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129 lines
4.9 KiB
Python
129 lines
4.9 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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"""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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import torch.nn.functional as F
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from pytorch_transformers.modeling_bert import (BertConfig, BertEmbeddings,
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BertLayerNorm, BertModel,
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BertPreTrainedModel, gelu)
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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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ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",
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'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
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'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
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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, token_type_ids=token_type_ids, position_ids=position_ids)
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class RobertaConfig(BertConfig):
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pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
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class RobertaModel(BertModel):
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"""
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Same as BertModel with:
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- a tiny embeddings tweak.
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- setup for Roberta pretrained models
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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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class RobertaForMaskedLM(BertPreTrainedModel):
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"""
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Roberta Model with a `language modeling` head on top.
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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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def forward(self, input_ids, token_type_ids=None, attention_mask=None, position_ids=None, head_mask=None):
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outputs = self.roberta(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
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attention_mask=attention_mask, 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:]
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return outputs
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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.weight = nn.Linear(config.hidden_size, config.vocab_size, bias=False).weight
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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 = F.linear(x, self.weight) + self.bias
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return x
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