199 lines
9.0 KiB
Python
199 lines
9.0 KiB
Python
# coding=utf-8
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# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
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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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""" LayoutLM model configuration"""
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from collections import OrderedDict
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from typing import Any, List, Mapping, Optional
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from ... import PretrainedConfig, PreTrainedTokenizer
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from ...onnx import OnnxConfig, PatchingSpec
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from ...utils import TensorType, is_torch_available, logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class LayoutLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
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LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the LayoutLM
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[microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.
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Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
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documentation from [`BertConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
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*inputs_ids* passed to the forward method of [`LayoutLMModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed into [`LayoutLMModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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pad_token_id (`int`, *optional*, defaults to 0):
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The value used to pad input_ids.
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum value that the 2D position embedding might ever used. Typically set this to something large
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just in case (e.g., 1024).
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Examples:
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```python
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>>> from transformers import LayoutLMConfig, LayoutLMModel
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>>> # Initializing a LayoutLM configuration
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>>> configuration = LayoutLMConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = LayoutLMModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "layoutlm"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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position_embedding_type="absolute",
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use_cache=True,
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max_2d_position_embeddings=1024,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.max_2d_position_embeddings = max_2d_position_embeddings
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class LayoutLMOnnxConfig(OnnxConfig):
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def __init__(
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self,
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config: PretrainedConfig,
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task: str = "default",
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patching_specs: List[PatchingSpec] = None,
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):
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super().__init__(config, task=task, patching_specs=patching_specs)
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self.max_2d_positions = config.max_2d_position_embeddings - 1
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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return OrderedDict(
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[
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("input_ids", {0: "batch", 1: "sequence"}),
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("bbox", {0: "batch", 1: "sequence"}),
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("attention_mask", {0: "batch", 1: "sequence"}),
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("token_type_ids", {0: "batch", 1: "sequence"}),
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]
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)
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def generate_dummy_inputs(
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self,
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tokenizer: PreTrainedTokenizer,
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batch_size: int = -1,
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seq_length: int = -1,
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is_pair: bool = False,
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framework: Optional[TensorType] = None,
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) -> Mapping[str, Any]:
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"""
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Generate inputs to provide to the ONNX exporter for the specific framework
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Args:
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tokenizer: The tokenizer associated with this model configuration
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batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
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seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
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is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
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framework: The framework (optional) the tokenizer will generate tensor for
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Returns:
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Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
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"""
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input_dict = super().generate_dummy_inputs(
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tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
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)
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# Generate a dummy bbox
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box = [48, 84, 73, 128]
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if not framework == TensorType.PYTORCH:
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raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")
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if not is_torch_available():
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raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
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import torch
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batch_size, seq_length = input_dict["input_ids"].shape
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input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1)
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return input_dict
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