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