# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py from transformers import PretrainedConfig class HunYuanVLVisionConfig(PretrainedConfig): model_type = "hunyuan_vl" base_config_key = "vision_config" def __init__( self, hidden_act="gelu", hidden_size=1152, intermediate_size=4304, interpolate_mode="bilinear", rms_norm_eps=1e-05, learnable_mlp_pooling_size=0, num_attention_heads=16, num_key_value_heads=None, num_channels=3, num_hidden_layers=27, out_hidden_size=4096, patch_size=16, remove_prenorm=True, spatial_merge_size=2, temporal_patch_size=1, resize_resolution=2048, img_max_token_num=4096, max_image_size=2048, video_max_image_size=768, video_min_image_size=256, min_image_size=512, anyres_vit_max_image_size=2048, max_vit_seq_len=16384, text_hidden_size=3072, **kwargs, ): super().__init__(**kwargs) self.hidden_act = hidden_act self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.interpolate_mode = interpolate_mode self.learnable_mlp_pooling_size = learnable_mlp_pooling_size self.num_attention_heads = num_attention_heads if not num_key_value_heads: self.num_key_value_heads = num_attention_heads else: self.num_key_value_heads = num_key_value_heads self.num_channels = num_channels self.num_hidden_layers = num_hidden_layers self.out_hidden_size = out_hidden_size self.patch_size = patch_size self.remove_prenorm = remove_prenorm self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.rms_norm_eps = rms_norm_eps self.resize_resolution = resize_resolution self.img_max_token_num = img_max_token_num self.max_image_size = max_image_size self.min_image_size = min_image_size self.video_max_image_size = video_max_image_size self.video_min_image_size = video_min_image_size self.anyres_vit_max_image_size = anyres_vit_max_image_size self.max_vit_seq_len = max_vit_seq_len self.text_hidden_size = text_hidden_size class HunYuanVLTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`HunYuanVLTextConfig`]. It is used to instantiate an HunYuan 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 HunYuan-7B. Hunyuan-7B-Instruct [tencent/Hunyuan-7B-Instruct](https://huggingface.co/tencent/Hunyuan-7B-Instruct). 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 290943): Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`HunYuanVLTextConfig`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations or shared 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*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_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`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. eod_token_id (int, *optional*, defaults to 3): Token ID representing the end-of-document marker. Used to indicate the termination of a text sequence. Example: In multi-document processing, this token helps the model distinguish between separate documents. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): 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/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. head_dim (`int`, *optional*, defaults to 128): The attention head dimension. """ # noqa: E501 model_type = "hunyuan_vl_text" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=290943, hidden_size=4096, intermediate_size: int = 11008, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, eod_token_id=3, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, head_dim=None, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings 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.head_dim = head_dim # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling # self._rope_scaling_validation() # TODO: Need validation? self.attention_bias = attention_bias self.attention_dropout = attention_dropout 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( "`rope_scaling` must be a dictionary with with two fields, `type` and " f"`factor` or `type` and `alpha`, got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_factor = self.rope_scaling.get("factor", None) rope_scaling_alpha = self.rope_scaling.get("alpha", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( "`rope_scaling`'s type field must be one of ['linear', 'dynamic'], " f"got {rope_scaling_type}" ) if rope_scaling_factor is None and rope_scaling_alpha is None: raise ValueError( "`rope_scaling`'s factor or alpha field must be have one, " "got both of none" ) if rope_scaling_factor is not None and ( not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0 ): raise ValueError( "`rope_scaling`'s factor field must be a float > 1.0, " f"got {rope_scaling_factor}" ) if rope_scaling_alpha is not None and ( not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0 ): raise ValueError( "`rope_scaling`'s alpha field must be a float > 1.0, " f"got {rope_scaling_alpha}" ) class HunYuanVLConfig(PretrainedConfig): model_type = "hunyuan_vl" sub_configs = { "vision_config": HunYuanVLVisionConfig, "text_config": HunYuanVLTextConfig, } keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, im_start_id=120118, im_end_id=120119, image_token_id=120120, im_newline_id=120121, video_start_id=120122, video_end_id=120123, **kwargs, ): # We need to init super() here so that it does not reset values # that are in text config to the BaseClass defaults. The Base # config has many text related defaults and not all defaults are # same as for `HunYuanVLTextConfig`. super().__init__(**kwargs) if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() if isinstance(text_config, dict): self.text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: # For BC use all kwargs to init `TextConfig` self.text_config = self.sub_configs["text_config"](**kwargs) self.image_token_id = image_token_id self.im_start_id = im_start_id self.im_end_id = im_end_id self.im_newline_id = im_newline_id self.video_start_id = video_start_id self.video_end_id = video_end_id self.vision_config.text_hidden_size = self.text_config.hidden_size # Attention implementation to use. It sets it recursively on sub-configs # so we call it again in the end. self._attn_implementation = kwargs.pop("attn_implementation", None) def __setattr__(self, key, value): if ( (text_config := super().__getattribute__("__dict__").get("text_config")) is not None and key not in ["dtype", "_attn_implementation_internal"] and key in text_config.__dict__ ): setattr(text_config, key, value) else: super().__setattr__(key, value) def __getattribute__(self, key): if "text_config" in super().__getattribute__("__dict__") and key not in [ "_name_or_path", "model_type", "dtype", "_attn_implementation_internal", ]: text_config = super().__getattribute__("text_config") if key in text_config.__dict__: return getattr(text_config, key) return super().__getattribute__(key)