configuration_hunyuan.py

# coding=utf-8
# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
""" 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,
        # capacity_factor: Union[int, List]=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,
        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.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

        # 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.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
        # MLA args
        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

        # DeepSeek related args
        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]

        # Vit args
        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

        # token id
        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}")