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1251 lines
50 KiB
1251 lines
50 KiB
# MIT License
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#
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# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from collections.abc import Callable
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from dataclasses import dataclass
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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 ... import initialization as init
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from ...activations import ACT2FN
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from ...masking_utils import create_bidirectional_mask
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from ...modeling_layers import GradientCheckpointingLayer
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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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NextSentencePredictorOutput,
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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 ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, TransformersKwargs, auto_docstring, logging
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from ...utils.generic import can_return_tuple, check_model_inputs
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from .configuration_mobilebert import MobileBertConfig
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logger = logging.get_logger(__name__)
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class NoNorm(nn.Module):
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def __init__(self, feat_size, eps=None):
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super().__init__()
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self.bias = nn.Parameter(torch.zeros(feat_size))
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self.weight = nn.Parameter(torch.ones(feat_size))
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def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
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return input_tensor * self.weight + self.bias
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NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm}
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class MobileBertEmbeddings(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.trigram_input = config.trigram_input
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self.embedding_size = config.embedding_size
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self.hidden_size = config.hidden_size
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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.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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embed_dim_multiplier = 3 if self.trigram_input else 1
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embedded_input_size = self.embedding_size * embed_dim_multiplier
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self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size)
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self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size)
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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(
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self,
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input_ids: torch.LongTensor | None = None,
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token_type_ids: torch.LongTensor | None = None,
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position_ids: torch.LongTensor | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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) -> torch.Tensor:
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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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if self.trigram_input:
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# From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
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# Devices (https://huggingface.co/papers/2004.02984)
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#
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# The embedding table in BERT models accounts for a substantial proportion of model size. To compress
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# the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
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# Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
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# dimensional output.
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inputs_embeds = torch.cat(
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[
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nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0),
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inputs_embeds,
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nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0),
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],
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dim=2,
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)
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if self.trigram_input or self.embedding_size != self.hidden_size:
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inputs_embeds = self.embedding_transformation(inputs_embeds)
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# Add positional embeddings and token type embeddings, then layer
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# normalize and perform dropout.
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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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# Copied from transformers.models.bert.modeling_bert.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float | None = None,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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attention_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class MobileBertSelfAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.true_hidden_size / 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.scaling = self.attention_head_size**-0.5
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self.query = nn.Linear(config.true_hidden_size, self.all_head_size)
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self.key = nn.Linear(config.true_hidden_size, self.all_head_size)
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self.value = nn.Linear(
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config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size
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)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.is_causal = False
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def forward(
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self,
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query_tensor: torch.Tensor,
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key_tensor: torch.Tensor,
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value_tensor: torch.Tensor,
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attention_mask: torch.FloatTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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input_shape = query_tensor.shape[:-1]
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hidden_shape = (*input_shape, -1, self.attention_head_size)
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# get all proj
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query_layer = self.query(query_tensor).view(*hidden_shape).transpose(1, 2)
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key_layer = self.key(key_tensor).view(*hidden_shape).transpose(1, 2)
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value_layer = self.value(value_tensor).view(*hidden_shape).transpose(1, 2)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout.p,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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return attn_output, attn_weights
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class MobileBertSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.use_bottleneck = config.use_bottleneck
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self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
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self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
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if not self.use_bottleneck:
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
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layer_outputs = self.dense(hidden_states)
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if not self.use_bottleneck:
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layer_outputs = self.dropout(layer_outputs)
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layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
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return layer_outputs
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class MobileBertAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = MobileBertSelfAttention(config)
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self.output = MobileBertSelfOutput(config)
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def forward(
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self,
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query_tensor: torch.Tensor,
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key_tensor: torch.Tensor,
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value_tensor: torch.Tensor,
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layer_input: torch.Tensor,
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attention_mask: torch.FloatTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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attention_output, attn_weights = self.self(
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query_tensor,
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key_tensor,
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value_tensor,
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attention_mask,
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**kwargs,
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)
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# Run a linear projection of `hidden_size` then add a residual
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# with `layer_input`.
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attention_output = self.output(attention_output, layer_input)
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return attention_output, attn_weights
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class MobileBertIntermediate(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.true_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: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class OutputBottleneck(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.true_hidden_size, config.hidden_size)
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self.LayerNorm = NORM2FN[config.normalization_type](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: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
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layer_outputs = self.dense(hidden_states)
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layer_outputs = self.dropout(layer_outputs)
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layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
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return layer_outputs
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class MobileBertOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.use_bottleneck = config.use_bottleneck
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self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
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self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
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if not self.use_bottleneck:
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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else:
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self.bottleneck = OutputBottleneck(config)
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def forward(
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self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor
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) -> torch.Tensor:
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layer_output = self.dense(intermediate_states)
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if not self.use_bottleneck:
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layer_output = self.dropout(layer_output)
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layer_output = self.LayerNorm(layer_output + residual_tensor_1)
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else:
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layer_output = self.LayerNorm(layer_output + residual_tensor_1)
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layer_output = self.bottleneck(layer_output, residual_tensor_2)
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return layer_output
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class BottleneckLayer(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.intra_bottleneck_size)
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self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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layer_input = self.dense(hidden_states)
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layer_input = self.LayerNorm(layer_input)
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return layer_input
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class Bottleneck(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
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self.use_bottleneck_attention = config.use_bottleneck_attention
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self.input = BottleneckLayer(config)
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if self.key_query_shared_bottleneck:
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self.attention = BottleneckLayer(config)
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def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor]:
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# This method can return three different tuples of values. These different values make use of bottlenecks,
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# which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
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# usage. These linear layer have weights that are learned during training.
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#
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# If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
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# key, query, value, and "layer input" to be used by the attention layer.
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# This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
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# in the attention self output, after the attention scores have been computed.
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#
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# If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
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# four values, three of which have been passed through a bottleneck: the query and key, passed through the same
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# bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
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#
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# Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
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# and the residual layer will be this value passed through a bottleneck.
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bottlenecked_hidden_states = self.input(hidden_states)
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if self.use_bottleneck_attention:
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return (bottlenecked_hidden_states,) * 4
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elif self.key_query_shared_bottleneck:
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shared_attention_input = self.attention(hidden_states)
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return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
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else:
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return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
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class FFNOutput(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.intermediate_size, config.true_hidden_size)
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self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
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layer_outputs = self.dense(hidden_states)
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layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
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return layer_outputs
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class FFNLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.intermediate = MobileBertIntermediate(config)
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self.output = FFNOutput(config)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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intermediate_output = self.intermediate(hidden_states)
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layer_outputs = self.output(intermediate_output, hidden_states)
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return layer_outputs
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class MobileBertLayer(GradientCheckpointingLayer):
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def __init__(self, config):
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super().__init__()
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self.use_bottleneck = config.use_bottleneck
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self.num_feedforward_networks = config.num_feedforward_networks
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self.attention = MobileBertAttention(config)
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self.intermediate = MobileBertIntermediate(config)
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self.output = MobileBertOutput(config)
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if self.use_bottleneck:
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self.bottleneck = Bottleneck(config)
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if config.num_feedforward_networks > 1:
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self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)])
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.FloatTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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if self.use_bottleneck:
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query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
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else:
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query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
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self_attention_output, _ = self.attention(
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query_tensor,
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key_tensor,
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value_tensor,
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layer_input,
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attention_mask,
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**kwargs,
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)
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attention_output = self_attention_output
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if self.num_feedforward_networks != 1:
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for ffn_module in self.ffn:
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attention_output = ffn_module(attention_output)
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output, hidden_states)
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return layer_output
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|
|
|
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class MobileBertEncoder(nn.Module):
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|
def __init__(self, config):
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super().__init__()
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self.layer = nn.ModuleList([MobileBertLayer(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: torch.Tensor,
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attention_mask: torch.FloatTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple | BaseModelOutput:
|
|
for i, layer_module in enumerate(self.layer):
|
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hidden_states = layer_module(
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hidden_states,
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attention_mask,
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|
**kwargs,
|
|
)
|
|
return BaseModelOutput(last_hidden_state=hidden_states)
|
|
|
|
|
|
class MobileBertPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.do_activate = config.classifier_activation
|
|
if self.do_activate:
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
if not self.do_activate:
|
|
return first_token_tensor
|
|
else:
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = torch.tanh(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class MobileBertPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MobileBertLMPredictionHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = MobileBertPredictionHeadTransform(config)
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False)
|
|
self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=True)
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0))
|
|
hidden_states += self.decoder.bias
|
|
return hidden_states
|
|
|
|
|
|
class MobileBertOnlyMLMHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = MobileBertLMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class MobileBertPreTrainingHeads(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = MobileBertLMPredictionHead(config)
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> tuple[torch.Tensor]:
|
|
prediction_scores = self.predictions(sequence_output)
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
@auto_docstring
|
|
class MobileBertPreTrainedModel(PreTrainedModel):
|
|
config: MobileBertConfig
|
|
base_model_prefix = "mobilebert"
|
|
supports_gradient_checkpointing = True
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": MobileBertLayer,
|
|
"attentions": MobileBertSelfAttention,
|
|
}
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
super()._init_weights(module)
|
|
if isinstance(module, NoNorm):
|
|
init.zeros_(module.bias)
|
|
init.ones_(module.weight)
|
|
elif isinstance(module, MobileBertLMPredictionHead):
|
|
init.zeros_(module.bias)
|
|
elif isinstance(module, MobileBertEmbeddings):
|
|
init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Output type of [`MobileBertForPreTraining`].
|
|
"""
|
|
)
|
|
class MobileBertForPreTrainingOutput(ModelOutput):
|
|
r"""
|
|
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
|
|
Total loss as the sum of the masked language modeling loss and the next sequence prediction
|
|
(classification) loss.
|
|
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
|
before SoftMax).
|
|
"""
|
|
|
|
loss: torch.FloatTensor | None = None
|
|
prediction_logits: torch.FloatTensor | None = None
|
|
seq_relationship_logits: torch.FloatTensor | None = None
|
|
hidden_states: tuple[torch.FloatTensor] | None = None
|
|
attentions: tuple[torch.FloatTensor] | None = None
|
|
|
|
|
|
@auto_docstring
|
|
class MobileBertModel(MobileBertPreTrainedModel):
|
|
"""
|
|
https://huggingface.co/papers/2004.02984
|
|
"""
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.gradient_checkpointing = False
|
|
|
|
self.embeddings = MobileBertEmbeddings(config)
|
|
self.encoder = MobileBertEncoder(config)
|
|
|
|
self.pooler = MobileBertPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | BaseModelOutputWithPooling:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=attention_mask,
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
|
|
`next sentence prediction (classification)` head.
|
|
"""
|
|
)
|
|
class MobileBertForPreTraining(MobileBertPreTrainedModel):
|
|
_tied_weights_keys = {
|
|
"cls.predictions.decoder.bias": "cls.predictions.bias",
|
|
"cls.predictions.decoder.weight": "mobilebert.embeddings.word_embeddings.weight",
|
|
}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.mobilebert = MobileBertModel(config)
|
|
self.cls = MobileBertPreTrainingHeads(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
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: int | None = None) -> nn.Embedding:
|
|
# resize dense output embedings at first
|
|
self.cls.predictions.dense = self._get_resized_lm_head(
|
|
self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
|
|
)
|
|
|
|
return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
next_sentence_label: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | MobileBertForPreTrainingOutput:
|
|
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]`
|
|
next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
|
|
(see `input_ids` docstring) Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, MobileBertForPreTraining
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
|
|
>>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
|
|
|
|
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
|
|
>>> # Batch size 1
|
|
>>> outputs = model(input_ids)
|
|
|
|
>>> prediction_logits = outputs.prediction_logits
|
|
>>> seq_relationship_logits = outputs.seq_relationship_logits
|
|
```"""
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
sequence_output, pooled_output = outputs[:2]
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
|
|
|
total_loss = None
|
|
if labels is not None and next_sentence_label is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
|
|
total_loss = masked_lm_loss + next_sentence_loss
|
|
|
|
return MobileBertForPreTrainingOutput(
|
|
loss=total_loss,
|
|
prediction_logits=prediction_scores,
|
|
seq_relationship_logits=seq_relationship_score,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class MobileBertForMaskedLM(MobileBertPreTrainedModel):
|
|
_tied_weights_keys = {
|
|
"cls.predictions.decoder.bias": "cls.predictions.bias",
|
|
"cls.predictions.decoder.weight": "mobilebert.embeddings.word_embeddings.weight",
|
|
}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
|
|
self.cls = MobileBertOnlyMLMHead(config)
|
|
self.config = 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
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: int | None = None) -> nn.Embedding:
|
|
# resize dense output embedings at first
|
|
self.cls.predictions.dense = self._get_resized_lm_head(
|
|
self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
|
|
)
|
|
return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]`
|
|
"""
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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))
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class MobileBertOnlyNSPHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return seq_relationship_score
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
MobileBert Model with a `next sentence prediction (classification)` head on top.
|
|
"""
|
|
)
|
|
class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.mobilebert = MobileBertModel(config)
|
|
self.cls = MobileBertOnlyNSPHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | NextSentencePredictorOutput:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
|
|
(see `input_ids` docstring) Indices should be in `[0, 1]`.
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, MobileBertForNextSentencePrediction
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
|
|
>>> model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")
|
|
|
|
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
|
|
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
|
|
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
seq_relationship_score = self.cls(pooled_output)
|
|
|
|
next_sentence_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1))
|
|
|
|
return NextSentencePredictorOutput(
|
|
loss=next_sentence_loss,
|
|
logits=seq_relationship_score,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
"""
|
|
)
|
|
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing
|
|
class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.mobilebert = MobileBertModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | 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).
|
|
"""
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering with Bert->MobileBert all-casing
|
|
class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
start_positions: torch.Tensor | None = None,
|
|
end_positions: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput:
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice with Bert->MobileBert all-casing
|
|
class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.mobilebert = MobileBertModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | 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)
|
|
"""
|
|
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.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification with Bert->MobileBert all-casing
|
|
class MobileBertForTokenClassification(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | 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]`.
|
|
"""
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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))
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"MobileBertForMaskedLM",
|
|
"MobileBertForMultipleChoice",
|
|
"MobileBertForNextSentencePrediction",
|
|
"MobileBertForPreTraining",
|
|
"MobileBertForQuestionAnswering",
|
|
"MobileBertForSequenceClassification",
|
|
"MobileBertForTokenClassification",
|
|
"MobileBertLayer",
|
|
"MobileBertModel",
|
|
"MobileBertPreTrainedModel",
|
|
]
|