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return context_layer
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class BERTSelfOutput(nn.Module):
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def __init__(self, config):
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super(BERTSelfOutput, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = BERTLayerNorm(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BERTAttention(nn.Module):
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def __init__(self, config):
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super(BERTAttention, self).__init__()
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self.self = BERTSelfAttention(config)
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self.output = BERTSelfOutput(config)
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def forward(self, input_tensor, attention_mask):
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self_output = self.self(input_tensor, attention_mask)
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attention_output = self.output(self_output, input_tensor)
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return attention_output
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class BERTIntermediate(nn.Module):
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def __init__(self, config):
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super(BERTIntermediate, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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self.intermediate_act_fn = gelu
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class BERTOutput(nn.Module):
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def __init__(self, config):
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super(BERTOutput, self).__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = BERTLayerNorm(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BERTLayer(nn.Module):
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def __init__(self, config):
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super(BERTLayer, self).__init__()
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self.attention = BERTAttention(config)
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self.intermediate = BERTIntermediate(config)
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self.output = BERTOutput(config)
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def forward(self, hidden_states, attention_mask):
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attention_output = self.attention(hidden_states, attention_mask)
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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class BERTEncoder(nn.Module):
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def __init__(self, config):
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super(BERTEncoder, self).__init__()
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layer = BERTLayer(config)
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self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
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def forward(self, hidden_states, attention_mask):
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all_encoder_layers = []
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for layer_module in self.layer:
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hidden_states = layer_module(hidden_states, attention_mask)
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all_encoder_layers.append(hidden_states)
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return all_encoder_layers
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class BERTPooler(nn.Module):
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def __init__(self, config):
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super(BERTPooler, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class BertModel(nn.Module):
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"""BERT model ("Bidirectional Embedding Representations from a Transformer").
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Example usage:
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```python
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# Already been converted into WordPiece token ids
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input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
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input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
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token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
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config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
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num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
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model = modeling.BertModel(config=config)
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all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
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```
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"""
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def __init__(self, config: BertConfig):
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"""Constructor for BertModel.
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Args:
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config: `BertConfig` instance.
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"""
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super(BertModel, self).__init__()
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self.embeddings = BERTEmbeddings(config)
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self.encoder = BERTEncoder(config)
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self.pooler = BERTPooler(config)
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def forward(self, input_ids, token_type_ids=None, attention_mask=None):
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if attention_mask is None:
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attention_mask = torch.ones_like(input_ids)
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, from_seq_length]
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# So we can broadcast to [batch_size, num_heads, to_seq_length, from_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and -10000.0 for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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extended_attention_mask = extended_attention_mask.float()
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extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
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embedding_output = self.embeddings(input_ids, token_type_ids)
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all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)
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sequence_output = all_encoder_layers[-1]
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pooled_output = self.pooler(sequence_output)
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return all_encoder_layers, pooled_output
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class BertForSequenceClassification(nn.Module):
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"""BERT model for classification.
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This module is composed of the BERT model with a linear layer on top of
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the pooled output.
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Example usage:
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```python
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# Already been converted into WordPiece token ids
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input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
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input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
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token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
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config = BertConfig(vocab_size=32000, hidden_size=512,
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num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
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num_labels = 2
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model = BertForSequenceClassification(config, num_labels)
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logits = model(input_ids, token_type_ids, input_mask)
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```
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"""
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def __init__(self, config, num_labels):
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super(BertForSequenceClassification, self).__init__()
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self.bert = BertModel(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.classifier = nn.Linear(config.hidden_size, num_labels)
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def init_weights(module):
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if isinstance(module, (nn.Linear, nn.Embedding)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(config.initializer_range)
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elif isinstance(module, BERTLayerNorm):
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module.beta.data.normal_(config.initializer_range)
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module.gamma.data.normal_(config.initializer_range)
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if isinstance(module, nn.Linear):
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module.bias.data.zero_()
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self.apply(init_weights)
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def forward(self, input_ids, token_type_ids, attention_mask, labels=None):
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_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask)
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pooled_output = self.dropout(pooled_output)
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logits = self.classifier(pooled_output)
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits, labels)
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return loss, logits
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else:
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return logits
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class BertForQuestionAnswering(nn.Module):
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"""BERT model for Question Answering (span extraction).
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This module is composed of the BERT model with a linear layer on top of
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the sequence output that computes start_logits and end_logits
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Example usage:
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```python
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# Already been converted into WordPiece token ids
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input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
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input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
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token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
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config = BertConfig(vocab_size=32000, hidden_size=512,
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num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
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model = BertForQuestionAnswering(config)
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start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
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```
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"""
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def __init__(self, config):
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super(BertForQuestionAnswering, self).__init__()
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self.bert = BertModel(config)
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# TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
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# self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.qa_outputs = nn.Linear(config.hidden_size, 2)
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def init_weights(module):
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if isinstance(module, (nn.Linear, nn.Embedding)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(config.initializer_range)
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|
elif isinstance(module, BERTLayerNorm):
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module.beta.data.normal_(config.initializer_range)
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|
module.gamma.data.normal_(config.initializer_range)
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if isinstance(module, nn.Linear):
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module.bias.data.zero_()
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|
self.apply(init_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids, attention_mask, start_positions=None, end_positions=None):
|
|
all_encoder_layers, _ = self.bert(input_ids, token_type_ids, attention_mask)
|
|
sequence_output = all_encoder_layers[-1]
|
|
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 start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension - if not this is a no-op
|
|
start_positions = start_positions.squeeze(-1)
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1) + 1
|
|
start_positions.clamp_(0, ignored_index)
|
|
end_positions.clamp_(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
return total_loss, (start_logits, end_logits)
|
|
else:
|
|
return start_logits, end_logits
|