935 lines
40 KiB
Python
935 lines
40 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch BERT model. """
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import math
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import torch
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import torch.utils.checkpoint
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from packaging import version
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers.modeling_utils import (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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from modelscope.metainfo import Models
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from modelscope.models import Model, TorchModel
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from modelscope.models.builder import MODELS
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from modelscope.outputs import AttentionBackboneModelOutput
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from modelscope.utils.constant import Tasks
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from modelscope.utils.logger import get_logger
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from modelscope.utils.nlp.utils import parse_labels_in_order
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from .configuration import BertConfig
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logger = get_logger()
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_CONFIG_FOR_DOC = 'BertConfig'
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class BertEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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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,
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config.hidden_size,
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padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings,
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config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
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config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model
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# variable name and be able to load any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and
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# exported when serialized
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self.position_embedding_type = getattr(config,
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'position_embedding_type',
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'absolute')
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self.register_buffer(
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'position_ids',
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torch.arange(config.max_position_embeddings).expand((1, -1)))
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if version.parse(torch.__version__) > version.parse('1.6.0'):
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self.register_buffer(
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'token_type_ids',
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torch.zeros(self.position_ids.size(), dtype=torch.long),
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persistent=False,
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)
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def forward(self,
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input_ids=None,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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past_key_values_length=0):
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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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
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+ past_key_values_length]
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# Setting the token_type_ids to the registered buffer in constructor
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# where it is all zeros, which usually occurs when its auto-generated,
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# registered buffer helps users when tracing the model without passing
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# token_type_ids, solves issue #5664
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if token_type_ids is None:
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if hasattr(self, 'token_type_ids'):
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buffered_token_type_ids = self.token_type_ids[:, :seq_length]
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buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
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input_shape[0], seq_length)
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token_type_ids = buffered_token_type_ids_expanded
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else:
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token_type_ids = torch.zeros(
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input_shape,
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dtype=torch.long,
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device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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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 += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class BertSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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super().__init__()
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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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raise ValueError(
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f'The hidden size ({config.hidden_size}) is not a multiple of the number of attention '
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f'heads ({config.num_attention_heads})')
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size
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/ config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = position_embedding_type or getattr(
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config, 'position_embedding_type', 'absolute')
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if self.position_embedding_type == 'relative_key' or self.position_embedding_type == 'relative_key_query':
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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,
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self.attention_head_size)
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self.is_decoder = config.is_decoder
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads,
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self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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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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mixed_query_layer = self.query(hidden_states)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention and past_key_value is not None:
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# reuse k,v, cross_attentions
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key_layer = past_key_value[0]
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value_layer = past_key_value[1]
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attention_mask = encoder_attention_mask
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elif is_cross_attention:
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key_layer = self.transpose_for_scores(
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self.key(encoder_hidden_states))
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value_layer = self.transpose_for_scores(
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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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query_layer = self.transpose_for_scores(mixed_query_layer)
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all
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# cross attention key/value_states. Further calls to cross_attention
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# layer can then reuse all cross-attention key/value_states (first
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# "if" case) if uni-directional self-attention (decoder) save
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# Tuple(torch.Tensor, torch.Tensor) of all previous decoder
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# key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected
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# key/value_states (third "elif" case) if encoder bi-directional
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# self-attention `past_key_value` is always `None`
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past_key_value = (key_layer, value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer,
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key_layer.transpose(-1, -2))
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if self.position_embedding_type == 'relative_key' or self.position_embedding_type == 'relative_key_query':
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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,
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device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(
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seq_length, dtype=torch.long,
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device=hidden_states.device).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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positional_embedding = positional_embedding.to(
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dtype=query_layer.dtype) # fp16 compatibility
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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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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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relative_position_scores_key = torch.einsum(
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'bhrd,lrd->bhlr', key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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attention_scores = attention_scores / math.sqrt(
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self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (
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self.all_head_size, )
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context_layer = context_layer.view(*new_context_layer_shape)
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outputs = (context_layer,
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attention_probs) if output_attentions else (context_layer, )
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if self.is_decoder:
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outputs = outputs + (past_key_value, )
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return outputs
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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(
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config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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super().__init__()
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self.self = BertSelfAttention(
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config, position_embedding_type=position_embedding_type)
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self.output = BertSelfOutput(config)
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self.pruned_heads = set()
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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, self.self.num_attention_heads,
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self.self.attention_head_size, self.pruned_heads)
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# Prune linear layers
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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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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(
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heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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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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outputs = (attention_output,
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) + self_outputs[1:] # add attentions if we output them
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return outputs
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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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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class BertOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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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.is_decoder = config.is_decoder
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self.add_cross_attention = config.add_cross_attention
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if self.add_cross_attention:
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if not self.is_decoder:
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raise ValueError(
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f'{self} should be used as a decoder model if cross attention is added'
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)
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self.crossattention = BertAttention(
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config, position_embedding_type='absolute')
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self.intermediate = BertIntermediate(config)
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self.output = BertOutput(config)
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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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# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
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self_attn_past_key_value = past_key_value[:
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2] if past_key_value is not None else None
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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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# if decoder, the last output is tuple of self-attn cache
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if self.is_decoder:
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outputs = self_attention_outputs[1:-1]
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present_key_value = self_attention_outputs[-1]
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else:
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outputs = self_attention_outputs[
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1:] # add self attentions if we output attention weights
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cross_attn_present_key_value = None
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if self.is_decoder and encoder_hidden_states is not None:
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if not hasattr(self, 'crossattention'):
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raise ValueError(
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f'If `encoder_hidden_states` are passed, {self} has to be instantiated '
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f'with cross-attention layers by setting `config.add_cross_attention=True`'
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)
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# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
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cross_attn_past_key_value = past_key_value[
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-2:] if past_key_value is not None else None
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cross_attention_outputs = self.crossattention(
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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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cross_attn_past_key_value,
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output_attentions,
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)
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attention_output = cross_attention_outputs[0]
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outputs = outputs + cross_attention_outputs[
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1:-1] # add cross attentions if we output attention weights
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# add cross-attn cache to positions 3,4 of present_key_value tuple
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cross_attn_present_key_value = cross_attention_outputs[-1]
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present_key_value = present_key_value + cross_attn_present_key_value
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layer_output = apply_chunking_to_forward(self.feed_forward_chunk,
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self.chunk_size_feed_forward,
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self.seq_len_dim,
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attention_output)
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outputs = (layer_output, ) + outputs
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# if decoder, return the attn key/values as the last output
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if self.is_decoder:
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outputs = outputs + (present_key_value, )
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return outputs
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def feed_forward_chunk(self, attention_output):
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intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class BertEncoder(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList(
|
|
[BertLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
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,
|
|
):
|
|
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, layer_module in enumerate(self.layer):
|
|
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 self.gradient_checkpointing and self.training:
|
|
|
|
if use_cache:
|
|
logger.warning(
|
|
'`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)
|
|
|
|
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,
|
|
)
|
|
|
|
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], )
|
|
if self.config.add_cross_attention:
|
|
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 AttentionBackboneModelOutput(
|
|
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):
|
|
# 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 BertPreTrainedModel(TorchModel, 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'
|
|
supports_gradient_checkpointing = True
|
|
_keys_to_ignore_on_load_missing = [r'position_ids']
|
|
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(config.name_or_path, **kwargs)
|
|
super(Model, self).__init__(config)
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# 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=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(
|
|
mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if isinstance(module, BertEncoder):
|
|
module.gradient_checkpointing = value
|
|
|
|
@classmethod
|
|
def _instantiate(cls, **kwargs):
|
|
"""Instantiate the model.
|
|
|
|
Args:
|
|
kwargs: Input args.
|
|
model_dir: The model dir used to load the checkpoint and the label information.
|
|
num_labels: An optional arg to tell the model how many classes to initialize.
|
|
Method will call utils.parse_label_mapping if num_labels not supplied.
|
|
If num_labels is not found, the model will use the default setting (2 classes).
|
|
|
|
Returns:
|
|
The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained
|
|
"""
|
|
|
|
model_dir = kwargs.pop('model_dir', None)
|
|
cfg = kwargs.pop('cfg', None)
|
|
model_args = parse_labels_in_order(model_dir, cfg, **kwargs)
|
|
if model_dir is None:
|
|
config = BertConfig(**model_args)
|
|
model = cls(config)
|
|
else:
|
|
model = super(Model, cls).from_pretrained(
|
|
pretrained_model_name_or_path=model_dir, **model_args)
|
|
model.model_dir = model_dir
|
|
return model
|
|
|
|
|
|
@MODELS.register_module(group_key=Tasks.backbone, module_name=Models.bert)
|
|
class BertModel(BertPreTrainedModel):
|
|
"""The Bert Model transformer outputting raw hidden-states without any
|
|
specific head on top.
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass
|
|
documentation for the generic methods the library implements for all its
|
|
model (such as downloading or saving, resizing the input embeddings, pruning
|
|
heads etc.)
|
|
|
|
This model is also a PyTorch
|
|
[torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
|
|
subclass. Use it as a regular PyTorch Module and refer to the PyTorch
|
|
documentation for all matter related to general usage and behavior.
|
|
|
|
Parameters:
|
|
config ([`BertConfig`]): Model configuration class with all the
|
|
parameters of the model.
|
|
Initializing with a config file does not load the weights associated
|
|
with the model, only the configuration. Check out the
|
|
[`~PreTrainedModel.from_pretrained`] method to load the model
|
|
weights.
|
|
|
|
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.
|
|
|
|
To behave as an decoder the model needs to be initialized with the
|
|
`is_decoder` argument of the configuration set to `True`. To be used in a
|
|
Seq2Seq model, the model needs to initialized with both `is_decoder`
|
|
argument and `add_cross_attention` set to `True`; an `encoder_hidden_states`
|
|
is then expected as an input to the forward pass.
|
|
|
|
|
|
"""
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.embeddings = BertEmbeddings(config)
|
|
self.encoder = BertEncoder(config)
|
|
|
|
self.pooler = BertPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@classmethod
|
|
def _instantiate(cls, model_dir=None, add_pooling_layer=True, **config):
|
|
config = BertConfig(**config)
|
|
model = cls(config, add_pooling_layer)
|
|
return model
|
|
|
|
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 forward(self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_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,
|
|
**kwargs) -> AttentionBackboneModelOutput:
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`BertTokenizer`]. See
|
|
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
|
|
for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask
|
|
values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the
|
|
inputs. Indices are selected in `[0, 1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position
|
|
embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers,
|
|
num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask
|
|
values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`,
|
|
*optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to
|
|
directly pass an embedded representation. This is useful if you want
|
|
more control over how to convert `input_ids` indices into associated
|
|
vectors than the model's internal embedding lookup matrix.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention
|
|
layers. See `attentions` under returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See
|
|
`hidden_states` under returned tensors for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~file_utils.ModelOutput`] instead of a
|
|
plain tuple.
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(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 (`torch.FloatTensor` of shape `(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 (`tuple(tuple(torch.FloatTensor))` of length
|
|
`config.n_layers` with each tuple having 4 tensors of shape
|
|
`(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 `past_key_values` are used, the user can optionally input only
|
|
the last `decoder_input_ids` (those that don't have their past key
|
|
value states given to this model) of shape `(batch_size, 1)` instead
|
|
of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned
|
|
and can be used to speed up decoding (see `past_key_values`).
|
|
Others (**kwargs)
|
|
some additional parameters might passed in from upstream pipeline,
|
|
which not influence the results.
|
|
"""
|
|
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 self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError(
|
|
'You cannot specify both input_ids and inputs_embeds at the same time'
|
|
)
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError(
|
|
'You have to specify either input_ids or inputs_embeds')
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
# past_key_values_length
|
|
past_key_values_length = past_key_values[0][0].shape[
|
|
2] if past_key_values is not None else 0
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(
|
|
((batch_size, seq_length + past_key_values_length)),
|
|
device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, 'token_type_ids'):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :
|
|
seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
|
|
batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(
|
|
input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
|
|
attention_mask, input_shape, device)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size(
|
|
)
|
|
encoder_hidden_shape = (encoder_batch_size,
|
|
encoder_sequence_length)
|
|
if 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 = None
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask,
|
|
self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
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,
|
|
)
|
|
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 AttentionBackboneModelOutput(
|
|
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,
|
|
)
|
|
|
|
def extract_sequence_outputs(self, outputs):
|
|
return outputs['last_hidden_state']
|
|
|
|
def extract_pooled_outputs(self, outputs):
|
|
return outputs['pooler_output']
|