1087 lines
46 KiB
Python
1087 lines
46 KiB
Python
# coding=utf-8
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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 os
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import warnings
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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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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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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replace_return_docstrings,
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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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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "bert-base-uncased"
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_CONFIG_FOR_DOC = "BertConfig"
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_TOKENIZER_FOR_DOC = "BertTokenizer"
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BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"bert-base-uncased",
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"bert-large-uncased",
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"bert-base-cased",
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"bert-large-cased",
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"bert-base-multilingual-uncased",
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"bert-base-multilingual-cased",
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"bert-base-chinese",
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"bert-base-german-cased",
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"bert-large-uncased-whole-word-masking",
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"bert-large-cased-whole-word-masking",
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"bert-large-uncased-whole-word-masking-finetuned-squad",
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"bert-large-cased-whole-word-masking-finetuned-squad",
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"bert-base-cased-finetuned-mrpc",
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"bert-base-german-dbmdz-cased",
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"bert-base-german-dbmdz-uncased",
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"cl-tohoku/bert-base-japanese",
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"cl-tohoku/bert-base-japanese-whole-word-masking",
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"cl-tohoku/bert-base-japanese-char",
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"cl-tohoku/bert-base-japanese-char-whole-word-masking",
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"TurkuNLP/bert-base-finnish-cased-v1",
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"TurkuNLP/bert-base-finnish-uncased-v1",
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"wietsedv/bert-base-dutch-cased",
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# See all BERT models at https://huggingface.co/models?filter=bert
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]
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def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
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"""Load tf checkpoints in a pytorch model."""
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try:
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import re
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import numpy as np
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import tensorflow as tf
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except ImportError:
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logger.error(
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"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
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"https://www.tensorflow.org/install/ for installation instructions."
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)
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raise
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tf_path = os.path.abspath(tf_checkpoint_path)
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logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
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# Load weights from TF model
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init_vars = tf.train.list_variables(tf_path)
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names = []
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arrays = []
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for name, shape in init_vars:
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logger.info(f"Loading TF weight {name} with shape {shape}")
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array = tf.train.load_variable(tf_path, name)
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names.append(name)
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arrays.append(array)
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for name, array in zip(names, arrays):
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name = name.split("/")
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# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
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# which are not required for using pretrained model
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if any(
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n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
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for n in name
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):
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logger.info(f"Skipping {'/'.join(name)}")
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continue
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pointer = model
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for m_name in name:
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if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
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scope_names = re.split(r"_(\d+)", m_name)
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else:
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scope_names = [m_name]
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if scope_names[0] == "kernel" or scope_names[0] == "gamma":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
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pointer = getattr(pointer, "bias")
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elif scope_names[0] == "output_weights":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "squad":
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pointer = getattr(pointer, "classifier")
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else:
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try:
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pointer = getattr(pointer, scope_names[0])
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except AttributeError:
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logger.info(f"Skipping {'/'.join(name)}")
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continue
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if len(scope_names) >= 2:
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num = int(scope_names[1])
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pointer = pointer[num]
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if m_name[-11:] == "_embeddings":
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pointer = getattr(pointer, "weight")
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elif m_name == "kernel":
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array = np.transpose(array)
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try:
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assert (
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pointer.shape == array.shape
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), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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logger.info(f"Initialize PyTorch weight {name}")
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pointer.data = torch.from_numpy(array)
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return model
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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(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = 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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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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def forward(
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self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
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):
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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[:, past_key_values_length : seq_length + past_key_values_length]
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if token_type_ids is None:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, 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):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(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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)
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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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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 = getattr(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(2 * config.max_position_embeddings - 1, self.attention_head_size)
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self.is_decoder = config.is_decoder
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# self.spatial_pos_encoder = nn.Embedding(5, self.num_attention_heads)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, 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(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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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 cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional 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, 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(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(seq_length, dtype=torch.long, 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(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(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("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("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("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(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_score(bs, head_num, length, length)， attention_mask->extended_attention_mask, (bs, 1, 1, length)
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# attention_bias = torch.zeros(attention_mask.shape, dtype=torch.float, device='cuda')
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# attention_bias[attention_mask == float('-inf')] = float('-inf')
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# attention_mask = torch.sigmoid(attention_mask)
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# attention_mask = attention_mask.unsqueeze(1).repeat(1, self.num_attention_heads, 1, 1)
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# attention_bias = attention_bias.unsqueeze(1).repeat(1, self.num_attention_heads, 1, 1)
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attention_scores = attention_scores + attention_mask
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# attention_scores = attention_scores + attention_mask + attention_bias
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# print("*attention_right"*30)
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# print(attention_scores)
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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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] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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outputs = (context_layer, 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(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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||
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class BertAttention(nn.Module):
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||
def __init__(self, config):
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super().__init__()
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||
self.self = BertSelfAttention(config)
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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):
|
||
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, self.self.attention_head_size, self.pruned_heads
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||
)
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||
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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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||
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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(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)
|
||
|
||
def forward(
|
||
self,
|
||
hidden_states,
|
||
attention_mask=None,
|
||
head_mask=None,
|
||
encoder_hidden_states=None,
|
||
encoder_attention_mask=None,
|
||
past_key_value=None,
|
||
output_attentions=False,
|
||
):
|
||
self_outputs = self.self(
|
||
hidden_states,
|
||
attention_mask,
|
||
head_mask,
|
||
encoder_hidden_states,
|
||
encoder_attention_mask,
|
||
past_key_value,
|
||
output_attentions,
|
||
)
|
||
attention_output = self.output(self_outputs[0], hidden_states)
|
||
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
||
return outputs
|
||
|
||
|
||
class BertIntermediate(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
||
if isinstance(config.hidden_act, str):
|
||
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
||
else:
|
||
self.intermediate_act_fn = config.hidden_act
|
||
|
||
def forward(self, hidden_states):
|
||
hidden_states = self.dense(hidden_states)
|
||
hidden_states = self.intermediate_act_fn(hidden_states)
|
||
return hidden_states
|
||
|
||
|
||
class BertOutput(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
||
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||
|
||
def forward(self, hidden_states, input_tensor):
|
||
hidden_states = self.dense(hidden_states)
|
||
hidden_states = self.dropout(hidden_states)
|
||
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
||
return hidden_states
|
||
|
||
|
||
class BertLayer(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
||
self.seq_len_dim = 1
|
||
self.attention = BertAttention(config)
|
||
self.is_decoder = config.is_decoder
|
||
self.add_cross_attention = config.add_cross_attention
|
||
if self.add_cross_attention:
|
||
assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
|
||
self.crossattention = BertAttention(config)
|
||
self.intermediate = BertIntermediate(config)
|
||
self.output = BertOutput(config)
|
||
|
||
def forward(
|
||
self,
|
||
hidden_states,
|
||
attention_mask=None,
|
||
head_mask=None,
|
||
encoder_hidden_states=None,
|
||
encoder_attention_mask=None,
|
||
past_key_value=None,
|
||
output_attentions=False,
|
||
):
|
||
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
||
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
||
self_attention_outputs = self.attention(
|
||
hidden_states,
|
||
attention_mask,
|
||
head_mask,
|
||
output_attentions=output_attentions,
|
||
past_key_value=self_attn_past_key_value,
|
||
)
|
||
attention_output = self_attention_outputs[0]
|
||
|
||
# if decoder, the last output is tuple of self-attn cache
|
||
if self.is_decoder:
|
||
outputs = self_attention_outputs[1:-1]
|
||
present_key_value = self_attention_outputs[-1]
|
||
else:
|
||
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
||
|
||
cross_attn_present_key_value = None
|
||
if self.is_decoder and encoder_hidden_states is not None:
|
||
assert hasattr(
|
||
self, "crossattention"
|
||
), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
|
||
|
||
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
|
||
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
|
||
cross_attention_outputs = self.crossattention(
|
||
attention_output,
|
||
attention_mask,
|
||
head_mask,
|
||
encoder_hidden_states,
|
||
encoder_attention_mask,
|
||
cross_attn_past_key_value,
|
||
output_attentions,
|
||
)
|
||
attention_output = cross_attention_outputs[0]
|
||
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
||
|
||
# add cross-attn cache to positions 3,4 of present_key_value tuple
|
||
cross_attn_present_key_value = cross_attention_outputs[-1]
|
||
present_key_value = present_key_value + cross_attn_present_key_value
|
||
|
||
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
|
||
|
||
# if decoder, return the attn key/values as the last output
|
||
if self.is_decoder:
|
||
outputs = outputs + (present_key_value,)
|
||
|
||
return outputs
|
||
|
||
def feed_forward_chunk(self, attention_output):
|
||
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)])
|
||
|
||
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 getattr(self.config, "gradient_checkpointing", False) and self.training:
|
||
|
||
if use_cache:
|
||
logger.warning(
|
||
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. 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 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):
|
||
# 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 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)
|
||
|
||
# The output weights are the same as the input embeddings, but there is
|
||
# an output-only bias for each token.
|
||
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
||
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
||
|
||
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
||
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 BertOnlyNSPHead(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
||
|
||
def forward(self, pooled_output):
|
||
seq_relationship_score = self.seq_relationship(pooled_output)
|
||
return seq_relationship_score
|
||
|
||
|
||
class BertPreTrainingHeads(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.predictions = BertLMPredictionHead(config)
|
||
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
||
|
||
def forward(self, sequence_output, pooled_output):
|
||
prediction_scores = self.predictions(sequence_output)
|
||
seq_relationship_score = self.seq_relationship(pooled_output)
|
||
return prediction_scores, seq_relationship_score
|
||
|
||
|
||
class BertPreTrainedModel(PreTrainedModel):
|
||
"""
|
||
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||
models.
|
||
"""
|
||
|
||
config_class = BertConfig
|
||
load_tf_weights = load_tf_weights_in_bert
|
||
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):
|
||
# 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)
|
||
|
||
|
||
BERT_START_DOCSTRING = r"""
|
||
|
||
This model inherits from :class:`~transformers.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 (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
|
||
weights.
|
||
"""
|
||
|
||
BERT_INPUTS_DOCSTRING = r"""
|
||
Args:
|
||
input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
|
||
Indices of input sequence tokens in the vocabulary.
|
||
|
||
Indices can be obtained using :class:`~transformers.BertTokenizer`. See
|
||
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
|
||
details.
|
||
|
||
`What are input IDs? <../glossary.html#input-ids>`__
|
||
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `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.html#attention-mask>`__
|
||
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `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.html#token-type-ids>`_
|
||
position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `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.html#position-ids>`_
|
||
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
|
||
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
|
||
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
|
||
vectors than the model's internal embedding lookup matrix.
|
||
output_attentions (:obj:`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 (:obj:`bool`, `optional`):
|
||
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
|
||
more detail.
|
||
return_dict (:obj:`bool`, `optional`):
|
||
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
|
||
"""
|
||
|
||
|
||
@add_start_docstrings(
|
||
"The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
|
||
BERT_START_DOCSTRING,
|
||
)
|
||
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.
|
||
|
||
To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration
|
||
set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
|
||
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=True):
|
||
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)
|
||
|
||
# @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
||
# @add_code_sample_docstrings(
|
||
# tokenizer_class=_TOKENIZER_FOR_DOC,
|
||
# checkpoint=_CHECKPOINT_FOR_DOC,
|
||
# output_type=BaseModelOutputWithPoolingAndCrossAttentions,
|
||
# config_class=_CONFIG_FOR_DOC,
|
||
# )
|
||
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,
|
||
distance_attention=None
|
||
):
|
||
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.ConstantPad2dTensor))` 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 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()
|
||
batch_size, seq_length = input_shape
|
||
elif inputs_embeds is not None:
|
||
input_shape = inputs_embeds.size()[:-1]
|
||
batch_size, seq_length = input_shape
|
||
else:
|
||
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||
|
||
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:
|
||
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)
|
||
extended_attention_mask = distance_attention
|
||
|
||
# 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 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,
|
||
)
|
||
|
||
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
|
||
class BertForMaskedLM(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)
|
||
|
||
if config.is_decoder:
|
||
logger.warning(
|
||
"If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
|
||
"bi-directional self-attention."
|
||
)
|
||
|
||
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
|
||
|
||
# @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
||
# @add_code_sample_docstrings(
|
||
# tokenizer_class=_TOKENIZER_FOR_DOC,
|
||
# checkpoint=_CHECKPOINT_FOR_DOC,
|
||
# output_type=MaskedLMOutput,
|
||
# config_class=_CONFIG_FOR_DOC,
|
||
# )
|
||
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,
|
||
labels=None,
|
||
output_attentions=None,
|
||
output_hidden_states=None,
|
||
return_dict=None,
|
||
distance_attention=None
|
||
):
|
||
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]``
|
||
"""
|
||
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
outputs = self.bert(
|
||
input_ids,
|
||
attention_mask=attention_mask,
|
||
token_type_ids=token_type_ids,
|
||
position_ids=position_ids,
|
||
head_mask=head_mask,
|
||
inputs_embeds=inputs_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,
|
||
distance_attention=distance_attention
|
||
)
|
||
|
||
sequence_output = outputs[0]
|
||
prediction_scores = self.cls(sequence_output)
|
||
|
||
masked_lm_loss = None
|
||
if labels is not None:
|
||
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
||
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
||
|
||
# print("*attention_left"*30)
|
||
# print(outputs.attentions)
|
||
|
||
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,
|
||
)
|
||
|
||
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
|
||
input_shape = input_ids.shape
|
||
effective_batch_size = input_shape[0]
|
||
|
||
# add a dummy token
|
||
assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
|
||
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
|
||
dummy_token = torch.full(
|
||
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
|
||
)
|
||
input_ids = torch.cat([input_ids, dummy_token], dim=1)
|
||
|
||
return {"input_ids": input_ids, "attention_mask": attention_mask}
|
||
|