[BIG] pytorch-transformers => transformers
This commit is contained in:
thomwolf
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transformers/modeling_roberta.py
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466
transformers/modeling_roberta.py
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# 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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"""PyTorch RoBERTa model. """
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import logging
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import torch
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import torch.nn as nn
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from torch.nn import CrossEntropyLoss, MSELoss
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from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
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from .configuration_roberta import RobertaConfig
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from .file_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
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'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
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'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
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}
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class RobertaEmbeddings(BertEmbeddings):
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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):
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super(RobertaEmbeddings, self).__init__(config)
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self.padding_idx = 1
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def forward(self, input_ids, token_type_ids=None, position_ids=None):
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seq_length = input_ids.size(1)
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if position_ids is None:
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# Position numbers begin at padding_idx+1. Padding symbols are ignored.
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# cf. fairseq's `utils.make_positions`
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position_ids = torch.arange(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=torch.long, device=input_ids.device)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
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return super(RobertaEmbeddings, self).forward(input_ids,
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token_type_ids=token_type_ids,
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position_ids=position_ids)
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ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
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`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
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by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
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Veselin Stoyanov. It is based on Google's BERT model released in 2018.
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It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
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objective and training with much larger mini-batches and learning rates.
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This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
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models.
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This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
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refer to the PyTorch documentation for all matter related to general usage and behavior.
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.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`:
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https://arxiv.org/abs/1907.11692
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.. _`torch.nn.Module`:
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https://pytorch.org/docs/stable/nn.html#module
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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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Inputs:
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**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Indices of input sequence tokens in the vocabulary.
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To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
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(a) For sequence pairs:
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``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
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(b) For single sequences:
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``tokens: <s> the dog is hairy . </s>``
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Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
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the ``add_special_tokens`` parameter set to ``True``.
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RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
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the right rather than the left.
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See :func:`transformers.PreTrainedTokenizer.encode` and
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:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
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**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
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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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**token_type_ids**: (`optional` need to be trained) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Optional segment token indices to indicate first and second portions of the inputs.
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This embedding matrice is not trained (not pretrained during RoBERTa pretraining), you will have to train it
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during finetuning.
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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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(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
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**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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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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**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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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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``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
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"""
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@add_start_docstrings("The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
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ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
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class RobertaModel(BertModel):
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r"""
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Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
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**last_hidden_state**: ``torch.FloatTensor`` of shape ``(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**: ``torch.FloatTensor`` of shape ``(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**: (`optional`, returned when ``config.output_hidden_states=True``)
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list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``)
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(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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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = RobertaModel.from_pretrained('roberta-base')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # 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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config_class = RobertaConfig
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pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
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base_model_prefix = "roberta"
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def __init__(self, config):
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super(RobertaModel, self).__init__(config)
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self.embeddings = RobertaEmbeddings(config)
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self.init_weights()
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def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
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if input_ids[:, 0].sum().item() != 0:
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logger.warning("A sequence with no special tokens has been passed to the RoBERTa model. "
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"This model requires special tokens in order to work. "
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"Please specify add_special_tokens=True in your encoding.")
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return super(RobertaModel, self).forward(input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask)
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@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """,
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ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
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class RobertaForMaskedLM(BertPreTrainedModel):
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r"""
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**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Labels for computing the masked language modeling loss.
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Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
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Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
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in ``[0, ..., config.vocab_size]``
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Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
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**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
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Masked language modeling loss.
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**prediction_scores**: ``torch.FloatTensor`` of shape ``(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**: (`optional`, returned when ``config.output_hidden_states=True``)
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list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``)
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(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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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = RobertaForMaskedLM.from_pretrained('roberta-base')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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outputs = model(input_ids, masked_lm_labels=input_ids)
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loss, prediction_scores = outputs[:2]
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"""
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config_class = RobertaConfig
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pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
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base_model_prefix = "roberta"
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def __init__(self, config):
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super(RobertaForMaskedLM, self).__init__(config)
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self.roberta = RobertaModel(config)
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self.lm_head = RobertaLMHead(config)
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self.init_weights()
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self.tie_weights()
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def tie_weights(self):
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""" Make sure we are sharing the input and output embeddings.
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Export to TorchScript can't handle parameter sharing so we are cloning them instead.
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"""
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self._tie_or_clone_weights(self.lm_head.decoder, self.roberta.embeddings.word_embeddings)
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def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
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masked_lm_labels=None):
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outputs = self.roberta(input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask)
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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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if masked_lm_labels is not None:
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loss_fct = CrossEntropyLoss(ignore_index=-1)
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masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
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outputs = (masked_lm_loss,) + outputs
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return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
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class RobertaLMHead(nn.Module):
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"""Roberta Head for masked language modeling."""
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def __init__(self, config):
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super(RobertaLMHead, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.bias = nn.Parameter(torch.zeros(config.vocab_size))
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def forward(self, features, **kwargs):
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x = self.dense(features)
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x = gelu(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) + self.bias
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return x
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@add_start_docstrings("""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, ROBERTA_INPUTS_DOCSTRING)
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class RobertaForSequenceClassification(BertPreTrainedModel):
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r"""
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**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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Labels for computing the sequence classification/regression loss.
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Indices should be in ``[0, ..., config.num_labels]``.
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If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
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If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
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Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
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**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
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Classification (or regression if config.num_labels==1) loss.
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**logits**: ``torch.FloatTensor`` of shape ``(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**: (`optional`, returned when ``config.output_hidden_states=True``)
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list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``)
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(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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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = RobertaForSequenceClassification.from_pretrained('roberta-base')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
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outputs = model(input_ids, labels=labels)
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loss, logits = outputs[:2]
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"""
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config_class = RobertaConfig
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pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
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base_model_prefix = "roberta"
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def __init__(self, config):
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super(RobertaForSequenceClassification, self).__init__(config)
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self.num_labels = config.num_labels
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self.roberta = RobertaModel(config)
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self.classifier = RobertaClassificationHead(config)
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def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
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labels=None):
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outputs = self.roberta(input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask)
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sequence_output = outputs[0]
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logits = self.classifier(sequence_output)
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outputs = (logits,) + outputs[2:]
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if labels is not None:
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if self.num_labels == 1:
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# We are doing regression
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loss_fct = MSELoss()
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loss = loss_fct(logits.view(-1), labels.view(-1))
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else:
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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outputs = (loss,) + outputs
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return outputs # (loss), logits, (hidden_states), (attentions)
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@add_start_docstrings("""Roberta Model with a multiple choice classification head on top (a linear layer on top of
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the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
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ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
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class RobertaForMultipleChoice(BertPreTrainedModel):
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r"""
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Inputs:
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**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
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Indices of input sequence tokens in the vocabulary.
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The second dimension of the input (`num_choices`) indicates the number of choices to score.
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To match pre-training, RoBerta input sequence should be formatted with [CLS] and [SEP] tokens as follows:
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(a) For sequence pairs:
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``tokens: [CLS] is this jack ##son ##ville ? [SEP] [SEP] no it is not . [SEP]``
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``token_type_ids: 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
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(b) For single sequences:
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``tokens: [CLS] the dog is hairy . [SEP]``
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``token_type_ids: 0 0 0 0 0 0 0``
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Indices can be obtained using :class:`transformers.BertTokenizer`.
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See :func:`transformers.PreTrainedTokenizer.encode` and
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:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
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**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
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Segment token indices to indicate first and second portions of the inputs.
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The second dimension of the input (`num_choices`) indicates the number of choices to score.
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Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
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**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
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Mask to avoid performing attention on padding token indices.
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The second dimension of the input (`num_choices`) indicates the number of choices to score.
|
||||
Mask values selected in ``[0, 1]``:
|
||||
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
||||
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
|
||||
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**.
|
||||
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
||||
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)
|
||||
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
||||
Classification loss.
|
||||
**classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
|
||||
of the input tensors. (see `input_ids` above).
|
||||
Classification scores (before SoftMax).
|
||||
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
||||
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
||||
of shape ``(batch_size, sequence_length, hidden_size)``:
|
||||
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
|
||||
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
|
||||
model = RobertaForMultipleChoice.from_pretrained('roberta-base')
|
||||
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
|
||||
input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
|
||||
labels = torch.tensor(1).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss, classification_scores = outputs[:2]
|
||||
|
||||
"""
|
||||
config_class = RobertaConfig
|
||||
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
base_model_prefix = "roberta"
|
||||
|
||||
def __init__(self, config):
|
||||
super(RobertaForMultipleChoice, self).__init__(config)
|
||||
|
||||
self.roberta = RobertaModel(config)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.classifier = nn.Linear(config.hidden_size, 1)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
|
||||
position_ids=None, head_mask=None):
|
||||
num_choices = input_ids.shape[1]
|
||||
|
||||
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
||||
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
||||
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
||||
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
||||
outputs = self.roberta(flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids,
|
||||
attention_mask=flat_attention_mask, head_mask=head_mask)
|
||||
pooled_output = outputs[1]
|
||||
|
||||
pooled_output = self.dropout(pooled_output)
|
||||
logits = self.classifier(pooled_output)
|
||||
reshaped_logits = logits.view(-1, num_choices)
|
||||
|
||||
outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here
|
||||
|
||||
if labels is not None:
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(reshaped_logits, labels)
|
||||
outputs = (loss,) + outputs
|
||||
|
||||
return outputs # (loss), reshaped_logits, (hidden_states), (attentions)
|
||||
|
||||
|
||||
|
||||
class RobertaClassificationHead(nn.Module):
|
||||
"""Head for sentence-level classification tasks."""
|
||||
|
||||
def __init__(self, config):
|
||||
super(RobertaClassificationHead, self).__init__()
|
||||
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
||||
|
||||
def forward(self, features, **kwargs):
|
||||
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
||||
x = self.dropout(x)
|
||||
x = self.dense(x)
|
||||
x = torch.tanh(x)
|
||||
x = self.dropout(x)
|
||||
x = self.out_proj(x)
|
||||
return x
|
||||
Reference in New Issue
Block a user