962 lines
38 KiB
Python
962 lines
38 KiB
Python
# coding=utf-8
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# Copyright 2020 The SqueezeBert 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 SqueezeBert model."""
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import math
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from typing import Optional, Union
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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auto_docstring,
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logging,
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)
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from .configuration_squeezebert import SqueezeBertConfig
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logger = logging.get_logger(__name__)
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class SqueezeBertEmbeddings(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(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, 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(config.hidden_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", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
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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[:, :seq_length]
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if token_type_ids is None:
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token_type_ids = torch.zeros(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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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + position_embeddings + token_type_embeddings
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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 MatMulWrapper(nn.Module):
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"""
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Wrapper for torch.matmul(). This makes flop-counting easier to implement. Note that if you directly call
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torch.matmul() in your code, the flop counter will typically ignore the flops of the matmul.
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"""
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def __init__(self):
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super().__init__()
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def forward(self, mat1, mat2):
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"""
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:param inputs: two torch tensors :return: matmul of these tensors
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Here are the typical dimensions found in BERT (the B is optional) mat1.shape: [B, <optional extra dims>, M, K]
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mat2.shape: [B, <optional extra dims>, K, N] output shape: [B, <optional extra dims>, M, N]
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"""
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return torch.matmul(mat1, mat2)
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class SqueezeBertLayerNorm(nn.LayerNorm):
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"""
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This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
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N = batch C = channels W = sequence length
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"""
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def __init__(self, hidden_size, eps=1e-12):
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nn.LayerNorm.__init__(self, normalized_shape=hidden_size, eps=eps) # instantiates self.{weight, bias, eps}
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def forward(self, x):
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x = x.permute(0, 2, 1)
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x = nn.LayerNorm.forward(self, x)
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return x.permute(0, 2, 1)
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class ConvDropoutLayerNorm(nn.Module):
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"""
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ConvDropoutLayerNorm: Conv, Dropout, LayerNorm
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"""
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def __init__(self, cin, cout, groups, dropout_prob):
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super().__init__()
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self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
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self.layernorm = SqueezeBertLayerNorm(cout)
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self.dropout = nn.Dropout(dropout_prob)
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def forward(self, hidden_states, input_tensor):
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x = self.conv1d(hidden_states)
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x = self.dropout(x)
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x = x + input_tensor
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x = self.layernorm(x)
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return x
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class ConvActivation(nn.Module):
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"""
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ConvActivation: Conv, Activation
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"""
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def __init__(self, cin, cout, groups, act):
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super().__init__()
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self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
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self.act = ACT2FN[act]
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def forward(self, x):
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output = self.conv1d(x)
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return self.act(output)
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class SqueezeBertSelfAttention(nn.Module):
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def __init__(self, config, cin, q_groups=1, k_groups=1, v_groups=1):
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"""
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config = used for some things; ignored for others (work in progress...) cin = input channels = output channels
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groups = number of groups to use in conv1d layers
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"""
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super().__init__()
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if cin % config.num_attention_heads != 0:
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raise ValueError(
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f"cin ({cin}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(cin / 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.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=q_groups)
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self.key = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=k_groups)
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self.value = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=v_groups)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.softmax = nn.Softmax(dim=-1)
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self.matmul_qk = MatMulWrapper()
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self.matmul_qkv = MatMulWrapper()
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def transpose_for_scores(self, x):
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"""
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- input: [N, C, W]
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- output: [N, C1, W, C2] where C1 is the head index, and C2 is one head's contents
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"""
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new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1]) # [N, C1, C2, W]
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x = x.view(*new_x_shape)
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return x.permute(0, 1, 3, 2) # [N, C1, C2, W] --> [N, C1, W, C2]
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def transpose_key_for_scores(self, x):
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"""
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- input: [N, C, W]
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- output: [N, C1, C2, W] where C1 is the head index, and C2 is one head's contents
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"""
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new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1]) # [N, C1, C2, W]
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x = x.view(*new_x_shape)
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# no `permute` needed
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return x
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def transpose_output(self, x):
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"""
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- input: [N, C1, W, C2]
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- output: [N, C, W]
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"""
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x = x.permute(0, 1, 3, 2).contiguous() # [N, C1, C2, W]
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new_x_shape = (x.size()[0], self.all_head_size, x.size()[3]) # [N, C, W]
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x = x.view(*new_x_shape)
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return x
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def forward(self, hidden_states, attention_mask, output_attentions):
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"""
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expects hidden_states in [N, C, W] data layout.
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The attention_mask data layout is [N, W], and it does not need to be transposed.
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"""
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(hidden_states)
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mixed_value_layer = self.value(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_key_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_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_score = self.matmul_qk(query_layer, key_layer)
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attention_score = attention_score / math.sqrt(self.attention_head_size)
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_score = attention_score + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = self.softmax(attention_score)
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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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context_layer = self.matmul_qkv(attention_probs, value_layer)
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context_layer = self.transpose_output(context_layer)
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result = {"context_layer": context_layer}
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if output_attentions:
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result["attention_score"] = attention_score
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return result
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class SqueezeBertModule(nn.Module):
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def __init__(self, config):
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"""
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- hidden_size = input chans = output chans for Q, K, V (they are all the same ... for now) = output chans for
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the module
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- intermediate_size = output chans for intermediate layer
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- groups = number of groups for all layers in the BertModule. (eventually we could change the interface to
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allow different groups for different layers)
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"""
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super().__init__()
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c0 = config.hidden_size
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c1 = config.hidden_size
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c2 = config.intermediate_size
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c3 = config.hidden_size
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self.attention = SqueezeBertSelfAttention(
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config=config, cin=c0, q_groups=config.q_groups, k_groups=config.k_groups, v_groups=config.v_groups
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)
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self.post_attention = ConvDropoutLayerNorm(
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cin=c0, cout=c1, groups=config.post_attention_groups, dropout_prob=config.hidden_dropout_prob
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)
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self.intermediate = ConvActivation(cin=c1, cout=c2, groups=config.intermediate_groups, act=config.hidden_act)
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self.output = ConvDropoutLayerNorm(
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cin=c2, cout=c3, groups=config.output_groups, dropout_prob=config.hidden_dropout_prob
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)
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def forward(self, hidden_states, attention_mask, output_attentions):
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att = self.attention(hidden_states, attention_mask, output_attentions)
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attention_output = att["context_layer"]
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post_attention_output = self.post_attention(attention_output, hidden_states)
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intermediate_output = self.intermediate(post_attention_output)
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layer_output = self.output(intermediate_output, post_attention_output)
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output_dict = {"feature_map": layer_output}
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if output_attentions:
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output_dict["attention_score"] = att["attention_score"]
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return output_dict
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class SqueezeBertEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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assert config.embedding_size == config.hidden_size, (
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"If you want embedding_size != intermediate hidden_size, "
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"please insert a Conv1d layer to adjust the number of channels "
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"before the first SqueezeBertModule."
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)
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self.layers = nn.ModuleList(SqueezeBertModule(config) for _ in range(config.num_hidden_layers))
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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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output_attentions=False,
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output_hidden_states=False,
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return_dict=True,
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):
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if head_mask is None:
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head_mask_is_all_none = True
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elif head_mask.count(None) == len(head_mask):
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head_mask_is_all_none = True
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else:
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head_mask_is_all_none = False
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assert head_mask_is_all_none is True, "head_mask is not yet supported in the SqueezeBert implementation."
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# [batch_size, sequence_length, hidden_size] --> [batch_size, hidden_size, sequence_length]
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hidden_states = hidden_states.permute(0, 2, 1)
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all_hidden_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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for layer in self.layers:
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if output_hidden_states:
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hidden_states = hidden_states.permute(0, 2, 1)
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all_hidden_states += (hidden_states,)
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hidden_states = hidden_states.permute(0, 2, 1)
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layer_output = layer.forward(hidden_states, attention_mask, output_attentions)
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hidden_states = layer_output["feature_map"]
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if output_attentions:
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all_attentions += (layer_output["attention_score"],)
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# [batch_size, hidden_size, sequence_length] --> [batch_size, sequence_length, hidden_size]
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hidden_states = hidden_states.permute(0, 2, 1)
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
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)
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class SqueezeBertPooler(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.activation = nn.Tanh()
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class SqueezeBertPredictionHeadTransform(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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if isinstance(config.hidden_act, str):
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self.transform_act_fn = ACT2FN[config.hidden_act]
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else:
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self.transform_act_fn = config.hidden_act
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.transform_act_fn(hidden_states)
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hidden_states = self.LayerNorm(hidden_states)
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return hidden_states
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class SqueezeBertLMPredictionHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.transform = SqueezeBertPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = 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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# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
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self.decoder.bias = self.bias
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def _tie_weights(self) -> None:
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self.decoder.bias = self.bias
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def forward(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = self.decoder(hidden_states)
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return hidden_states
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class SqueezeBertOnlyMLMHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.predictions = SqueezeBertLMPredictionHead(config)
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def forward(self, sequence_output):
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prediction_scores = self.predictions(sequence_output)
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return prediction_scores
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@auto_docstring
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class SqueezeBertPreTrainedModel(PreTrainedModel):
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config: SqueezeBertConfig
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base_model_prefix = "transformer"
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, (nn.Linear, nn.Conv1d)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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elif isinstance(module, SqueezeBertLMPredictionHead):
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module.bias.data.zero_()
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@auto_docstring
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class SqueezeBertModel(SqueezeBertPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.embeddings = SqueezeBertEmbeddings(config)
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self.encoder = SqueezeBertEncoder(config)
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self.pooler = SqueezeBertPooler(config)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, new_embeddings):
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self.embeddings.word_embeddings = new_embeddings
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def _prune_heads(self, heads_to_prune):
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"""
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Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
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class PreTrainedModel
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"""
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for layer, heads in heads_to_prune.items():
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self.encoder.layer[layer].attention.prune_heads(heads)
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@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPooling]:
|
|
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:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
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)
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
hidden_states=embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output)
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.transformer = SqueezeBertModel(config)
|
|
self.cls = SqueezeBertOnlyMLMHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
self.cls.predictions.bias = new_embeddings.bias
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, MaskedLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
SqueezeBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
"""
|
|
)
|
|
class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.transformer = SqueezeBertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.transformer = SqueezeBertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, MultipleChoiceModelOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *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#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *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#position-ids)
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where *num_choices* is the size of the second dimension of the input tensors. (see
|
|
*input_ids* above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = SqueezeBertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = SqueezeBertModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, QuestionAnsweringModelOutput]:
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"SqueezeBertForMaskedLM",
|
|
"SqueezeBertForMultipleChoice",
|
|
"SqueezeBertForQuestionAnswering",
|
|
"SqueezeBertForSequenceClassification",
|
|
"SqueezeBertForTokenClassification",
|
|
"SqueezeBertModel",
|
|
"SqueezeBertModule",
|
|
"SqueezeBertPreTrainedModel",
|
|
]
|