1354 lines
55 KiB
Python
1354 lines
55 KiB
Python
# coding=utf-8
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# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
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# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
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# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch I-BERT model."""
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import math
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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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 gelu
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from ...modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPoolingAndCrossAttentions,
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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 ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
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from .configuration_ibert import IBertConfig
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from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base"
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_CONFIG_FOR_DOC = "IBertConfig"
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from ..deprecated._archive_maps import IBERT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class IBertEmbeddings(nn.Module):
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"""
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Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
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"""
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def __init__(self, config):
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super().__init__()
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self.quant_mode = config.quant_mode
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self.embedding_bit = 8
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self.embedding_act_bit = 16
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self.act_bit = 8
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self.ln_input_bit = 22
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self.ln_output_bit = 32
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self.word_embeddings = QuantEmbedding(
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config.vocab_size,
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config.hidden_size,
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padding_idx=config.pad_token_id,
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weight_bit=self.embedding_bit,
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quant_mode=self.quant_mode,
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)
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self.token_type_embeddings = QuantEmbedding(
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config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode
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)
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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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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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# End copy
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self.padding_idx = config.pad_token_id
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self.position_embeddings = QuantEmbedding(
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config.max_position_embeddings,
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config.hidden_size,
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padding_idx=self.padding_idx,
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weight_bit=self.embedding_bit,
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quant_mode=self.quant_mode,
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)
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# Integer-only addition between embeddings
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self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
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self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
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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 = IntLayerNorm(
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config.hidden_size,
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eps=config.layer_norm_eps,
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output_bit=self.ln_output_bit,
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quant_mode=self.quant_mode,
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force_dequant=config.force_dequant,
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)
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self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
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):
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if position_ids is None:
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if input_ids is not None:
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# Create the position ids from the input token ids. Any padded tokens remain padded.
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position_ids = create_position_ids_from_input_ids(
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input_ids, self.padding_idx, past_key_values_length
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).to(input_ids.device)
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else:
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position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
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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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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, inputs_embeds_scaling_factor = self.word_embeddings(input_ids)
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else:
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inputs_embeds_scaling_factor = None
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token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids)
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embeddings, embeddings_scaling_factor = self.embeddings_act1(
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inputs_embeds,
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inputs_embeds_scaling_factor,
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identity=token_type_embeddings,
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identity_scaling_factor=token_type_embeddings_scaling_factor,
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)
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if self.position_embedding_type == "absolute":
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position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids)
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embeddings, embeddings_scaling_factor = self.embeddings_act1(
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embeddings,
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embeddings_scaling_factor,
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identity=position_embeddings,
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identity_scaling_factor=position_embeddings_scaling_factor,
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)
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embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor)
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embeddings = self.dropout(embeddings)
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embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor)
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return embeddings, embeddings_scaling_factor
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def create_position_ids_from_inputs_embeds(self, inputs_embeds):
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"""
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We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
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Args:
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inputs_embeds: torch.Tensor
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Returns: torch.Tensor
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"""
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input_shape = inputs_embeds.size()[:-1]
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sequence_length = input_shape[1]
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position_ids = torch.arange(
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self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
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)
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return position_ids.unsqueeze(0).expand(input_shape)
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class IBertSelfAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.quant_mode = config.quant_mode
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self.weight_bit = 8
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self.bias_bit = 32
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self.act_bit = 8
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.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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# Q, K, V Linear layers
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self.query = QuantLinear(
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config.hidden_size,
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self.all_head_size,
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bias=True,
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weight_bit=self.weight_bit,
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bias_bit=self.bias_bit,
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quant_mode=self.quant_mode,
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per_channel=True,
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)
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self.key = QuantLinear(
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config.hidden_size,
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self.all_head_size,
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bias=True,
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weight_bit=self.weight_bit,
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bias_bit=self.bias_bit,
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quant_mode=self.quant_mode,
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per_channel=True,
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)
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self.value = QuantLinear(
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config.hidden_size,
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self.all_head_size,
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bias=True,
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weight_bit=self.weight_bit,
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bias_bit=self.bias_bit,
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quant_mode=self.quant_mode,
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per_channel=True,
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)
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# Requantization (32bit -> 8bit) for Q, K, V activations
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self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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if self.position_embedding_type != "absolute":
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raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`")
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self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self,
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hidden_states,
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hidden_states_scaling_factor,
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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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):
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# Projection
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mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor)
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mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor)
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mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor)
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# Requantization
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query_layer, query_layer_scaling_factor = self.query_activation(
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mixed_query_layer, mixed_query_layer_scaling_factor
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)
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key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor)
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value_layer, value_layer_scaling_factor = self.value_activation(
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mixed_value_layer, mixed_value_layer_scaling_factor
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)
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# Transpose
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query_layer = self.transpose_for_scores(query_layer)
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key_layer = self.transpose_for_scores(key_layer)
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value_layer = self.transpose_for_scores(value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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scale = math.sqrt(self.attention_head_size)
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attention_scores = attention_scores / scale
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if self.quant_mode:
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attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale
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else:
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attention_scores_scaling_factor = None
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in IBertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs, attention_probs_scaling_factor = self.softmax(
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attention_scores, attention_scores_scaling_factor
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)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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if attention_probs_scaling_factor is not None:
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context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor
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else:
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context_layer_scaling_factor = None
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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# requantization: 32-bit -> 8-bit
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context_layer, context_layer_scaling_factor = self.output_activation(
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context_layer, context_layer_scaling_factor
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)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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output_scaling_factor = (
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(context_layer_scaling_factor, attention_probs_scaling_factor)
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if output_attentions
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else (context_layer_scaling_factor,)
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)
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return outputs, output_scaling_factor
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class IBertSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.quant_mode = config.quant_mode
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self.act_bit = 8
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self.weight_bit = 8
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self.bias_bit = 32
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self.ln_input_bit = 22
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self.ln_output_bit = 32
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self.dense = QuantLinear(
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config.hidden_size,
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config.hidden_size,
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bias=True,
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weight_bit=self.weight_bit,
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bias_bit=self.bias_bit,
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quant_mode=self.quant_mode,
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per_channel=True,
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)
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self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
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self.LayerNorm = IntLayerNorm(
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config.hidden_size,
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eps=config.layer_norm_eps,
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output_bit=self.ln_output_bit,
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quant_mode=self.quant_mode,
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force_dequant=config.force_dequant,
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)
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self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
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hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
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hidden_states = self.dropout(hidden_states)
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hidden_states, hidden_states_scaling_factor = self.ln_input_act(
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hidden_states,
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hidden_states_scaling_factor,
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identity=input_tensor,
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identity_scaling_factor=input_tensor_scaling_factor,
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)
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hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)
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hidden_states, hidden_states_scaling_factor = self.output_activation(
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hidden_states, hidden_states_scaling_factor
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)
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return hidden_states, hidden_states_scaling_factor
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class IBertAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.quant_mode = config.quant_mode
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self.self = IBertSelfAttention(config)
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self.output = IBertSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states,
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hidden_states_scaling_factor,
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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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):
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self_outputs, self_outputs_scaling_factor = self.self(
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hidden_states,
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hidden_states_scaling_factor,
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attention_mask,
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head_mask,
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output_attentions,
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)
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attention_output, attention_output_scaling_factor = self.output(
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self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor
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)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:]
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return outputs, outputs_scaling_factor
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class IBertIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.quant_mode = config.quant_mode
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self.act_bit = 8
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self.weight_bit = 8
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self.bias_bit = 32
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self.dense = QuantLinear(
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config.hidden_size,
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config.intermediate_size,
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bias=True,
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weight_bit=self.weight_bit,
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bias_bit=self.bias_bit,
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quant_mode=self.quant_mode,
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per_channel=True,
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)
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if config.hidden_act != "gelu":
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raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`")
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self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant)
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self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
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def forward(self, hidden_states, hidden_states_scaling_factor):
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hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
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hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn(
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hidden_states, hidden_states_scaling_factor
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)
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# Requantization: 32bit -> 8-bit
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hidden_states, hidden_states_scaling_factor = self.output_activation(
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hidden_states, hidden_states_scaling_factor
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)
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return hidden_states, hidden_states_scaling_factor
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class IBertOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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|
self.quant_mode = config.quant_mode
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self.act_bit = 8
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self.weight_bit = 8
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self.bias_bit = 32
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self.ln_input_bit = 22
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self.ln_output_bit = 32
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self.dense = QuantLinear(
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config.intermediate_size,
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config.hidden_size,
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bias=True,
|
|
weight_bit=self.weight_bit,
|
|
bias_bit=self.bias_bit,
|
|
quant_mode=self.quant_mode,
|
|
per_channel=True,
|
|
)
|
|
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
|
|
self.LayerNorm = IntLayerNorm(
|
|
config.hidden_size,
|
|
eps=config.layer_norm_eps,
|
|
output_bit=self.ln_output_bit,
|
|
quant_mode=self.quant_mode,
|
|
force_dequant=config.force_dequant,
|
|
)
|
|
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
|
|
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states, hidden_states_scaling_factor = self.ln_input_act(
|
|
hidden_states,
|
|
hidden_states_scaling_factor,
|
|
identity=input_tensor,
|
|
identity_scaling_factor=input_tensor_scaling_factor,
|
|
)
|
|
hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)
|
|
|
|
hidden_states, hidden_states_scaling_factor = self.output_activation(
|
|
hidden_states, hidden_states_scaling_factor
|
|
)
|
|
return hidden_states, hidden_states_scaling_factor
|
|
|
|
|
|
class IBertLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.quant_mode = config.quant_mode
|
|
self.act_bit = 8
|
|
|
|
self.seq_len_dim = 1
|
|
self.attention = IBertAttention(config)
|
|
self.intermediate = IBertIntermediate(config)
|
|
self.output = IBertOutput(config)
|
|
|
|
self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
|
|
self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
hidden_states_scaling_factor,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
self_attention_outputs, self_attention_outputs_scaling_factor = self.attention(
|
|
hidden_states,
|
|
hidden_states_scaling_factor,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
attention_output_scaling_factor = self_attention_outputs_scaling_factor[0]
|
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
layer_output, layer_output_scaling_factor = self.feed_forward_chunk(
|
|
attention_output, attention_output_scaling_factor
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output, attention_output_scaling_factor):
|
|
attention_output, attention_output_scaling_factor = self.pre_intermediate_act(
|
|
attention_output, attention_output_scaling_factor
|
|
)
|
|
intermediate_output, intermediate_output_scaling_factor = self.intermediate(
|
|
attention_output, attention_output_scaling_factor
|
|
)
|
|
|
|
intermediate_output, intermediate_output_scaling_factor = self.pre_output_act(
|
|
intermediate_output, intermediate_output_scaling_factor
|
|
)
|
|
layer_output, layer_output_scaling_factor = self.output(
|
|
intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor
|
|
)
|
|
return layer_output, layer_output_scaling_factor
|
|
|
|
|
|
class IBertEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.quant_mode = config.quant_mode
|
|
self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
hidden_states_scaling_factor,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = None # `config.add_cross_attention` is not supported
|
|
next_decoder_cache = None # `config.use_cache` is not supported
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
hidden_states_scaling_factor,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
next_decoder_cache,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_decoder_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class IBertPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.quant_mode = config.quant_mode
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states):
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class IBertPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = IBertConfig
|
|
base_model_prefix = "ibert"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (QuantLinear, nn.Linear)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, (QuantEmbedding, nn.Embedding)):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, (IntLayerNorm, nn.LayerNorm)):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
def resize_token_embeddings(self, new_num_tokens=None):
|
|
raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.")
|
|
|
|
|
|
IBERT_START_DOCSTRING = r"""
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`IBertConfig`]): Model configuration class with all the parameters of the
|
|
model. Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
IBERT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
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)
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, 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.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.",
|
|
IBERT_START_DOCSTRING,
|
|
)
|
|
class IBertModel(IBertPreTrainedModel):
|
|
"""
|
|
|
|
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
|
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
|
|
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
|
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
|
|
|
|
"""
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.quant_mode = config.quant_mode
|
|
|
|
self.embeddings = IBertEmbeddings(config)
|
|
self.encoder = IBertEncoder(config)
|
|
|
|
self.pooler = IBertPooler(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
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]:
|
|
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")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = 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, embedding_output_scaling_factor = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
embedding_output_scaling_factor,
|
|
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 self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING)
|
|
class IBertForMaskedLM(IBertPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.ibert = IBertModel(config, add_pooling_layer=False)
|
|
self.lm_head = IBertLMHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.decoder = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MaskedLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
mask="<mask>",
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
|
|
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]`
|
|
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
|
|
Used to hide legacy arguments that have been deprecated.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.ibert(
|
|
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.lm_head(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
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,
|
|
)
|
|
|
|
|
|
class IBertLMHead(nn.Module):
|
|
"""I-BERT Head for masked language modeling."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = self.dense(features)
|
|
x = gelu(x)
|
|
x = self.layer_norm(x)
|
|
|
|
# project back to size of vocabulary with bias
|
|
x = self.decoder(x)
|
|
|
|
return x
|
|
|
|
def _tie_weights(self):
|
|
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
|
|
self.bias = self.decoder.bias
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
|
|
output) e.g. for GLUE tasks.
|
|
""",
|
|
IBERT_START_DOCSTRING,
|
|
)
|
|
class IBertForSequenceClassification(IBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.ibert = IBertModel(config, add_pooling_layer=False)
|
|
self.classifier = IBertClassificationHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=SequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
|
|
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.ibert(
|
|
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.classifier(sequence_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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
I-BERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
|
|
softmax) e.g. for RocStories/SWAG tasks.
|
|
""",
|
|
IBERT_START_DOCSTRING,
|
|
)
|
|
class IBertForMultipleChoice(IBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.ibert = IBertModel(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()
|
|
|
|
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
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]
|
|
|
|
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
flat_inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.ibert(
|
|
flat_input_ids,
|
|
position_ids=flat_position_ids,
|
|
token_type_ids=flat_token_type_ids,
|
|
attention_mask=flat_attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=flat_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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
|
|
Named-Entity-Recognition (NER) tasks.
|
|
""",
|
|
IBERT_START_DOCSTRING,
|
|
)
|
|
class IBertForTokenClassification(IBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.ibert = IBertModel(config, add_pooling_layer=False)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=TokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
|
|
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.ibert(
|
|
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,
|
|
)
|
|
|
|
|
|
class IBertClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
hidden_states = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = torch.tanh(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.out_proj(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
I-BERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
|
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
IBERT_START_DOCSTRING,
|
|
)
|
|
class IBertForQuestionAnswering(IBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.ibert = IBertModel(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()
|
|
|
|
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=QuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.ibert(
|
|
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,
|
|
)
|
|
|
|
|
|
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
|
|
"""
|
|
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
|
|
are ignored. This is modified from fairseq's *utils.make_positions*.
|
|
|
|
Args:
|
|
input_ids (`torch.LongTensor`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
|
|
mask = input_ids.ne(padding_idx).int()
|
|
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
|
|
return incremental_indices.long() + padding_idx
|