# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # 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. """UDOP model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class UdopConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`UdopForConditionalGeneration`]. It is used to instantiate a UDOP 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 UDOP [microsoft/udop-large](https://huggingface.co/microsoft/udop-large) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 33201): Vocabulary size of the UDOP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`UdopForConditionalGeneration`]. d_model (`int`, *optional*, defaults to 1024): Size of the encoder layers and the pooler layer. d_kv (`int`, *optional*, defaults to 64): Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will be defined as `num_heads * d_kv`. d_ff (`int`, *optional*, defaults to 4096): Size of the intermediate feed forward layer in each `UdopBlock`. num_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder and decoder. num_decoder_layers (`int`, *optional*): Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. num_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder and decoder. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. relative_bias_args (`List[dict]`, *optional*, defaults to `[{'type': '1d'}, {'type': 'horizontal'}, {'type': 'vertical'}]`): A list of dictionaries containing the arguments for the relative bias layers. dropout_rate (`float`, *optional*, defaults to 0.1): The ratio for all dropout layers. layer_norm_epsilon (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). feed_forward_proj (`string`, *optional*, defaults to `"relu"`): Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. Udopv1.1 uses the `"gated-gelu"` feed forward projection. Original Udop uses `"relu"`. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model should behave as an encoder/decoder or not. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 1): The id of the end-of-sequence token in the vocabulary. max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum absolute position embeddings for relative position encoding. image_size (`int`, *optional*, defaults to 224): The size of the input images. patch_size (`int`, *optional*, defaults to 16): The patch size used by the vision encoder. num_channels (`int`, *optional*, defaults to 3): The number of channels in the input images. """ model_type = "udop" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} def __init__( self, vocab_size=33201, d_model=1024, d_kv=64, d_ff=4096, num_layers=24, num_decoder_layers=None, num_heads=16, relative_attention_num_buckets=32, relative_attention_max_distance=128, relative_bias_args=[{"type": "1d"}, {"type": "horizontal"}, {"type": "vertical"}], dropout_rate=0.1, layer_norm_epsilon=1e-6, initializer_factor=1.0, feed_forward_proj="relu", is_encoder_decoder=True, use_cache=True, pad_token_id=0, eos_token_id=1, max_2d_position_embeddings=1024, image_size=224, patch_size=16, num_channels=3, **kwargs, ): self.vocab_size = vocab_size self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff self.num_layers = num_layers self.num_decoder_layers = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry self.num_heads = num_heads self.relative_attention_num_buckets = relative_attention_num_buckets self.relative_attention_max_distance = relative_attention_max_distance self.dropout_rate = dropout_rate self.layer_norm_epsilon = layer_norm_epsilon self.initializer_factor = initializer_factor self.feed_forward_proj = feed_forward_proj self.use_cache = use_cache # UDOP attributes self.max_2d_position_embeddings = max_2d_position_embeddings self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels if not isinstance(relative_bias_args, list): raise TypeError("`relative_bias_args` should be a list of dictionaries.") self.relative_bias_args = relative_bias_args act_info = self.feed_forward_proj.split("-") self.dense_act_fn = act_info[-1] self.is_gated_act = act_info[0] == "gated" if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2: raise ValueError( f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. " "'gated-gelu' or 'relu'" ) super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, **kwargs, ) __all__ = ["UdopConfig"]