1258 lines
55 KiB
Python
1258 lines
55 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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 PEER model. """
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import math
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from transformers.activations import ACT2FN, get_activation
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from transformers.file_utils import ModelOutput, add_start_docstrings
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from transformers.modeling_outputs import \
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BaseModelOutputWithPastAndCrossAttentions
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from transformers.modeling_utils import (PreTrainedModel,
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apply_chunking_to_forward,
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find_pruneable_heads_and_indices,
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prune_linear_layer)
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from modelscope.models import Model, TorchModel
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from modelscope.utils import logger as logging
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from modelscope.utils.nlp.utils import parse_labels_in_order
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from .configuration import PeerConfig
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from .sas_utils import SequenceSideInfo
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logger = logging.get_logger()
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PEER_PRETRAINED_MODEL_ARCHIVE_LIST = [
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'google/peer-small-generator',
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'google/peer-base-generator',
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'google/peer-large-generator',
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'google/peer-small-discriminator',
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'google/peer-base-discriminator',
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'google/peer-large-discriminator',
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# See all PEER models at https://huggingface.co/models?filter=peer
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]
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class PeerEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(
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config.vocab_size,
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config.embedding_size,
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padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings,
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config.embedding_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
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config.embedding_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(
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config.embedding_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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'position_ids',
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torch.arange(config.max_position_embeddings).expand((1, -1)))
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self.position_embedding_type = getattr(config,
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'position_embedding_type',
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['absolute'])
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if 'absolute_token_position_in_sentence' in self.position_embedding_type:
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self.side_info_size = 16
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self.position_embeddings__token_position_in_sentence = nn.Embedding(
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self.side_info_size, config.embedding_size)
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# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
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def forward(
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self,
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input_ids=None,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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past_key_values_length=0,
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side_info_sets=dict(),
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):
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:,
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past_key_values_length:seq_length
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+ past_key_values_length]
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if token_type_ids is None:
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token_type_ids = torch.zeros(
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input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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if 'absolute' in self.position_embedding_type:
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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if 'absolute_token_position_in_sentence' in self.position_embedding_type:
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position_idx = torch.clamp(
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side_info_sets['ss_token_position_in_sentence'],
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min=0,
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max=self.side_info_size - 1)
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position_embeddings__token_position_in_sentence = self.position_embeddings__token_position_in_sentence(
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position_idx)
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embeddings += position_embeddings__token_position_in_sentence
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# Pass to attention layers to calcualte position-2-position attention scores
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if 'absolute_self_only' in self.position_embedding_type:
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if 'embeddings' not in side_info_sets:
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side_info_sets['embeddings'] = dict()
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side_info_sets['embeddings'][
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'ss_token_position_in_sequence'] = self.position_embeddings(
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position_ids)
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class PeerSelfAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
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config, 'embedding_size'):
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raise ValueError(
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'The hidden size (%d) is not a multiple of the number of attention '
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'heads (%d)' %
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(config.hidden_size, config.num_attention_heads))
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size
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/ config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = getattr(config,
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'position_embedding_type',
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['absolute'])
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if 'relative_scalar_bias' in self.position_embedding_type:
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self.max_relative_position_embeddings = config.max_position_embeddings // 4
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self.distance_embedding = nn.Embedding(
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2 * self.max_relative_position_embeddings,
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self.num_attention_heads)
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elif 'relative_scalar_bias_with_side_info_token' in self.position_embedding_type:
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self.max_relative_position_embeddings = config.max_position_embeddings // 4
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self.side_info_size = 16 # leverage the information of token_position_in_sentence
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self.distance_embedding = nn.Embedding(
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(2 * self.max_relative_position_embeddings)
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* self.side_info_size, self.num_attention_heads)
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elif 'relative_scalar_bias_token_plus_sentence' in self.position_embedding_type:
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self.max_relative_position_embeddings = config.max_position_embeddings // 4
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self.max_sen_relative_position_embeddings = self.max_relative_position_embeddings // 4
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self.distance_embedding = nn.Embedding(
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2 * self.max_relative_position_embeddings,
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self.num_attention_heads)
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self.distance_embedding_sentence = nn.Embedding(
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2 * self.max_sen_relative_position_embeddings,
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self.num_attention_heads)
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elif 'relative_scalar_bias_with_side_info_sentence' in self.position_embedding_type:
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self.max_relative_position_embeddings = config.max_position_embeddings // 4
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self.max_sen_relative_position_embeddings = self.max_relative_position_embeddings // 4
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vocab = (2 * self.max_relative_position_embeddings) * (
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2 * self.max_sen_relative_position_embeddings)
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self.distance_embedding = nn.Embedding(vocab,
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self.num_attention_heads)
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elif 'relative_key' in self.position_embedding_type or 'relative_key_query' in self.position_embedding_type:
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self.max_relative_position_embeddings = config.max_position_embeddings // 4
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self.distance_embedding = nn.Embedding(
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2 * self.max_relative_position_embeddings,
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self.attention_head_size)
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self.is_decoder = config.is_decoder
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads,
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self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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side_info_sets=dict(),
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):
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mixed_query_layer = self.query(hidden_states)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention and past_key_value is not None:
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# reuse k,v, cross_attentions
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key_layer = past_key_value[0]
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value_layer = past_key_value[1]
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attention_mask = encoder_attention_mask
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elif is_cross_attention:
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key_layer = self.transpose_for_scores(
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self.key(encoder_hidden_states))
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value_layer = self.transpose_for_scores(
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self.value(encoder_hidden_states))
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attention_mask = encoder_attention_mask
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elif past_key_value is not None:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
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value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
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else:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional self-attention `past_key_value` is always `None`
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past_key_value = (key_layer, value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(
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-1, -2)) / math.sqrt(self.attention_head_size)
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attention_scores_terms = 1
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if 'absolute_self_only' in self.position_embedding_type:
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attention_scores += side_info_sets[
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'side_info_attention_scores'] # already normalized by sqrt(attention_head_size)
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attention_scores_terms += 1
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if 'relative_key' in self.position_embedding_type or 'relative_key_query' in self.position_embedding_type \
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or 'relative_scalar_bias' in self.position_embedding_type \
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or 'relative_scalar_bias_with_side_info_token' in self.position_embedding_type \
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or 'relative_scalar_bias_token_plus_sentence' in self.position_embedding_type \
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or 'relative_scalar_bias_with_side_info_sentence' in self.position_embedding_type:
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distance_idx = side_info_sets['distance_idx']
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positional_embedding = self.distance_embedding(distance_idx)
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positional_embedding = positional_embedding.to(
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dtype=query_layer.dtype) # fp16 compatibility
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if 'relative_scalar_bias' in self.position_embedding_type:
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relative_scalar_bias = positional_embedding.permute(
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[2, 0, 1]).unsqueeze(0)
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attention_scores = attention_scores / math.sqrt(
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attention_scores_terms) + relative_scalar_bias
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elif ('relative_scalar_bias_with_side_info_token'
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in self.position_embedding_type
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or 'relative_scalar_bias_with_side_info_sentence'
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in self.position_embedding_type):
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relative_scalar_bias = positional_embedding.permute(
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[0, 3, 1, 2])
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attention_scores = attention_scores / math.sqrt(
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attention_scores_terms) + relative_scalar_bias
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elif 'relative_scalar_bias_token_plus_sentence' in self.position_embedding_type:
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relative_scalar_bias = positional_embedding.permute(
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[2, 0, 1]).unsqueeze(0)
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distance_idx_sentence = side_info_sets['distance_idx_sentence']
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positional_embedding_sentence = self.distance_embedding_sentence(
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distance_idx_sentence)
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positional_embedding_sentence = positional_embedding_sentence.to(
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dtype=query_layer.dtype) # fp16 compatibility
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relative_scalar_bias_sentence = positional_embedding_sentence.permute(
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[0, 3, 1, 2])
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attention_scores = attention_scores / math.sqrt(
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attention_scores_terms
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) + relative_scalar_bias + relative_scalar_bias_sentence
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elif 'relative_key' in self.position_embedding_type:
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relative_position_scores = torch.einsum(
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'bhld,lrd->bhlr', query_layer,
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positional_embedding) / math.sqrt(self.attention_head_size)
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attention_scores_terms += 1
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attention_scores = (attention_scores + relative_position_scores
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) / math.sqrt(attention_scores_terms)
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elif 'relative_key_query' in self.position_embedding_type:
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relative_position_scores_query = torch.einsum(
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'bhld,lrd->bhlr', query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum(
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'bhrd,lrd->bhlr', key_layer, positional_embedding)
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relative_position_scores = (
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relative_position_scores_query
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+ relative_position_scores_key) / math.sqrt(
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self.attention_head_size)
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attention_scores_terms += 2
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attention_scores = (attention_scores + relative_position_scores
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) / math.sqrt(attention_scores_terms)
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else:
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attention_scores = attention_scores / math.sqrt(
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attention_scores_terms)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in PeerModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (
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self.all_head_size, )
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context_layer = context_layer.view(*new_context_layer_shape)
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outputs = (context_layer,
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attention_probs) if output_attentions else (context_layer, )
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if self.is_decoder:
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outputs = outputs + (past_key_value, )
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return outputs
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class PeerSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class PeerAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = PeerSelfAttention(config)
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self.output = PeerSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads,
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self.self.attention_head_size, self.pruned_heads)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(
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heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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side_info_sets=dict(),
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):
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self_outputs = self.self(
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hidden_states,
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attention_mask,
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head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_value,
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output_attentions,
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side_info_sets,
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)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,
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) + self_outputs[1:] # add attentions if we output them
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return outputs
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class PeerIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class PeerOutput(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(
|
|
config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states, input_tensor):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class PeerLayer(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = PeerAttention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
assert self.is_decoder, f'{self} should be used as a decoder model if cross attention is added'
|
|
self.crossattention = PeerAttention(config)
|
|
self.intermediate = PeerIntermediate(config)
|
|
self.output = PeerOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_value=None,
|
|
output_attentions=False,
|
|
side_info_sets=dict(),
|
|
):
|
|
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
|
self_attn_past_key_value = past_key_value[:
|
|
2] if past_key_value is not None else None
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
past_key_value=self_attn_past_key_value,
|
|
side_info_sets=side_info_sets,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
present_key_value = self_attention_outputs[-1]
|
|
else:
|
|
outputs = self_attention_outputs[
|
|
1:] # add self attentions if we output attention weights
|
|
|
|
cross_attn_present_key_value = None
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
assert hasattr(
|
|
self, 'crossattention'
|
|
), f'If `encoder_hidden_states` are passed, {self} has to be instantiated \
|
|
with cross-attention layers by setting `config.add_cross_attention=True`'
|
|
|
|
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
|
|
cross_attn_past_key_value = past_key_value[
|
|
-2:] if past_key_value is not None else None
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
cross_attn_past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[
|
|
1:-1] # add cross attentions if we output attention weights
|
|
|
|
# add cross-attn cache to positions 3,4 of present_key_value tuple
|
|
cross_attn_present_key_value = cross_attention_outputs[-1]
|
|
present_key_value = present_key_value + cross_attn_present_key_value
|
|
|
|
layer_output = apply_chunking_to_forward(self.feed_forward_chunk,
|
|
self.chunk_size_feed_forward,
|
|
self.seq_len_dim,
|
|
attention_output)
|
|
outputs = (layer_output, ) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (present_key_value, )
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class PeerEncoder(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList(
|
|
[PeerLayer(config) for _ in range(config.num_hidden_layers)])
|
|
|
|
self.position_embedding_type = getattr(config,
|
|
'position_embedding_type',
|
|
['absolute'])
|
|
if 'absolute_self_only' in self.position_embedding_type:
|
|
# To be used/shared in all self-attention layers. Copy their dimensions here to be consistent.
|
|
self.self_attention = self.layer[0].attention.self
|
|
|
|
self.num_attention_heads = self.self_attention.num_attention_heads
|
|
self.attention_head_size = self.self_attention.attention_head_size
|
|
self.all_head_size = self.self_attention.all_head_size
|
|
|
|
self.pos_query = nn.Linear(self.self_attention.query.in_features,
|
|
self.self_attention.query.out_features)
|
|
self.pos_key = nn.Linear(self.self_attention.key.in_features,
|
|
self.self_attention.key.out_features)
|
|
|
|
def get_position_attention_score(self, hidden_states):
|
|
query_layer = self.self_attention.transpose_for_scores(
|
|
self.pos_query(hidden_states))
|
|
key_layer = self.self_attention.transpose_for_scores(
|
|
self.pos_key(hidden_states))
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer,
|
|
key_layer.transpose(-1, -2))
|
|
attention_scores = attention_scores / math.sqrt(
|
|
self.attention_head_size)
|
|
return attention_scores
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
side_info_sets=dict(),
|
|
return_dict=True,
|
|
):
|
|
|
|
if 'absolute_self_only' in self.position_embedding_type:
|
|
side_info_attention_scores = self.get_position_attention_score(
|
|
hidden_states=side_info_sets['embeddings']
|
|
['ss_token_position_in_sequence'])
|
|
side_info_sets[
|
|
'side_info_attention_scores'] = side_info_attention_scores
|
|
|
|
if 'relative_key' in self.position_embedding_type or 'relative_key_query' in self.position_embedding_type \
|
|
or 'relative_scalar_bias' in self.position_embedding_type \
|
|
or 'relative_scalar_bias_with_side_info_token' in self.position_embedding_type \
|
|
or 'relative_scalar_bias_token_plus_sentence' in self.position_embedding_type \
|
|
or 'relative_scalar_bias_with_side_info_sentence' in self.position_embedding_type:
|
|
seq_length = hidden_states.shape[1]
|
|
batch_size = hidden_states.shape[0]
|
|
|
|
position_ids_l = torch.arange(
|
|
seq_length, dtype=torch.long,
|
|
device=hidden_states.device).view(-1, 1)
|
|
position_ids_r = torch.arange(
|
|
seq_length, dtype=torch.long,
|
|
device=hidden_states.device).view(1, -1)
|
|
max_relative_position_embeddings = self.layer[
|
|
0].attention.self.max_relative_position_embeddings
|
|
distance_idx = torch.clamp(
|
|
position_ids_l - position_ids_r
|
|
+ max_relative_position_embeddings - 2,
|
|
min=0,
|
|
max=2 * max_relative_position_embeddings - 4)
|
|
distance_idx[
|
|
0, :] = 2 * max_relative_position_embeddings - 3 # CLS-to-others
|
|
distance_idx[:,
|
|
0] = 2 * max_relative_position_embeddings - 2 # others-to-CLS
|
|
distance_idx[
|
|
0, 0] = 2 * max_relative_position_embeddings - 1 # CLS-to-CLS
|
|
distance_idx_max = 2 * max_relative_position_embeddings
|
|
|
|
# token position-aware relative position
|
|
if 'relative_scalar_bias_with_side_info_token' in self.position_embedding_type:
|
|
idx1 = torch.clamp(
|
|
side_info_sets['ss_token_position_in_sentence'],
|
|
min=0,
|
|
max=self.layer[0].attention.self.side_info_size
|
|
- 1).unsqueeze(2).repeat(1, 1, seq_length)
|
|
idx2 = distance_idx.unsqueeze(0).repeat(batch_size, 1, 1)
|
|
distance_idx = idx1 * distance_idx_max + idx2
|
|
# relative token position + relative sentence position
|
|
elif 'relative_scalar_bias_with_side_info_sentence' in self.position_embedding_type:
|
|
sen_position_ids_l = side_info_sets[
|
|
'ss_sentence_position_in_sequence'].view(
|
|
batch_size, -1, 1)
|
|
sen_position_ids_r = side_info_sets[
|
|
'ss_sentence_position_in_sequence'].view(
|
|
batch_size, 1, -1)
|
|
max_sen_relative_position_embeddings = self.layer[
|
|
0].attention.self.max_sen_relative_position_embeddings
|
|
idx1 = torch.clamp(
|
|
sen_position_ids_l - sen_position_ids_r
|
|
+ max_sen_relative_position_embeddings,
|
|
min=0,
|
|
max=2 * max_sen_relative_position_embeddings - 1)
|
|
idx2 = distance_idx.unsqueeze(0).repeat(batch_size, 1, 1)
|
|
distance_idx = idx1 * distance_idx_max + idx2
|
|
elif 'relative_scalar_bias_token_plus_sentence' in self.position_embedding_type:
|
|
sen_position_ids_l = side_info_sets[
|
|
'ss_sentence_position_in_sequence'].view(
|
|
batch_size, -1, 1)
|
|
sen_position_ids_r = side_info_sets[
|
|
'ss_sentence_position_in_sequence'].view(
|
|
batch_size, 1, -1)
|
|
max_sen_relative_position_embeddings = self.layer[
|
|
0].attention.self.max_sen_relative_position_embeddings
|
|
idx1 = torch.clamp(
|
|
sen_position_ids_l - sen_position_ids_r
|
|
+ max_sen_relative_position_embeddings,
|
|
min=0,
|
|
max=2 * max_sen_relative_position_embeddings - 1)
|
|
side_info_sets['distance_idx_sentence'] = idx1
|
|
|
|
side_info_sets['distance_idx'] = distance_idx
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = (
|
|
) if output_attentions and self.config.add_cross_attention else None
|
|
|
|
next_decoder_cache = () if use_cache else None
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states, )
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
past_key_value = past_key_values[
|
|
i] if past_key_values is not None else None
|
|
if getattr(self.config, 'gradient_checkpointing', False):
|
|
|
|
def create_custom_forward(module):
|
|
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, past_key_value,
|
|
output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(layer_module),
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
side_info_sets,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
side_info_sets,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[-1], )
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (
|
|
layer_outputs[1], )
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (
|
|
layer_outputs[2], )
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states, )
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [
|
|
hidden_states,
|
|
next_decoder_cache,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
] if v is not None)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_decoder_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class PeerDiscriminatorPredictions(nn.Module):
|
|
"""Prediction module for the discriminator, made up of two dense layers."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.dense_prediction = nn.Linear(config.hidden_size, 1)
|
|
self.config = config
|
|
|
|
def forward(self, discriminator_hidden_states):
|
|
hidden_states = self.dense(discriminator_hidden_states)
|
|
hidden_states = get_activation(self.config.hidden_act)(hidden_states)
|
|
logits = self.dense_prediction(hidden_states).squeeze(-1)
|
|
|
|
return logits
|
|
|
|
|
|
class PeerGeneratorPredictions(nn.Module):
|
|
"""Prediction module for the generator, made up of two dense layers."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.LayerNorm = nn.LayerNorm(config.embedding_size)
|
|
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
|
|
|
|
def forward(self, generator_hidden_states):
|
|
hidden_states = self.dense(generator_hidden_states)
|
|
hidden_states = get_activation('gelu')(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class PeerPreTrainedModel(TorchModel, PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = PeerConfig
|
|
base_model_prefix = 'teams1_shared_bottom'
|
|
_keys_to_ignore_on_load_missing = [r'position_ids']
|
|
_keys_to_ignore_on_load_unexpected = [
|
|
r'peer\.embeddings_project\.weight', r'peer\.embeddings_project\.bias'
|
|
]
|
|
|
|
def _init_weights(self, module):
|
|
""" Initialize the weights """
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(
|
|
mean=0.0, std=self.config.initializer_range)
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
@classmethod
|
|
def _instantiate(cls, **kwargs):
|
|
"""Instantiate the model.
|
|
|
|
Args:
|
|
kwargs: Input args.
|
|
model_dir: The model dir used to load the checkpoint and the label information.
|
|
num_labels: An optional arg to tell the model how many classes to initialize.
|
|
Method will call utils.parse_label_mapping if num_labels is not input.
|
|
label2id: An optional label2id mapping, which will cover the label2id in configuration (if exists).
|
|
|
|
Returns:
|
|
The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained
|
|
"""
|
|
|
|
model_dir = kwargs.pop('model_dir', None)
|
|
cfg = kwargs.pop('cfg', None)
|
|
model_args = parse_labels_in_order(model_dir, cfg, **kwargs)
|
|
|
|
if model_dir is None:
|
|
config = PeerConfig(**model_args)
|
|
model = cls(config)
|
|
else:
|
|
model = super(Model, cls).from_pretrained(
|
|
pretrained_model_name_or_path=model_dir, **model_args)
|
|
return model
|
|
|
|
|
|
@dataclass
|
|
class PeerForRTDOutput(ModelOutput):
|
|
"""
|
|
Output type of :class:`~transformers.PeerForRTD`.
|
|
|
|
Args:
|
|
loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
|
|
Total loss of the PEER objective.
|
|
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
|
|
Prediction scores of the head (scores for each token before SoftMax).
|
|
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`,
|
|
returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
|
|
Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`,
|
|
returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
|
|
Tuple of :obj:`torch.FloatTensor` (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.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class PeerForPreTrainingOutput(ModelOutput):
|
|
"""
|
|
Output type of :class:`~transformers.PeerForPreTraining`.
|
|
|
|
Args:
|
|
loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
|
|
Total loss of the PEER objective.
|
|
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
|
|
Prediction scores of the head (scores for each token before SoftMax).
|
|
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`,
|
|
returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
|
|
Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`,
|
|
returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
|
|
Tuple of :obj:`torch.FloatTensor` (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.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
mlm_loss: Optional[torch.FloatTensor] = None
|
|
rtd_loss: Optional[torch.FloatTensor] = None
|
|
mlm_logits: torch.FloatTensor = None
|
|
rtd_logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
PEER_START_DOCSTRING = r"""
|
|
|
|
This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
|
|
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
|
|
pruning heads etc.)
|
|
|
|
This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
|
|
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
|
|
general usage and behavior.
|
|
|
|
Parameters:
|
|
config (:class:`~transformers.PeerConfig`): 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.
|
|
"""
|
|
|
|
PEER_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using :class:`~transformers.PeerTokenizer`. See
|
|
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
|
|
details.
|
|
|
|
`What are input IDs? <../glossary.html#input-ids>`__
|
|
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
|
|
1]``:
|
|
|
|
- 0 corresponds to a `sentence A` token,
|
|
- 1 corresponds to a `sentence B` token.
|
|
|
|
`What are token type IDs? <../glossary.html#token-type-ids>`_
|
|
position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
|
|
config.max_position_embeddings - 1]``.
|
|
|
|
`What are position IDs? <../glossary.html#position-ids>`_
|
|
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
|
|
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
|
|
vectors than the model's internal embedding lookup matrix.
|
|
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
|
the model is configured as a decoder.
|
|
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
|
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (:obj:`bool`, `optional`):
|
|
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (:obj:`bool`, `optional`):
|
|
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
|
|
more detail.
|
|
return_dict (:obj:`bool`, `optional`):
|
|
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
'The bare Peer Model transformer outputting raw hidden-states without any specific head on top. Identical to '
|
|
'the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the '
|
|
'hidden size and embedding size are different.'
|
|
''
|
|
'Both the generator and discriminator checkpoints may be loaded into this model.',
|
|
PEER_START_DOCSTRING,
|
|
)
|
|
class PeerModel(PeerPreTrainedModel):
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.embeddings = PeerEmbeddings(config)
|
|
|
|
if config.embedding_size != config.hidden_size:
|
|
self.embeddings_project = nn.Linear(config.embedding_size,
|
|
config.hidden_size)
|
|
|
|
self.encoder = PeerEncoder(config)
|
|
self.config = config
|
|
self.init_weights()
|
|
|
|
if self.config.seq_side_info_embeddings:
|
|
self.input_sequence_side_info = dict()
|
|
self.sequence_side_info = SequenceSideInfo()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
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
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
def update_seq_side_info(self, side_info_sets, input_ids):
|
|
|
|
device = input_ids.device
|
|
if 'input_sequence_side_info' not in side_info_sets or len(
|
|
side_info_sets['input_sequence_side_info']) == 0:
|
|
input_sequence_side_info = self.sequence_side_info.generate_seq_side_info(
|
|
self.config.seq_side_info_embeddings, input_ids)
|
|
|
|
else:
|
|
# Save compute in PEER pre-training
|
|
# (Save the extra side info into cpu in the first epoch; Directly retrieve it from cpu in later epochs)
|
|
input_sequence_side_info = side_info_sets[
|
|
'input_sequence_side_info']
|
|
|
|
for ss in input_sequence_side_info.keys():
|
|
input_sequence_side_info[ss] = input_sequence_side_info[ss].to(
|
|
device=device).long()
|
|
side_info_sets = {**side_info_sets, **input_sequence_side_info}
|
|
return side_info_sets
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
side_info_sets=dict(),
|
|
return_dict=None,
|
|
):
|
|
if self.config.seq_side_info_embeddings:
|
|
side_info_sets = self.update_seq_side_info(side_info_sets,
|
|
input_ids)
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else
|
|
self.config.output_hidden_states)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError(
|
|
'You cannot specify both input_ids and inputs_embeds at the same time'
|
|
)
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError(
|
|
'You have to specify either input_ids or inputs_embeds')
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(
|
|
input_shape, dtype=torch.long, device=device)
|
|
|
|
extended_attention_mask = self.get_extended_attention_mask(
|
|
attention_mask, input_shape, device)
|
|
head_mask = self.get_head_mask(head_mask,
|
|
self.config.num_hidden_layers)
|
|
|
|
hidden_states = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
side_info_sets=side_info_sets,
|
|
)
|
|
|
|
if hasattr(self, 'embeddings_project'):
|
|
hidden_states = self.embeddings_project(hidden_states)
|
|
|
|
hidden_states = self.encoder(
|
|
hidden_states,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
side_info_sets=side_info_sets,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class PeerTopModel(PeerPreTrainedModel):
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.encoder = PeerEncoder(config)
|
|
self.config = config
|
|
self.init_weights()
|
|
|
|
if self.config.seq_side_info_embeddings:
|
|
self.input_sequence_side_info = dict()
|
|
self.sequence_side_info = SequenceSideInfo()
|
|
|
|
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
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
def update_seq_side_info(self, side_info_sets, input_ids):
|
|
|
|
device = input_ids.device
|
|
if 'input_sequence_side_info' not in side_info_sets or len(
|
|
side_info_sets['input_sequence_side_info']) == 0:
|
|
input_sequence_side_info = self.sequence_side_info.generate_seq_side_info(
|
|
self.config.seq_side_info_embeddings, input_ids)
|
|
|
|
else:
|
|
# Save compute in PEER pre-training
|
|
# (Save the extra side info into cpu in the first epoch; Directly retrieve it from cpu in later epochs)
|
|
input_sequence_side_info = side_info_sets[
|
|
'input_sequence_side_info']
|
|
|
|
for ss in input_sequence_side_info.keys():
|
|
input_sequence_side_info[ss] = input_sequence_side_info[ss].to(
|
|
device=device).long()
|
|
side_info_sets = {**side_info_sets, **input_sequence_side_info}
|
|
return side_info_sets
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
side_info_sets=dict(),
|
|
return_dict=None,
|
|
):
|
|
|
|
if self.config.seq_side_info_embeddings:
|
|
side_info_sets = self.update_seq_side_info(side_info_sets,
|
|
input_ids)
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else
|
|
self.config.output_hidden_states)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError(
|
|
'You cannot specify both input_ids and inputs_embeds at the same time'
|
|
)
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError(
|
|
'You have to specify either input_ids or inputs_embeds')
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(
|
|
input_shape, dtype=torch.long, device=device)
|
|
|
|
extended_attention_mask = self.get_extended_attention_mask(
|
|
attention_mask, input_shape, device)
|
|
head_mask = self.get_head_mask(head_mask,
|
|
self.config.num_hidden_layers)
|
|
|
|
hidden_states = self.encoder(
|
|
hidden_states,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
side_info_sets=side_info_sets,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class PeerClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__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 = get_activation('gelu')(
|
|
x
|
|
) # although BERT uses tanh here, it seems Peer authors used gelu here
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|