from typing import List, Union
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class HunyuanImage3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HunyuanImage3Model`]. 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.
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 32000):
Vocabulary size of the Hunyuan Image 3 model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`HunyuanImage3Model`]
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.
moe_intermediate_size (`int` or `List`, *optional*, defaults to 11008):
Dimension of the MLP representations in MoE. Use a list if you want a different size per layer.
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://arxiv.org/pdf/2305.13245.pdf). 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-06):
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*):
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.
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.
use_qk_norm (`bool`, *optional*, defaults to `False`):
Whether query and key in attention use norm
use_cla (`bool`, *optional*, defaults to `False`):
Whether to use CLA in attention
cla_share_factor (`int`, *optional*, defaults to 1):
The share factor of CLA
num_experts (`int` or `List`, *optional*, defaults to 1):
The number of experts for moe. If it is a list, it will be used as the number of experts for each layer.
num_shared_expert (`int` or `List`, *optional*, defaults to 1):
The number of shared experts for moe. If it is a list, it will be used as the number of shared experts
for each layer.
moe_topk (`int` or `List`, *optional*, defaults to 1):
The topk value for moe. If it is a list, it will be used as the topk value for each layer.
capacity_factor (Not used) (`float` or `List`, *optional*, defaults to 1.0):
The capacity factor for moe. If it is a list, it will be used as the capacity factor for each layer.
moe_layer_num_skipped (`int`, *optional*, defaults to 0):
First moe_layer_num_skipped layers do not use MoE.
"""
model_type = "Hunyuan"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=290943,
hidden_size=4096,
intermediate_size: int = 11008,
moe_intermediate_size: Union[int, List] = None,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
attention_head_dim=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,
im_start_id=4,
im_end_id=5,
text_start_id=6,
text_end_id=7,
image_token_id=8,
video_start_id=9,
video_end_id=10,
im_newline_id=11,
mask_init_id=12,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
mlp_bias=False,
attention_dropout=0.0,
use_qk_norm=False,
use_rotary_pos_emb=True,
use_cla=False,
cla_share_factor=1,
norm_type="hf_rms",
num_experts: Union[int, List] = 1,
use_mixed_mlp_moe=False,
num_shared_expert: Union[int, List] = 1,
moe_topk: Union[int, List] = 1,
capacity_factor: int = 1.0,
moe_drop_tokens=False,
moe_random_routing_dropped_token=False,
use_mla=False,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
moe_layer_num_skipped=0,
norm_topk_prob=True,
routed_scaling_factor=1.0,
group_limited_greedy=False,
n_group=None,
topk_group=None,
add_classification_head=False,
class_num=0,
pool_type="last",
pad_id=-1,
moe_impl="eager",
vae_downsample_factor=(16, 16),
img_proj_type="unet",
patch_size=1,
patch_embed_hidden_dim=1024,
image_base_size=1024,
vae=None,
vit=None,
vit_processor=None,
vit_aligner=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.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.moe_impl = moe_impl
self.num_experts = num_experts
self.use_mixed_mlp_moe = use_mixed_mlp_moe
self.num_shared_expert = num_shared_expert
self.moe_topk = moe_topk
self.capacity_factor = capacity_factor
self.moe_drop_tokens = moe_drop_tokens
self.moe_random_routing_dropped_token = moe_random_routing_dropped_token
if attention_head_dim is not None:
self.attention_head_dim = attention_head_dim
else:
self.attention_head_dim = self.hidden_size // num_attention_heads
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.attention_bias = attention_bias
self.mlp_bias = mlp_bias
self.attention_dropout = attention_dropout
self.use_qk_norm = use_qk_norm
self.use_rotary_pos_emb = use_rotary_pos_emb
self.use_cla = use_cla
self.cla_share_factor = cla_share_factor
self.norm_type = norm_type
self.use_mla = use_mla
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.v_head_dim = v_head_dim
self.moe_layer_num_skipped = moe_layer_num_skipped
self.norm_topk_prob = norm_topk_prob
self.routed_scaling_factor = routed_scaling_factor
self.group_limited_greedy = group_limited_greedy
self.n_group = n_group
self.topk_group = topk_group
self.add_classification_head = add_classification_head
self.class_num = class_num
self.pool_type = pool_type
self.pad_id = pad_id
if self.class_num is not None:
self.dense_list = [self.hidden_size, self.class_num]
self.vit = vit
self.vit_processor = vit_processor
self.vit_aligner = vit_aligner
self.vae = vae
self.vae_downsample_factor = vae_downsample_factor
self.img_proj_type = img_proj_type
self.patch_size = patch_size
self.patch_embed_hidden_dim = patch_embed_hidden_dim
self.image_base_size = image_base_size
self.eod_token_id = eod_token_id
self.im_start_id = im_start_id
self.im_end_id = im_end_id
self.text_start_id = text_start_id
self.text_end_id = text_end_id
self.image_token_id = image_token_id
self.video_start_id = video_start_id
self.video_end_id = video_end_id
self.im_newline_id = im_newline_id
self.mask_init_id = mask_init_id
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,
)
