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# 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 List, Optional, Tuple, Union
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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 torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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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 PreTrainedModel
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from transformers.models.bert.configuration_bert import BertConfig
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from transformers.pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from transformers.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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logging,
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replace_return_docstrings,
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)
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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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# TokenClassification docstring
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_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "dbmdz/bert-large-cased-finetuned-conll03-english"
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_TOKEN_CLASS_EXPECTED_OUTPUT = (
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"['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] ")
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_TOKEN_CLASS_EXPECTED_LOSS = 0.01
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# QuestionAnswering docstring
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_CHECKPOINT_FOR_QA = "deepset/bert-base-cased-squad2"
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_QA_EXPECTED_OUTPUT = "'a nice puppet'"
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_QA_EXPECTED_LOSS = 7.41
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_QA_TARGET_START_INDEX = 14
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_QA_TARGET_END_INDEX = 15
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# SequenceClassification docstring
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_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "textattack/bert-base-uncased-yelp-polarity"
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_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
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_SEQ_CLASS_EXPECTED_LOSS = 0.01
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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("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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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(n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
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for n in name):
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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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if pointer.shape != array.shape:
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raise ValueError(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.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.register_buffer("position_ids", 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(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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past_key_values_length: int = 0,
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) -> torch.Tensor:
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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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# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
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# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
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# 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(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(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, 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(config, "embedding_size"):
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raise ValueError(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 / 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(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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def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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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: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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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
|
|
|
|
# all previous decoder key/value_states. Further calls to uni-directional self-attention
|
|
|
|
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
|
|
|
|
# if encoder bi-directional self-attention `past_key_value` is always `None`
|
|
|
|
past_key_value = (key_layer, value_layer)
|
|
|
|
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
|
|
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
|
|
seq_length = hidden_states.size()[1]
|
|
|
|
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
|
|
|
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
|
|
|
distance = position_ids_l - position_ids_r
|
|
|
|
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
2023-04-14 08:27:48 +00:00
|
|
|
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
2022-11-18 06:04:49 +00:00
|
|
|
|
|
|
|
if self.position_embedding_type == "relative_key":
|
|
|
|
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
|
|
attention_scores = attention_scores + relative_position_scores
|
|
|
|
elif self.position_embedding_type == "relative_key_query":
|
|
|
|
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
2023-06-08 06:28:34 +00:00
|
|
|
relative_position_scores_key = torch.einsum("bhld,lrd->bhlr", key_layer, positional_embedding)
|
2022-11-18 06:04:49 +00:00
|
|
|
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
|
|
|
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
|
|
if attention_mask is not None:
|
|
|
|
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
|
|
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
|
|
|
|
# Mask heads if we want to
|
|
|
|
if head_mask is not None:
|
|
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
|
|
context_layer = context_layer.view(new_context_layer_shape)
|
|
|
|
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
|
|
|
|
if self.is_decoder:
|
|
|
|
outputs = outputs + (past_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
|
|
|
|
|
|
class BertSelfOutput(nn.Module):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__()
|
|
|
|
self.dense = nn.Linear(config.hidden_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: torch.Tensor, input_tensor: torch.Tensor) -> torch.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 BertAttention(nn.Module):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config, position_embedding_type=None):
|
|
|
|
super().__init__()
|
|
|
|
self.self = BertSelfAttention(config, position_embedding_type=position_embedding_type)
|
|
|
|
self.output = BertSelfOutput(config)
|
|
|
|
self.pruned_heads = set()
|
|
|
|
|
|
|
|
def prune_heads(self, heads):
|
|
|
|
if len(heads) == 0:
|
|
|
|
return
|
2023-04-14 08:27:48 +00:00
|
|
|
heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads,
|
|
|
|
self.self.attention_head_size, self.pruned_heads)
|
2022-11-18 06:04:49 +00:00
|
|
|
|
|
|
|
# Prune linear layers
|
|
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
|
|
|
|
# Update hyper params and store pruned heads
|
|
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
hidden_states: torch.Tensor,
|
|
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
|
|
output_attentions: Optional[bool] = False,
|
|
|
|
) -> Tuple[torch.Tensor]:
|
|
|
|
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)
|
2023-04-14 08:27:48 +00:00
|
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
2022-11-18 06:04:49 +00:00
|
|
|
return outputs
|
|
|
|
|
|
|
|
|
|
|
|
class BertIntermediate(nn.Module):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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: torch.Tensor) -> torch.Tensor:
|
|
|
|
hidden_states = self.dense(hidden_states)
|
|
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
|
|
|
|
|
|
class BertOutput(nn.Module):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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: torch.Tensor, input_tensor: torch.Tensor) -> torch.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):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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:
|
|
|
|
if not self.is_decoder:
|
|
|
|
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
|
|
|
|
self.crossattention = BertAttention(config, position_embedding_type="absolute")
|
|
|
|
self.intermediate = BertIntermediate(config)
|
|
|
|
self.output = BertOutput(config)
|
|
|
|
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
hidden_states: torch.Tensor,
|
|
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
|
|
output_attentions: Optional[bool] = False,
|
|
|
|
) -> Tuple[torch.Tensor]:
|
|
|
|
# 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:
|
2023-04-14 08:27:48 +00:00
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
2022-11-18 06:04:49 +00:00
|
|
|
|
|
|
|
cross_attn_present_key_value = None
|
|
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
|
|
if not hasattr(self, "crossattention"):
|
|
|
|
raise ValueError(
|
|
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
|
2023-04-14 08:27:48 +00:00
|
|
|
" by setting `config.add_cross_attention=True`")
|
2022-11-18 06:04:49 +00:00
|
|
|
|
|
|
|
# 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]
|
2023-04-14 08:27:48 +00:00
|
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
2022-11-18 06:04:49 +00:00
|
|
|
|
|
|
|
# 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
|
|
|
|
|
2023-04-14 08:27:48 +00:00
|
|
|
layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward,
|
|
|
|
self.seq_len_dim, attention_output)
|
2022-11-18 06:04:49 +00:00
|
|
|
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):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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: torch.Tensor,
|
|
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
|
|
use_cache: Optional[bool] = None,
|
|
|
|
output_attentions: Optional[bool] = False,
|
|
|
|
output_hidden_states: Optional[bool] = False,
|
|
|
|
return_dict: Optional[bool] = True,
|
|
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
|
|
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(
|
2023-04-14 08:27:48 +00:00
|
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
|
2022-11-18 06:04:49 +00:00
|
|
|
use_cache = False
|
|
|
|
|
|
|
|
def create_custom_forward(module):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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:
|
2023-04-14 08:27:48 +00:00
|
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return tuple(v for v in [
|
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|
hidden_states,
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|
next_decoder_cache,
|
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|
all_hidden_states,
|
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all_self_attentions,
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|
all_cross_attentions,
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] if v is not None)
|
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|
return BaseModelOutputWithPastAndCrossAttentions(
|
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|
|
last_hidden_state=hidden_states,
|
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|
|
past_key_values=next_decoder_cache,
|
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|
hidden_states=all_hidden_states,
|
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|
attentions=all_self_attentions,
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|
cross_attentions=all_cross_attentions,
|
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|
)
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|
class BertPooler(nn.Module):
|
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2022-11-18 06:04:49 +00:00
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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.activation = nn.Tanh()
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|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
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|
# We "pool" the model by simply taking the hidden state corresponding
|
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|
|
# to the first token.
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|
first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class BertPredictionHeadTransform(nn.Module):
|
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2022-11-18 06:04:49 +00:00
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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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if isinstance(config.hidden_act, str):
|
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self.transform_act_fn = ACT2FN[config.hidden_act]
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else:
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self.transform_act_fn = config.hidden_act
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
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|
hidden_states = self.dense(hidden_states)
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|
hidden_states = self.transform_act_fn(hidden_states)
|
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|
hidden_states = self.LayerNorm(hidden_states)
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|
return hidden_states
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|
class BertLMPredictionHead(nn.Module):
|
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|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config):
|
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|
super().__init__()
|
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|
self.transform = BertPredictionHeadTransform(config)
|
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|
# The output weights are the same as the input embeddings, but there is
|
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|
|
# an output-only bias for each token.
|
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|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
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|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
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|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
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|
self.decoder.bias = self.bias
|
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|
|
|
|
|
def forward(self, hidden_states):
|
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|
|
hidden_states = self.transform(hidden_states)
|
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|
|
hidden_states = self.decoder(hidden_states)
|
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|
|
return hidden_states
|
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|
|
class BertOnlyMLMHead(nn.Module):
|
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|
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|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__()
|
|
|
|
self.predictions = BertLMPredictionHead(config)
|
|
|
|
|
|
|
|
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
|
|
|
|
prediction_scores = self.predictions(sequence_output)
|
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|
|
return prediction_scores
|
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|
|
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|
|
class BertOnlyNSPHead(nn.Module):
|
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|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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):
|
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|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
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"
|
|
|
|
supports_gradient_checkpointing = True
|
|
|
|
_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)
|
|
|
|
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
|
|
if isinstance(module, BertEncoder):
|
|
|
|
module.gradient_checkpointing = value
|
|
|
|
|
|
|
|
|
|
|
|
@dataclass
|
|
|
|
class BertForPreTrainingOutput(ModelOutput):
|
|
|
|
"""
|
|
|
|
Output type of [`BertForPreTraining`].
|
|
|
|
|
|
|
|
Args:
|
|
|
|
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
|
|
|
|
Total loss as the sum of the masked language modeling loss and the next sequence prediction
|
|
|
|
(classification) loss.
|
|
|
|
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
|
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
|
|
seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
|
|
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
|
|
|
before SoftMax).
|
|
|
|
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
|
|
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
|
|
|
|
shape `(batch_size, sequence_length, hidden_size)`.
|
|
|
|
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
|
|
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
|
|
|
sequence_length)`.
|
|
|
|
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
|
|
heads.
|
|
|
|
"""
|
|
|
|
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
|
|
prediction_logits: torch.FloatTensor = None
|
|
|
|
seq_relationship_logits: torch.FloatTensor = None
|
|
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
|
|
|
|
|
|
BERT_START_DOCSTRING = r"""
|
|
|
|
|
|
|
|
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.
|
|
|
|
"""
|
|
|
|
|
|
|
|
BERT_INPUTS_DOCSTRING = r"""
|
|
|
|
Args:
|
|
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
|
|
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 `({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#attention-mask)
|
|
|
|
token_type_ids (`torch.LongTensor` of shape `({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#token-type-ids)
|
|
|
|
position_ids (`torch.LongTensor` of shape `({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#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 `({0}, 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 [`~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 `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.config = 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()
|
|
|
|
|
|
|
|
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(
|
|
|
|
processor_class=_TOKENIZER_FOR_DOC,
|
|
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
|
|
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
|
|
|
|
config_class=_CONFIG_FOR_DOC,
|
|
|
|
)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
|
|
use_cache: Optional[bool] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
|
|
r"""
|
|
|
|
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`).
|
|
|
|
"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
2023-04-14 08:27:48 +00:00
|
|
|
output_hidden_states = (output_hidden_states
|
|
|
|
if output_hidden_states is not None else self.config.output_hidden_states)
|
2022-11-18 06:04:49 +00:00
|
|
|
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)
|
|
|
|
|
|
|
|
# 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 two heads on top as done during the pretraining: a `masked language modeling` head and a `next
|
|
|
|
sentence prediction (classification)` head.
|
|
|
|
""",
|
|
|
|
BERT_START_DOCSTRING,
|
|
|
|
)
|
|
|
|
class BertForPreTraining(BertPreTrainedModel):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__(config)
|
|
|
|
|
|
|
|
self.bert = BertModel(config)
|
|
|
|
self.cls = BertPreTrainingHeads(config)
|
|
|
|
|
|
|
|
# Initialize weights and apply final processing
|
|
|
|
self.post_init()
|
|
|
|
|
|
|
|
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"))
|
|
|
|
@replace_return_docstrings(output_type=BertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
labels: Optional[torch.Tensor] = None,
|
|
|
|
next_sentence_label: Optional[torch.Tensor] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
) -> Union[Tuple[torch.Tensor], BertForPreTrainingOutput]:
|
|
|
|
r"""
|
|
|
|
labels (`torch.LongTensor` of shape `(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]`
|
|
|
|
next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
|
|
|
|
pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
|
|
|
|
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
|
|
|
|
Used to hide legacy arguments that have been deprecated.
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
|
|
Example:
|
|
|
|
|
|
|
|
```python
|
|
|
|
>>> from transformers import BertTokenizer, BertForPreTraining
|
|
|
|
>>> import torch
|
|
|
|
|
|
|
|
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
|
|
|
>>> model = BertForPreTraining.from_pretrained("bert-base-uncased")
|
|
|
|
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
|
|
|
|
|
|
>>> prediction_logits = outputs.prediction_logits
|
|
|
|
>>> seq_relationship_logits = outputs.seq_relationship_logits
|
|
|
|
```
|
|
|
|
"""
|
|
|
|
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,
|
|
|
|
output_attentions=output_attentions,
|
|
|
|
output_hidden_states=output_hidden_states,
|
|
|
|
return_dict=return_dict,
|
|
|
|
)
|
|
|
|
|
|
|
|
sequence_output, pooled_output = outputs[:2]
|
|
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
|
|
|
|
|
|
|
total_loss = None
|
|
|
|
if labels is not None and next_sentence_label is not None:
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
|
|
|
|
total_loss = masked_lm_loss + next_sentence_loss
|
|
|
|
|
|
|
|
if not return_dict:
|
|
|
|
output = (prediction_scores, seq_relationship_score) + outputs[2:]
|
|
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
|
|
|
|
return BertForPreTrainingOutput(
|
|
|
|
loss=total_loss,
|
|
|
|
prediction_logits=prediction_scores,
|
|
|
|
seq_relationship_logits=seq_relationship_score,
|
|
|
|
hidden_states=outputs.hidden_states,
|
|
|
|
attentions=outputs.attentions,
|
|
|
|
)
|
|
|
|
|
|
|
|
|
2023-04-14 08:27:48 +00:00
|
|
|
@add_start_docstrings("""Bert Model with a `language modeling` head on top for CLM fine-tuning.""",
|
|
|
|
BERT_START_DOCSTRING)
|
2022-11-18 06:04:49 +00:00
|
|
|
class BertLMHeadModel(BertPreTrainedModel):
|
|
|
|
|
|
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
|
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
|
|
|
|
|
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__(config)
|
|
|
|
|
|
|
|
if not config.is_decoder:
|
|
|
|
logger.warning("If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`")
|
|
|
|
|
|
|
|
self.bert = BertModel(config, add_pooling_layer=False)
|
|
|
|
self.cls = BertOnlyMLMHead(config)
|
|
|
|
|
|
|
|
# Initialize weights and apply final processing
|
|
|
|
self.post_init()
|
|
|
|
|
|
|
|
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(
|
|
|
|
processor_class=_TOKENIZER_FOR_DOC,
|
|
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
|
|
output_type=CausalLMOutputWithCrossAttentions,
|
|
|
|
config_class=_CONFIG_FOR_DOC,
|
|
|
|
)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
labels: Optional[torch.Tensor] = None,
|
|
|
|
past_key_values: Optional[List[torch.Tensor]] = None,
|
|
|
|
use_cache: Optional[bool] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
|
|
|
r"""
|
|
|
|
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**.
|
|
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
|
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
|
|
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
|
|
|
|
past_key_values (`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`).
|
|
|
|
"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if labels is not None:
|
|
|
|
use_cache = False
|
|
|
|
|
|
|
|
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,
|
|
|
|
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 = outputs[0]
|
|
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
|
|
|
|
lm_loss = None
|
|
|
|
if labels is not None:
|
|
|
|
# we are doing next-token prediction; shift prediction scores and input ids by one
|
|
|
|
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
|
|
|
|
labels = labels[:, 1:].contiguous()
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
|
|
|
|
if not return_dict:
|
|
|
|
output = (prediction_scores,) + outputs[2:]
|
|
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
|
|
loss=lm_loss,
|
|
|
|
logits=prediction_scores,
|
|
|
|
past_key_values=outputs.past_key_values,
|
|
|
|
hidden_states=outputs.hidden_states,
|
|
|
|
attentions=outputs.attentions,
|
|
|
|
cross_attentions=outputs.cross_attentions,
|
|
|
|
)
|
|
|
|
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
|
|
|
|
input_shape = input_ids.shape
|
|
|
|
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
|
|
|
if attention_mask is None:
|
|
|
|
attention_mask = input_ids.new_ones(input_shape)
|
|
|
|
|
|
|
|
# cut decoder_input_ids if past is used
|
|
|
|
if past is not None:
|
|
|
|
input_ids = input_ids[:, -1:]
|
|
|
|
|
|
|
|
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
|
|
|
|
|
|
|
|
def _reorder_cache(self, past, beam_idx):
|
|
|
|
reordered_past = ()
|
|
|
|
for layer_past in past:
|
|
|
|
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
|
|
|
return reordered_past
|
|
|
|
|
|
|
|
|
|
|
|
@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:
|
2023-04-14 08:27:48 +00:00
|
|
|
logger.warning("If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
|
|
|
|
"bi-directional self-attention.")
|
2022-11-18 06:04:49 +00:00
|
|
|
|
|
|
|
self.bert = BertModel(config, add_pooling_layer=False)
|
|
|
|
self.cls = BertOnlyMLMHead(config)
|
|
|
|
|
|
|
|
# Initialize weights and apply final processing
|
|
|
|
self.post_init()
|
|
|
|
|
|
|
|
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(
|
|
|
|
processor_class=_TOKENIZER_FOR_DOC,
|
|
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
|
|
output_type=MaskedLMOutput,
|
|
|
|
config_class=_CONFIG_FOR_DOC,
|
|
|
|
expected_output="'paris'",
|
|
|
|
expected_loss=0.88,
|
|
|
|
)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
labels: Optional[torch.Tensor] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
|
|
|
|
r"""
|
|
|
|
labels (`torch.LongTensor` of shape `(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,
|
|
|
|
)
|
|
|
|
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
|
|
|
|
masked_lm_loss = None
|
|
|
|
if labels is not None:
|
2023-04-14 08:27:48 +00:00
|
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
2022-11-18 06:04:49 +00:00
|
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
|
|
|
|
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
|
|
|
|
if self.config.pad_token_id is None:
|
|
|
|
raise ValueError("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)
|
2023-04-14 08:27:48 +00:00
|
|
|
dummy_token = torch.full((effective_batch_size, 1),
|
|
|
|
self.config.pad_token_id,
|
|
|
|
dtype=torch.long,
|
|
|
|
device=input_ids.device)
|
2022-11-18 06:04:49 +00:00
|
|
|
input_ids = torch.cat([input_ids, dummy_token], dim=1)
|
|
|
|
|
|
|
|
return {"input_ids": input_ids, "attention_mask": attention_mask}
|
|
|
|
|
|
|
|
|
|
|
|
@add_start_docstrings(
|
|
|
|
"""Bert Model with a `next sentence prediction (classification)` head on top.""",
|
|
|
|
BERT_START_DOCSTRING,
|
|
|
|
)
|
|
|
|
class BertForNextSentencePrediction(BertPreTrainedModel):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__(config)
|
|
|
|
|
|
|
|
self.bert = BertModel(config)
|
|
|
|
self.cls = BertOnlyNSPHead(config)
|
|
|
|
|
|
|
|
# Initialize weights and apply final processing
|
|
|
|
self.post_init()
|
|
|
|
|
|
|
|
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
|
|
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
labels: Optional[torch.Tensor] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
**kwargs,
|
|
|
|
) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
|
|
|
|
r"""
|
|
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
|
|
|
|
(see `input_ids` docstring). Indices should be in `[0, 1]`:
|
|
|
|
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
|
|
Example:
|
|
|
|
|
|
|
|
```python
|
|
|
|
>>> from transformers import BertTokenizer, BertForNextSentencePrediction
|
|
|
|
>>> import torch
|
|
|
|
|
|
|
|
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
|
|
|
>>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")
|
|
|
|
|
|
|
|
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
|
|
|
|
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
|
|
|
|
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
|
|
|
|
|
|
|
|
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
|
|
|
|
>>> logits = outputs.logits
|
|
|
|
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
|
|
|
|
```
|
|
|
|
"""
|
|
|
|
|
|
|
|
if "next_sentence_label" in kwargs:
|
|
|
|
warnings.warn(
|
|
|
|
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
|
|
|
|
" `labels` instead.",
|
|
|
|
FutureWarning,
|
|
|
|
)
|
|
|
|
labels = kwargs.pop("next_sentence_label")
|
|
|
|
|
|
|
|
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,
|
|
|
|
output_attentions=output_attentions,
|
|
|
|
output_hidden_states=output_hidden_states,
|
|
|
|
return_dict=return_dict,
|
|
|
|
)
|
|
|
|
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
|
|
|
|
seq_relationship_scores = self.cls(pooled_output)
|
|
|
|
|
|
|
|
next_sentence_loss = None
|
|
|
|
if labels is not None:
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
|
|
|
|
|
|
|
|
if not return_dict:
|
|
|
|
output = (seq_relationship_scores,) + outputs[2:]
|
|
|
|
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
|
|
|
|
|
|
|
|
return NextSentencePredictorOutput(
|
|
|
|
loss=next_sentence_loss,
|
|
|
|
logits=seq_relationship_scores,
|
|
|
|
hidden_states=outputs.hidden_states,
|
|
|
|
attentions=outputs.attentions,
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
@add_start_docstrings(
|
|
|
|
"""
|
|
|
|
Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
|
|
|
|
output) e.g. for GLUE tasks.
|
|
|
|
""",
|
|
|
|
BERT_START_DOCSTRING,
|
|
|
|
)
|
|
|
|
class BertForSequenceClassification(BertPreTrainedModel):
|
2023-04-14 08:27:48 +00:00
|
|
|
|
2022-11-18 06:04:49 +00:00
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
|
|
self.config = config
|
|
|
|
|
|
|
|
self.bert = BertModel(config)
|
2023-04-14 08:27:48 +00:00
|
|
|
classifier_dropout = (config.classifier_dropout
|
|
|
|
if config.classifier_dropout is not None else config.hidden_dropout_prob)
|
2022-11-18 06:04:49 +00:00
|
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
|
|
|
|
# Initialize weights and apply final processing
|
|
|
|
self.post_init()
|
|
|
|
|
|
|
|
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
|
|
@add_code_sample_docstrings(
|
|
|
|
processor_class=_TOKENIZER_FOR_DOC,
|
|
|
|
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
|
|
|
|
output_type=SequenceClassifierOutput,
|
|
|
|
config_class=_CONFIG_FOR_DOC,
|
|
|
|
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
|
|
|
|
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
|
|
|
|
)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
labels: Optional[torch.Tensor] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
|
|
r"""
|
|
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
|
|
"""
|
|
|
|
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,
|
|
|
|
output_attentions=output_attentions,
|
|
|
|
output_hidden_states=output_hidden_states,
|
|
|
|
return_dict=return_dict,
|
|
|
|
)
|
|
|
|
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
|
|
|
|
loss = None
|
|
|
|
if labels is not None:
|
|
|
|
if self.config.problem_type is None:
|
|
|
|
if self.num_labels == 1:
|
|
|
|
self.config.problem_type = "regression"
|
|
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
|
|
self.config.problem_type = "single_label_classification"
|
|
|
|
else:
|
|
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
|
|
|
|
if self.config.problem_type == "regression":
|
|
|
|
loss_fct = MSELoss()
|
|
|
|
if self.num_labels == 1:
|
|
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
|
|
else:
|
|
|
|
loss = loss_fct(logits, labels)
|
|
|
|
elif self.config.problem_type == "single_label_classification":
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
|
|
loss_fct = BCEWithLogitsLoss()
|
|
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
|
|
output = (logits,) + outputs[2:]
|
|
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
|
|
|
|
return SequenceClassifierOutput(
|
|
|
|
loss=loss,
|
|
|
|
logits=logits,
|
|
|
|
hidden_states=outputs.hidden_states,
|
|
|
|
attentions=outputs.attentions,
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
@add_start_docstrings(
|
|
|
|
"""
|
|
|
|
Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
|
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softmax) e.g. for RocStories/SWAG tasks.
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""",
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BERT_START_DOCSTRING,
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)
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class BertForMultipleChoice(BertPreTrainedModel):
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2023-04-14 08:27:48 +00:00
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2022-11-18 06:04:49 +00:00
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def __init__(self, config):
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super().__init__(config)
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self.bert = BertModel(config)
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2023-04-14 08:27:48 +00:00
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classifier_dropout = (config.classifier_dropout
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if config.classifier_dropout is not None else config.hidden_dropout_prob)
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2022-11-18 06:04:49 +00:00
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self.dropout = nn.Dropout(classifier_dropout)
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self.classifier = nn.Linear(config.hidden_size, 1)
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# Initialize weights and apply final processing
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self.post_init()
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@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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output_type=MultipleChoiceModelOutput,
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config_class=_CONFIG_FOR_DOC,
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)
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def forward(
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self,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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labels: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
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num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
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`input_ids` above)
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
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input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
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attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
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token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
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position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
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2023-04-14 08:27:48 +00:00
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inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
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if inputs_embeds is not None else None)
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2022-11-18 06:04:49 +00:00
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outputs = self.bert(
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input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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pooled_output = outputs[1]
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pooled_output = self.dropout(pooled_output)
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logits = self.classifier(pooled_output)
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reshaped_logits = logits.view(-1, num_choices)
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loss = None
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(reshaped_logits, labels)
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if not return_dict:
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output = (reshaped_logits,) + outputs[2:]
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return ((loss,) + output) if loss is not None else output
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return MultipleChoiceModelOutput(
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loss=loss,
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logits=reshaped_logits,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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@add_start_docstrings(
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"""
|
|
|
|
Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
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Named-Entity-Recognition (NER) tasks.
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""",
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BERT_START_DOCSTRING,
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)
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class BertForTokenClassification(BertPreTrainedModel):
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_keys_to_ignore_on_load_unexpected = [r"pooler"]
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def __init__(self, config):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.bert = BertModel(config, add_pooling_layer=False)
|
2023-04-14 08:27:48 +00:00
|
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classifier_dropout = (config.classifier_dropout
|
|
|
|
if config.classifier_dropout is not None else config.hidden_dropout_prob)
|
2022-11-18 06:04:49 +00:00
|
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self.dropout = nn.Dropout(classifier_dropout)
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|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
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# Initialize weights and apply final processing
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self.post_init()
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@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
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|
@add_code_sample_docstrings(
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|
processor_class=_TOKENIZER_FOR_DOC,
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|
checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
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|
output_type=TokenClassifierOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
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|
expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
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|
)
|
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|
def forward(
|
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|
|
self,
|
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|
|
input_ids: Optional[torch.Tensor] = None,
|
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|
attention_mask: Optional[torch.Tensor] = None,
|
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|
token_type_ids: Optional[torch.Tensor] = None,
|
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|
position_ids: Optional[torch.Tensor] = None,
|
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|
head_mask: Optional[torch.Tensor] = None,
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|
inputs_embeds: Optional[torch.Tensor] = None,
|
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|
labels: Optional[torch.Tensor] = None,
|
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|
output_attentions: Optional[bool] = None,
|
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|
output_hidden_states: Optional[bool] = None,
|
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|
return_dict: Optional[bool] = None,
|
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|
|
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
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|
|
r"""
|
|
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
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|
|
"""
|
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|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
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|
outputs = self.bert(
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|
input_ids,
|
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|
attention_mask=attention_mask,
|
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|
token_type_ids=token_type_ids,
|
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|
|
position_ids=position_ids,
|
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|
head_mask=head_mask,
|
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|
inputs_embeds=inputs_embeds,
|
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|
output_attentions=output_attentions,
|
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|
output_hidden_states=output_hidden_states,
|
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|
return_dict=return_dict,
|
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)
|
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|
sequence_output = outputs[0]
|
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|
sequence_output = self.dropout(sequence_output)
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|
logits = self.classifier(sequence_output)
|
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|
|
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|
|
loss = None
|
|
|
|
if labels is not None:
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
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|
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|
|
|
|
if not return_dict:
|
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|
|
output = (logits,) + outputs[2:]
|
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|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
|
|
|
|
return TokenClassifierOutput(
|
|
|
|
loss=loss,
|
|
|
|
logits=logits,
|
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|
hidden_states=outputs.hidden_states,
|
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|
attentions=outputs.attentions,
|
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)
|
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|
@add_start_docstrings(
|
|
|
|
"""
|
|
|
|
Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
|
|
|
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
|
|
""",
|
|
|
|
BERT_START_DOCSTRING,
|
|
|
|
)
|
|
|
|
class BertForQuestionAnswering(BertPreTrainedModel):
|
|
|
|
|
|
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
|
|
|
|
|
def __init__(self, config):
|
|
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
|
|
|
|
|
|
self.bert = BertModel(config, add_pooling_layer=False)
|
|
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
|
|
|
|
# Initialize weights and apply final processing
|
|
|
|
self.post_init()
|
|
|
|
|
|
|
|
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
|
|
@add_code_sample_docstrings(
|
|
|
|
processor_class=_TOKENIZER_FOR_DOC,
|
|
|
|
checkpoint=_CHECKPOINT_FOR_QA,
|
|
|
|
output_type=QuestionAnsweringModelOutput,
|
|
|
|
config_class=_CONFIG_FOR_DOC,
|
|
|
|
qa_target_start_index=_QA_TARGET_START_INDEX,
|
|
|
|
qa_target_end_index=_QA_TARGET_END_INDEX,
|
|
|
|
expected_output=_QA_EXPECTED_OUTPUT,
|
|
|
|
expected_loss=_QA_EXPECTED_LOSS,
|
|
|
|
)
|
|
|
|
def forward(
|
|
|
|
self,
|
|
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
|
|
output_attentions: Optional[bool] = None,
|
|
|
|
output_hidden_states: Optional[bool] = None,
|
|
|
|
return_dict: Optional[bool] = None,
|
|
|
|
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
|
|
|
r"""
|
|
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
|
|
are not taken into account for computing the loss.
|
|
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
|
|
are not taken into account for computing the loss.
|
|
|
|
"""
|
|
|
|
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,
|
|
|
|
output_attentions=output_attentions,
|
|
|
|
output_hidden_states=output_hidden_states,
|
|
|
|
return_dict=return_dict,
|
|
|
|
)
|
|
|
|
|
|
|
|
sequence_output = outputs[0]
|
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|
|
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
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|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
|
|
|
|
total_loss = None
|
|
|
|
if start_positions is not None and end_positions is not None:
|
|
|
|
# If we are on multi-GPU, split add a dimension
|
|
|
|
if len(start_positions.size()) > 1:
|
|
|
|
start_positions = start_positions.squeeze(-1)
|
|
|
|
if len(end_positions.size()) > 1:
|
|
|
|
end_positions = end_positions.squeeze(-1)
|
|
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
|
|
ignored_index = start_logits.size(1)
|
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|
|
start_positions = start_positions.clamp(0, ignored_index)
|
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|
|
end_positions = end_positions.clamp(0, ignored_index)
|
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|
|
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
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|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
|
|
|
|
if not return_dict:
|
|
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
|
|
loss=total_loss,
|
|
|
|
start_logits=start_logits,
|
|
|
|
end_logits=end_logits,
|
|
|
|
hidden_states=outputs.hidden_states,
|
|
|
|
attentions=outputs.attentions,
|
|
|
|
)
|