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""" |
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Adapted from salesforce@LAVIS. Below is the original copyright: |
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* Copyright (c) 2023, salesforce.com, inc. |
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* All rights reserved. |
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* SPDX-License-Identifier: BSD-3-Clause |
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* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause |
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* By Junnan Li |
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* Based on hf.rst.imde base |
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* https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert |
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""" |
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|
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import math |
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import os |
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import warnings |
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from dataclasses import dataclass |
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from typing import Optional, Tuple, Dict, Any |
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|
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import torch |
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from torch import Tensor, device, dtype, nn |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.nn import CrossEntropyLoss |
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import torch.nn.functional as F |
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|
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from transformers.activations import ACT2FN |
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from transformers.file_utils import ( |
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ModelOutput, |
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) |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPastAndCrossAttentions, |
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BaseModelOutputWithPoolingAndCrossAttentions, |
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CausalLMOutputWithCrossAttentions, |
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MaskedLMOutput, |
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MultipleChoiceModelOutput, |
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NextSentencePredictorOutput, |
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QuestionAnsweringModelOutput, |
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SequenceClassifierOutput, |
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TokenClassifierOutput, |
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) |
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from transformers.modeling_utils import ( |
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PreTrainedModel, |
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apply_chunking_to_forward, |
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find_pruneable_heads_and_indices, |
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prune_linear_layer, |
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) |
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from transformers.utils import logging |
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from transformers.models.bert.configuration_bert import BertConfig |
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|
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logger = logging.get_logger(__name__) |
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|
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class BertEmbeddings(nn.Module): |
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"""Construct the embeddings from word and position embeddings.""" |
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|
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def __init__(self, config): |
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super().__init__() |
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self.word_embeddings = nn.Embedding( |
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config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id |
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) |
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self.position_embeddings = nn.Embedding( |
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config.max_position_embeddings, config.hidden_size |
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) |
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|
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
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self.dropout = nn.Dropout(config.hidden_dropout_prob) |
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|
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self.register_buffer( |
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) |
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) |
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self.position_embedding_type = getattr( |
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config, "position_embedding_type", "absolute" |
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) |
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|
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self.config = config |
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|
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def forward( |
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self, |
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input_ids=None, |
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position_ids=None, |
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query_embeds=None, |
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past_key_values_length=0, |
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): |
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if input_ids is not None: |
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seq_length = input_ids.size()[1] |
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else: |
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seq_length = 0 |
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|
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if position_ids is None: |
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position_ids = self.position_ids[ |
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:, past_key_values_length : seq_length + past_key_values_length |
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].clone() |
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|
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if input_ids is not None: |
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embeddings = self.word_embeddings(input_ids) |
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if self.position_embedding_type == "absolute": |
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position_embeddings = self.position_embeddings(position_ids) |
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embeddings = embeddings + position_embeddings |
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|
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if query_embeds is not None: |
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embeddings = torch.cat((query_embeds, embeddings), dim=1) |
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else: |
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embeddings = query_embeds |
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|
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embeddings = self.LayerNorm(embeddings) |
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embeddings = self.dropout(embeddings) |
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return embeddings |
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|
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class BertSelfAttention(nn.Module): |
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def __init__(self, config, is_cross_attention): |
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super().__init__() |
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self.config = config |
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr( |
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config, "embedding_size" |
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): |
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raise ValueError( |
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"The hidden size (%d) is not a multiple of the number of attention " |
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"heads (%d)" % (config.hidden_size, config.num_attention_heads) |
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) |
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|
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self.num_attention_heads = config.num_attention_heads |
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads) |
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self.all_head_size = self.num_attention_heads * self.attention_head_size |
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|
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self.query = nn.Linear(config.hidden_size, self.all_head_size) |
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if is_cross_attention: |
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self.key = nn.Linear(config.encoder_width, self.all_head_size) |
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self.value = nn.Linear(config.encoder_width, self.all_head_size) |
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else: |
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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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|
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob) |
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self.position_embedding_type = getattr( |
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config, "position_embedding_type", "absolute" |
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) |
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if ( |
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self.position_embedding_type == "relative_key" |
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or self.position_embedding_type == "relative_key_query" |
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): |
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self.max_position_embeddings = config.max_position_embeddings |
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self.distance_embedding = nn.Embedding( |
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2 * config.max_position_embeddings - 1, self.attention_head_size |
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) |
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self.save_attention = False |
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|
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def save_attn_gradients(self, attn_gradients): |
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self.attn_gradients = attn_gradients |
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|
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def get_attn_gradients(self): |
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return self.attn_gradients |
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|
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def save_attention_map(self, attention_map): |
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self.attention_map = attention_map |
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|
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def get_attention_map(self): |
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return self.attention_map |
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|
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def transpose_for_scores(self, x): |
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new_x_shape = x.size()[:-1] + ( |
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self.num_attention_heads, |
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self.attention_head_size, |
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) |
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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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|
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def forward( |
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self, |
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hidden_states, |
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attention_mask=None, |
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head_mask=None, |
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encoder_hidden_states=None, |
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encoder_attention_mask=None, |
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past_key_value=None, |
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output_attentions=False, |
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): |
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is_cross_attention = encoder_hidden_states is not None |
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|
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if is_cross_attention: |
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key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) |
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value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) |
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attention_mask = encoder_attention_mask |
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elif past_key_value is not None: |
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key_layer = self.transpose_for_scores(self.key(hidden_states)) |
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value_layer = self.transpose_for_scores(self.value(hidden_states)) |
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key_layer = torch.cat([past_key_value[0], key_layer], dim=2) |
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value_layer = torch.cat([past_key_value[1], value_layer], dim=2) |
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else: |
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key_layer = self.transpose_for_scores(self.key(hidden_states)) |
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value_layer = self.transpose_for_scores(self.value(hidden_states)) |
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mixed_query_layer = self.query(hidden_states) |
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query_layer = self.transpose_for_scores(mixed_query_layer) |
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past_key_value = (key_layer, value_layer) |
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) |
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|
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if ( |
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self.position_embedding_type == "relative_key" |
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or self.position_embedding_type == "relative_key_query" |
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): |
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seq_length = hidden_states.size()[1] |
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position_ids_l = torch.arange( |
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seq_length, dtype=torch.long, device=hidden_states.device |
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).view(-1, 1) |
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position_ids_r = torch.arange( |
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seq_length, dtype=torch.long, device=hidden_states.device |
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).view(1, -1) |
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distance = position_ids_l - position_ids_r |
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positional_embedding = self.distance_embedding( |
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distance + self.max_position_embeddings - 1 |
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) |
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positional_embedding = positional_embedding.to( |
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dtype=query_layer.dtype |
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) |
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|
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if self.position_embedding_type == "relative_key": |
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relative_position_scores = torch.einsum( |
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"bhld,lrd->bhlr", query_layer, positional_embedding |
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) |
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attention_scores = attention_scores + relative_position_scores |
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elif self.position_embedding_type == "relative_key_query": |
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relative_position_scores_query = torch.einsum( |
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"bhld,lrd->bhlr", query_layer, positional_embedding |
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) |
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relative_position_scores_key = torch.einsum( |
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"bhrd,lrd->bhlr", key_layer, positional_embedding |
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) |
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attention_scores = ( |
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attention_scores |
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+ relative_position_scores_query |
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+ relative_position_scores_key |
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) |
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attention_scores = attention_scores / math.sqrt(self.attention_head_size) |
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if attention_mask is not None: |
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attention_scores = attention_scores + attention_mask |
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attention_probs = nn.Softmax(dim=-1)(attention_scores) |
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if is_cross_attention and self.save_attention: |
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self.save_attention_map(attention_probs) |
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attention_probs.register_hook(self.save_attn_gradients) |
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attention_probs_dropped = self.dropout(attention_probs) |
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if head_mask is not None: |
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attention_probs_dropped = attention_probs_dropped * head_mask |
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context_layer = torch.matmul(attention_probs_dropped, value_layer) |
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous() |
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) |
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context_layer = context_layer.view(*new_context_layer_shape) |
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outputs = ( |
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(context_layer, attention_probs) if output_attentions else (context_layer,) |
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) |
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outputs = outputs + (past_key_value,) |
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return outputs |
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|
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class BertSelfOutput(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
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self.dropout = nn.Dropout(config.hidden_dropout_prob) |
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|
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def forward(self, hidden_states, input_tensor): |
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hidden_states = self.dense(hidden_states) |
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hidden_states = self.dropout(hidden_states) |
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hidden_states = self.LayerNorm(hidden_states + input_tensor) |
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return hidden_states |
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|
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class BertAttention(nn.Module): |
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def __init__(self, config, is_cross_attention=False): |
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super().__init__() |
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self.self = BertSelfAttention(config, is_cross_attention) |
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self.output = BertSelfOutput(config) |
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self.pruned_heads = set() |
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|
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def prune_heads(self, heads): |
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if len(heads) == 0: |
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return |
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heads, index = find_pruneable_heads_and_indices( |
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heads, |
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self.self.num_attention_heads, |
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self.self.attention_head_size, |
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self.pruned_heads, |
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) |
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self.self.query = prune_linear_layer(self.self.query, index) |
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self.self.key = prune_linear_layer(self.self.key, index) |
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self.self.value = prune_linear_layer(self.self.value, index) |
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) |
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads) |
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self.self.all_head_size = ( |
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self.self.attention_head_size * self.self.num_attention_heads |
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) |
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self.pruned_heads = self.pruned_heads.union(heads) |
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|
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def forward( |
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self, |
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hidden_states, |
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attention_mask=None, |
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head_mask=None, |
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encoder_hidden_states=None, |
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encoder_attention_mask=None, |
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past_key_value=None, |
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output_attentions=False, |
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): |
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self_outputs = self.self( |
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hidden_states, |
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attention_mask, |
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head_mask, |
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encoder_hidden_states, |
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encoder_attention_mask, |
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past_key_value, |
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output_attentions, |
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) |
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attention_output = self.output(self_outputs[0], hidden_states) |
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|
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outputs = (attention_output,) + self_outputs[ |
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1: |
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] |
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return outputs |
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|
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class BertIntermediate(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size) |
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if isinstance(config.hidden_act, str): |
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self.intermediate_act_fn = ACT2FN[config.hidden_act] |
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else: |
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self.intermediate_act_fn = config.hidden_act |
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|
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def forward(self, hidden_states): |
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hidden_states = self.dense(hidden_states) |
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hidden_states = self.intermediate_act_fn(hidden_states) |
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return hidden_states |
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|
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class BertOutput(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size) |
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
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self.dropout = nn.Dropout(config.hidden_dropout_prob) |
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|
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def forward(self, hidden_states, input_tensor): |
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hidden_states = self.dense(hidden_states) |
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hidden_states = self.dropout(hidden_states) |
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hidden_states = self.LayerNorm(hidden_states + input_tensor) |
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return hidden_states |
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|
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class BertLayer(nn.Module): |
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def __init__(self, config, layer_num): |
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super().__init__() |
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self.config = config |
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self.chunk_size_feed_forward = config.chunk_size_feed_forward |
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self.seq_len_dim = 1 |
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self.attention = BertAttention(config) |
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self.layer_num = layer_num |
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if ( |
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self.config.add_cross_attention |
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and layer_num % self.config.cross_attention_freq == 0 |
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): |
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self.crossattention = BertAttention( |
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config, is_cross_attention=self.config.add_cross_attention |
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) |
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self.has_cross_attention = True |
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else: |
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self.has_cross_attention = False |
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self.intermediate = BertIntermediate(config) |
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self.output = BertOutput(config) |
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|
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self.intermediate_query = BertIntermediate(config) |
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self.output_query = BertOutput(config) |
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|
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def forward( |
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self, |
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hidden_states, |
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attention_mask=None, |
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head_mask=None, |
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encoder_hidden_states=None, |
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encoder_attention_mask=None, |
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past_key_value=None, |
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output_attentions=False, |
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query_length=0, |
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): |
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|
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self_attn_past_key_value = ( |
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past_key_value[:2] if past_key_value is not None else None |
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) |
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self_attention_outputs = self.attention( |
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hidden_states, |
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attention_mask, |
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head_mask, |
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output_attentions=output_attentions, |
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past_key_value=self_attn_past_key_value, |
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) |
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attention_output = self_attention_outputs[0] |
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outputs = self_attention_outputs[1:-1] |
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|
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present_key_value = self_attention_outputs[-1] |
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|
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if query_length > 0: |
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query_attention_output = attention_output[:, :query_length, :] |
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|
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if self.has_cross_attention: |
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assert ( |
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encoder_hidden_states is not None |
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), "encoder_hidden_states must be given for cross-attention layers" |
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cross_attention_outputs = self.crossattention( |
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query_attention_output, |
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attention_mask, |
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head_mask, |
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encoder_hidden_states, |
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encoder_attention_mask, |
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output_attentions=output_attentions, |
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) |
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query_attention_output = cross_attention_outputs[0] |
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outputs = ( |
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outputs + cross_attention_outputs[1:-1] |
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) |
|
|
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layer_output = apply_chunking_to_forward( |
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self.feed_forward_chunk_query, |
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self.chunk_size_feed_forward, |
|
self.seq_len_dim, |
|
query_attention_output, |
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) |
|
if attention_output.shape[1] > query_length: |
|
layer_output_text = apply_chunking_to_forward( |
|
self.feed_forward_chunk, |
|
self.chunk_size_feed_forward, |
|
self.seq_len_dim, |
|
attention_output[:, query_length:, :], |
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) |
|
layer_output = torch.cat([layer_output, layer_output_text], dim=1) |
|
else: |
|
layer_output = apply_chunking_to_forward( |
|
self.feed_forward_chunk, |
|
self.chunk_size_feed_forward, |
|
self.seq_len_dim, |
|
attention_output, |
|
) |
|
outputs = (layer_output,) + outputs |
|
|
|
outputs = outputs + (present_key_value,) |
|
|
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return outputs |
|
|
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def feed_forward_chunk(self, attention_output): |
|
intermediate_output = self.intermediate(attention_output) |
|
layer_output = self.output(intermediate_output, attention_output) |
|
return layer_output |
|
|
|
def feed_forward_chunk_query(self, attention_output): |
|
intermediate_output = self.intermediate_query(attention_output) |
|
layer_output = self.output_query(intermediate_output, attention_output) |
|
return layer_output |
|
|
|
|
|
class BertEncoder(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.config = config |
|
self.layer = nn.ModuleList( |
|
[BertLayer(config, i) for i in range(config.num_hidden_layers)] |
|
) |
|
|
|
def forward( |
|
self, |
|
hidden_states, |
|
attention_mask=None, |
|
head_mask=None, |
|
encoder_hidden_states=None, |
|
encoder_attention_mask=None, |
|
past_key_values=None, |
|
use_cache=None, |
|
output_attentions=False, |
|
output_hidden_states=False, |
|
return_dict=True, |
|
query_length=0, |
|
): |
|
all_hidden_states = () if output_hidden_states else None |
|
all_self_attentions = () if output_attentions else None |
|
all_cross_attentions = ( |
|
() if output_attentions and self.config.add_cross_attention else None |
|
) |
|
|
|
next_decoder_cache = () if use_cache else None |
|
|
|
for i in range(self.config.num_hidden_layers): |
|
layer_module = self.layer[i] |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None |
|
past_key_value = past_key_values[i] if past_key_values is not None else None |
|
|
|
if getattr(self.config, "gradient_checkpointing", False) and self.training: |
|
|
|
if use_cache: |
|
logger.warn( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
|
) |
|
use_cache = False |
|
|
|
def create_custom_forward(module): |
|
def custom_forward(*inputs): |
|
return module( |
|
*inputs, past_key_value, output_attentions, query_length |
|
) |
|
|
|
return custom_forward |
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint( |
|
create_custom_forward(layer_module), |
|
hidden_states, |
|
attention_mask, |
|
layer_head_mask, |
|
encoder_hidden_states, |
|
encoder_attention_mask, |
|
) |
|
else: |
|
layer_outputs = layer_module( |
|
hidden_states, |
|
attention_mask, |
|
layer_head_mask, |
|
encoder_hidden_states, |
|
encoder_attention_mask, |
|
past_key_value, |
|
output_attentions, |
|
query_length, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
if use_cache: |
|
next_decoder_cache += (layer_outputs[-1],) |
|
if output_attentions: |
|
all_self_attentions = all_self_attentions + (layer_outputs[1],) |
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],) |
|
|
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if not return_dict: |
|
return tuple( |
|
v |
|
for v in [ |
|
hidden_states, |
|
next_decoder_cache, |
|
all_hidden_states, |
|
all_self_attentions, |
|
all_cross_attentions, |
|
] |
|
if v is not None |
|
) |
|
return BaseModelOutputWithPastAndCrossAttentions( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_decoder_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attentions, |
|
cross_attentions=all_cross_attentions, |
|
) |
|
|
|
|
|
class BertPooler(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
|
self.activation = nn.Tanh() |
|
|
|
def forward(self, hidden_states): |
|
|
|
|
|
first_token_tensor = hidden_states[:, 0] |
|
pooled_output = self.dense(first_token_tensor) |
|
pooled_output = self.activation(pooled_output) |
|
return pooled_output |
|
|
|
|
|
class BertPredictionHeadTransform(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
|
if isinstance(config.hidden_act, str): |
|
self.transform_act_fn = ACT2FN[config.hidden_act] |
|
else: |
|
self.transform_act_fn = config.hidden_act |
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
|
|
|
def forward(self, hidden_states): |
|
hidden_states = self.dense(hidden_states) |
|
hidden_states = self.transform_act_fn(hidden_states) |
|
hidden_states = self.LayerNorm(hidden_states) |
|
return hidden_states |
|
|
|
|
|
class BertLMPredictionHead(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.transform = BertPredictionHeadTransform(config) |
|
|
|
|
|
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
|
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size)) |
|
|
|
|
|
self.decoder.bias = self.bias |
|
|
|
def forward(self, hidden_states): |
|
hidden_states = self.transform(hidden_states) |
|
hidden_states = self.decoder(hidden_states) |
|
return hidden_states |
|
|
|
|
|
class BertOnlyMLMHead(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.predictions = BertLMPredictionHead(config) |
|
|
|
def forward(self, sequence_output): |
|
prediction_scores = self.predictions(sequence_output) |
|
return prediction_scores |
|
|
|
|
|
class BertPreTrainedModel(PreTrainedModel): |
|
""" |
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
|
models. |
|
""" |
|
|
|
config_class = BertConfig |
|
base_model_prefix = "bert" |
|
_keys_to_ignore_on_load_missing = [r"position_ids"] |
|
|
|
def _init_weights(self, module): |
|
"""Initialize the weights""" |
|
if isinstance(module, (nn.Linear, nn.Embedding)): |
|
|
|
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
elif isinstance(module, nn.LayerNorm): |
|
module.bias.data.zero_() |
|
module.weight.data.fill_(1.0) |
|
if isinstance(module, nn.Linear) and module.bias is not None: |
|
module.bias.data.zero_() |
|
|
|
|
|
class BertModel(BertPreTrainedModel): |
|
""" |
|
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of |
|
cross-attention is added between the self-attention layers, following the architecture described in `Attention is |
|
all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, |
|
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. |
|
argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an |
|
input to the forward pass. |
|
""" |
|
|
|
def __init__(self, config, add_pooling_layer=False): |
|
super().__init__(config) |
|
self.config = config |
|
|
|
self.embeddings = BertEmbeddings(config) |
|
|
|
self.encoder = BertEncoder(config) |
|
|
|
self.pooler = BertPooler(config) if add_pooling_layer else None |
|
|
|
self.init_weights() |
|
|
|
def get_input_embeddings(self): |
|
return self.embeddings.word_embeddings |
|
|
|
def set_input_embeddings(self, value): |
|
self.embeddings.word_embeddings = value |
|
|
|
def _prune_heads(self, heads_to_prune): |
|
""" |
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base |
|
class PreTrainedModel |
|
""" |
|
for layer, heads in heads_to_prune.items(): |
|
self.encoder.layer[layer].attention.prune_heads(heads) |
|
|
|
def get_extended_attention_mask( |
|
self, |
|
attention_mask: Tensor, |
|
input_shape: Tuple[int], |
|
device: device, |
|
is_decoder: bool, |
|
has_query: bool = False, |
|
) -> Tensor: |
|
""" |
|
Makes broadcastable attention and causal masks so that future and masked tokens are ignored. |
|
|
|
Arguments: |
|
attention_mask (:obj:`torch.Tensor`): |
|
Mask with ones indicating tokens to attend to, zeros for tokens to ignore. |
|
input_shape (:obj:`Tuple[int]`): |
|
The shape of the input to the model. |
|
device: (:obj:`torch.device`): |
|
The device of the input to the model. |
|
|
|
Returns: |
|
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. |
|
""" |
|
|
|
|
|
if attention_mask.dim() == 3: |
|
extended_attention_mask = attention_mask[:, None, :, :] |
|
elif attention_mask.dim() == 2: |
|
|
|
|
|
|
|
if is_decoder: |
|
batch_size, seq_length = input_shape |
|
|
|
seq_ids = torch.arange(seq_length, device=device) |
|
causal_mask = ( |
|
seq_ids[None, None, :].repeat(batch_size, seq_length, 1) |
|
<= seq_ids[None, :, None] |
|
) |
|
|
|
|
|
|
|
causal_mask = causal_mask.to(attention_mask.dtype) |
|
|
|
if causal_mask.shape[1] < attention_mask.shape[1]: |
|
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] |
|
if has_query: |
|
causal_mask = torch.cat( |
|
[ |
|
torch.zeros( |
|
(batch_size, prefix_seq_len, seq_length), |
|
device=device, |
|
dtype=causal_mask.dtype, |
|
), |
|
causal_mask, |
|
], |
|
axis=1, |
|
) |
|
causal_mask = torch.cat( |
|
[ |
|
torch.ones( |
|
(batch_size, causal_mask.shape[1], prefix_seq_len), |
|
device=device, |
|
dtype=causal_mask.dtype, |
|
), |
|
causal_mask, |
|
], |
|
axis=-1, |
|
) |
|
extended_attention_mask = ( |
|
causal_mask[:, None, :, :] * attention_mask[:, None, None, :] |
|
) |
|
else: |
|
extended_attention_mask = attention_mask[:, None, None, :] |
|
else: |
|
raise ValueError( |
|
"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( |
|
input_shape, attention_mask.shape |
|
) |
|
) |
|
|
|
|
|
|
|
|
|
|
|
|
|
extended_attention_mask = extended_attention_mask.to( |
|
dtype=self.dtype |
|
) |
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 |
|
return extended_attention_mask |
|
|
|
def forward( |
|
self, |
|
input_ids=None, |
|
attention_mask=None, |
|
position_ids=None, |
|
head_mask=None, |
|
query_embeds=None, |
|
encoder_hidden_states=None, |
|
encoder_attention_mask=None, |
|
past_key_values=None, |
|
use_cache=None, |
|
output_attentions=None, |
|
output_hidden_states=None, |
|
return_dict=None, |
|
is_decoder=False, |
|
): |
|
r""" |
|
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): |
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if |
|
the model is configured as a decoder. |
|
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in |
|
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: |
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): |
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. |
|
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` |
|
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` |
|
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. |
|
use_cache (:obj:`bool`, `optional`): |
|
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up |
|
decoding (see :obj:`past_key_values`). |
|
""" |
|
output_attentions = ( |
|
output_attentions |
|
if output_attentions is not None |
|
else self.config.output_attentions |
|
) |
|
output_hidden_states = ( |
|
output_hidden_states |
|
if output_hidden_states is not None |
|
else self.config.output_hidden_states |
|
) |
|
return_dict = ( |
|
return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
|
|
|
|
|
|
if input_ids is None: |
|
assert ( |
|
query_embeds is not None |
|
), "You have to specify query_embeds when input_ids is None" |
|
|
|
|
|
past_key_values_length = ( |
|
past_key_values[0][0].shape[2] - self.config.query_length |
|
if past_key_values is not None |
|
else 0 |
|
) |
|
|
|
query_length = query_embeds.shape[1] if query_embeds is not None else 0 |
|
|
|
embedding_output = self.embeddings( |
|
input_ids=input_ids, |
|
position_ids=position_ids, |
|
query_embeds=query_embeds, |
|
past_key_values_length=past_key_values_length, |
|
) |
|
|
|
input_shape = embedding_output.size()[:-1] |
|
batch_size, seq_length = input_shape |
|
device = embedding_output.device |
|
|
|
if attention_mask is None: |
|
attention_mask = torch.ones( |
|
((batch_size, seq_length + past_key_values_length)), device=device |
|
) |
|
|
|
|
|
|
|
if is_decoder: |
|
extended_attention_mask = self.get_extended_attention_mask( |
|
attention_mask, |
|
input_ids.shape, |
|
device, |
|
is_decoder, |
|
has_query=(query_embeds is not None), |
|
) |
|
else: |
|
extended_attention_mask = self.get_extended_attention_mask( |
|
attention_mask, input_shape, device, is_decoder |
|
) |
|
|
|
|
|
|
|
if encoder_hidden_states is not None: |
|
if type(encoder_hidden_states) == list: |
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[ |
|
0 |
|
].size() |
|
else: |
|
( |
|
encoder_batch_size, |
|
encoder_sequence_length, |
|
_, |
|
) = encoder_hidden_states.size() |
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) |
|
|
|
if type(encoder_attention_mask) == list: |
|
encoder_extended_attention_mask = [ |
|
self.invert_attention_mask(mask) for mask in encoder_attention_mask |
|
] |
|
elif encoder_attention_mask is None: |
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) |
|
encoder_extended_attention_mask = self.invert_attention_mask( |
|
encoder_attention_mask |
|
) |
|
else: |
|
encoder_extended_attention_mask = self.invert_attention_mask( |
|
encoder_attention_mask |
|
) |
|
else: |
|
encoder_extended_attention_mask = None |
|
|
|
|
|
|
|
|
|
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) |
|
|
|
encoder_outputs = self.encoder( |
|
embedding_output, |
|
attention_mask=extended_attention_mask, |
|
head_mask=head_mask, |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_extended_attention_mask, |
|
past_key_values=past_key_values, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
query_length=query_length, |
|
) |
|
sequence_output = encoder_outputs[0] |
|
pooled_output = ( |
|
self.pooler(sequence_output) if self.pooler is not None else None |
|
) |
|
|
|
if not return_dict: |
|
return (sequence_output, pooled_output) + encoder_outputs[1:] |
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions( |
|
last_hidden_state=sequence_output, |
|
pooler_output=pooled_output, |
|
past_key_values=encoder_outputs.past_key_values, |
|
hidden_states=encoder_outputs.hidden_states, |
|
attentions=encoder_outputs.attentions, |
|
cross_attentions=encoder_outputs.cross_attentions, |
|
) |
|
|
|
|
|
class BertLMHeadModel(BertPreTrainedModel): |
|
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"] |
|
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.bert = BertModel(config, add_pooling_layer=False) |
|
self.cls = BertOnlyMLMHead(config) |
|
|
|
self.init_weights() |
|
|
|
def get_output_embeddings(self): |
|
return self.cls.predictions.decoder |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.cls.predictions.decoder = new_embeddings |
|
|
|
def forward( |
|
self, |
|
input_ids=None, |
|
attention_mask=None, |
|
position_ids=None, |
|
head_mask=None, |
|
query_embeds=None, |
|
encoder_hidden_states=None, |
|
encoder_attention_mask=None, |
|
labels=None, |
|
past_key_values=None, |
|
use_cache=True, |
|
output_attentions=None, |
|
output_hidden_states=None, |
|
return_dict=None, |
|
return_logits=False, |
|
is_decoder=True, |
|
reduction="mean", |
|
): |
|
r""" |
|
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): |
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if |
|
the model is configured as a decoder. |
|
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in |
|
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: |
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in |
|
``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are |
|
ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` |
|
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): |
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. |
|
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` |
|
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` |
|
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. |
|
use_cache (:obj:`bool`, `optional`): |
|
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up |
|
decoding (see :obj:`past_key_values`). |
|
Returns: |
|
Example:: |
|
>>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig |
|
>>> import torch |
|
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') |
|
>>> config = BertConfig.from_pretrained("bert-base-cased") |
|
>>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) |
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") |
|
>>> outputs = model(**inputs) |
|
>>> prediction_logits = outputs.logits |
|
""" |
|
return_dict = ( |
|
return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
if labels is not None: |
|
use_cache = False |
|
if past_key_values is not None: |
|
query_embeds = None |
|
|
|
outputs = self.bert( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
query_embeds=query_embeds, |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
past_key_values=past_key_values, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
is_decoder=is_decoder, |
|
) |
|
|
|
sequence_output = outputs[0] |
|
if query_embeds is not None: |
|
sequence_output = outputs[0][:, query_embeds.shape[1] :, :] |
|
|
|
prediction_scores = self.cls(sequence_output) |
|
|
|
if return_logits: |
|
return prediction_scores[:, :-1, :].contiguous() |
|
|
|
lm_loss = None |
|
if labels is not None: |
|
|
|
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() |
|
labels = labels[:, 1:].contiguous() |
|
loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) |
|
lm_loss = loss_fct( |
|
shifted_prediction_scores.view(-1, self.config.vocab_size), |
|
labels.view(-1), |
|
) |
|
if reduction == "none": |
|
lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1) |
|
|
|
if not return_dict: |
|
output = (prediction_scores,) + outputs[2:] |
|
return ((lm_loss,) + output) if lm_loss is not None else output |
|
|
|
return CausalLMOutputWithCrossAttentions( |
|
loss=lm_loss, |
|
logits=prediction_scores, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
cross_attentions=outputs.cross_attentions, |
|
) |
|
|
|
def prepare_inputs_for_generation( |
|
self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs |
|
): |
|
|
|
if attention_mask is None: |
|
attention_mask = input_ids.new_ones(input_ids.shape) |
|
query_mask = input_ids.new_ones(query_embeds.shape[:-1]) |
|
attention_mask = torch.cat([query_mask, attention_mask], dim=-1) |
|
|
|
|
|
if past is not None: |
|
input_ids = input_ids[:, -1:] |
|
|
|
return { |
|
"input_ids": input_ids, |
|
"query_embeds": query_embeds, |
|
"attention_mask": attention_mask, |
|
"past_key_values": past, |
|
"encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), |
|
"encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), |
|
"is_decoder": True, |
|
} |
|
|
|
def _reorder_cache(self, past, beam_idx): |
|
reordered_past = () |
|
for layer_past in past: |
|
reordered_past += ( |
|
tuple( |
|
past_state.index_select(0, beam_idx) for past_state in layer_past |
|
), |
|
) |
|
return reordered_past |
|
|
|
|
|
class BertForMaskedLM(BertPreTrainedModel): |
|
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"] |
|
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] |
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|
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def __init__(self, config): |
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super().__init__(config) |
|
|
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self.bert = BertModel(config, add_pooling_layer=False) |
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self.cls = BertOnlyMLMHead(config) |
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|
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self.init_weights() |
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|
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def get_output_embeddings(self): |
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return self.cls.predictions.decoder |
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|
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def set_output_embeddings(self, new_embeddings): |
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self.cls.predictions.decoder = new_embeddings |
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|
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def forward( |
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self, |
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input_ids=None, |
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attention_mask=None, |
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position_ids=None, |
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head_mask=None, |
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query_embeds=None, |
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encoder_hidden_states=None, |
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encoder_attention_mask=None, |
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labels=None, |
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output_attentions=None, |
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output_hidden_states=None, |
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return_dict=None, |
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return_logits=False, |
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is_decoder=False, |
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): |
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r""" |
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
|
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ..., |
|
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored |
|
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` |
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""" |
|
|
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return_dict = ( |
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return_dict if return_dict is not None else self.config.use_return_dict |
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) |
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|
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outputs = self.bert( |
|
input_ids, |
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attention_mask=attention_mask, |
|
position_ids=position_ids, |
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head_mask=head_mask, |
|
query_embeds=query_embeds, |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
is_decoder=is_decoder, |
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) |
|
|
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if query_embeds is not None: |
|
sequence_output = outputs[0][:, query_embeds.shape[1] :, :] |
|
prediction_scores = self.cls(sequence_output) |
|
|
|
if return_logits: |
|
return prediction_scores |
|
|
|
masked_lm_loss = None |
|
if labels is not None: |
|
loss_fct = CrossEntropyLoss() |
|
masked_lm_loss = loss_fct( |
|
prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) |
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) |
|
|
|
if not return_dict: |
|
output = (prediction_scores,) + outputs[2:] |
|
return ( |
|
((masked_lm_loss,) + output) if masked_lm_loss is not None else output |
|
) |
|
|
|
return MaskedLMOutput( |
|
loss=masked_lm_loss, |
|
logits=prediction_scores, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|