d4c2cb402d
* Kill model archive maps * Fixup * Also kill model_archive_map for MaskedBertPreTrainedModel * Unhook config_archive_map * Tokenizers: align with model id changes * make style && make quality * Fix CI
510 lines
24 KiB
Python
510 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" TF 2.0 RoBERTa model. """
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import logging
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import tensorflow as tf
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from .configuration_roberta import RobertaConfig
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from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
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from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu
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from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
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logger = logging.getLogger(__name__)
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TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"roberta-base",
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"roberta-large",
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"roberta-large-mnli",
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"distilroberta-base",
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# See all RoBERTa models at https://huggingface.co/models?filter=roberta
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]
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class TFRobertaEmbeddings(TFBertEmbeddings):
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"""
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Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
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"""
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def __init__(self, config, **kwargs):
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super().__init__(config, **kwargs)
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self.padding_idx = 1
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def create_position_ids_from_input_ids(self, x):
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""" Replace non-padding symbols with their position numbers. Position numbers begin at
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padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
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`utils.make_positions`.
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:param tf.Tensor x:
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:return tf.Tensor:
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"""
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mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
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incremental_indicies = tf.math.cumsum(mask, axis=1) * mask
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return incremental_indicies + self.padding_idx
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def create_position_ids_from_inputs_embeds(self, inputs_embeds):
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""" We are provided embeddings directly. We cannot infer which are padded so just generate
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sequential position ids.
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:param tf.Tensor inputs_embeds:
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:return tf.Tensor:
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"""
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seq_length = shape_list(inputs_embeds)[1]
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position_ids = tf.range(self.padding_idx + 1, seq_length + self.padding_idx + 1, dtype=tf.int32)[tf.newaxis, :]
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return position_ids
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def _embedding(self, inputs, training=False):
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"""Applies embedding based on inputs tensor."""
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input_ids, position_ids, token_type_ids, inputs_embeds = inputs
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if position_ids is None:
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if input_ids is not None:
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# Create the position ids from the input token ids. Any padded tokens remain padded.
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position_ids = self.create_position_ids_from_input_ids(input_ids)
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else:
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position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
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return super()._embedding([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
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class TFRobertaMainLayer(TFBertMainLayer):
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"""
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Same as TFBertMainLayer but uses TFRobertaEmbeddings.
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"""
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def __init__(self, config, **kwargs):
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super().__init__(config, **kwargs)
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self.embeddings = TFRobertaEmbeddings(config, name="embeddings")
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def get_input_embeddings(self):
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return self.embeddings
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class TFRobertaPreTrainedModel(TFPreTrainedModel):
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""" An abstract class to handle weights initialization and
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a simple interface for downloading and loading pretrained models.
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"""
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config_class = RobertaConfig
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base_model_prefix = "roberta"
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ROBERTA_START_DOCSTRING = r"""
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This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
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Use it as a regular TF 2.0 Keras Model and
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refer to the TF 2.0 documentation for all matter related to general usage and behavior.
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.. note::
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TF 2.0 models accepts two formats as inputs:
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- having all inputs as keyword arguments (like PyTorch models), or
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- having all inputs as a list, tuple or dict in the first positional arguments.
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This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
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all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
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If you choose this second option, there are three possibilities you can use to gather all the input Tensors
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in the first positional argument :
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- a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
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- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
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- a dictionary with one or several input Tensors associated to the input names given in the docstring:
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:obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
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Parameters:
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config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
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Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
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"""
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ROBERTA_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary.
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Indices can be obtained using :class:`transformers.RobertaTokenizer`.
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See :func:`transformers.PreTrainedTokenizer.encode` and
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:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
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`What are input IDs? <../glossary.html#input-ids>`__
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attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
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Mask to avoid performing attention on padding token indices.
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Mask values selected in ``[0, 1]``:
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``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
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`What are attention masks? <../glossary.html#attention-mask>`__
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token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
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Segment token indices to indicate first and second portions of the inputs.
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Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
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corresponds to a `sentence B` token
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`What are token type IDs? <../glossary.html#token-type-ids>`__
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position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
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Indices of positions of each input sequence tokens in the position embeddings.
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Selected in the range ``[0, config.max_position_embeddings - 1]``.
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`What are position IDs? <../glossary.html#position-ids>`__
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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`):
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Mask to nullify selected heads of the self-attention modules.
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Mask values selected in ``[0, 1]``:
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:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
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inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`, defaults to :obj:`None`):
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Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert `input_ids` indices into associated vectors
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than the model's internal embedding lookup matrix.
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training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
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Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
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(if set to :obj:`False`) for evaluation.
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"""
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@add_start_docstrings(
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"The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
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ROBERTA_START_DOCSTRING,
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)
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class TFRobertaModel(TFRobertaPreTrainedModel):
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def __init__(self, config, *inputs, **kwargs):
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super().__init__(config, *inputs, **kwargs)
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self.roberta = TFRobertaMainLayer(config, name="roberta")
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@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
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def call(self, inputs, **kwargs):
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r"""
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Returns:
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:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
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last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token)
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further processed by a Linear layer and a Tanh activation function. The Linear
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layer weights are trained from the next sentence prediction (classification)
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objective during Bert pretraining. This output is usually *not* a good summary
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of the semantic content of the input, you're often better with averaging or pooling
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the sequence of hidden-states for the whole input sequence.
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hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
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tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
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of shape :obj:`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
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tuple of :obj:`tf.Tensor` (one for each layer) of shape
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:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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import tensorflow as tf
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from transformers import RobertaTokenizer, TFRobertaModel
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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = TFRobertaModel.from_pretrained('roberta-base')
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input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
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outputs = model(input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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"""
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outputs = self.roberta(inputs, **kwargs)
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return outputs
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class TFRobertaLMHead(tf.keras.layers.Layer):
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"""Roberta Head for masked language modeling."""
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def __init__(self, config, input_embeddings, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = config.vocab_size
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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.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
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self.act = tf.keras.layers.Activation(gelu)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = input_embeddings
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def build(self, input_shape):
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self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
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super().build(input_shape)
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def call(self, features):
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x = self.dense(features)
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x = self.act(x)
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x = self.layer_norm(x)
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# project back to size of vocabulary with bias
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x = self.decoder(x, mode="linear") + self.bias
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return x
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@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
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class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
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def __init__(self, config, *inputs, **kwargs):
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super().__init__(config, *inputs, **kwargs)
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self.roberta = TFRobertaMainLayer(config, name="roberta")
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self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
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def get_output_embeddings(self):
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return self.lm_head.decoder
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@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
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def call(self, inputs, **kwargs):
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r"""
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Return:
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:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
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prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
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tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
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of shape :obj:`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
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tuple of :obj:`tf.Tensor` (one for each layer) of shape
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:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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import tensorflow as tf
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from transformers import RobertaTokenizer, TFRobertaForMaskedLM
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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
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input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
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outputs = model(input_ids)
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prediction_scores = outputs[0]
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"""
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outputs = self.roberta(inputs, **kwargs)
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sequence_output = outputs[0]
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prediction_scores = self.lm_head(sequence_output)
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outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
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return outputs # prediction_scores, (hidden_states), (attentions)
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class TFRobertaClassificationHead(tf.keras.layers.Layer):
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"""Head for sentence-level classification tasks."""
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def __init__(self, config, **kwargs):
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super().__init__(config, **kwargs)
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self.dense = tf.keras.layers.Dense(
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config.hidden_size,
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kernel_initializer=get_initializer(config.initializer_range),
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activation="tanh",
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name="dense",
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)
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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self.out_proj = tf.keras.layers.Dense(
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config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
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)
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def call(self, features, training=False):
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x = features[:, 0, :] # take <s> token (equiv. to [CLS])
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x = self.dropout(x, training=training)
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x = self.dense(x)
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x = self.dropout(x, training=training)
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x = self.out_proj(x)
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return x
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@add_start_docstrings(
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"""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
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on top of the pooled output) e.g. for GLUE tasks. """,
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ROBERTA_START_DOCSTRING,
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)
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class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
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def __init__(self, config, *inputs, **kwargs):
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super().__init__(config, *inputs, **kwargs)
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self.num_labels = config.num_labels
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self.roberta = TFRobertaMainLayer(config, name="roberta")
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self.classifier = TFRobertaClassificationHead(config, name="classifier")
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@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
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def call(self, inputs, **kwargs):
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r"""
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Return:
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:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
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logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
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Classification (or regression if config.num_labels==1) scores (before SoftMax).
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hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
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tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
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of shape :obj:`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
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tuple of :obj:`tf.Tensor` (one for each layer) of shape
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:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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import tensorflow as tf
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from transformers import RobertaTokenizer, TFRobertaForSequenceClassification
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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = TFRobertaForSequenceClassification.from_pretrained('roberta-base')
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input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
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labels = tf.constant([1])[None, :] # Batch size 1
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outputs = model(input_ids)
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logits = outputs[0]
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"""
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outputs = self.roberta(inputs, **kwargs)
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sequence_output = outputs[0]
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logits = self.classifier(sequence_output, training=kwargs.get("training", False))
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outputs = (logits,) + outputs[2:]
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return outputs # logits, (hidden_states), (attentions)
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@add_start_docstrings(
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"""RoBERTa Model with a token classification head on top (a linear layer on top of
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the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
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ROBERTA_START_DOCSTRING,
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)
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class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
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def __init__(self, config, *inputs, **kwargs):
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super().__init__(config, *inputs, **kwargs)
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self.num_labels = config.num_labels
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self.roberta = TFRobertaMainLayer(config, name="roberta")
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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self.classifier = tf.keras.layers.Dense(
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config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
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)
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@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
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def call(self, inputs, **kwargs):
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r"""
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Return:
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:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
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scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
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Classification scores (before SoftMax).
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hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
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tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
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of shape :obj:`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
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tuple of :obj:`tf.Tensor` (one for each layer) of shape
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:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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import tensorflow as tf
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from transformers import RobertaTokenizer, TFRobertaForTokenClassification
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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = TFRobertaForTokenClassification.from_pretrained('roberta-base')
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input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
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outputs = model(input_ids)
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scores = outputs[0]
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"""
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outputs = self.roberta(inputs, **kwargs)
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sequence_output = outputs[0]
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sequence_output = self.dropout(sequence_output, training=kwargs.get("training", False))
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logits = self.classifier(sequence_output)
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outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
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return outputs # scores, (hidden_states), (attentions)
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@add_start_docstrings(
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"""RoBERTa 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`). """,
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ROBERTA_START_DOCSTRING,
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)
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class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel):
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def __init__(self, config, *inputs, **kwargs):
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super().__init__(config, *inputs, **kwargs)
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self.num_labels = config.num_labels
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|
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self.roberta = TFRobertaMainLayer(config, name="roberta")
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self.qa_outputs = tf.keras.layers.Dense(
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config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
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)
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@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
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def call(self, inputs, **kwargs):
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r"""
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|
Return:
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:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
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|
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
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|
Span-start scores (before SoftMax).
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|
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
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|
Span-end scores (before SoftMax).
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|
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::
|
|
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|
# The checkpoint roberta-base is not fine-tuned for question answering. Please see the
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|
# examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
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|
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|
import tensorflow as tf
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|
from transformers import RobertaTokenizer, TFRobertaForQuestionAnswering
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
|
|
model = TFRobertaForQuestionAnswering.from_pretrained('roberta-base')
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|
input_ids = tokenizer.encode("Who was Jim Henson?", "Jim Henson was a nice puppet")
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|
start_scores, end_scores = model(tf.constant(input_ids)[None, :]) # Batch size 1
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|
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|
all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
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|
answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
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|
|
|
"""
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|
outputs = self.roberta(inputs, **kwargs)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
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|
start_logits, end_logits = tf.split(logits, 2, axis=-1)
|
|
start_logits = tf.squeeze(start_logits, axis=-1)
|
|
end_logits = tf.squeeze(end_logits, axis=-1)
|
|
|
|
outputs = (start_logits, end_logits,) + outputs[2:]
|
|
|
|
return outputs # start_logits, end_logits, (hidden_states), (attentions)
|