1250 lines
60 KiB
Python
1250 lines
60 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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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from __future__ import absolute_import, division, print_function, unicode_literals
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import copy
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import json
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import logging
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import math
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import os
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import shutil
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import tarfile
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import tempfile
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import sys
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from io import open
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from torch.utils import checkpoint
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from file_utils import cached_path
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logger = logging.getLogger(__name__)
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PRETRAINED_MODEL_ARCHIVE_MAP = {
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'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
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'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
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'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
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'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
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'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
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'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
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'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
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}
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CONFIG_NAME = 'bert_config.json'
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WEIGHTS_NAME = 'pytorch_model.bin'
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TF_WEIGHTS_NAME = 'model.ckpt'
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def load_tf_weights_in_bert(model, tf_checkpoint_path):
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""" Load tf checkpoints in a pytorch model
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"""
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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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print("Loading a TensorFlow models 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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print("Converting TensorFlow checkpoint from {}".format(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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print("Loading TF weight {} with shape {}".format(name, 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"] for n in name):
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print("Skipping {}".format("/".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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l = re.split(r'_(\d+)', m_name)
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else:
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l = [m_name]
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if l[0] == 'kernel' or l[0] == 'gamma':
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pointer = getattr(pointer, 'weight')
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elif l[0] == 'output_bias' or l[0] == 'beta':
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pointer = getattr(pointer, 'bias')
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elif l[0] == 'output_weights':
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pointer = getattr(pointer, 'weight')
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else:
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pointer = getattr(pointer, l[0])
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if len(l) >= 2:
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num = int(l[1])
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pointer = pointer[num]
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if m_name[-11:] == '_embeddings':
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pointer = getattr(pointer, 'weight')
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elif m_name == 'kernel':
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array = np.transpose(array)
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try:
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assert pointer.shape == array.shape
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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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print("Initialize PyTorch weight {}".format(name))
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pointer.data = torch.from_numpy(array)
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return model
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def gelu(x):
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"""Implementation of the gelu activation function.
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For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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Also see https://arxiv.org/abs/1606.08415
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"""
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return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
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def swish(x):
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return x * torch.sigmoid(x)
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ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
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class BertConfig(object):
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"""Configuration class to store the configuration of a `BertModel`.
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"""
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def __init__(self,
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vocab_size_or_config_json_file,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02):
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"""Constructs BertConfig.
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Args:
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vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
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hidden_size: Size of the encoder layers and the pooler layer.
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num_hidden_layers: Number of hidden layers in the Transformer encoder.
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num_attention_heads: Number of attention heads for each attention layer in
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the Transformer encoder.
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intermediate_size: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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hidden_act: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
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hidden_dropout_prob: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob: The dropout ratio for the attention
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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type_vocab_size: The vocabulary size of the `token_type_ids` passed into
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`BertModel`.
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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"""
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if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.vocab_size = vocab_size_or_config_json_file
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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else:
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raise ValueError("First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)")
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@classmethod
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def from_dict(cls, json_object):
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
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config = BertConfig(vocab_size_or_config_json_file=-1)
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for key, value in json_object.items():
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config.__dict__[key] = value
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return config
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@classmethod
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def from_json_file(cls, json_file):
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"""Constructs a `BertConfig` from a json file of parameters."""
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with open(json_file, "r", encoding='utf-8') as reader:
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text = reader.read()
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return cls.from_dict(json.loads(text))
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def __repr__(self):
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return str(self.to_json_string())
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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try:
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from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
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except ImportError:
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print("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.")
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class BertLayerNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-12):
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"""Construct a layernorm module in the TF style (epsilon inside the square root).
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"""
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super(BertLayerNorm, self).__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.bias = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = eps
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def forward(self, x):
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u = x.mean(-1, keepdim=True)
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s = (x - u).pow(2).mean(-1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.variance_epsilon)
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return self.weight * x + self.bias
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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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"""
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def __init__(self, config):
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super(BertEmbeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
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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 = BertLayerNorm(config.hidden_size, eps=1e-12)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, input_ids, token_type_ids=None):
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seq_length = input_ids.size(1)
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position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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words_embeddings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = words_embeddings + position_embeddings + token_type_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class BertSelfAttention(nn.Module):
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def __init__(self, config):
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super(BertSelfAttention, self).__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads))
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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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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(self, hidden_states, attention_mask):
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(hidden_states)
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mixed_value_layer = self.value(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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return context_layer
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class BertSelfOutput(nn.Module):
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def __init__(self, config):
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super(BertSelfOutput, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertAttention(nn.Module):
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def __init__(self, config):
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super(BertAttention, self).__init__()
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self.self = BertSelfAttention(config)
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self.output = BertSelfOutput(config)
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def forward(self, input_tensor, attention_mask):
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self_output = self.self(input_tensor, attention_mask)
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attention_output = self.output(self_output, input_tensor)
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return attention_output
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class BertIntermediate(nn.Module):
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def __init__(self, config):
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super(BertIntermediate, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class BertOutput(nn.Module):
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def __init__(self, config):
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super(BertOutput, self).__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertLayer(nn.Module):
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def __init__(self, config):
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super(BertLayer, self).__init__()
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self.attention = BertAttention(config)
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self.intermediate = BertIntermediate(config)
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self.output = BertOutput(config)
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def forward(self, hidden_states, attention_mask):
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attention_output = self.attention(hidden_states, attention_mask)
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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class BertEncoder(nn.Module):
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def __init__(self, config):
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super(BertEncoder, self).__init__()
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layer = BertLayer(config)
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self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
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# def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
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# all_encoder_layers = []
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# for layer_module in self.layer:
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# hidden_states = layer_module(hidden_states, attention_mask)
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# if output_all_encoded_layers:
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# all_encoder_layers.append(hidden_states)
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# if not output_all_encoded_layers:
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# all_encoder_layers.append(hidden_states)
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# return all_encoder_layers
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def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True, checkpoint_activations=False):
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all_encoder_layers = []
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def custom(start, end):
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def custom_forward(*inputs):
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layers = self.layer[start:end]
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x_ = inputs[0]
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for layer in layers:
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x_ = layer(x_, inputs[1])
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return x_
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return custom_forward
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if checkpoint_activations:
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l = 0
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num_layers = len(self.layer)
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chunk_length = math.ceil(math.sqrt(num_layers))
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while l < num_layers:
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hidden_states = checkpoint.checkpoint(custom(l, l+chunk_length), hidden_states, attention_mask*1)
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l += chunk_length
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# decoder layers
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else:
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for i,layer_module in enumerate(self.layer):
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hidden_states = layer_module(hidden_states, attention_mask)
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if output_all_encoded_layers:
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all_encoder_layers.append(hidden_states)
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if not output_all_encoded_layers or checkpoint_activations:
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all_encoder_layers.append(hidden_states)
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return all_encoder_layers
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#class BertEncoder(nn.Module):
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# def __init__(self, config):
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# super(BertEncoder, self).__init__()
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# layer = BertLayer(config)
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|
# self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
|
|
#
|
|
# def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
|
|
# all_encoder_layers = []
|
|
# for layer_module in self.layer:
|
|
# hidden_states = layer_module(hidden_states, attention_mask)
|
|
# if output_all_encoded_layers:
|
|
# all_encoder_layers.append(hidden_states)
|
|
# if not output_all_encoded_layers:
|
|
# all_encoder_layers.append(hidden_states)
|
|
# return all_encoder_layers
|
|
|
|
|
|
class BertPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super(BertPooler, self).__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states):
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class BertPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super(BertPredictionHeadTransform, self).__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class BertLMPredictionHead(nn.Module):
|
|
def __init__(self, config, bert_model_embedding_weights):
|
|
super(BertLMPredictionHead, self).__init__()
|
|
self.transform = BertPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
|
|
bert_model_embedding_weights.size(0),
|
|
bias=False)
|
|
self.decoder.weight = bert_model_embedding_weights
|
|
self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states) + self.bias
|
|
return hidden_states
|
|
|
|
|
|
class BertOnlyMLMHead(nn.Module):
|
|
def __init__(self, config, bert_model_embedding_weights):
|
|
super(BertOnlyMLMHead, self).__init__()
|
|
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
|
|
|
|
def forward(self, sequence_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class BertOnlyNSPHead(nn.Module):
|
|
def __init__(self, config):
|
|
super(BertOnlyNSPHead, self).__init__()
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, pooled_output):
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return seq_relationship_score
|
|
|
|
|
|
class BertPreTrainingHeads(nn.Module):
|
|
def __init__(self, config, bert_model_embedding_weights):
|
|
super(BertPreTrainingHeads, self).__init__()
|
|
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
|
|
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(nn.Module):
|
|
""" An abstract class to handle weights initialization and
|
|
a simple interface for dowloading and loading pretrained models.
|
|
"""
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super(BertPreTrainedModel, self).__init__()
|
|
if not isinstance(config, BertConfig):
|
|
raise ValueError(
|
|
"Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
|
|
"To create a model from a Google pretrained model use "
|
|
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
|
|
self.__class__.__name__, self.__class__.__name__
|
|
))
|
|
self.config = config
|
|
|
|
def init_bert_weights(self, module):
|
|
""" Initialize the weights.
|
|
"""
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
elif isinstance(module, BertLayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
|
|
from_tf=False, *inputs, **kwargs):
|
|
"""
|
|
Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
|
|
Download and cache the pre-trained model file if needed.
|
|
|
|
Params:
|
|
pretrained_model_name_or_path: either:
|
|
- a str with the name of a pre-trained model to load selected in the list of:
|
|
. `bert-base-uncased`
|
|
. `bert-large-uncased`
|
|
. `bert-base-cased`
|
|
. `bert-large-cased`
|
|
. `bert-base-multilingual-uncased`
|
|
. `bert-base-multilingual-cased`
|
|
. `bert-base-chinese`
|
|
- a path or url to a pretrained model archive containing:
|
|
. `bert_config.json` a configuration file for the model
|
|
. `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
|
|
- a path or url to a pretrained model archive containing:
|
|
. `bert_config.json` a configuration file for the model
|
|
. `model.chkpt` a TensorFlow checkpoint
|
|
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
|
|
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
|
|
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
|
|
*inputs, **kwargs: additional input for the specific Bert class
|
|
(ex: num_labels for BertForSequenceClassification)
|
|
"""
|
|
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
|
|
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
|
|
else:
|
|
archive_file = pretrained_model_name_or_path
|
|
# redirect to the cache, if necessary
|
|
try:
|
|
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
|
|
except EnvironmentError:
|
|
logger.error(
|
|
"Model name '{}' was not found in model name list ({}). "
|
|
"We assumed '{}' was a path or url but couldn't find any file "
|
|
"associated to this path or url.".format(
|
|
pretrained_model_name_or_path,
|
|
', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
|
|
archive_file))
|
|
return None
|
|
if resolved_archive_file == archive_file:
|
|
logger.info("loading archive file {}".format(archive_file))
|
|
else:
|
|
logger.info("loading archive file {} from cache at {}".format(
|
|
archive_file, resolved_archive_file))
|
|
tempdir = None
|
|
if os.path.isdir(resolved_archive_file) or from_tf:
|
|
serialization_dir = resolved_archive_file
|
|
else:
|
|
# Extract archive to temp dir
|
|
tempdir = tempfile.mkdtemp()
|
|
logger.info("extracting archive file {} to temp dir {}".format(
|
|
resolved_archive_file, tempdir))
|
|
with tarfile.open(resolved_archive_file, 'r:gz') as archive:
|
|
archive.extractall(tempdir)
|
|
serialization_dir = tempdir
|
|
# Load config
|
|
config_file = os.path.join(serialization_dir, CONFIG_NAME)
|
|
config = BertConfig.from_json_file(config_file)
|
|
logger.info("Model config {}".format(config))
|
|
# Instantiate model.
|
|
model = cls(config, *inputs, **kwargs)
|
|
if state_dict is None and not from_tf:
|
|
weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
|
|
state_dict = torch.load(weights_path, map_location='cpu' if not torch.cuda.is_available() else None)
|
|
if tempdir:
|
|
# Clean up temp dir
|
|
shutil.rmtree(tempdir)
|
|
if from_tf:
|
|
# Directly load from a TensorFlow checkpoint
|
|
weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
|
|
return load_tf_weights_in_bert(model, weights_path)
|
|
# Load from a PyTorch state_dict
|
|
old_keys = []
|
|
new_keys = []
|
|
for key in state_dict.keys():
|
|
new_key = None
|
|
if 'gamma' in key:
|
|
new_key = key.replace('gamma', 'weight')
|
|
if 'beta' in key:
|
|
new_key = key.replace('beta', 'bias')
|
|
if new_key:
|
|
old_keys.append(key)
|
|
new_keys.append(new_key)
|
|
for old_key, new_key in zip(old_keys, new_keys):
|
|
state_dict[new_key] = state_dict.pop(old_key)
|
|
|
|
missing_keys = []
|
|
unexpected_keys = []
|
|
error_msgs = []
|
|
# copy state_dict so _load_from_state_dict can modify it
|
|
metadata = getattr(state_dict, '_metadata', None)
|
|
state_dict = state_dict.copy()
|
|
if metadata is not None:
|
|
state_dict._metadata = metadata
|
|
|
|
def load(module, prefix=''):
|
|
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
|
|
module._load_from_state_dict(
|
|
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
|
|
for name, child in module._modules.items():
|
|
if child is not None:
|
|
load(child, prefix + name + '.')
|
|
start_prefix = ''
|
|
if not hasattr(model, 'bert') and any(s.startswith('bert.') for s in state_dict.keys()):
|
|
start_prefix = 'bert.'
|
|
load(model, prefix=start_prefix)
|
|
if len(missing_keys) > 0:
|
|
logger.info("Weights of {} not initialized from pretrained model: {}".format(
|
|
model.__class__.__name__, missing_keys))
|
|
if len(unexpected_keys) > 0:
|
|
logger.info("Weights from pretrained model not used in {}: {}".format(
|
|
model.__class__.__name__, unexpected_keys))
|
|
if len(error_msgs) > 0:
|
|
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
|
|
model.__class__.__name__, "\n\t".join(error_msgs)))
|
|
return model
|
|
|
|
|
|
class BertModel(BertPreTrainedModel):
|
|
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
|
|
|
|
Params:
|
|
config: a BertConfig class instance with the configuration to build a new model
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
|
|
|
|
Outputs: Tuple of (encoded_layers, pooled_output)
|
|
`encoded_layers`: controled by `output_all_encoded_layers` argument:
|
|
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
|
|
of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
|
|
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
|
|
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
|
|
to the last attention block of shape [batch_size, sequence_length, hidden_size],
|
|
`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
|
|
classifier pretrained on top of the hidden state associated to the first character of the
|
|
input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = modeling.BertModel(config=config)
|
|
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config):
|
|
super(BertModel, self).__init__(config)
|
|
self.embeddings = BertEmbeddings(config)
|
|
self.encoder = BertEncoder(config)
|
|
self.pooler = BertPooler(config)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True, checkpoint_activations=False):
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones_like(input_ids)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros_like(input_ids)
|
|
|
|
# We create a 3D attention mask from a 2D tensor mask.
|
|
# Sizes are [batch_size, 1, 1, to_seq_length]
|
|
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
|
|
# this attention mask is more simple than the triangular masking of causal attention
|
|
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
|
|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
|
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
|
# masked positions, this operation will create a tensor which is 0.0 for
|
|
# positions we want to attend and -10000.0 for masked positions.
|
|
# Since we are adding it to the raw scores before the softmax, this is
|
|
# effectively the same as removing these entirely.
|
|
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
|
|
embedding_output = self.embeddings(input_ids, token_type_ids)
|
|
encoded_layers = self.encoder(embedding_output,
|
|
extended_attention_mask,
|
|
output_all_encoded_layers=output_all_encoded_layers, checkpoint_activations=checkpoint_activations)
|
|
sequence_output = encoded_layers[-1]
|
|
pooled_output = self.pooler(sequence_output)
|
|
if not output_all_encoded_layers:
|
|
encoded_layers = encoded_layers[-1]
|
|
return encoded_layers, pooled_output
|
|
|
|
|
|
class BertForPreTraining(BertPreTrainedModel):
|
|
"""BERT model with pre-training heads.
|
|
This module comprises the BERT model followed by the two pre-training heads:
|
|
- the masked language modeling head, and
|
|
- the next sentence classification head.
|
|
|
|
Params:
|
|
config: a BertConfig class instance with the configuration to build a new model.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
|
|
with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
|
|
is only computed for the labels set in [0, ..., vocab_size]
|
|
`next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
|
|
with indices selected in [0, 1].
|
|
0 => next sentence is the continuation, 1 => next sentence is a random sentence.
|
|
|
|
Outputs:
|
|
if `masked_lm_labels` and `next_sentence_label` are not `None`:
|
|
Outputs the total_loss which is the sum of the masked language modeling loss and the next
|
|
sentence classification loss.
|
|
if `masked_lm_labels` or `next_sentence_label` is `None`:
|
|
Outputs a tuple comprising
|
|
- the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
|
|
- the next sentence classification logits of shape [batch_size, 2].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = BertForPreTraining(config)
|
|
masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config):
|
|
super(BertForPreTraining, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, checkpoint_activations=False):
|
|
sequence_output, pooled_output = self.bert(input_ids, token_type_ids, attention_mask,
|
|
output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
|
|
|
if masked_lm_labels is not None and next_sentence_label is not None:
|
|
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
|
|
#print("loss is {} {}".format(masked_lm_loss, next_sentence_loss))
|
|
total_loss = masked_lm_loss + next_sentence_loss
|
|
return total_loss
|
|
else:
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
class BertForMaskedLM(BertPreTrainedModel):
|
|
"""BERT model with the masked language modeling head.
|
|
This module comprises the BERT model followed by the masked language modeling head.
|
|
|
|
Params:
|
|
config: a BertConfig class instance with the configuration to build a new model.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
|
|
with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
|
|
is only computed for the labels set in [0, ..., vocab_size]
|
|
|
|
Outputs:
|
|
if `masked_lm_labels` is not `None`:
|
|
Outputs the masked language modeling loss.
|
|
if `masked_lm_labels` is `None`:
|
|
Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = BertForMaskedLM(config)
|
|
masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config):
|
|
super(BertForMaskedLM, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, checkpoint_activations=False):
|
|
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask,
|
|
output_all_encoded_layers=False)
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
if masked_lm_labels is not None:
|
|
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
|
|
return masked_lm_loss
|
|
else:
|
|
return prediction_scores
|
|
|
|
|
|
class BertForNextSentencePrediction(BertPreTrainedModel):
|
|
"""BERT model with next sentence prediction head.
|
|
This module comprises the BERT model followed by the next sentence classification head.
|
|
|
|
Params:
|
|
config: a BertConfig class instance with the configuration to build a new model.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
|
|
with indices selected in [0, 1].
|
|
0 => next sentence is the continuation, 1 => next sentence is a random sentence.
|
|
|
|
Outputs:
|
|
if `next_sentence_label` is not `None`:
|
|
Outputs the total_loss which is the sum of the masked language modeling loss and the next
|
|
sentence classification loss.
|
|
if `next_sentence_label` is `None`:
|
|
Outputs the next sentence classification logits of shape [batch_size, 2].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = BertForNextSentencePrediction(config)
|
|
seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config):
|
|
super(BertForNextSentencePrediction, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
self.cls = BertOnlyNSPHead(config)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None, checkpoint_activations=False):
|
|
_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask,
|
|
output_all_encoded_layers=False)
|
|
seq_relationship_score = self.cls( pooled_output)
|
|
|
|
if next_sentence_label is not None:
|
|
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
|
|
return next_sentence_loss
|
|
else:
|
|
return seq_relationship_score
|
|
|
|
|
|
class BertForSequenceClassification(BertPreTrainedModel):
|
|
"""BERT model for classification.
|
|
This module is composed of the BERT model with a linear layer on top of
|
|
the pooled output.
|
|
|
|
Params:
|
|
`config`: a BertConfig class instance with the configuration to build a new model.
|
|
`num_labels`: the number of classes for the classifier. Default = 2.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
|
|
with indices selected in [0, ..., num_labels].
|
|
|
|
Outputs:
|
|
if `labels` is not `None`:
|
|
Outputs the CrossEntropy classification loss of the output with the labels.
|
|
if `labels` is `None`:
|
|
Outputs the classification logits of shape [batch_size, num_labels].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
num_labels = 2
|
|
|
|
model = BertForSequenceClassification(config, num_labels)
|
|
logits = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config, num_labels):
|
|
super(BertForSequenceClassification, self).__init__(config)
|
|
self.num_labels = num_labels
|
|
self.bert = BertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, num_labels)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, checkpoint_activations=False):
|
|
_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
return loss
|
|
else:
|
|
return logits
|
|
|
|
|
|
class BertForMultipleChoice(BertPreTrainedModel):
|
|
"""BERT model for multiple choice tasks.
|
|
This module is composed of the BERT model with a linear layer on top of
|
|
the pooled output.
|
|
|
|
Params:
|
|
`config`: a BertConfig class instance with the configuration to build a new model.
|
|
`num_choices`: the number of classes for the classifier. Default = 2.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
|
|
with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
|
|
and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
|
|
with indices selected in [0, ..., num_choices].
|
|
|
|
Outputs:
|
|
if `labels` is not `None`:
|
|
Outputs the CrossEntropy classification loss of the output with the labels.
|
|
if `labels` is `None`:
|
|
Outputs the classification logits of shape [batch_size, num_labels].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
|
|
input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
|
|
token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
num_choices = 2
|
|
|
|
model = BertForMultipleChoice(config, num_choices)
|
|
logits = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config, num_choices):
|
|
super(BertForMultipleChoice, self).__init__(config)
|
|
self.num_choices = num_choices
|
|
self.bert = BertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, checkpoint_activations=False):
|
|
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
|
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
|
|
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
|
|
_, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False)
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, self.num_choices)
|
|
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
return loss
|
|
else:
|
|
return reshaped_logits
|
|
|
|
|
|
class BertForTokenClassification(BertPreTrainedModel):
|
|
"""BERT model for token-level classification.
|
|
This module is composed of the BERT model with a linear layer on top of
|
|
the full hidden state of the last layer.
|
|
|
|
Params:
|
|
`config`: a BertConfig class instance with the configuration to build a new model.
|
|
`num_labels`: the number of classes for the classifier. Default = 2.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
|
|
with indices selected in [0, ..., num_labels].
|
|
|
|
Outputs:
|
|
if `labels` is not `None`:
|
|
Outputs the CrossEntropy classification loss of the output with the labels.
|
|
if `labels` is `None`:
|
|
Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
num_labels = 2
|
|
|
|
model = BertForTokenClassification(config, num_labels)
|
|
logits = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config, num_labels):
|
|
super(BertForTokenClassification, self).__init__(config)
|
|
self.num_labels = num_labels
|
|
self.bert = BertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, num_labels)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, checkpoint_activations=False):
|
|
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# Only keep active parts of the loss
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1) == 1
|
|
active_logits = logits.view(-1, self.num_labels)[active_loss]
|
|
active_labels = labels.view(-1)[active_loss]
|
|
loss = loss_fct(active_logits, active_labels)
|
|
else:
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
return loss
|
|
else:
|
|
return logits
|
|
|
|
|
|
class BertForQuestionAnswering(BertPreTrainedModel):
|
|
"""BERT model for Question Answering (span extraction).
|
|
This module is composed of the BERT model with a linear layer on top of
|
|
the sequence output that computes start_logits and end_logits
|
|
|
|
Params:
|
|
`config`: a BertConfig class instance with the configuration to build a new model.
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
|
|
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
|
|
into account for computing the loss.
|
|
`end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
|
|
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
|
|
into account for computing the loss.
|
|
|
|
Outputs:
|
|
if `start_positions` and `end_positions` are not `None`:
|
|
Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
|
|
if `start_positions` or `end_positions` is `None`:
|
|
Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
|
|
position tokens of shape [batch_size, sequence_length].
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = BertForQuestionAnswering(config)
|
|
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config):
|
|
super(BertForQuestionAnswering, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
# TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
|
|
# self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None, checkpoint_activations=False):
|
|
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1)
|
|
end_logits = end_logits.squeeze(-1)
|
|
|
|
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)
|
|
start_positions.clamp_(0, ignored_index)
|
|
end_positions.clamp_(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
return total_loss
|
|
else:
|
|
return start_logits, end_logits
|