# coding=utf-8 # Copyright 2010, The Microsoft Research Asia LayoutLM Team authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """LayoutLM model configuration""" from collections import OrderedDict from typing import Any, List, Mapping, Optional from ... import PretrainedConfig, PreTrainedTokenizer from ...onnx import OnnxConfig, PatchingSpec from ...utils import TensorType, is_torch_available, logging logger = logging.get_logger(__name__) class LayoutLMConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LayoutLM [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture. Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the documentation from [`BertConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`LayoutLMModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed into [`LayoutLMModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): The value used to pad input_ids. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the 2D position embedding might ever used. Typically set this to something large just in case (e.g., 1024). Examples: ```python >>> from transformers import LayoutLMConfig, LayoutLMModel >>> # Initializing a LayoutLM configuration >>> configuration = LayoutLMConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = LayoutLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "layoutlm" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, max_2d_position_embeddings=1024, **kwargs, ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.use_cache = use_cache self.max_2d_position_embeddings = max_2d_position_embeddings class LayoutLMOnnxConfig(OnnxConfig): def __init__( self, config: PretrainedConfig, task: str = "default", patching_specs: List[PatchingSpec] = None, ): super().__init__(config, task=task, patching_specs=patching_specs) self.max_2d_positions = config.max_2d_position_embeddings - 1 @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("bbox", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ("token_type_ids", {0: "batch", 1: "sequence"}), ] ) def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: """ Generate inputs to provide to the ONNX exporter for the specific framework Args: tokenizer: The tokenizer associated with this model configuration batch_size: The batch size (int) to export the model for (-1 means dynamic axis) seq_length: The sequence length (int) to export the model for (-1 means dynamic axis) is_pair: Indicate if the input is a pair (sentence 1, sentence 2) framework: The framework (optional) the tokenizer will generate tensor for Returns: Mapping[str, Tensor] holding the kwargs to provide to the model's forward function """ input_dict = super().generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) # Generate a dummy bbox box = [48, 84, 73, 128] if not framework == TensorType.PYTORCH: raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.") if not is_torch_available(): raise ValueError("Cannot generate dummy inputs without PyTorch installed.") import torch batch_size, seq_length = input_dict["input_ids"].shape input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1) return input_dict __all__ = ["LayoutLMConfig", "LayoutLMOnnxConfig"]