1084 lines
44 KiB
Python
1084 lines
44 KiB
Python
# coding=utf-8
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# Copyright 2022 Microsoft Research Asia and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch MarkupLM model."""
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import os
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from typing import Callable, Optional, 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_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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MaskedLMOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import (
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ALL_ATTENTION_FUNCTIONS,
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PreTrainedModel,
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apply_chunking_to_forward,
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find_pruneable_heads_and_indices,
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prune_linear_layer,
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)
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from ...utils import auto_docstring, can_return_tuple, logging
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from ...utils.deprecation import deprecate_kwarg
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from .configuration_markuplm import MarkupLMConfig
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logger = logging.get_logger(__name__)
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class XPathEmbeddings(nn.Module):
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"""Construct the embeddings from xpath tags and subscripts.
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We drop tree-id in this version, as its info can be covered by xpath.
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"""
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def __init__(self, config):
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super().__init__()
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self.max_depth = config.max_depth
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self.xpath_unitseq2_embeddings = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.activation = nn.ReLU()
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self.xpath_unitseq2_inner = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, 4 * config.hidden_size)
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self.inner2emb = nn.Linear(4 * config.hidden_size, config.hidden_size)
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self.xpath_tag_sub_embeddings = nn.ModuleList(
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[
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nn.Embedding(config.max_xpath_tag_unit_embeddings, config.xpath_unit_hidden_size)
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for _ in range(self.max_depth)
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]
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)
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self.xpath_subs_sub_embeddings = nn.ModuleList(
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[
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nn.Embedding(config.max_xpath_subs_unit_embeddings, config.xpath_unit_hidden_size)
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for _ in range(self.max_depth)
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]
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)
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def forward(self, xpath_tags_seq=None, xpath_subs_seq=None):
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xpath_tags_embeddings = []
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xpath_subs_embeddings = []
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for i in range(self.max_depth):
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xpath_tags_embeddings.append(self.xpath_tag_sub_embeddings[i](xpath_tags_seq[:, :, i]))
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xpath_subs_embeddings.append(self.xpath_subs_sub_embeddings[i](xpath_subs_seq[:, :, i]))
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xpath_tags_embeddings = torch.cat(xpath_tags_embeddings, dim=-1)
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xpath_subs_embeddings = torch.cat(xpath_subs_embeddings, dim=-1)
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xpath_embeddings = xpath_tags_embeddings + xpath_subs_embeddings
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xpath_embeddings = self.inner2emb(self.dropout(self.activation(self.xpath_unitseq2_inner(xpath_embeddings))))
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return xpath_embeddings
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# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
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def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
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"""
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
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are ignored. This is modified from fairseq's `utils.make_positions`.
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Args:
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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) + past_key_values_length) * mask
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return incremental_indices.long() + padding_idx
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class MarkupLMEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.config = config
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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.max_depth = config.max_depth
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self.xpath_embeddings = XPathEmbeddings(config)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_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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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.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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# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_inputs_embeds
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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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def forward(
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self,
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input_ids=None,
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xpath_tags_seq=None,
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xpath_subs_seq=None,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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past_key_values_length=0,
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):
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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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device = input_ids.device if input_ids is not None else inputs_embeds.device
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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(input_ids, self.padding_idx, past_key_values_length)
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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 token_type_ids is None:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=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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# prepare xpath seq
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if xpath_tags_seq is None:
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xpath_tags_seq = self.config.tag_pad_id * torch.ones(
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tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device
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)
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if xpath_subs_seq is None:
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xpath_subs_seq = self.config.subs_pad_id * torch.ones(
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tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device
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)
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words_embeddings = inputs_embeds
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
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embeddings = words_embeddings + position_embeddings + token_type_embeddings + xpath_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->MarkupLM
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class MarkupLMSelfOutput(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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# Copied from transformers.models.bert.modeling_bert.BertIntermediate
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class MarkupLMIntermediate(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 with Bert->MarkupLM
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class MarkupLMOutput(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:
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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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# Copied from transformers.models.bert.modeling_bert.BertPooler
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class MarkupLMPooler(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->MarkupLM
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class MarkupLMPredictionHeadTransform(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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if isinstance(config.hidden_act, str):
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self.transform_act_fn = ACT2FN[config.hidden_act]
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else:
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self.transform_act_fn = config.hidden_act
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.transform_act_fn(hidden_states)
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hidden_states = self.LayerNorm(hidden_states)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->MarkupLM
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class MarkupLMLMPredictionHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.transform = MarkupLMPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.bias = nn.Parameter(torch.zeros(config.vocab_size))
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# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
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self.decoder.bias = self.bias
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def _tie_weights(self):
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self.decoder.bias = self.bias
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def forward(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = self.decoder(hidden_states)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->MarkupLM
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class MarkupLMOnlyMLMHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.predictions = MarkupLMLMPredictionHead(config)
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def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
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prediction_scores = self.predictions(sequence_output)
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return prediction_scores
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# Copied from transformers.models.align.modeling_align.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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):
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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if head_mask is not None:
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attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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# Copied from transformers.models.align.modeling_align.AlignTextSelfAttention with AlignText->MarkupLM
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class MarkupLMSelfAttention(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.config = config
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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.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.attention_dropout = config.attention_probs_dropout_prob
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self.scaling = self.attention_head_size**-0.5
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@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
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@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
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@deprecate_kwarg("past_key_value", version="4.54.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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**kwargs,
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) -> tuple[torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.attention_head_size)
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query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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head_mask=head_mask,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
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return outputs
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# Copied from transformers.models.align.modeling_align.AlignTextAttention with AlignText->MarkupLM
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class MarkupLMAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = MarkupLMSelfAttention(config)
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self.output = MarkupLMSelfOutput(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
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.align.modeling_align.AlignTextLayer with AlignText->MarkupLM
|
|
class MarkupLMLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = MarkupLMAttention(config)
|
|
self.intermediate = MarkupLMIntermediate(config)
|
|
self.output = MarkupLMOutput(config)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
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
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
# Copied from transformers.models.align.modeling_align.AlignTextEncoder with AlignText->MarkupLM
|
|
class MarkupLMEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([MarkupLMLayer(config) for i in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_values", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
**kwargs,
|
|
) -> 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
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
|
|
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,)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class MarkupLMPreTrainedModel(PreTrainedModel):
|
|
config: MarkupLMConfig
|
|
base_model_prefix = "markuplm"
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights with Bert->MarkupLM
|
|
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)
|
|
elif isinstance(module, MarkupLMLMPredictionHead):
|
|
module.bias.data.zero_()
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
|
|
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
|
|
|
|
@auto_docstring
|
|
class MarkupLMModel(MarkupLMPreTrainedModel):
|
|
# Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->MarkupLM
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = MarkupLMEmbeddings(config)
|
|
self.encoder = MarkupLMEncoder(config)
|
|
|
|
self.pooler = MarkupLMPooler(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)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
xpath_tags_seq: Optional[torch.LongTensor] = None,
|
|
xpath_subs_seq: 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[tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Tag IDs for each token in the input sequence, padded up to config.max_depth.
|
|
xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Subscript IDs for each token in the input sequence, padded up to config.max_depth.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, MarkupLMModel
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
|
|
>>> model = MarkupLMModel.from_pretrained("microsoft/markuplm-base")
|
|
|
|
>>> html_string = "<html> <head> <title>Page Title</title> </head> </html>"
|
|
|
|
>>> encoding = processor(html_string, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
>>> list(last_hidden_states.shape)
|
|
[1, 4, 768]
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
|
|
if head_mask is not None:
|
|
if head_mask.dim() == 1:
|
|
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
|
|
head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
|
|
elif head_mask.dim() == 2:
|
|
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
|
|
head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
|
|
else:
|
|
head_mask = [None] * self.config.num_hidden_layers
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
xpath_tags_seq=xpath_tags_seq,
|
|
xpath_subs_seq=xpath_subs_seq,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel):
|
|
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with bert->markuplm, Bert->MarkupLM
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.markuplm = MarkupLMModel(config, add_pooling_layer=False)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
xpath_tags_seq: Optional[torch.Tensor] = None,
|
|
xpath_subs_seq: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
|
r"""
|
|
xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Tag IDs for each token in the input sequence, padded up to config.max_depth.
|
|
xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Subscript IDs for each token in the input sequence, padded up to config.max_depth.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, MarkupLMForQuestionAnswering
|
|
>>> import torch
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
|
|
>>> model = MarkupLMForQuestionAnswering.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
|
|
|
|
>>> html_string = "<html> <head> <title>My name is Niels</title> </head> </html>"
|
|
>>> question = "What's his name?"
|
|
|
|
>>> encoding = processor(html_string, questions=question, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... 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]
|
|
>>> processor.decode(predict_answer_tokens).strip()
|
|
'Niels'
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.markuplm(
|
|
input_ids,
|
|
xpath_tags_seq=xpath_tags_seq,
|
|
xpath_subs_seq=xpath_subs_seq,
|
|
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=True,
|
|
)
|
|
|
|
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.clamp_(0, ignored_index)
|
|
end_positions.clamp_(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
MarkupLM Model with a `token_classification` head on top.
|
|
"""
|
|
)
|
|
class MarkupLMForTokenClassification(MarkupLMPreTrainedModel):
|
|
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with bert->markuplm, Bert->MarkupLM
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.markuplm = MarkupLMModel(config, add_pooling_layer=False)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
xpath_tags_seq: Optional[torch.Tensor] = None,
|
|
xpath_subs_seq: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
|
|
r"""
|
|
xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Tag IDs for each token in the input sequence, padded up to config.max_depth.
|
|
xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Subscript IDs for each token in the input sequence, padded up to config.max_depth.
|
|
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]`.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForTokenClassification
|
|
>>> import torch
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
|
|
>>> processor.parse_html = False
|
|
>>> model = AutoModelForTokenClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)
|
|
|
|
>>> nodes = ["hello", "world"]
|
|
>>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]
|
|
>>> node_labels = [1, 2]
|
|
>>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**encoding)
|
|
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.markuplm(
|
|
input_ids,
|
|
xpath_tags_seq=xpath_tags_seq,
|
|
xpath_subs_seq=xpath_subs_seq,
|
|
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=True,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.classifier(sequence_output) # (batch_size, seq_length, node_type_size)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(
|
|
prediction_scores.view(-1, self.config.num_labels),
|
|
labels.view(-1),
|
|
)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
MarkupLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
"""
|
|
)
|
|
class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel):
|
|
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with bert->markuplm, Bert->MarkupLM
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.markuplm = MarkupLMModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
xpath_tags_seq: Optional[torch.Tensor] = None,
|
|
xpath_subs_seq: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
r"""
|
|
xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Tag IDs for each token in the input sequence, padded up to config.max_depth.
|
|
xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
|
|
Subscript IDs for each token in the input sequence, padded up to config.max_depth.
|
|
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).
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForSequenceClassification
|
|
>>> import torch
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
|
|
>>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)
|
|
|
|
>>> html_string = "<html> <head> <title>Page Title</title> </head> </html>"
|
|
>>> encoding = processor(html_string, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**encoding)
|
|
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.markuplm(
|
|
input_ids,
|
|
xpath_tags_seq=xpath_tags_seq,
|
|
xpath_subs_seq=xpath_subs_seq,
|
|
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=True,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"MarkupLMForQuestionAnswering",
|
|
"MarkupLMForSequenceClassification",
|
|
"MarkupLMForTokenClassification",
|
|
"MarkupLMModel",
|
|
"MarkupLMPreTrainedModel",
|
|
]
|