# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # 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. """LayoutLMv3 model configuration""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...utils import logging if TYPE_CHECKING: from ...processing_utils import ProcessorMixin from ...utils import TensorType logger = logging.get_logger(__name__) class LayoutLMv3Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an LayoutLMv3 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 LayoutLMv3 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50265): Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LayoutLMv3Model`]. hidden_size (`int`, *optional*, defaults to 768): Dimension 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): Dimension 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"`, `"selu"` 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 when calling [`LayoutLMv3Model`]. 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-5): The epsilon used by the layer normalization layers. max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024). coordinate_size (`int`, *optional*, defaults to `128`): Dimension of the coordinate embeddings. shape_size (`int`, *optional*, defaults to `128`): Dimension of the width and height embeddings. has_relative_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a relative attention bias in the self-attention mechanism. rel_pos_bins (`int`, *optional*, defaults to 32): The number of relative position bins to be used in the self-attention mechanism. max_rel_pos (`int`, *optional*, defaults to 128): The maximum number of relative positions to be used in the self-attention mechanism. max_rel_2d_pos (`int`, *optional*, defaults to 256): The maximum number of relative 2D positions in the self-attention mechanism. rel_2d_pos_bins (`int`, *optional*, defaults to 64): The number of 2D relative position bins in the self-attention mechanism. has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a spatial attention bias in the self-attention mechanism. visual_embed (`bool`, *optional*, defaults to `True`): Whether or not to add patch embeddings. input_size (`int`, *optional*, defaults to `224`): The size (resolution) of the images. num_channels (`int`, *optional*, defaults to `3`): The number of channels of the images. patch_size (`int`, *optional*, defaults to `16`) The size (resolution) of the patches. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. Example: ```python >>> from transformers import LayoutLMv3Config, LayoutLMv3Model >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration >>> configuration = LayoutLMv3Config() >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration >>> model = LayoutLMv3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "layoutlmv3" def __init__( self, vocab_size=50265, 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-5, pad_token_id=1, bos_token_id=0, eos_token_id=2, max_2d_position_embeddings=1024, coordinate_size=128, shape_size=128, has_relative_attention_bias=True, rel_pos_bins=32, max_rel_pos=128, rel_2d_pos_bins=64, max_rel_2d_pos=256, has_spatial_attention_bias=True, text_embed=True, visual_embed=True, input_size=224, num_channels=3, patch_size=16, classifier_dropout=None, **kwargs, ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_eps=layer_norm_eps, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.max_2d_position_embeddings = max_2d_position_embeddings self.coordinate_size = coordinate_size self.shape_size = shape_size self.has_relative_attention_bias = has_relative_attention_bias self.rel_pos_bins = rel_pos_bins self.max_rel_pos = max_rel_pos self.has_spatial_attention_bias = has_spatial_attention_bias self.rel_2d_pos_bins = rel_2d_pos_bins self.max_rel_2d_pos = max_rel_2d_pos self.text_embed = text_embed self.visual_embed = visual_embed self.input_size = input_size self.num_channels = num_channels self.patch_size = patch_size self.classifier_dropout = classifier_dropout class LayoutLMv3OnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.12") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: # The order of inputs is different for question answering and sequence classification if self.task in ["question-answering", "sequence-classification"]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ("bbox", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) else: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("bbox", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels"}), ] ) @property def atol_for_validation(self) -> float: return 1e-5 @property def default_onnx_opset(self) -> int: return 12 def generate_dummy_inputs( self, processor: "ProcessorMixin", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional["TensorType"] = None, num_channels: int = 3, image_width: int = 40, image_height: int = 40, ) -> Mapping[str, Any]: """ Generate inputs to provide to the ONNX exporter for the specific framework Args: processor ([`ProcessorMixin`]): The processor associated with this model configuration. batch_size (`int`, *optional*, defaults to -1): The batch size to export the model for (-1 means dynamic axis). seq_length (`int`, *optional*, defaults to -1): The sequence length to export the model for (-1 means dynamic axis). is_pair (`bool`, *optional*, defaults to `False`): Indicate if the input is a pair (sentence 1, sentence 2). framework (`TensorType`, *optional*, defaults to `None`): The framework (PyTorch or TensorFlow) that the processor will generate tensors for. num_channels (`int`, *optional*, defaults to 3): The number of channels of the generated images. image_width (`int`, *optional*, defaults to 40): The width of the generated images. image_height (`int`, *optional*, defaults to 40): The height of the generated images. Returns: Mapping[str, Any]: holding the kwargs to provide to the model's forward function """ # A dummy image is used so OCR should not be applied setattr(processor.image_processor, "apply_ocr", False) # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = processor.tokenizer.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence dummy_text = [[" ".join([processor.tokenizer.unk_token]) * seq_length]] * batch_size # Generate dummy bounding boxes dummy_bboxes = [[[48, 84, 73, 128]]] * batch_size # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) inputs = dict( processor( dummy_image, text=dummy_text, boxes=dummy_bboxes, return_tensors=framework, ) ) return inputs __all__ = ["LayoutLMv3Config", "LayoutLMv3OnnxConfig"]