# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team and Alibaba inc. # # 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, division, print_function import copy import math import json import numpy as np import six import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint 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, emb_size=-1, num_hidden_layers=12, transformer_type='original', transition_function='linear', weighted_transformer=0, num_rolled_layers=3, 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, attention_type='self', rezero=False, pre_ln=False, squeeze_excitation=False, transfer_matrix=False, dim_dropout=False, roberta_style=False, set_mask_zero=False, init_scale=False, safer_fp16=False, grad_checkpoint=False): """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 probability 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 stdev of the truncated_normal_initializer for initializing all weight matrices. """ self.vocab_size = vocab_size self.hidden_size = hidden_size self.emb_size = emb_size self.num_hidden_layers = num_hidden_layers self.transformer_type = transformer_type self.transition_function = transition_function self.weighted_transformer = weighted_transformer self.num_rolled_layers = num_rolled_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 self.attention_type = attention_type self.rezero = rezero self.pre_ln = pre_ln self.squeeze_excitation = squeeze_excitation self.transfer_matrix = transfer_matrix self.dim_dropout = dim_dropout self.set_mask_zero = set_mask_zero self.roberta_style = roberta_style self.init_scale = init_scale self.safer_fp16 = safer_fp16 self.grad_checkpoint = grad_checkpoint @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', encoding='utf-8') 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, special_size=None): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(BERTLayerNorm, self).__init__() self.config = config hidden_size = special_size if special_size is not None else config.hidden_size self.gamma = nn.Parameter(torch.ones(hidden_size)) self.beta = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = variance_epsilon if not config.roberta_style else 1e-5 def forward(self, x): previous_type = x.type() if self.config.safer_fp16: x = x.float() u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) if self.config.safer_fp16: return (self.gamma * x + self.beta).type(previous_type) else: 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. """ hidden_size = config.hidden_size if config.emb_size < 0 else config.emb_size self.word_embeddings = nn.Embedding( config.vocab_size, hidden_size, padding_idx=1 if config.roberta_style else None) self.position_embeddings = nn.Embedding( config.max_position_embeddings, hidden_size, padding_idx=1 if config.roberta_style else None) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, hidden_size) self.config = config self.proj = None if config.emb_size < 0 else nn.Linear( config.emb_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, special_size=hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None, adv_embedding=None): seq_length = input_ids.size(1) if not self.config.roberta_style: position_ids = torch.arange( seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) else: mask = input_ids.ne(1).int() position_ids = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + 1 if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) words_embeddings = self.word_embeddings( input_ids) if adv_embedding is None else adv_embedding if self.config.set_mask_zero: words_embeddings[input_ids == 103] = 0. position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) if not self.config.roberta_style: embeddings = words_embeddings + position_embeddings + token_type_embeddings else: embeddings = words_embeddings + position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) if self.proj is not None: embeddings = self.proj(embeddings) embeddings = self.dropout(embeddings) else: return embeddings, words_embeddings class BERTFactorizedAttention(nn.Module): def __init__(self, config): super(BERTFactorizedAttention, 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, *size): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(size) 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, 0, 2, 3, 1) key_layer = self.transpose_for_scores(mixed_key_layer, 0, 2, 1, 3) value_layer = self.transpose_for_scores(mixed_value_layer, 0, 2, 1, 3) s_attention_scores = query_layer + attention_mask s_attention_probs = nn.Softmax(dim=-1)(s_attention_scores) s_attention_probs = self.dropout(s_attention_probs) c_attention_probs = nn.Softmax(dim=-1)(key_layer) s_context_layer = torch.matmul(s_attention_probs, value_layer) context_layer = torch.matmul(c_attention_probs, s_context_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 def dim_dropout(x, p=0, dim=-1, training=False): if not training or p == 0: return x a = (1 - p) b = (x.data.new(x.size()).zero_() + 1) dropout_mask = torch.bernoulli(a * b) return dropout_mask * (dropout_mask.size(dim) / torch.sum( dropout_mask, dim=dim, keepdim=True)) * x 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) self.config = config if config.pre_ln: self.LayerNorm = BERTLayerNorm(config) 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, head_mask=None): if self.config.pre_ln: hidden_states = self.LayerNorm(hidden_states) 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) if head_mask is not None and not self.training: for i, mask in enumerate(head_mask): if head_mask[i] == 1: attention_scores[:, i, :, :] = 0. 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. if not self.config.dim_dropout: attention_probs = self.dropout(attention_probs) else: attention_probs = dim_dropout( attention_probs, p=self.config.attention_probs_dropout_prob, dim=-1, training=self.training) 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.config = config self.dense = nn.Linear(config.hidden_size, config.hidden_size) if not config.pre_ln and not config.rezero: self.LayerNorm = BERTLayerNorm(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) 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): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(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 BERTAttention(nn.Module): def __init__(self, config): super(BERTAttention, self).__init__() if config.attention_type.lower() == 'self': self.self = BERTSelfAttention(config) elif config.attention_type.lower() == 'factorized': self.self = BERTFactorizedAttention(config) else: raise ValueError( 'Attention type must in [self, factorized], but got {}'.format( config.attention_type)) self.output = BERTSelfOutput(config) def forward(self, input_tensor, attention_mask, head_mask=None): self_output = self.self(input_tensor, attention_mask, head_mask) attention_output = self.output(self_output, input_tensor) return attention_output class DepthwiseSeparableConv1d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False): super(DepthwiseSeparableConv1d, self).__init__() padding = (kernel_size - 1) // 2 self.depthwise = nn.Conv1d( in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias) self.pointwise = nn.Conv1d( in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias) def forward(self, x): x = self.depthwise(x) x = self.pointwise(x) return x class BERTIntermediate(nn.Module): def __init__(self, config): super(BERTIntermediate, self).__init__() self.config = config if self.config.pre_ln: self.LayerNorm = BERTLayerNorm(config) self.intermediate_act_fn = gelu if config.transition_function.lower() == 'linear': self.dense = nn.Linear(config.hidden_size, config.intermediate_size) elif config.transition_function.lower() == 'cnn': 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