# coding=utf-8 # Copyright 2024 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. """ Spec-Vision model configuration""" from typing import Dict, Optional, Union from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class SpecVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SpecVisionModel`]. It is used to instantiate a Spec-Vision model according to the specified arguments, defining the model 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 32064): Vocabulary size of the model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SpecVisionModel`]. hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): Number of key/value heads for implementing Grouped Query Attention. resid_pdrop (`float`, *optional*, defaults to 0.0): Dropout probability for MLP outputs. embd_pdrop (`float`, *optional*, defaults to 0.0): The dropout ratio for embeddings. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after computing attention scores. hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function in the decoder. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon value used for RMSNorm. use_cache (`bool`, *optional*, defaults to `True`): Whether to use the past key/values attentions for faster inference. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`dict`, *optional*): Configuration for RoPE scaling strategy. embd_layer (`dict`, *optional*): Configuration for the embedding layer, including image embedding settings. """ model_type = "spec_vision" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int = 32064, hidden_size: int = 3072, intermediate_size: int = 8192, num_hidden_layers: int = 32, num_attention_heads: int = 32, num_key_value_heads: Optional[int] = None, resid_pdrop: float = 0.0, embd_pdrop: float = 0.0, attention_dropout: float = 0.0, hidden_act: str = "silu", max_position_embeddings: int = 4096, initializer_range: float = 0.02, rms_norm_eps: float = 1e-5, use_cache: bool = True, rope_theta: float = 10000.0, rope_scaling: Optional[Dict] = None, embd_layer: Dict[str, Union[str, bool]] = { "embedding_cls": "image", "hd_transform_order": "sub_glb", "projection_cls": "mlp", "use_hd_transform": True, "with_learnable_separator": True }, bos_token_id: int = 1, eos_token_id: int = 32000, pad_token_id: int = 32000, tie_word_embeddings: bool = False, **kwargs, ): self.vocab_size = vocab_size 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.num_key_value_heads = num_key_value_heads or num_attention_heads self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attention_dropout = attention_dropout self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.embd_layer = embd_layer super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_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) != 3: raise ValueError( "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, " f"got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_short_factor = self.rope_scaling.get("short_factor", None) rope_scaling_long_factor = self.rope_scaling.get("long_factor", None) if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]: raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}") head_dim = self.hidden_size // self.num_attention_heads // 2 for factor, name in [(rope_scaling_short_factor, "short_factor"), (rope_scaling_long_factor, "long_factor")]: if not (isinstance(factor, list) and all(isinstance(x, (int, float)) for x in factor)): raise ValueError(f"`rope_scaling`'s {name} field must be a list of numbers, got {factor}") if len(factor) != head_dim: raise ValueError(f"`rope_scaling`'s {name} field must have length {head_dim}, got {len(factor)}")