934 lines
39 KiB
Python
934 lines
39 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team and Alibaba inc.
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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, division, print_function
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import copy
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import math
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import json
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import numpy as np
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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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import torch.nn.functional as F
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import torch.utils.checkpoint
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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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emb_size=-1,
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num_hidden_layers=12,
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transformer_type='original',
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transition_function='linear',
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weighted_transformer=0,
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num_rolled_layers=3,
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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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attention_type='self',
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rezero=False,
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pre_ln=False,
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squeeze_excitation=False,
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transfer_matrix=False,
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dim_dropout=False,
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roberta_style=False,
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set_mask_zero=False,
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init_scale=False,
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safer_fp16=False,
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grad_checkpoint=False):
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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.emb_size = emb_size
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self.num_hidden_layers = num_hidden_layers
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self.transformer_type = transformer_type
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self.transition_function = transition_function
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self.weighted_transformer = weighted_transformer
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self.num_rolled_layers = num_rolled_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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self.attention_type = attention_type
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self.rezero = rezero
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self.pre_ln = pre_ln
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self.squeeze_excitation = squeeze_excitation
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self.transfer_matrix = transfer_matrix
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self.dim_dropout = dim_dropout
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self.set_mask_zero = set_mask_zero
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self.roberta_style = roberta_style
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self.init_scale = init_scale
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self.safer_fp16 = safer_fp16
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self.grad_checkpoint = grad_checkpoint
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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', encoding='utf-8') 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, special_size=None):
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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.config = config
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hidden_size = special_size if special_size is not None else config.hidden_size
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self.gamma = nn.Parameter(torch.ones(hidden_size))
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self.beta = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = variance_epsilon if not config.roberta_style else 1e-5
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def forward(self, x):
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previous_type = x.type()
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if self.config.safer_fp16:
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x = x.float()
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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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if self.config.safer_fp16:
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return (self.gamma * x + self.beta).type(previous_type)
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else:
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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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hidden_size = config.hidden_size if config.emb_size < 0 else config.emb_size
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self.word_embeddings = nn.Embedding(
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config.vocab_size,
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hidden_size,
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padding_idx=1 if config.roberta_style else None)
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self.position_embeddings = nn.Embedding(
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config.max_position_embeddings,
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hidden_size,
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padding_idx=1 if config.roberta_style else None)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
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hidden_size)
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self.config = config
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self.proj = None if config.emb_size < 0 else nn.Linear(
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config.emb_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, special_size=hidden_size)
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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, adv_embedding=None):
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seq_length = input_ids.size(1)
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if not self.config.roberta_style:
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position_ids = torch.arange(
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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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else:
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mask = input_ids.ne(1).int()
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position_ids = (torch.cumsum(mask, dim=1).type_as(mask)
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* mask).long() + 1
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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(
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input_ids) if adv_embedding is None else adv_embedding
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if self.config.set_mask_zero:
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words_embeddings[input_ids == 103] = 0.
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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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if not self.config.roberta_style:
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embeddings = words_embeddings + position_embeddings + token_type_embeddings
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else:
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embeddings = words_embeddings + position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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if self.proj is not None:
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embeddings = self.proj(embeddings)
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embeddings = self.dropout(embeddings)
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else:
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return embeddings, words_embeddings
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class BERTFactorizedAttention(nn.Module):
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def __init__(self, config):
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super(BERTFactorizedAttention, 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)' %
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(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
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/ 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, *size):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads,
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self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(size)
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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, 0, 2, 3, 1)
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key_layer = self.transpose_for_scores(mixed_key_layer, 0, 2, 1, 3)
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value_layer = self.transpose_for_scores(mixed_value_layer, 0, 2, 1, 3)
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s_attention_scores = query_layer + attention_mask
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s_attention_probs = nn.Softmax(dim=-1)(s_attention_scores)
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s_attention_probs = self.dropout(s_attention_probs)
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c_attention_probs = nn.Softmax(dim=-1)(key_layer)
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s_context_layer = torch.matmul(s_attention_probs, value_layer)
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context_layer = torch.matmul(c_attention_probs, s_context_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] + (
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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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def dim_dropout(x, p=0, dim=-1, training=False):
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if not training or p == 0:
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return x
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a = (1 - p)
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b = (x.data.new(x.size()).zero_() + 1)
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dropout_mask = torch.bernoulli(a * b)
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return dropout_mask * (dropout_mask.size(dim) / torch.sum(
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dropout_mask, dim=dim, keepdim=True)) * x
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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)' %
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(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
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/ 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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self.config = config
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if config.pre_ln:
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self.LayerNorm = BERTLayerNorm(config)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads,
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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, head_mask=None):
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if self.config.pre_ln:
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hidden_states = self.LayerNorm(hidden_states)
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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,
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key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(
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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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if head_mask is not None and not self.training:
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for i, mask in enumerate(head_mask):
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if head_mask[i] == 1:
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attention_scores[:, i, :, :] = 0.
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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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if not self.config.dim_dropout:
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attention_probs = self.dropout(attention_probs)
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else:
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attention_probs = dim_dropout(
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attention_probs,
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p=self.config.attention_probs_dropout_prob,
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dim=-1,
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training=self.training)
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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] + (
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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.config = config
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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if not config.pre_ln and not config.rezero:
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self.LayerNorm = BERTLayerNorm(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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if config.rezero:
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self.res_factor = nn.Parameter(
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torch.Tensor(1).fill_(0.99).to(
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dtype=next(self.parameters()).dtype))
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self.factor = nn.Parameter(
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torch.ones(1).to(dtype=next(self.parameters()).dtype))
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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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if not self.config.rezero and not self.config.pre_ln:
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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elif self.config.rezero:
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hidden_states = hidden_states + self.factor * input_tensor
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else:
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pass
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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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if config.attention_type.lower() == 'self':
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self.self = BERTSelfAttention(config)
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elif config.attention_type.lower() == 'factorized':
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self.self = BERTFactorizedAttention(config)
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else:
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raise ValueError(
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'Attention type must in [self, factorized], but got {}'.format(
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config.attention_type))
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self.output = BERTSelfOutput(config)
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def forward(self, input_tensor, attention_mask, head_mask=None):
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self_output = self.self(input_tensor, attention_mask, head_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 DepthwiseSeparableConv1d(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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dilation=1,
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bias=False):
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super(DepthwiseSeparableConv1d, self).__init__()
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padding = (kernel_size - 1) // 2
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self.depthwise = nn.Conv1d(
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in_channels,
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in_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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groups=in_channels,
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bias=bias)
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self.pointwise = nn.Conv1d(
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in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias)
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def forward(self, x):
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x = self.depthwise(x)
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x = self.pointwise(x)
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return x
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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.config = config
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if self.config.pre_ln:
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self.LayerNorm = BERTLayerNorm(config)
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self.intermediate_act_fn = gelu
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if config.transition_function.lower() == 'linear':
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self.dense = nn.Linear(config.hidden_size,
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config.intermediate_size)
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elif config.transition_function.lower() == 'cnn':
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self.cnn = DepthwiseSeparableConv1d(
|
|
config.hidden_size, 4 * config.hidden_size, kernel_size=7)
|
|
elif config.config.hidden_size.lower() == 'rnn':
|
|
raise NotImplementedError(
|
|
'rnn transition function is not implemented yet')
|
|
else:
|
|
raise ValueError('Only support linear/cnn/rnn')
|
|
|
|
def forward(self, hidden_states):
|
|
if self.config.pre_ln:
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
if self.config.transition_function.lower() == 'linear':
|
|
hidden_states = self.dense(hidden_states)
|
|
elif self.config.transition_function.lower() == 'cnn':
|
|
hidden_states = self.cnn(hidden_states.transpose(-1,
|
|
-2)).transpose(
|
|
-1, -2)
|
|
else:
|
|
pass
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class SqueezeExcitationBlock(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(SqueezeExcitationBlock, self).__init__()
|
|
self.down_sampling = nn.Linear(config.hidden_size,
|
|
config.hidden_size // 4)
|
|
self.up_sampling = nn.Linear(config.hidden_size // 4,
|
|
config.hidden_size)
|
|
|
|
def forward(self, hidden_states):
|
|
squeeze = torch.mean(hidden_states, 1, keepdim=True)
|
|
excitation = torch.sigmoid(
|
|
self.up_sampling(gelu(self.down_sampling(squeeze))))
|
|
return hidden_states * excitation
|
|
|
|
|
|
class BERTOutput(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(BERTOutput, self).__init__()
|
|
self.config = config
|
|
if config.transition_function.lower() == 'linear':
|
|
self.dense = nn.Linear(config.intermediate_size,
|
|
config.hidden_size)
|
|
elif config.transition_function.lower() == 'cnn':
|
|
self.cnn = DepthwiseSeparableConv1d(
|
|
4 * config.hidden_size, config.hidden_size, kernel_size=7)
|
|
elif config.config.hidden_size.lower() == 'rnn':
|
|
raise NotImplementedError(
|
|
'rnn transition function is not implemented yet')
|
|
else:
|
|
raise ValueError('Only support linear/cnn/rnn')
|
|
if not config.pre_ln and not config.rezero:
|
|
self.LayerNorm = BERTLayerNorm(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
if config.squeeze_excitation:
|
|
self.SEblock = SqueezeExcitationBlock(config)
|
|
if config.rezero:
|
|
self.res_factor = nn.Parameter(
|
|
torch.Tensor(1).fill_(0.99).to(
|
|
dtype=next(self.parameters()).dtype))
|
|
self.factor = nn.Parameter(
|
|
torch.ones(1).to(dtype=next(self.parameters()).dtype))
|
|
|
|
def forward(self, hidden_states, input_tensor):
|
|
if self.config.transition_function.lower() == 'linear':
|
|
hidden_states = self.dense(hidden_states)
|
|
elif self.config.transition_function.lower() == 'cnn':
|
|
hidden_states = self.cnn(hidden_states.transpose(-1,
|
|
-2)).transpose(
|
|
-1, -2)
|
|
else:
|
|
pass
|
|
hidden_states = self.dropout(hidden_states)
|
|
if self.config.squeeze_excitation:
|
|
hidden_states = self.SEblock(hidden_states)
|
|
if not self.config.rezero and not self.config.pre_ln:
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
elif self.config.rezero:
|
|
hidden_states = hidden_states + self.factor * input_tensor
|
|
else:
|
|
pass
|
|
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, head_mask=None):
|
|
attention_output = self.attention(hidden_states, attention_mask,
|
|
head_mask)
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return attention_output, layer_output
|
|
|
|
|
|
class BERTWeightedLayer(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(BERTWeightedLayer, self).__init__()
|
|
self.config = config
|
|
self.self = BERTSelfAttention(config)
|
|
self.attention_head_size = self.self.attention_head_size
|
|
|
|
self.w_o = nn.ModuleList([
|
|
nn.Linear(self.attention_head_size, config.hidden_size)
|
|
for _ in range(config.num_attention_heads)
|
|
])
|
|
self.w_kp = torch.rand(config.num_attention_heads)
|
|
self.w_kp = nn.Parameter(self.w_kp / self.w_kp.sum())
|
|
self.w_a = torch.rand(config.num_attention_heads)
|
|
self.w_a = nn.Parameter(self.w_a / self.w_a.sum())
|
|
|
|
self.intermediate = BERTIntermediate(config)
|
|
self.output = 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, attention_mask):
|
|
self_output = self.self(hidden_states, attention_mask)
|
|
self_outputs = self_output.split(self.self.attention_head_size, dim=-1)
|
|
self_outputs = [
|
|
self.w_o[i](self_outputs[i]) for i in range(len(self_outputs))
|
|
]
|
|
self_outputs = [
|
|
self.dropout(self_outputs[i]) for i in range(len(self_outputs))
|
|
]
|
|
self_outputs = [
|
|
kappa * output for kappa, output in zip(self.w_kp, self_outputs)
|
|
]
|
|
self_outputs = [
|
|
self.intermediate(self_outputs[i])
|
|
for i in range(len(self_outputs))
|
|
]
|
|
self_outputs = [
|
|
self.output(self_outputs[i]) for i in range(len(self_outputs))
|
|
]
|
|
self_outputs = [
|
|
self.dropout(self_outputs[i]) for i in range(len(self_outputs))
|
|
]
|
|
self_outputs = [
|
|
alpha * output for alpha, output in zip(self.w_a, self_outputs)
|
|
]
|
|
output = sum(self_outputs)
|
|
return self.LayerNorm(hidden_states + output)
|
|
|
|
|
|
class BERTEncoder(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(BERTEncoder, self).__init__()
|
|
self.layer = nn.ModuleList()
|
|
for _ in range(config.num_hidden_layers):
|
|
if config.weighted_transformer:
|
|
self.layer.append(BERTWeightedLayer(config))
|
|
else:
|
|
self.layer.append(BERTLayer(config))
|
|
if config.rezero:
|
|
for index, layer in enumerate(self.layer):
|
|
layer.output.res_factor = nn.Parameter(
|
|
torch.Tensor(1).fill_(1.).to(
|
|
dtype=next(self.parameters()).dtype))
|
|
layer.output.factor = nn.Parameter(
|
|
torch.Tensor(1).fill_(1).to(
|
|
dtype=next(self.parameters()).dtype))
|
|
layer.attention.output.res_factor = layer.output.res_factor
|
|
layer.attention.output.factor = layer.output.factor
|
|
self.config = config
|
|
|
|
def forward(self,
|
|
hidden_states,
|
|
attention_mask,
|
|
epoch_id=-1,
|
|
head_masks=None):
|
|
all_encoder_layers = [hidden_states]
|
|
if epoch_id != -1:
|
|
detach_index = int(len(self.layer) / 3) * (2 - epoch_id) - 1
|
|
else:
|
|
detach_index = -1
|
|
for index, layer_module in enumerate(self.layer):
|
|
if head_masks is None:
|
|
if not self.config.grad_checkpoint:
|
|
self_out, hidden_states = layer_module(
|
|
hidden_states, attention_mask, None)
|
|
else:
|
|
self_out, hidden_states = torch.utils.checkpoint.checkpoint(
|
|
layer_module, hidden_states, attention_mask, None)
|
|
else:
|
|
self_out, hidden_states = layer_module(hidden_states,
|
|
attention_mask,
|
|
head_masks[index])
|
|
if detach_index == index:
|
|
hidden_states.detach_()
|
|
all_encoder_layers.append(self_out)
|
|
all_encoder_layers.append(hidden_states)
|
|
return all_encoder_layers
|
|
|
|
|
|
class BERTEncoderRolled(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(BERTEncoderRolled, self).__init__()
|
|
layer = BERTLayer(config)
|
|
self.config = config
|
|
self.layer = nn.ModuleList(
|
|
[copy.deepcopy(layer) for _ in range(config.num_rolled_layers)])
|
|
|
|
def forward(self,
|
|
hidden_states,
|
|
attention_mask,
|
|
epoch_id=-1,
|
|
head_masks=None):
|
|
all_encoder_layers = [hidden_states]
|
|
for i in range(self.config.num_hidden_layers):
|
|
if self.config.transformer_type.lower() == 'universal':
|
|
hidden_states = self.layer[i % self.config.num_rolled_layers](
|
|
hidden_states, attention_mask)
|
|
elif self.config.transformer_type.lower() == 'albert':
|
|
a = i // (
|
|
self.config.num_hidden_layers
|
|
// self.config.num_rolled_layers)
|
|
hidden_states = self.layer[a](hidden_states, attention_mask)
|
|
all_encoder_layers.append(hidden_states)
|
|
return all_encoder_layers
|
|
|
|
|
|
class BERTEncoderACT(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(BERTEncoderACT, self).__init__()
|
|
self.layer = BERTLayer(config)
|
|
p = nn.Linear(config.hidden_size, 1)
|
|
self.p = nn.ModuleList(
|
|
[copy.deepcopy(p) for _ in range(config.num_hidden_layers)])
|
|
# Following act paper, set bias init ones
|
|
for module in self.p:
|
|
module.bias.data.fill_(1.)
|
|
self.config = config
|
|
self.act_max_steps = config.num_hidden_layers
|
|
self.threshold = 0.99
|
|
|
|
def should_continue(self, halting_probability, n_updates):
|
|
return (halting_probability.lt(self.threshold).__and__(
|
|
n_updates.lt(self.act_max_steps))).any()
|
|
|
|
def forward(self, hidden_states, attention_mask):
|
|
all_encoder_layers = [hidden_states]
|
|
batch_size, seq_len, hdim = hidden_states.size()
|
|
halting_probability = torch.zeros(batch_size, seq_len).cuda()
|
|
remainders = torch.zeros(batch_size, seq_len).cuda()
|
|
n_updates = torch.zeros(batch_size, seq_len).cuda()
|
|
for i in range(self.act_max_steps):
|
|
p = torch.sigmoid(self.p[i](hidden_states).squeeze(2))
|
|
still_running = halting_probability.lt(1.0).float()
|
|
new_halted = (halting_probability + p * still_running).gt(
|
|
self.threshold).float() * still_running
|
|
still_running = (halting_probability + p * still_running).le(
|
|
self.threshold).float() * still_running
|
|
halting_probability = halting_probability + p * still_running
|
|
remainders = remainders + new_halted * (1 - halting_probability)
|
|
halting_probability = halting_probability + new_halted * remainders
|
|
n_updates = n_updates + still_running + new_halted
|
|
update_weights = (p * still_running
|
|
+ new_halted * remainders).unsqueeze(2)
|
|
transformed_states = self.layer(hidden_states, attention_mask)
|
|
hidden_states = transformed_states * update_weights + hidden_states * (
|
|
1 - update_weights)
|
|
all_encoder_layers.append(hidden_states)
|
|
if not self.should_continue(halting_probability, n_updates):
|
|
break
|
|
return all_encoder_layers, torch.mean(n_updates + remainders)
|
|
|
|
|
|
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:
|
|
>>> # 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.config = config
|
|
self.embeddings = BERTEmbeddings(config)
|
|
if config.transformer_type.lower() == 'original':
|
|
self.encoder = BERTEncoder(config)
|
|
elif config.transformer_type.lower() == 'universal':
|
|
self.encoder = BERTEncoderRolled(config)
|
|
elif config.transformer_type.lower() == 'albert':
|
|
self.encoder = BERTEncoderRolled(config)
|
|
elif config.transformer_type.lower() == 'act':
|
|
self.encoder = BERTEncoderACT(config)
|
|
elif config.transformer_type.lower() == 'textnas':
|
|
from textnas_final import input_dict, op_dict, skip_dict
|
|
self.encoder = TextNASEncoder(config, op_dict, input_dict,
|
|
skip_dict)
|
|
else:
|
|
raise ValueError('Not support transformer type: {}'.format(
|
|
config.transformer_type.lower()))
|
|
self.pooler = BERTPooler(config)
|
|
|
|
def forward(self,
|
|
input_ids,
|
|
token_type_ids=None,
|
|
attention_mask=None,
|
|
epoch_id=-1,
|
|
head_masks=None,
|
|
adv_embedding=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, 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, word_embeddings = self.embeddings(
|
|
input_ids, token_type_ids, adv_embedding)
|
|
if self.config.transformer_type.lower() == 'act':
|
|
all_encoder_layers, act_loss = self.encoder(
|
|
embedding_output, extended_attention_mask)
|
|
elif self.config.transformer_type.lower() == 'reformer':
|
|
sequence_output = self.encoder(embedding_output)
|
|
all_encoder_layers = [sequence_output, sequence_output]
|
|
else:
|
|
all_encoder_layers = self.encoder(embedding_output,
|
|
extended_attention_mask,
|
|
epoch_id, head_masks)
|
|
all_encoder_layers.insert(0, word_embeddings)
|
|
sequence_output = all_encoder_layers[-1]
|
|
if not self.config.safer_fp16:
|
|
pooled_output = self.pooler(sequence_output)
|
|
else:
|
|
pooled_output = sequence_output[:, 0]
|
|
return all_encoder_layers, pooled_output
|
|
|
|
|
|
class BertForSequenceClassificationMultiTask(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:
|
|
>>> # 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, label_list, core_encoder):
|
|
super(BertForSequenceClassificationMultiTask, self).__init__()
|
|
if core_encoder.lower() == 'bert':
|
|
self.bert = BertModel(config)
|
|
elif core_encoder.lower() == 'lstm':
|
|
self.bert = LSTMModel(config)
|
|
else:
|
|
raise ValueError(
|
|
'Only support lstm or bert, but got {}'.format(core_encoder))
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.ModuleList()
|
|
for label in label_list:
|
|
self.classifier.append(nn.Linear(config.hidden_size, len(label)))
|
|
self.label_list = label_list
|
|
|
|
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,
|
|
labels_index=None,
|
|
epoch_id=-1,
|
|
head_masks=None,
|
|
adv_embedding=None,
|
|
return_embedding=False,
|
|
loss_weight=None):
|
|
all_encoder_layers, pooled_output = self.bert(input_ids,
|
|
token_type_ids,
|
|
attention_mask, epoch_id,
|
|
head_masks,
|
|
adv_embedding)
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = [classifier(pooled_output) for classifier in self.classifier]
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss(reduction='none')
|
|
regression_loss_fct = nn.MSELoss(reduction='none')
|
|
labels_lst = torch.unbind(labels, 1)
|
|
loss_lst = []
|
|
for index, (label, logit) in enumerate(zip(labels_lst, logits)):
|
|
if len(self.label_list[index]) != 1:
|
|
loss = loss_fct(logit, label.long())
|
|
else:
|
|
loss = regression_loss_fct(logit.squeeze(-1), label)
|
|
labels_mask = (labels_index == index).to(
|
|
dtype=next(self.parameters()).dtype)
|
|
if loss_weight is not None:
|
|
loss = loss * loss_weight[index]
|
|
loss = torch.mean(loss * labels_mask)
|
|
loss_lst.append(loss)
|
|
if not return_embedding:
|
|
return sum(loss_lst), logits
|
|
else:
|
|
return sum(loss_lst), logits, all_encoder_layers[0]
|
|
else:
|
|
return logits
|