1175 lines
55 KiB
Python
1175 lines
55 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" TF 2.0 BERT model. """
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import logging
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import numpy as np
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import tensorflow as tf
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from .configuration_bert import BertConfig
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from .file_utils import MULTIPLE_CHOICE_DUMMY_INPUTS, add_start_docstrings, add_start_docstrings_to_callable
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from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list
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from .tokenization_utils import BatchEncoding
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logger = logging.getLogger(__name__)
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TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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"bert-base-uncased": "https://cdn.huggingface.co/bert-base-uncased-tf_model.h5",
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"bert-large-uncased": "https://cdn.huggingface.co/bert-large-uncased-tf_model.h5",
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"bert-base-cased": "https://cdn.huggingface.co/bert-base-cased-tf_model.h5",
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"bert-large-cased": "https://cdn.huggingface.co/bert-large-cased-tf_model.h5",
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"bert-base-multilingual-uncased": "https://cdn.huggingface.co/bert-base-multilingual-uncased-tf_model.h5",
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"bert-base-multilingual-cased": "https://cdn.huggingface.co/bert-base-multilingual-cased-tf_model.h5",
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"bert-base-chinese": "https://cdn.huggingface.co/bert-base-chinese-tf_model.h5",
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"bert-base-german-cased": "https://cdn.huggingface.co/bert-base-german-cased-tf_model.h5",
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"bert-large-uncased-whole-word-masking": "https://cdn.huggingface.co/bert-large-uncased-whole-word-masking-tf_model.h5",
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"bert-large-cased-whole-word-masking": "https://cdn.huggingface.co/bert-large-cased-whole-word-masking-tf_model.h5",
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"bert-large-uncased-whole-word-masking-finetuned-squad": "https://cdn.huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad-tf_model.h5",
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"bert-large-cased-whole-word-masking-finetuned-squad": "https://cdn.huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad-tf_model.h5",
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"bert-base-cased-finetuned-mrpc": "https://cdn.huggingface.co/bert-base-cased-finetuned-mrpc-tf_model.h5",
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"bert-base-japanese": "https://cdn.huggingface.co/cl-tohoku/bert-base-japanese/tf_model.h5",
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"bert-base-japanese-whole-word-masking": "https://cdn.huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/tf_model.h5",
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"bert-base-japanese-char": "https://cdn.huggingface.co/cl-tohoku/bert-base-japanese-char/tf_model.h5",
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"bert-base-japanese-char-whole-word-masking": "https://cdn.huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/tf_model.h5",
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"bert-base-finnish-cased-v1": "https://cdn.huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/tf_model.h5",
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"bert-base-finnish-uncased-v1": "https://cdn.huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/tf_model.h5",
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"bert-base-dutch-cased": "https://cdn.huggingface.co/wietsedv/bert-base-dutch-cased/tf_model.h5",
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}
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def gelu(x):
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""" Gaussian Error Linear Unit.
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Original Implementation of the gelu activation function in Google Bert repo when initially created.
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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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cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
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return x * cdf
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def gelu_new(x):
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"""Gaussian Error Linear Unit.
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This is a smoother version of the RELU.
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Original paper: https://arxiv.org/abs/1606.08415
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Args:
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x: float Tensor to perform activation.
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Returns:
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`x` with the GELU activation applied.
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"""
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cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
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return x * cdf
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def swish(x):
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return x * tf.sigmoid(x)
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ACT2FN = {
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"gelu": tf.keras.layers.Activation(gelu),
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"relu": tf.keras.activations.relu,
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"swish": tf.keras.layers.Activation(swish),
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"gelu_new": tf.keras.layers.Activation(gelu_new),
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}
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class TFBertEmbeddings(tf.keras.layers.Layer):
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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, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = config.vocab_size
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self.hidden_size = config.hidden_size
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self.initializer_range = config.initializer_range
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self.position_embeddings = tf.keras.layers.Embedding(
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config.max_position_embeddings,
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config.hidden_size,
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embeddings_initializer=get_initializer(self.initializer_range),
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name="position_embeddings",
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)
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self.token_type_embeddings = tf.keras.layers.Embedding(
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config.type_vocab_size,
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config.hidden_size,
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embeddings_initializer=get_initializer(self.initializer_range),
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name="token_type_embeddings",
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)
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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 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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def build(self, input_shape):
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"""Build shared word embedding layer """
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with tf.name_scope("word_embeddings"):
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# Create and initialize weights. The random normal initializer was chosen
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# arbitrarily, and works well.
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self.word_embeddings = self.add_weight(
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"weight",
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shape=[self.vocab_size, self.hidden_size],
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initializer=get_initializer(self.initializer_range),
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)
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super().build(input_shape)
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def call(self, inputs, mode="embedding", training=False):
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"""Get token embeddings of inputs.
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Args:
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inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
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mode: string, a valid value is one of "embedding" and "linear".
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Returns:
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outputs: (1) If mode == "embedding", output embedding tensor, float32 with
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shape [batch_size, length, embedding_size]; (2) mode == "linear", output
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linear tensor, float32 with shape [batch_size, length, vocab_size].
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Raises:
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ValueError: if mode is not valid.
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Shared weights logic adapted from
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https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
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"""
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if mode == "embedding":
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return self._embedding(inputs, training=training)
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elif mode == "linear":
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return self._linear(inputs)
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else:
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raise ValueError("mode {} is not valid.".format(mode))
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def _embedding(self, inputs, training=False):
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"""Applies embedding based on inputs tensor."""
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input_ids, position_ids, token_type_ids, inputs_embeds = inputs
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if input_ids is not None:
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input_shape = shape_list(input_ids)
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else:
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input_shape = shape_list(inputs_embeds)[:-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 = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
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if token_type_ids is None:
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token_type_ids = tf.fill(input_shape, 0)
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if inputs_embeds is None:
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inputs_embeds = tf.gather(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 = inputs_embeds + position_embeddings + token_type_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings, training=training)
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return embeddings
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def _linear(self, inputs):
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"""Computes logits by running inputs through a linear layer.
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Args:
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inputs: A float32 tensor with shape [batch_size, length, hidden_size]
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Returns:
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float32 tensor with shape [batch_size, length, vocab_size].
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"""
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batch_size = shape_list(inputs)[0]
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length = shape_list(inputs)[1]
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x = tf.reshape(inputs, [-1, self.hidden_size])
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logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
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return tf.reshape(logits, [batch_size, length, self.vocab_size])
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class TFBertSelfAttention(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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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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)
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self.output_attentions = config.output_attentions
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self.num_attention_heads = config.num_attention_heads
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assert config.hidden_size % config.num_attention_heads == 0
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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 = tf.keras.layers.Dense(
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self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
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)
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self.key = tf.keras.layers.Dense(
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self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
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)
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self.value = tf.keras.layers.Dense(
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self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
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)
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self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x, batch_size):
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x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
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return tf.transpose(x, perm=[0, 2, 1, 3])
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def call(self, inputs, training=False):
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hidden_states, attention_mask, head_mask = inputs
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batch_size = shape_list(hidden_states)[0]
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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, batch_size)
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key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
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value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = tf.matmul(
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query_layer, key_layer, transpose_b=True
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) # (batch size, num_heads, seq_len_q, seq_len_k)
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dk = tf.cast(shape_list(key_layer)[-1], tf.float32) # scale attention_scores
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attention_scores = attention_scores / tf.math.sqrt(dk)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in TFBertModel call() 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 = tf.nn.softmax(attention_scores, axis=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs, training=training)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = tf.matmul(attention_probs, value_layer)
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context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
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context_layer = tf.reshape(
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context_layer, (batch_size, -1, self.all_head_size)
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) # (batch_size, seq_len_q, all_head_size)
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outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
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return outputs
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class TFBertSelfOutput(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.dense = tf.keras.layers.Dense(
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config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
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)
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self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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def call(self, inputs, training=False):
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hidden_states, input_tensor = inputs
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states, training=training)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class TFBertAttention(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.self_attention = TFBertSelfAttention(config, name="self")
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self.dense_output = TFBertSelfOutput(config, name="output")
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def prune_heads(self, heads):
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raise NotImplementedError
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def call(self, inputs, training=False):
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input_tensor, attention_mask, head_mask = inputs
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self_outputs = self.self_attention([input_tensor, attention_mask, head_mask], training=training)
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attention_output = self.dense_output([self_outputs[0], input_tensor], training=training)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class TFBertIntermediate(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.dense = tf.keras.layers.Dense(
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config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
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)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def call(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 TFBertOutput(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.dense = tf.keras.layers.Dense(
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config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
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)
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self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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def call(self, inputs, training=False):
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hidden_states, input_tensor = inputs
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states, training=training)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class TFBertLayer(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.attention = TFBertAttention(config, name="attention")
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self.intermediate = TFBertIntermediate(config, name="intermediate")
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self.bert_output = TFBertOutput(config, name="output")
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def call(self, inputs, training=False):
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hidden_states, attention_mask, head_mask = inputs
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attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
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attention_output = attention_outputs[0]
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.bert_output([intermediate_output, attention_output], training=training)
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outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
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return outputs
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class TFBertEncoder(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.output_attentions = config.output_attentions
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self.output_hidden_states = config.output_hidden_states
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self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
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def call(self, inputs, training=False):
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hidden_states, attention_mask, head_mask = inputs
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all_hidden_states = ()
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all_attentions = ()
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for i, layer_module in enumerate(self.layer):
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if self.output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_outputs = layer_module([hidden_states, attention_mask, head_mask[i]], training=training)
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hidden_states = layer_outputs[0]
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if self.output_attentions:
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all_attentions = all_attentions + (layer_outputs[1],)
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# Add last layer
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if self.output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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outputs = (hidden_states,)
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if self.output_hidden_states:
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outputs = outputs + (all_hidden_states,)
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if self.output_attentions:
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outputs = outputs + (all_attentions,)
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return outputs # outputs, (hidden states), (attentions)
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class TFBertPooler(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.dense = tf.keras.layers.Dense(
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config.hidden_size,
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kernel_initializer=get_initializer(config.initializer_range),
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activation="tanh",
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name="dense",
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)
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def call(self, hidden_states):
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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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return pooled_output
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class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.dense = tf.keras.layers.Dense(
|
|
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
|
)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
|
|
|
def call(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 TFBertLMPredictionHead(tf.keras.layers.Layer):
|
|
def __init__(self, config, input_embeddings, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.vocab_size = config.vocab_size
|
|
self.transform = TFBertPredictionHeadTransform(config, name="transform")
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.input_embeddings = input_embeddings
|
|
|
|
def build(self, input_shape):
|
|
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
|
|
super().build(input_shape)
|
|
|
|
def call(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
|
hidden_states = hidden_states + self.bias
|
|
return hidden_states
|
|
|
|
|
|
class TFBertMLMHead(tf.keras.layers.Layer):
|
|
def __init__(self, config, input_embeddings, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.predictions = TFBertLMPredictionHead(config, input_embeddings, name="predictions")
|
|
|
|
def call(self, sequence_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class TFBertNSPHead(tf.keras.layers.Layer):
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.seq_relationship = tf.keras.layers.Dense(
|
|
2, kernel_initializer=get_initializer(config.initializer_range), name="seq_relationship"
|
|
)
|
|
|
|
def call(self, pooled_output):
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return seq_relationship_score
|
|
|
|
|
|
@keras_serializable
|
|
class TFBertMainLayer(tf.keras.layers.Layer):
|
|
config_class = BertConfig
|
|
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.num_hidden_layers = config.num_hidden_layers
|
|
|
|
self.embeddings = TFBertEmbeddings(config, name="embeddings")
|
|
self.encoder = TFBertEncoder(config, name="encoder")
|
|
self.pooler = TFBertPooler(config, name="pooler")
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings
|
|
|
|
def _resize_token_embeddings(self, new_num_tokens):
|
|
raise NotImplementedError
|
|
|
|
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
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def call(
|
|
self,
|
|
inputs,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
training=False,
|
|
):
|
|
if isinstance(inputs, (tuple, list)):
|
|
input_ids = inputs[0]
|
|
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
|
|
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
|
|
position_ids = inputs[3] if len(inputs) > 3 else position_ids
|
|
head_mask = inputs[4] if len(inputs) > 4 else head_mask
|
|
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
|
|
assert len(inputs) <= 6, "Too many inputs."
|
|
elif isinstance(inputs, (dict, BatchEncoding)):
|
|
input_ids = inputs.get("input_ids")
|
|
attention_mask = inputs.get("attention_mask", attention_mask)
|
|
token_type_ids = inputs.get("token_type_ids", token_type_ids)
|
|
position_ids = inputs.get("position_ids", position_ids)
|
|
head_mask = inputs.get("head_mask", head_mask)
|
|
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
|
|
assert len(inputs) <= 6, "Too many inputs."
|
|
else:
|
|
input_ids = inputs
|
|
|
|
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 = shape_list(input_ids)
|
|
elif inputs_embeds is not None:
|
|
input_shape = shape_list(inputs_embeds)[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
if attention_mask is None:
|
|
attention_mask = tf.fill(input_shape, 1)
|
|
if token_type_ids is None:
|
|
token_type_ids = tf.fill(input_shape, 0)
|
|
|
|
# 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[:, tf.newaxis, tf.newaxis, :]
|
|
|
|
# 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 = tf.cast(extended_attention_mask, tf.float32)
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
|
|
# 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]
|
|
if head_mask is not None:
|
|
raise NotImplementedError
|
|
else:
|
|
head_mask = [None] * self.num_hidden_layers
|
|
# head_mask = tf.constant([0] * self.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
|
|
encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
|
|
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output)
|
|
|
|
outputs = (sequence_output, pooled_output,) + encoder_outputs[
|
|
1:
|
|
] # add hidden_states and attentions if they are here
|
|
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
|
|
|
|
|
|
class TFBertPreTrainedModel(TFPreTrainedModel):
|
|
""" An abstract class to handle weights initialization and
|
|
a simple interface for downloading and loading pretrained models.
|
|
"""
|
|
|
|
config_class = BertConfig
|
|
pretrained_model_archive_map = TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
|
base_model_prefix = "bert"
|
|
|
|
|
|
BERT_START_DOCSTRING = r"""
|
|
This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
|
|
Use it as a regular TF 2.0 Keras Model and
|
|
refer to the TF 2.0 documentation for all matter related to general usage and behavior.
|
|
|
|
.. note::
|
|
|
|
TF 2.0 models accepts two formats as inputs:
|
|
|
|
- having all inputs as keyword arguments (like PyTorch models), or
|
|
- having all inputs as a list, tuple or dict in the first positional arguments.
|
|
|
|
This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
|
|
all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
|
|
|
|
If you choose this second option, there are three possibilities you can use to gather all the input Tensors
|
|
in the first positional argument :
|
|
|
|
- a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
|
|
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
|
|
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
|
|
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
|
|
:obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
|
|
|
|
Parameters:
|
|
config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the configuration.
|
|
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
|
|
"""
|
|
|
|
BERT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using :class:`transformers.BertTokenizer`.
|
|
See :func:`transformers.PreTrainedTokenizer.encode` and
|
|
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
|
|
|
|
`What are input IDs? <../glossary.html#input-ids>`__
|
|
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
|
|
Mask to avoid performing attention on padding token indices.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
|
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
|
|
Segment token indices to indicate first and second portions of the inputs.
|
|
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
|
|
corresponds to a `sentence B` token
|
|
|
|
`What are token type IDs? <../glossary.html#token-type-ids>`__
|
|
position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
|
|
Indices of positions of each input sequence tokens in the position embeddings.
|
|
Selected in the range ``[0, config.max_position_embeddings - 1]``.
|
|
|
|
`What are position IDs? <../glossary.html#position-ids>`__
|
|
head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
|
|
Mask to nullify selected heads of the self-attention modules.
|
|
Mask values selected in ``[0, 1]``:
|
|
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
|
|
inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`, defaults to :obj:`None`):
|
|
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
|
|
Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
|
|
(if set to :obj:`False`) for evaluation.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
|
|
BERT_START_DOCSTRING,
|
|
)
|
|
class TFBertModel(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Returns:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the model.
|
|
pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
|
|
Last layer hidden-state of the first token of the sequence (classification token)
|
|
further processed by a Linear layer and a Tanh activation function. The Linear
|
|
layer weights are trained from the next sentence prediction (classification)
|
|
objective during Bert pretraining. This output is usually *not* a good summary
|
|
of the semantic content of the input, you're often better with averaging or pooling
|
|
the sequence of hidden-states for the whole input sequence.
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertModel
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertModel.from_pretrained('bert-base-uncased')
|
|
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
|
|
outputs = model(input_ids)
|
|
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
return outputs
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Bert Model with two heads on top as done during the pre-training:
|
|
a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
|
|
BERT_START_DOCSTRING,
|
|
)
|
|
class TFBertForPreTraining(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.nsp = TFBertNSPHead(config, name="nsp___cls")
|
|
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
|
|
|
|
def get_output_embeddings(self):
|
|
return self.bert.embeddings
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
seq_relationship_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`):
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForPreTraining
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
|
|
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
|
|
outputs = model(input_ids)
|
|
prediction_scores, seq_relationship_scores = outputs[:2]
|
|
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
|
|
sequence_output, pooled_output = outputs[:2]
|
|
prediction_scores = self.mlm(sequence_output, training=kwargs.get("training", False))
|
|
seq_relationship_score = self.nsp(pooled_output)
|
|
|
|
outputs = (prediction_scores, seq_relationship_score,) + outputs[
|
|
2:
|
|
] # add hidden states and attention if they are here
|
|
|
|
return outputs # prediction_scores, seq_relationship_score, (hidden_states), (attentions)
|
|
|
|
|
|
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
|
|
class TFBertForMaskedLM(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
|
|
|
|
def get_output_embeddings(self):
|
|
return self.bert.embeddings
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForMaskedLM
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForMaskedLM.from_pretrained('bert-base-uncased')
|
|
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
|
|
outputs = model(input_ids)
|
|
prediction_scores = outputs[0]
|
|
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.mlm(sequence_output, training=kwargs.get("training", False))
|
|
|
|
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
|
|
|
|
return outputs # prediction_scores, (hidden_states), (attentions)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Bert Model with a `next sentence prediction (classification)` head on top. """, BERT_START_DOCSTRING,
|
|
)
|
|
class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.nsp = TFBertNSPHead(config, name="nsp___cls")
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
seq_relationship_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`)
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForNextSentencePrediction
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
|
|
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
|
|
outputs = model(input_ids)
|
|
seq_relationship_scores = outputs[0]
|
|
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
|
|
pooled_output = outputs[1]
|
|
seq_relationship_score = self.nsp(pooled_output)
|
|
|
|
outputs = (seq_relationship_score,) + outputs[2:] # add hidden states and attention if they are here
|
|
|
|
return outputs # seq_relationship_score, (hidden_states), (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 TFBertForSequenceClassification(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = tf.keras.layers.Dense(
|
|
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
|
|
)
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForSequenceClassification
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
|
|
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
|
|
outputs = model(input_ids)
|
|
logits = outputs[0]
|
|
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output, training=kwargs.get("training", False))
|
|
logits = self.classifier(pooled_output)
|
|
|
|
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
|
|
|
|
return outputs # logits, (hidden_states), (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 softmax) e.g. for RocStories/SWAG tasks. """,
|
|
BERT_START_DOCSTRING,
|
|
)
|
|
class TFBertForMultipleChoice(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = tf.keras.layers.Dense(
|
|
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
|
|
)
|
|
|
|
@property
|
|
def dummy_inputs(self):
|
|
""" Dummy inputs to build the network.
|
|
|
|
Returns:
|
|
tf.Tensor with dummy inputs
|
|
"""
|
|
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(
|
|
self,
|
|
inputs,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
training=False,
|
|
):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
classification_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`:
|
|
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
|
|
|
|
Classification scores (before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForMultipleChoice
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForMultipleChoice.from_pretrained('bert-base-uncased')
|
|
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
|
|
input_ids = tf.constant([tokenizer.encode(s) for s in choices])[None, :] # Batch size 1, 2 choices
|
|
outputs = model(input_ids)
|
|
classification_scores = outputs[0]
|
|
|
|
"""
|
|
if isinstance(inputs, (tuple, list)):
|
|
input_ids = inputs[0]
|
|
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
|
|
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
|
|
position_ids = inputs[3] if len(inputs) > 3 else position_ids
|
|
head_mask = inputs[4] if len(inputs) > 4 else head_mask
|
|
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
|
|
assert len(inputs) <= 6, "Too many inputs."
|
|
elif isinstance(inputs, dict):
|
|
input_ids = inputs.get("input_ids")
|
|
attention_mask = inputs.get("attention_mask", attention_mask)
|
|
token_type_ids = inputs.get("token_type_ids", token_type_ids)
|
|
position_ids = inputs.get("position_ids", position_ids)
|
|
head_mask = inputs.get("head_mask", head_mask)
|
|
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
|
|
assert len(inputs) <= 6, "Too many inputs."
|
|
else:
|
|
input_ids = inputs
|
|
|
|
if input_ids is not None:
|
|
num_choices = shape_list(input_ids)[1]
|
|
seq_length = shape_list(input_ids)[2]
|
|
else:
|
|
num_choices = shape_list(inputs_embeds)[1]
|
|
seq_length = shape_list(inputs_embeds)[2]
|
|
|
|
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
|
|
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
|
|
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
|
|
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
|
|
|
|
flat_inputs = [
|
|
flat_input_ids,
|
|
flat_attention_mask,
|
|
flat_token_type_ids,
|
|
flat_position_ids,
|
|
head_mask,
|
|
inputs_embeds,
|
|
]
|
|
|
|
outputs = self.bert(flat_inputs, training=training)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output, training=training)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = tf.reshape(logits, (-1, num_choices))
|
|
|
|
outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here
|
|
|
|
return outputs # reshaped_logits, (hidden_states), (attentions)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Bert Model with a token classification head on top (a linear layer on top of
|
|
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
|
|
BERT_START_DOCSTRING,
|
|
)
|
|
class TFBertForTokenClassification(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = tf.keras.layers.Dense(
|
|
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
|
|
)
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
|
|
Classification scores (before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForTokenClassification
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForTokenClassification.from_pretrained('bert-base-uncased')
|
|
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
|
|
outputs = model(input_ids)
|
|
scores = outputs[0]
|
|
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output, training=kwargs.get("training", False))
|
|
logits = self.classifier(sequence_output)
|
|
|
|
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
|
|
|
|
return outputs # scores, (hidden_states), (attentions)
|
|
|
|
|
|
@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 TFBertForQuestionAnswering(TFBertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.bert = TFBertMainLayer(config, name="bert")
|
|
self.qa_outputs = tf.keras.layers.Dense(
|
|
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
|
|
)
|
|
|
|
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
|
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
|
|
Span-start scores (before SoftMax).
|
|
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
|
|
Span-end scores (before SoftMax).
|
|
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
|
|
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
|
|
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
|
|
tuple of :obj:`tf.Tensor` (one for each layer) of shape
|
|
:obj:`(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.
|
|
|
|
Examples::
|
|
|
|
import tensorflow as tf
|
|
from transformers import BertTokenizer, TFBertForQuestionAnswering
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = TFBertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
|
|
|
|
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
encoding = tokenizer.encode_plus(question, text)
|
|
input_ids, token_type_ids = encoding["input_ids"], encoding["token_type_ids"]
|
|
start_scores, end_scores = model(tf.constant(input_ids)[None, :], token_type_ids=tf.constant(token_type_ids)[None, :])
|
|
|
|
all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
|
|
answer = ' '.join(all_tokens[tf.math.argmax(tf.squeeze(start_scores)) : tf.math.argmax(tf.squeeze(end_scores))+1])
|
|
assert answer == "a nice puppet"
|
|
|
|
"""
|
|
outputs = self.bert(inputs, **kwargs)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = tf.split(logits, 2, axis=-1)
|
|
start_logits = tf.squeeze(start_logits, axis=-1)
|
|
end_logits = tf.squeeze(end_logits, axis=-1)
|
|
|
|
outputs = (start_logits, end_logits,) + outputs[2:]
|
|
|
|
return outputs # start_logits, end_logits, (hidden_states), (attentions)
|