# Copyright 2023 Adept AI 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. """Fuyu model configuration""" from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging from ..auto import CONFIG_MAPPING, AutoConfig logger = logging.get_logger(__name__) class FuyuConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`FuyuForCausalLM`]. It is used to instantiate an Fuyu 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 [adept/fuyu-8b](https://huggingface.co/adept/fuyu-8b). 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 262144): Vocabulary size of the Fuyu model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FuyuForCausalLM`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 16384): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that this model might ever be used with. image_size (`int`, *optional*, defaults to 300): The input image size. patch_size (`int`, *optional*, defaults to 30): The input vision transformer encoding patch size. num_channels (`int`, *optional*, defaults to 3): The input image number of channels. 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-05): The epsilon used by the rms normalization layers. 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`. Whether to tie weight embeddings tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie input and output embeddings. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. qk_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to normalize the Queries and Keys after projecting the hidden states hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after applying the MLP to the hidden states. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after computing the attention scores. pad_token_id (`int`, *optional*): The id of the *padding* token. bos_token_id (`int`, *optional*, defaults to 1): The id of the *beginning-of-sequence* token. eos_token_id (`Union[int, list[int]]`, *optional*, defaults to 2): The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens. image_token_id (`int`, *optional*, defaults to 71011): The id of the image placeholder token. text_config (`dict`, *optional*): Dictionary of configuration options used to initialize the `language``[`Aut`]. ```python >>> from transformers import FuyuConfig >>> # Initializing a Fuyu fuyu-7b style configuration >>> configuration = FuyuConfig() ```""" model_type = "fuyu" sub_configs = {"text_config": AutoConfig} keys_to_ignore_at_inference = ["past_key_values"] default_theta = 25000.0 def __init__( self, vocab_size: int | None = 262144, hidden_size: int | None = 4096, intermediate_size: int | None = 16384, num_hidden_layers: int | None = 36, num_attention_heads: int | None = 64, hidden_act: str | None = "relu2", max_position_embeddings: int | None = 16384, image_size: int | None = 300, patch_size: int | None = 30, num_channels: int | None = 3, initializer_range: float | None = 0.02, layer_norm_eps: int | None = 1e-5, use_cache: bool | None = True, tie_word_embeddings: bool | None = False, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, qk_layernorm: bool | None = True, hidden_dropout: float | None = 0.0, attention_dropout: float | None = 0.0, pad_token_id: int | None = None, bos_token_id: int | None = 1, eos_token_id: int | None = 2, image_token_id: int | None = 71011, text_config: dict | None = None, **kwargs, ): if text_config is None: text_config = { "vocab_size": vocab_size, "max_position_embeddings": max_position_embeddings, "hidden_size": hidden_size, "intermediate_size": intermediate_size, "num_hidden_layers": num_hidden_layers, "num_attention_heads": num_attention_heads, "hidden_act": hidden_act, "initializer_range": initializer_range, "layer_norm_eps": layer_norm_eps, "use_cache": use_cache, "rope_parameters": rope_parameters, "qk_layernorm": qk_layernorm, "hidden_dropout": hidden_dropout, "attention_dropout": attention_dropout, "pad_token_id": pad_token_id, "bos_token_id": bos_token_id, "eos_token_id": eos_token_id, } logger.info("text_config is None. initializing the text model with default values.") text_model_type = text_config.get("model_type", "persimmon") self.text_config = CONFIG_MAPPING[text_model_type](**text_config) self._vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.qk_layernorm = qk_layernorm self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.image_token_id = image_token_id self.rope_parameters = rope_parameters kwargs.setdefault("partial_rotary_factor", 0.5) # assign default for BC self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) __all__ = ["FuyuConfig"]