# coding=utf-8 # 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 ...utils import logging from ..auto import CONFIG_MAPPING 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_theta (`float`, *optional*, defaults to 25000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. 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. partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding. 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. 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" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=262144, hidden_size=4096, intermediate_size=16384, num_hidden_layers=36, num_attention_heads=64, hidden_act="relu2", max_position_embeddings=16384, image_size=300, patch_size=30, num_channels=3, initializer_range=0.02, layer_norm_eps=1e-5, use_cache=True, tie_word_embeddings=False, rope_theta=25000.0, rope_scaling=None, qk_layernorm=True, hidden_dropout=0.0, attention_dropout=0.0, partial_rotary_factor=0.5, pad_token_id=None, bos_token_id=1, eos_token_id=2, text_config=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_theta": rope_theta, "rope_scaling": rope_scaling, "qk_layernorm": qk_layernorm, "hidden_dropout": hidden_dropout, "attention_dropout": attention_dropout, "partial_rotary_factor": partial_rotary_factor, "pad_token_id": pad_token_id, "bos_token_id": bos_token_id, "eos_token_id": eos_token_id, "tie_word_embeddings": tie_word_embeddings, } logger.info("text_config is None. initializing the text model with default values.") text_model_type = text_config["model_type"] if "model_type" in text_config else "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.rope_theta = rope_theta self.rope_scaling = rope_scaling self.qk_layernorm = qk_layernorm self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.partial_rotary_factor = partial_rotary_factor self._rope_scaling_validation() super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) def _rope_scaling_validation(self): """ Validate the `rope_scaling` configuration. """ if self.rope_scaling is None: return if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: raise ValueError( f"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_factor = self.rope_scaling.get("factor", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" ) if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") __all__ = ["FuyuConfig"]