# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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.
"""PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
import math
import six
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertConfig(object):
"""Configuration class to store the configuration of a `BertModel`.
"""
def __init__(self,
vocab_size,
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=16,
initializer_range=0.02):
"""Constructs BertConfig.
Args:
vocab_size: 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.
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.
"""
self.vocab_size = vocab_size
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
@classmethod
def from_dict(cls, json_object):
"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = BertConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
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") as reader:
text = reader.read()
return cls.from_dict(json.loads(text))
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"
class BERTLayerNorm(nn.Module):
def __init__(self, config, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BERTLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(config.hidden_size))
self.beta = nn.Parameter(torch.zeros(config.hidden_size))
self.variance_epsilon = variance_epsilon
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.gamma * x + self.beta
class BERTEmbeddings(nn.Module):
def __init__(self, config):
super(BERTEmbeddings, self).__init__()
"""Construct the embedding module from word, position and token_type embeddings.
"""
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)
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
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)
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 = self.self(input_tensor, attention_mask)
attention_output = self.output(self_output, input_tensor)
return attention_output
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 = gelu
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)
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 = self.attention(hidden_states, attention_mask)
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
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):
all_encoder_layers = []
for layer_module in self.layer:
hidden_states = layer_module(hidden_states, attention_mask)
all_encoder_layers.append(hidden_states)
return all_encoder_layers
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 BertModel(nn.Module):
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
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, 2, 0]])
config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
model = modeling.BertModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config: BertConfig):
"""Constructor for BertModel.
Args:
config: `BertConfig` instance.
"""
super(BertModel, self).__init__()
self.embeddings = BERTEmbeddings(config)
self.encoder = BERTEncoder(config)
self.pooler = BERTPooler(config)
def forward(self, input_ids, token_type_ids=None, attention_mask=None):
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, from_seq_length]
# So we can broadcast to [batch_size, num_heads, to_seq_length, from_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.float()
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
embedding_output = self.embeddings(input_ids, token_type_ids)
all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)
sequence_output = all_encoder_layers[-1]
pooled_output = self.pooler(sequence_output)
return all_encoder_layers, pooled_output
class BertForSequenceClassification(nn.Module):
"""BERT model for classification.
This module is composed of the BERT model with a linear layer on top of
the pooled output.
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, 2, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
num_labels = 2
model = BertForSequenceClassification(config, num_labels)
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_labels):
super(BertForSequenceClassification, self).__init__()
self.bert = BertModel(config)
self.hidden_size = config.hidden_size
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(self.hidden_size, 1)
def init_weights(module):
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=config.initializer_range)
elif isinstance(module, BERTLayerNorm):
module.beta.data.normal_(mean=0.0, std=config.initializer_range)
module.gamma.data.normal_(mean=0.0, std=config.initializer_range)
if isinstance(module, nn.Linear):
module.bias.data.zero_()
self.apply(init_weights)
def forward(self, input_ids, token_type_ids, attention_mask, labels=None):
pooled_outputs = []
for i in range(input_ids.size(1)):
_, pooled_output = self.bert(input_ids[:, i, :], token_type_ids[:, i, :], attention_mask[:, i, :])
pooled_output = self.dropout(pooled_output)
pooled_outputs.append(pooled_output.unsqueeze_(1))
logits = self.classifier(torch.cat(pooled_outputs, 1).view(-1, self.hidden_size))
logits = logits.view(-1, input_ids.size(1))
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits, labels)
return loss, logits
else:
return logits
class BertForQuestionAnswering(nn.Module):
"""BERT model for Question Answering (span extraction).
This module is composed of the BERT model with a linear layer on top of
the sequence output that computes start_logits and end_logits
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, 2, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
model = BertForQuestionAnswering(config)
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config):
super(BertForQuestionAnswering, self).__init__()
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)
def init_weights(module):
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=config.initializer_range)
elif isinstance(module, BERTLayerNorm):
module.beta.data.normal_(mean=0.0, std=config.initializer_range)
module.gamma.data.normal_(mean=0.0, std=config.initializer_range)
if isinstance(module, nn.Linear):
module.bias.data.zero_()
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 len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 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)
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
else:
return start_logits, end_logits
