48ff6d5109
* remove the implied defaults to :obj:`None` * fix bug in the original * replace to :obj:`True`, :obj:`False`
1337 lines
56 KiB
Python
1337 lines
56 KiB
Python
# coding=utf-8
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# Copyright 2018 The OpenAI Team Authors and 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 ALBERT model. """
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import tensorflow as tf
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from .configuration_albert import AlbertConfig
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from .file_utils import (
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MULTIPLE_CHOICE_DUMMY_INPUTS,
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ModelOutput,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_callable,
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replace_return_docstrings,
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)
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from .modeling_tf_bert import ACT2FN, TFBertSelfAttention
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from .modeling_tf_outputs import (
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TFBaseModelOutput,
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TFBaseModelOutputWithPooling,
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TFMaskedLMOutput,
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TFMultipleChoiceModelOutput,
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TFQuestionAnsweringModelOutput,
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TFSequenceClassifierOutput,
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TFTokenClassifierOutput,
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)
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from .modeling_tf_utils import (
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TFMaskedLanguageModelingLoss,
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TFMultipleChoiceLoss,
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TFPreTrainedModel,
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TFQuestionAnsweringLoss,
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TFSequenceClassificationLoss,
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TFTokenClassificationLoss,
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get_initializer,
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keras_serializable,
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shape_list,
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)
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from .tokenization_utils import BatchEncoding
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from .utils import logging
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "AlbertConfig"
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_TOKENIZER_FOR_DOC = "AlbertTokenizer"
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TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"albert-base-v1",
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"albert-large-v1",
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"albert-xlarge-v1",
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"albert-xxlarge-v1",
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"albert-base-v2",
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"albert-large-v2",
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"albert-xlarge-v2",
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"albert-xxlarge-v2",
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# See all ALBERT models at https://huggingface.co/models?filter=albert
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]
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class TFAlbertEmbeddings(tf.keras.layers.Layer):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.config = config
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self.vocab_size = config.vocab_size
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self.position_embeddings = tf.keras.layers.Embedding(
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config.max_position_embeddings,
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config.embedding_size,
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embeddings_initializer=get_initializer(self.config.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.embedding_size,
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embeddings_initializer=get_initializer(self.config.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.config.vocab_size, self.config.embedding_size],
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initializer=get_initializer(self.config.initializer_range),
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)
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super().build(input_shape)
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def call(
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self,
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input_ids=None,
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position_ids=None,
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token_type_ids=None,
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inputs_embeds=None,
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mode="embedding",
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training=False,
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):
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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(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
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elif mode == "linear":
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return self._linear(input_ids)
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else:
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raise ValueError("mode {} is not valid.".format(mode))
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def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
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"""Applies embedding based on inputs tensor."""
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assert not (input_ids is None and inputs_embeds is None)
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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, embedding_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.config.embedding_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.config.vocab_size])
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class TFAlbertSelfAttention(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.num_attention_heads = config.num_attention_heads
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assert (
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config.hidden_size % config.num_attention_heads == 0
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), f"Hidden size {config.hidden_size} not dividable by number of 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.output_attentions = config.output_attentions
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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, hidden_states, attention_mask, head_mask, output_attentions, training=False):
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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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# (batch size, num_heads, seq_len_q, seq_len_k)
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attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
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# scale attention_scores
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dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
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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 TFAlbertModel 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 output_attentions else (context_layer,)
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return outputs
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class TFAlbertSelfOutput(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, hidden_states, input_tensor, training=False):
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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 TFAlbertAttention(TFBertSelfAttention):
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""" Contains the complete attention sublayer, including both dropouts and layer norm. """
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def __init__(self, config, **kwargs):
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super().__init__(config, **kwargs)
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self.hidden_size = config.hidden_size
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self.output_attentions = config.output_attentions
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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.pruned_heads = set()
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# Two different dropout probabilities; see https://github.com/google-research/albert/blob/master/modeling.py#L971-L993
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self.attention_dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
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self.output_dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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def prune_heads(self, heads):
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raise NotImplementedError
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def call(self, input_tensor, attention_mask, head_mask, output_attentions, training=False):
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batch_size = shape_list(input_tensor)[0]
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mixed_query_layer = self.query(input_tensor)
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mixed_key_layer = self.key(input_tensor)
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mixed_value_layer = self.value(input_tensor)
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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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# (batch size, num_heads, seq_len_q, seq_len_k)
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attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
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# scale attention_scores
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dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
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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.attention_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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self_outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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hidden_states = self_outputs[0]
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hidden_states = self.dense(hidden_states)
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hidden_states = self.output_dropout(hidden_states, training=training)
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attention_output = self.LayerNorm(hidden_states + input_tensor)
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# add attentions if we output them
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outputs = (attention_output,) + self_outputs[1:]
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return outputs
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class TFAlbertLayer(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 = TFAlbertAttention(config, name="attention")
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self.ffn = tf.keras.layers.Dense(
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config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn"
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)
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if isinstance(config.hidden_act, str):
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self.activation = ACT2FN[config.hidden_act]
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else:
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self.activation = config.hidden_act
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self.ffn_output = tf.keras.layers.Dense(
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config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn_output"
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)
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self.full_layer_layer_norm = tf.keras.layers.LayerNormalization(
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epsilon=config.layer_norm_eps, name="full_layer_layer_norm"
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)
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
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attention_outputs = self.attention(
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hidden_states, attention_mask, head_mask, output_attentions, training=training
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)
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ffn_output = self.ffn(attention_outputs[0])
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ffn_output = self.activation(ffn_output)
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ffn_output = self.ffn_output(ffn_output)
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ffn_output = self.dropout(ffn_output, training=training)
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hidden_states = self.full_layer_layer_norm(ffn_output + attention_outputs[0])
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# add attentions if we output them
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outputs = (hidden_states,) + attention_outputs[1:]
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return outputs
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class TFAlbertLayerGroup(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.albert_layers = [
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TFAlbertLayer(config, name="albert_layers_._{}".format(i)) for i in range(config.inner_group_num)
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]
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def call(self, hidden_states, attention_mask, head_mask, output_attentions, output_hidden_states, training=False):
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layer_hidden_states = ()
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layer_attentions = ()
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for layer_index, albert_layer in enumerate(self.albert_layers):
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layer_output = albert_layer(
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hidden_states, attention_mask, head_mask[layer_index], output_attentions, training=training
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)
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hidden_states = layer_output[0]
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if output_attentions:
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layer_attentions = layer_attentions + (layer_output[1],)
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if output_hidden_states:
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layer_hidden_states = layer_hidden_states + (hidden_states,)
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outputs = (hidden_states,)
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if output_hidden_states:
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outputs = outputs + (layer_hidden_states,)
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if output_attentions:
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outputs = outputs + (layer_attentions,)
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# last-layer hidden state, (layer hidden states), (layer attentions)
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return outputs
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class TFAlbertTransformer(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.config = config
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self.embedding_hidden_mapping_in = 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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name="embedding_hidden_mapping_in",
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)
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self.albert_layer_groups = [
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TFAlbertLayerGroup(config, name="albert_layer_groups_._{}".format(i))
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for i in range(config.num_hidden_groups)
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]
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def call(
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self,
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hidden_states,
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attention_mask,
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head_mask,
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output_attentions,
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output_hidden_states,
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return_dict,
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training=False,
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):
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hidden_states = self.embedding_hidden_mapping_in(hidden_states)
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all_attentions = () if output_attentions else None
|
|
all_hidden_states = (hidden_states,) if output_hidden_states else None
|
|
|
|
for i in range(self.config.num_hidden_layers):
|
|
# Number of layers in a hidden group
|
|
layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
|
|
|
|
# Index of the hidden group
|
|
group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
|
|
|
|
layer_group_output = self.albert_layer_groups[group_idx](
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
|
|
output_attentions,
|
|
output_hidden_states,
|
|
training=training,
|
|
)
|
|
hidden_states = layer_group_output[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + layer_group_output[-1]
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
|
|
return TFBaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
class TFAlbertPreTrainedModel(TFPreTrainedModel):
|
|
"""An abstract class to handle weights initialization and
|
|
a simple interface for downloading and loading pretrained models.
|
|
"""
|
|
|
|
config_class = AlbertConfig
|
|
base_model_prefix = "albert"
|
|
|
|
|
|
class TFAlbertMLMHead(tf.keras.layers.Layer):
|
|
def __init__(self, config, input_embeddings, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.dense = tf.keras.layers.Dense(
|
|
config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
|
)
|
|
if isinstance(config.hidden_act, str):
|
|
self.activation = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.activation = config.hidden_act
|
|
|
|
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder = input_embeddings
|
|
|
|
def build(self, input_shape):
|
|
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
|
|
self.decoder_bias = self.add_weight(
|
|
shape=(self.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
|
|
)
|
|
super().build(input_shape)
|
|
|
|
def call(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
hidden_states = self.decoder(hidden_states, mode="linear") + self.decoder_bias
|
|
return hidden_states
|
|
|
|
|
|
@keras_serializable
|
|
class TFAlbertMainLayer(tf.keras.layers.Layer):
|
|
config_class = AlbertConfig
|
|
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.num_hidden_layers = config.num_hidden_layers
|
|
self.output_attentions = config.output_attentions
|
|
self.output_hidden_states = config.output_hidden_states
|
|
self.return_dict = config.use_return_dict
|
|
|
|
self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
|
|
self.encoder = TFAlbertTransformer(config, name="encoder")
|
|
self.pooler = tf.keras.layers.Dense(
|
|
config.hidden_size,
|
|
kernel_initializer=get_initializer(config.initializer_range),
|
|
activation="tanh",
|
|
name="pooler",
|
|
)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
self.embeddings.vocab_size = value.shape[0]
|
|
|
|
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,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=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
|
|
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
|
|
output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
|
|
return_dict = inputs[8] if len(inputs) > 8 else return_dict
|
|
assert len(inputs) <= 9, "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)
|
|
output_attentions = inputs.get("output_attentions", output_attentions)
|
|
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
|
|
return_dict = inputs.get("return_dict", return_dict)
|
|
assert len(inputs) <= 9, "Too many inputs."
|
|
else:
|
|
input_ids = inputs
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.output_attentions
|
|
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
|
|
return_dict = return_dict if return_dict is not None else self.return_dict
|
|
|
|
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,
|
|
output_attentions,
|
|
output_hidden_states,
|
|
return_dict,
|
|
training=training,
|
|
)
|
|
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output[:, 0])
|
|
|
|
if not return_dict:
|
|
return (
|
|
sequence_output,
|
|
pooled_output,
|
|
) + encoder_outputs[1:]
|
|
|
|
return TFBaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class TFAlbertForPreTrainingOutput(ModelOutput):
|
|
"""
|
|
Output type of :class:`~transformers.TFAlbertForPreTrainingModel`.
|
|
|
|
Args:
|
|
prediction_logits (: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).
|
|
sop_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 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 ``output_hidden_states=True`` is passed or when ``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 ``output_attentions=True`` is passed or 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.
|
|
"""
|
|
|
|
prediction_logits: tf.Tensor = None
|
|
sop_logits: tf.Tensor = None
|
|
hidden_states: Optional[Tuple[tf.Tensor]] = None
|
|
attentions: Optional[Tuple[tf.Tensor]] = None
|
|
|
|
|
|
ALBERT_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.
|
|
|
|
.. _`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`:
|
|
https://arxiv.org/abs/1909.11942
|
|
|
|
.. _`tf.keras.Model`:
|
|
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
|
|
|
|
.. 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})`
|
|
|
|
Args:
|
|
config (:class:`~transformers.AlbertConfig`): 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.
|
|
"""
|
|
|
|
ALBERT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using :class:`transformers.AlbertTokenizer`.
|
|
See :func:`transformers.PreTrainedTokenizer.encode` and
|
|
:func:`transformers.PreTrainedTokenizer.__call__` for details.
|
|
|
|
`What are input IDs? <../glossary.html#input-ids>`__
|
|
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
|
|
Mask to avoid performing attention on padding token indices.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
|
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
|
|
Segment token indices to indicate first and second portions of the inputs.
|
|
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
|
|
corresponds to a `sentence B` token
|
|
|
|
`What are token type IDs? <../glossary.html#token-type-ids>`_
|
|
position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
|
|
Indices of positions of each input sequence tokens in the position embeddings.
|
|
Selected in the range ``[0, config.max_position_embeddings - 1]``.
|
|
|
|
`What are position IDs? <../glossary.html#position-ids>`_
|
|
head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
|
|
Mask to nullify selected heads of the self-attention modules.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
|
|
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
|
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `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.
|
|
output_attentions (:obj:`bool`, `optional`):
|
|
If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
|
|
output_hidden_states (:obj:`bool`, `optional`):
|
|
If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
|
|
return_dict (:obj:`bool`, `optional`):
|
|
If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
|
|
plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Albert Model transformer outputing raw hidden-states without any specific head on top.",
|
|
ALBERT_START_DOCSTRING,
|
|
)
|
|
class TFAlbertModel(TFAlbertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
|
|
@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="albert-base-v2",
|
|
output_type=TFBaseModelOutputWithPooling,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def call(self, inputs, **kwargs):
|
|
outputs = self.albert(inputs, **kwargs)
|
|
return outputs
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Albert Model with two heads on top for pre-training:
|
|
a `masked language modeling` head and a `sentence order prediction` (classification) head. """,
|
|
ALBERT_START_DOCSTRING,
|
|
)
|
|
class TFAlbertForPreTraining(TFAlbertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
|
|
self.sop_classifier = TFAlbertSOPHead(config, name="sop_classifier")
|
|
|
|
def get_output_embeddings(self):
|
|
return self.albert.embeddings
|
|
|
|
@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
|
|
def call(self, inputs, **kwargs):
|
|
r"""
|
|
Return:
|
|
|
|
Examples::
|
|
import tensorflow as tf
|
|
from transformers import AlbertTokenizer, TFAlbertForPreTraining
|
|
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
|
|
model = TFAlbertForPreTraining.from_pretrained('albert-base-v2')
|
|
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, sop_scores = outputs[:2]
|
|
"""
|
|
return_dict = kwargs.get("return_dict")
|
|
return_dict = return_dict if return_dict is not None else self.albert.return_dict
|
|
outputs = self.albert(inputs, **kwargs)
|
|
sequence_output, pooled_output = outputs[:2]
|
|
prediction_scores = self.predictions(sequence_output)
|
|
sop_scores = self.sop_classifier(pooled_output, training=kwargs.get("training", False))
|
|
|
|
if not return_dict:
|
|
return (prediction_scores, sop_scores) + outputs[2:]
|
|
|
|
return TFAlbertForPreTrainingOutput(
|
|
prediction_logits=prediction_scores,
|
|
sop_logits=sop_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class TFAlbertSOPHead(tf.keras.layers.Layer):
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
self.dropout = tf.keras.layers.Dropout(config.classifier_dropout_prob)
|
|
self.classifier = tf.keras.layers.Dense(
|
|
config.num_labels,
|
|
kernel_initializer=get_initializer(config.initializer_range),
|
|
name="classifier",
|
|
)
|
|
|
|
def call(self, pooled_output, training: bool):
|
|
dropout_pooled_output = self.dropout(pooled_output, training=training)
|
|
logits = self.classifier(dropout_pooled_output)
|
|
return logits
|
|
|
|
|
|
@add_start_docstrings("""Albert Model with a `language modeling` head on top. """, ALBERT_START_DOCSTRING)
|
|
class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
|
|
|
|
def get_output_embeddings(self):
|
|
return self.albert.embeddings
|
|
|
|
@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="albert-base-v2",
|
|
output_type=TFMaskedLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def call(
|
|
self,
|
|
inputs=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
labels=None,
|
|
training=False,
|
|
):
|
|
r"""
|
|
labels (:obj::obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Labels for computing the masked language modeling loss.
|
|
Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
|
|
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
|
|
in ``[0, ..., config.vocab_size]``
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.albert.return_dict
|
|
if isinstance(inputs, (tuple, list)):
|
|
labels = inputs[9] if len(inputs) > 9 else labels
|
|
if len(inputs) > 9:
|
|
inputs = inputs[:9]
|
|
elif isinstance(inputs, (dict, BatchEncoding)):
|
|
labels = inputs.pop("labels", labels)
|
|
|
|
outputs = self.albert(
|
|
inputs,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
training=training,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.predictions(sequence_output, training=training)
|
|
|
|
loss = None if labels is None else self.compute_loss(labels, prediction_scores)
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TFMaskedLMOutput(
|
|
loss=loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of
|
|
the pooled output) e.g. for GLUE tasks. """,
|
|
ALBERT_START_DOCSTRING,
|
|
)
|
|
class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClassificationLoss):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
self.dropout = tf.keras.layers.Dropout(config.classifier_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(ALBERT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="albert-base-v2",
|
|
output_type=TFSequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def call(
|
|
self,
|
|
inputs=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
labels=None,
|
|
training=False,
|
|
):
|
|
r"""
|
|
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
|
|
Labels for computing the sequence classification/regression loss.
|
|
Indices should be in ``[0, ..., config.num_labels - 1]``.
|
|
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
|
|
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.albert.return_dict
|
|
if isinstance(inputs, (tuple, list)):
|
|
labels = inputs[9] if len(inputs) > 9 else labels
|
|
if len(inputs) > 9:
|
|
inputs = inputs[:9]
|
|
elif isinstance(inputs, (dict, BatchEncoding)):
|
|
labels = inputs.pop("labels", labels)
|
|
|
|
outputs = self.albert(
|
|
inputs,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
training=training,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output, training=training)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None if labels is None else self.compute_loss(labels, logits)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TFSequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Albert 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. """,
|
|
ALBERT_START_DOCSTRING,
|
|
)
|
|
class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
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(ALBERT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="albert-base-v2",
|
|
output_type=TFTokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def call(
|
|
self,
|
|
inputs=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
labels=None,
|
|
training=False,
|
|
):
|
|
r"""
|
|
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Labels for computing the token classification loss.
|
|
Indices should be in ``[0, ..., config.num_labels - 1]``.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.albert.return_dict
|
|
if isinstance(inputs, (tuple, list)):
|
|
labels = inputs[9] if len(inputs) > 9 else labels
|
|
if len(inputs) > 9:
|
|
inputs = inputs[:9]
|
|
elif isinstance(inputs, (dict, BatchEncoding)):
|
|
labels = inputs.pop("labels", labels)
|
|
|
|
outputs = self.albert(
|
|
inputs,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
training=training,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output, training=training)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None if labels is None else self.compute_loss(labels, logits)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TFTokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Albert 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`). """,
|
|
ALBERT_START_DOCSTRING,
|
|
)
|
|
class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
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(ALBERT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="albert-base-v2",
|
|
output_type=TFQuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def call(
|
|
self,
|
|
inputs=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
start_positions=None,
|
|
end_positions=None,
|
|
training=False,
|
|
):
|
|
r"""
|
|
start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`).
|
|
Position outside of the sequence are not taken into account for computing the loss.
|
|
end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`).
|
|
Position outside of the sequence are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.albert.return_dict
|
|
if isinstance(inputs, (tuple, list)):
|
|
start_positions = inputs[9] if len(inputs) > 9 else start_positions
|
|
end_positions = inputs[10] if len(inputs) > 10 else end_positions
|
|
if len(inputs) > 9:
|
|
inputs = inputs[:9]
|
|
elif isinstance(inputs, (dict, BatchEncoding)):
|
|
start_positions = inputs.pop("start_positions", start_positions)
|
|
end_positions = inputs.pop("end_positions", start_positions)
|
|
|
|
outputs = self.albert(
|
|
inputs,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
training=training,
|
|
)
|
|
|
|
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)
|
|
|
|
loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
labels = {"start_position": start_positions}
|
|
labels["end_position"] = end_positions
|
|
loss = self.compute_loss(labels, (start_logits, end_logits))
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TFQuestionAnsweringModelOutput(
|
|
loss=loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Albert 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. """,
|
|
ALBERT_START_DOCSTRING,
|
|
)
|
|
class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.albert = TFAlbertMainLayer(config, name="albert")
|
|
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(ALBERT_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="albert-base-v2",
|
|
output_type=TFMultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def call(
|
|
self,
|
|
inputs,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
labels=None,
|
|
training=False,
|
|
):
|
|
r"""
|
|
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
|
|
Labels for computing the multiple choice classification loss.
|
|
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
|
|
of the input tensors. (see `input_ids` above)
|
|
"""
|
|
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
|
|
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
|
|
output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
|
|
return_dict = inputs[8] if len(inputs) > 8 else return_dict
|
|
labels = inputs[9] if len(inputs) > 9 else labels
|
|
assert len(inputs) <= 10, "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)
|
|
output_attentions = inputs.get("output_attentions", output_attentions)
|
|
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
|
|
return_dict = inputs.get("return_dict", return_dict)
|
|
labels = inputs.get("labels", labels)
|
|
assert len(inputs) <= 10, "Too many inputs."
|
|
else:
|
|
input_ids = inputs
|
|
return_dict = return_dict if return_dict is not None else self.albert.return_dict
|
|
|
|
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_embeds = (
|
|
tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.albert(
|
|
flat_input_ids,
|
|
flat_attention_mask,
|
|
flat_token_type_ids,
|
|
flat_position_ids,
|
|
head_mask,
|
|
flat_inputs_embeds,
|
|
output_attentions,
|
|
output_hidden_states,
|
|
return_dict=return_dict,
|
|
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))
|
|
|
|
loss = None if labels is None else self.compute_loss(labels, reshaped_logits)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TFMultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|