Source code for transformers.modeling_tf_albert

# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
""" TF 2.0 ALBERT model. """


import logging

import tensorflow as tf

from .configuration_albert import AlbertConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_bert import ACT2FN, TFBertSelfAttention
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list


logger = logging.getLogger(__name__)

TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "albert-base-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v1-tf_model.h5",
    "albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v1-tf_model.h5",
    "albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v1-tf_model.h5",
    "albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v1-tf_model.h5",
    "albert-base-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-tf_model.h5",
    "albert-large-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-tf_model.h5",
    "albert-xlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-tf_model.h5",
    "albert-xxlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-tf_model.h5",
}


class TFAlbertEmbeddings(tf.keras.layers.Layer):
    """Construct the embeddings from word, position and token_type embeddings.
    """

    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.config = config
        self.position_embeddings = tf.keras.layers.Embedding(
            config.max_position_embeddings,
            config.embedding_size,
            embeddings_initializer=get_initializer(self.config.initializer_range),
            name="position_embeddings",
        )
        self.token_type_embeddings = tf.keras.layers.Embedding(
            config.type_vocab_size,
            config.embedding_size,
            embeddings_initializer=get_initializer(self.config.initializer_range),
            name="token_type_embeddings",
        )

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

    def build(self, input_shape):
        """Build shared word embedding layer """
        with tf.name_scope("word_embeddings"):
            # Create and initialize weights. The random normal initializer was chosen
            # arbitrarily, and works well.
            self.word_embeddings = self.add_weight(
                "weight",
                shape=[self.config.vocab_size, self.config.embedding_size],
                initializer=get_initializer(self.config.initializer_range),
            )
        super().build(input_shape)

    def call(self, inputs, mode="embedding", training=False):
        """Get token embeddings of inputs.
        Args:
            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
            mode: string, a valid value is one of "embedding" and "linear".
        Returns:
            outputs: (1) If mode == "embedding", output embedding tensor, float32 with
                shape [batch_size, length, embedding_size]; (2) mode == "linear", output
                linear tensor, float32 with shape [batch_size, length, vocab_size].
        Raises:
            ValueError: if mode is not valid.

        Shared weights logic adapted from
            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
        """
        if mode == "embedding":
            return self._embedding(inputs, training=training)
        elif mode == "linear":
            return self._linear(inputs)
        else:
            raise ValueError("mode {} is not valid.".format(mode))

    def _embedding(self, inputs, training=False):
        """Applies embedding based on inputs tensor."""
        input_ids, position_ids, token_type_ids, inputs_embeds = inputs

        if input_ids is not None:
            input_shape = shape_list(input_ids)
        else:
            input_shape = shape_list(inputs_embeds)[:-1]

        seq_length = input_shape[1]
        if position_ids is None:
            position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
        if token_type_ids is None:
            token_type_ids = tf.fill(input_shape, 0)

        if inputs_embeds is None:
            inputs_embeds = tf.gather(self.word_embeddings, input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings, training=training)
        return embeddings

    def _linear(self, inputs):
        """Computes logits by running inputs through a linear layer.
            Args:
                inputs: A float32 tensor with shape [batch_size, length, embedding_size]
            Returns:
                float32 tensor with shape [batch_size, length, vocab_size].
        """
        batch_size = shape_list(inputs)[0]
        length = shape_list(inputs)[1]
        x = tf.reshape(inputs, [-1, self.config.embedding_size])
        logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
        return tf.reshape(logits, [batch_size, length, self.config.vocab_size])


class TFAlbertSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )
        self.output_attentions = config.output_attentions

        self.num_attention_heads = config.num_attention_heads
        assert config.hidden_size % config.num_attention_heads == 0
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = tf.keras.layers.Dense(
            self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )

        self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, inputs, training=False):
        hidden_states, attention_mask, head_mask = inputs

        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        # scale attention_scores
        dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
        attention_scores = attention_scores / tf.math.sqrt(dk)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in TFAlbertModel call() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = tf.nn.softmax(attention_scores, axis=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = tf.matmul(attention_probs, value_layer)

        context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
        context_layer = tf.reshape(
            context_layer, (batch_size, -1, self.all_head_size)
        )  # (batch_size, seq_len_q, all_head_size)

        outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
        return outputs


class TFAlbertSelfOutput(tf.keras.layers.Layer):
    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"
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

    def call(self, inputs, training=False):
        hidden_states, input_tensor = inputs

        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class TFAlbertAttention(TFBertSelfAttention):
    def __init__(self, config, **kwargs):
        super().__init__(config, **kwargs)

        self.hidden_size = config.hidden_size
        self.dense = tf.keras.layers.Dense(
            config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.pruned_heads = set()

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(self, inputs, training=False):
        input_tensor, attention_mask, head_mask = inputs

        batch_size = shape_list(input_tensor)[0]
        mixed_query_layer = self.query(input_tensor)
        mixed_key_layer = self.key(input_tensor)
        mixed_value_layer = self.value(input_tensor)

        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        # scale attention_scores
        dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
        attention_scores = attention_scores / tf.math.sqrt(dk)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = tf.nn.softmax(attention_scores, axis=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = tf.matmul(attention_probs, value_layer)

        context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
        context_layer = tf.reshape(
            context_layer, (batch_size, -1, self.all_head_size)
        )  # (batch_size, seq_len_q, all_head_size)

        self_outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)

        hidden_states = self_outputs[0]

        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        attention_output = self.LayerNorm(hidden_states + input_tensor)

        # add attentions if we output them
        outputs = (attention_output,) + self_outputs[1:]
        return outputs


class TFAlbertLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFAlbertAttention(config, name="attention")

        self.ffn = tf.keras.layers.Dense(
            config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn"
        )

        if isinstance(config.hidden_act, str):
            self.activation = ACT2FN[config.hidden_act]
        else:
            self.activation = config.hidden_act

        self.ffn_output = tf.keras.layers.Dense(
            config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn_output"
        )
        self.full_layer_layer_norm = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="full_layer_layer_norm"
        )
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

    def call(self, inputs, training=False):
        hidden_states, attention_mask, head_mask = inputs

        attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
        ffn_output = self.ffn(attention_outputs[0])
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)

        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.full_layer_layer_norm(ffn_output + attention_outputs[0])

        # add attentions if we output them
        outputs = (hidden_states,) + attention_outputs[1:]
        return outputs


class TFAlbertLayerGroup(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.albert_layers = [
            TFAlbertLayer(config, name="albert_layers_._{}".format(i)) for i in range(config.inner_group_num)
        ]

    def call(self, inputs, training=False):
        hidden_states, attention_mask, head_mask = inputs

        layer_hidden_states = ()
        layer_attentions = ()

        for layer_index, albert_layer in enumerate(self.albert_layers):
            layer_output = albert_layer([hidden_states, attention_mask, head_mask[layer_index]], training=training)
            hidden_states = layer_output[0]

            if self.output_attentions:
                layer_attentions = layer_attentions + (layer_output[1],)

            if self.output_hidden_states:
                layer_hidden_states = layer_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if self.output_hidden_states:
            outputs = outputs + (layer_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (layer_attentions,)
        # last-layer hidden state, (layer hidden states), (layer attentions)
        return outputs


class TFAlbertTransformer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.config = config
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.embedding_hidden_mapping_in = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="embedding_hidden_mapping_in",
        )
        self.albert_layer_groups = [
            TFAlbertLayerGroup(config, name="albert_layer_groups_._{}".format(i))
            for i in range(config.num_hidden_groups)
        ]

    def call(self, inputs, training=False):
        hidden_states, attention_mask, head_mask = inputs

        hidden_states = self.embedding_hidden_mapping_in(hidden_states)
        all_attentions = ()

        if self.output_hidden_states:
            all_hidden_states = (hidden_states,)

        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],
                ],
                training=training,
            )
            hidden_states = layer_group_output[0]

            if self.output_attentions:
                all_attentions = all_attentions + layer_group_output[-1]

            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (all_attentions,)

        # last-layer hidden state, (all hidden states), (all attentions)
        return outputs


class TFAlbertPreTrainedModel(TFPreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = AlbertConfig
    pretrained_model_archive_map = TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
    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
        hidden_states = hidden_states + self.bias
        return hidden_states


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:`(batch_size, sequence_length)`):
            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.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]``:
            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
        input_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `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.
"""


[docs]@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, **kwargs): super().__init__(config, **kwargs) self.num_hidden_layers = config.num_hidden_layers 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", )
[docs] 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
[docs] @add_start_docstrings_to_callable(ALBERT_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""" Returns: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) 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 Albert 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 AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = TFAlbertModel.from_pretrained('albert-base-v2') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[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 """ 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 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[:, 0]) # add hidden_states and attentions if they are here outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] # sequence_output, pooled_output, (hidden_states), (attentions) return outputs
[docs]@add_start_docstrings("""Albert Model with a `language modeling` head on top. """, ALBERT_START_DOCSTRING) class TFAlbertForMaskedLM(TFAlbertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super(TFAlbertForMaskedLM, self).__init__(config, *inputs, **kwargs) self.albert = TFAlbertModel(config, name="albert") self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
[docs] def get_output_embeddings(self): return self.albert.embeddings
[docs] @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING) def call(self, inputs, **kwargs): r""" Returns: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) 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 AlbertTokenizer, TFAlbertForMaskedLM tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = TFAlbertForMaskedLM.from_pretrained('albert-base-v2') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 outputs = model(input_ids) prediction_scores = outputs[0] """ outputs = self.albert(inputs, **kwargs) sequence_output = outputs[0] prediction_scores = self.predictions(sequence_output, training=kwargs.get("training", False)) # Add hidden states and attention if they are here outputs = (prediction_scores,) + outputs[2:] return outputs # prediction_scores, (hidden_states), (attentions)
[docs]@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): def __init__(self, config, *inputs, **kwargs): super(TFAlbertForSequenceClassification, self).__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.albert = TFAlbertModel(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" )
[docs] @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING) def call(self, inputs, **kwargs): r""" Returns: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) 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 AlbertTokenizer, TFAlbertForSequenceClassification tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 outputs = model(input_ids) logits = outputs[0] """ outputs = self.albert(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)