| |
| |
| """ HunYuan model configuration""" |
| from torch import nn |
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
| from typing import List, Union, Optional |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class HunYuanConfig(PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`HunYuanModel`]. 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 model. Defines the number of different tokens that can be represented by the |
| `inputs_ids` passed when calling [`HunYuanModel`] |
| 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, |
| org_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, |
| sep_token_id=4, |
| im_start_id=5, |
| im_end_id=6, |
| text_start_id=7, |
| text_end_id=8, |
| image_token_id=9, |
| video_start_id=10, |
| video_end_id=11, |
| im_newline_id=12, |
| mask_init_id=13, |
| 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, |
| |
| 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, |
| vit_path=None, |
| num_media_embeds=257, |
| vit_type="AnyResVit", |
| vit_input_resolution=224, |
| vit_token=64, |
| vit_patch=1, |
| vit_mapping_type="simple_conv_mlp", |
| vit_norm_type="fused", |
| vit_used_rms_norm=True, |
| vit_remove_prenorm=True, |
| vit_add_patchemb_bias=True, |
| anyres_vit_max_image_size=2048, |
| anyres_pooling_size=2, |
| anyres_vit_two_views=False, |
| skip_cls_token=False, |
| position_embedding_xdrope=False, |
| xdrope_section=None, |
| add_classification_head=False, |
| class_num=0, |
| pool_type="last", |
| pad_id=-1, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| self.org_vocab_size = org_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.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.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_path = vit_path |
| self.num_media_embeds = num_media_embeds |
| self.vit_type = vit_type |
| self.vit_input_resolution = vit_input_resolution |
| self.vit_token = vit_token |
| self.vit_patch = vit_patch |
| self.vit_mapping_type = vit_mapping_type |
| self.vit_norm_type = vit_norm_type |
| self.vit_used_rms_norm = vit_used_rms_norm |
| self.vit_remove_prenorm = vit_remove_prenorm |
| self.vit_add_patchemb_bias = vit_add_patchemb_bias |
| self.anyres_vit_max_image_size = anyres_vit_max_image_size |
| self.anyres_pooling_size = anyres_pooling_size |
| self.anyres_vit_two_views = anyres_vit_two_views |
| self.skip_cls_token = skip_cls_token |
| self.position_embedding_xdrope = position_embedding_xdrope |
| self.xdrope_section = xdrope_section |
|
|
| |
| 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, |
| sep_token_id=sep_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 `factor` or `type` and `alpha`, " |
| f"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( |
| f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], 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: |
| if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: |
| raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}") |
| if rope_scaling_alpha is not None: |
| if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0: |
| raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}") |
|
|
