1187 lines
52 KiB
Python
1187 lines
52 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. 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 LiLT 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 ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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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 apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
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from .configuration_lilt import LiltConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "LiltConfig"
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from ..deprecated._archive_maps import LILT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class LiltTextEmbeddings(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_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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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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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 = nn.Embedding(
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config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
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)
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def forward(
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self,
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input_ids=None,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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):
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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 = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to(
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input_ids.device
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)
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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 = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings, position_ids
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def create_position_ids_from_input_ids(self, input_ids, padding_idx):
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"""
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Args:
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
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symbols are ignored. This is modified from fairseq's `utils.make_positions`.
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x: torch.Tensor x:
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Returns: torch.Tensor
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"""
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# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
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mask = input_ids.ne(padding_idx).int()
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incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
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return incremental_indices.long() + padding_idx
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def create_position_ids_from_inputs_embeds(self, inputs_embeds):
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"""
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Args:
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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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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 LiltLayoutEmbeddings(nn.Module):
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def __init__(self, config):
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super().__init__()
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# we divide the hidden_size by 6 here as there are 6 different layout embeddings,
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# namely left_position, upper_position, right_position, lower_position, height, width
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self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
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self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
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self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
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self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
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self.padding_idx = config.pad_token_id
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self.box_position_embeddings = nn.Embedding(
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config.max_position_embeddings,
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config.hidden_size // config.channel_shrink_ratio,
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padding_idx=self.padding_idx,
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)
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self.box_linear_embeddings = nn.Linear(
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in_features=config.hidden_size, out_features=config.hidden_size // config.channel_shrink_ratio
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)
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self.LayerNorm = nn.LayerNorm(config.hidden_size // config.channel_shrink_ratio, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, bbox=None, position_ids=None):
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try:
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left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
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upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
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right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
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lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
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except IndexError as e:
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raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
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h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
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w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
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spatial_position_embeddings = torch.cat(
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[
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left_position_embeddings,
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upper_position_embeddings,
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right_position_embeddings,
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lower_position_embeddings,
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h_position_embeddings,
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w_position_embeddings,
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],
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dim=-1,
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)
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spatial_position_embeddings = self.box_linear_embeddings(spatial_position_embeddings)
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box_position_embeddings = self.box_position_embeddings(position_ids)
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spatial_position_embeddings = spatial_position_embeddings + box_position_embeddings
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spatial_position_embeddings = self.LayerNorm(spatial_position_embeddings)
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spatial_position_embeddings = self.dropout(spatial_position_embeddings)
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return spatial_position_embeddings
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class LiltSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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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.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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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.layout_query = nn.Linear(
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config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
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)
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self.layout_key = nn.Linear(
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config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
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)
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self.layout_value = nn.Linear(
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config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
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)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = position_embedding_type or getattr(
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config, "position_embedding_type", "absolute"
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)
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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self.channel_shrink_ratio = config.channel_shrink_ratio
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def transpose_for_scores(self, x, r=1):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size // r)
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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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layout_inputs,
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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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layout_value_layer = self.transpose_for_scores(self.layout_value(layout_inputs), r=self.channel_shrink_ratio)
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layout_key_layer = self.transpose_for_scores(self.layout_key(layout_inputs), r=self.channel_shrink_ratio)
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layout_query_layer = self.transpose_for_scores(self.layout_query(layout_inputs), r=self.channel_shrink_ratio)
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mixed_query_layer = self.query(hidden_states)
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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layout_attention_scores = torch.matmul(layout_query_layer, layout_key_layer.transpose(-1, -2))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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seq_length = hidden_states.size()[1]
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position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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tmp_attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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tmp_layout_attention_scores = layout_attention_scores / math.sqrt(
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self.attention_head_size // self.channel_shrink_ratio
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)
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attention_scores = tmp_attention_scores + tmp_layout_attention_scores
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layout_attention_scores = tmp_layout_attention_scores + tmp_attention_scores
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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layout_attention_scores = layout_attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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layout_attention_probs = nn.Softmax(dim=-1)(layout_attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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layout_attention_probs = self.dropout(layout_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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layout_attention_probs = layout_attention_probs * head_mask
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layout_context_layer = torch.matmul(layout_attention_probs, layout_value_layer)
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layout_context_layer = layout_context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = layout_context_layer.size()[:-2] + (self.all_head_size // self.channel_shrink_ratio,)
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layout_context_layer = layout_context_layer.view(*new_context_layer_shape)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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outputs = (
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((context_layer, layout_context_layer), attention_probs)
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if output_attentions
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else ((context_layer, layout_context_layer),)
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)
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
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class LiltSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(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, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class LiltAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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super().__init__()
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self.self = LiltSelfAttention(config, position_embedding_type=position_embedding_type)
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self.output = LiltSelfOutput(config)
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self.pruned_heads = set()
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ori_hidden_size = config.hidden_size
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config.hidden_size = config.hidden_size // config.channel_shrink_ratio
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self.layout_output = LiltSelfOutput(config)
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config.hidden_size = ori_hidden_size
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# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
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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: torch.Tensor,
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layout_inputs: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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self_outputs = self.self(
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hidden_states,
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layout_inputs,
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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 = self.output(self_outputs[0][0], hidden_states)
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layout_attention_output = self.layout_output(self_outputs[0][1], layout_inputs)
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outputs = ((attention_output, layout_attention_output),) + self_outputs[1:] # add attentions if we output them
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate
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class LiltIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: 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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# Copied from transformers.models.bert.modeling_bert.BertOutput
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class LiltOutput(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.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class LiltLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = LiltAttention(config)
|
|
self.intermediate = LiltIntermediate(config)
|
|
self.output = LiltOutput(config)
|
|
|
|
ori_hidden_size = config.hidden_size
|
|
ori_intermediate_size = config.intermediate_size
|
|
config.hidden_size = config.hidden_size // config.channel_shrink_ratio
|
|
config.intermediate_size = config.intermediate_size // config.channel_shrink_ratio
|
|
self.layout_intermediate = LiltIntermediate(config)
|
|
self.layout_output = LiltOutput(config)
|
|
config.hidden_size = ori_hidden_size
|
|
config.intermediate_size = ori_intermediate_size
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
layout_inputs: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
layout_inputs,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0][0]
|
|
layout_attention_output = self_attention_outputs[0][1]
|
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
layout_layer_output = apply_chunking_to_forward(
|
|
self.layout_feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, layout_attention_output
|
|
)
|
|
outputs = ((layer_output, layout_layer_output),) + outputs
|
|
|
|
return outputs
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
def layout_feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.layout_intermediate(attention_output)
|
|
layer_output = self.layout_output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class LiltEncoder(nn.Module):
|
|
# Copied from transformers.models.bert.modeling_bert.BertEncoder.__init__ with Bert->Lilt
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([LiltLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
layout_inputs: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
layout_inputs,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
layout_inputs,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0][0]
|
|
layout_inputs = layer_outputs[0][1]
|
|
|
|
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,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler
|
|
class LiltPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
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]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class LiltPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LiltConfig
|
|
base_model_prefix = "lilt"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = []
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, 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, 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, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
LILT_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 ([`LiltConfig`]): 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.
|
|
"""
|
|
|
|
LILT_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)
|
|
|
|
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
|
|
Bounding boxes of each input sequence tokens. Selected in the range `[0,
|
|
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
|
|
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
|
|
y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
|
|
|
|
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 LiLT Model transformer outputting raw hidden-states without any specific head on top.",
|
|
LILT_START_DOCSTRING,
|
|
)
|
|
class LiltModel(LiltPreTrainedModel):
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = LiltTextEmbeddings(config)
|
|
self.layout_embeddings = LiltLayoutEmbeddings(config)
|
|
self.encoder = LiltEncoder(config)
|
|
|
|
self.pooler = LiltPooler(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(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
bbox: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
|
|
r"""
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AutoModel
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
>>> model = AutoModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```"""
|
|
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 bbox is None:
|
|
bbox = torch.zeros(input_shape + (4,), dtype=torch.long, device=device)
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
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, position_ids = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
layout_embedding_output = self.layout_embeddings(bbox=bbox, position_ids=position_ids)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
layout_embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
LiLT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
|
|
output) e.g. for GLUE tasks.
|
|
""",
|
|
LILT_START_DOCSTRING,
|
|
)
|
|
class LiltForSequenceClassification(LiltPreTrainedModel):
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Lilt, roberta->lilt
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.lilt = LiltModel(config, add_pooling_layer=False)
|
|
self.classifier = LiltClassificationHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
bbox: Optional[torch.Tensor] = 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[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).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
>>> model = AutoModelForSequenceClassification.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> predicted_class_idx = outputs.logits.argmax(-1).item()
|
|
>>> predicted_class = model.config.id2label[predicted_class_idx]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.lilt(
|
|
input_ids,
|
|
bbox=bbox,
|
|
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:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
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(
|
|
"""
|
|
Lilt 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.
|
|
""",
|
|
LILT_START_DOCSTRING,
|
|
)
|
|
class LiltForTokenClassification(LiltPreTrainedModel):
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Lilt, roberta->lilt
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.lilt = LiltModel(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()
|
|
|
|
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
bbox: 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[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]`.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AutoModelForTokenClassification
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
>>> model = AutoModelForTokenClassification.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> predicted_class_indices = outputs.logits.argmax(-1)
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.lilt(
|
|
input_ids,
|
|
bbox=bbox,
|
|
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:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
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,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Lilt
|
|
class LiltClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
x = self.dropout(x)
|
|
x = self.dense(x)
|
|
x = torch.tanh(x)
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Lilt 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`).
|
|
""",
|
|
LILT_START_DOCSTRING,
|
|
)
|
|
class LiltForQuestionAnswering(LiltPreTrainedModel):
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Lilt, roberta->lilt
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.lilt = LiltModel(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(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
bbox: 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[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
|
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.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
>>> model = AutoModelForQuestionAnswering.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
|
|
>>> answer_start_index = outputs.start_logits.argmax()
|
|
>>> answer_end_index = outputs.end_logits.argmax()
|
|
|
|
>>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
|
|
>>> predicted_answer = tokenizer.decode(predict_answer_tokens)
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.lilt(
|
|
input_ids,
|
|
bbox=bbox,
|
|
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,
|
|
)
|