sgl-project--sglang/python/sglang/srt/models/hunyuan.py

# coding=utf-8
# Copyright 2024 The HunYuan team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only HunYuan model compatible with HuggingFace weights."""

import re
from typing import Any, Dict, Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig

from sglang.srt.distributed import (
    tensor_model_parallel_all_reduce,
)
from sglang.srt.eplb.expert_distribution import ExpertDistributionRecorder
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
from sglang.srt.layers.moe.topk import TopK
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.sampler import create_sampler
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import (
    default_weight_loader,
    kv_cache_scales_loader,
    maybe_remap_kv_scale_name,
)
from sglang.srt.runtime_context import get_parallel
from sglang.srt.utils import is_hip
from sglang.srt.utils.hf_transformers_utils import get_rope_config

expert_distribution_recorder = ExpertDistributionRecorder()


def _is_moe(config: PretrainedConfig) -> bool:
    if getattr(config, "num_experts", None) and (
        (isinstance(config.num_experts, int) and config.num_experts > 1)
        or (isinstance(config.num_experts, list) and max(config.num_experts) > 1)
    ):
        return True
    else:
        return False


def _get_cla_factor(config: PretrainedConfig) -> int:
    if not getattr(config, "use_cla", False):
        return 1
    return getattr(config, "cla_share_factor", 1)


class HunYuanMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
        prefix: str = "",
        reduce_results: bool = True,
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[intermediate_size] * 2,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            input_size=intermediate_size,
            output_size=hidden_size,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
            reduce_results=reduce_results,
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. "
                "Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class HunYuanSparseMoeBlock(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        layer_id: int = -1,
    ):
        super().__init__()
        self.tp_size = get_parallel().tp_size

        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.num_experts}."
            )

        # Get layer_id topk if config.moe_topk is a list
        if isinstance(config.moe_topk, list):
            assert layer_id >= 0
            assert len(config.moe_topk) > layer_id
            top_k = config.moe_topk[layer_id]
        else:
            top_k = config.moe_topk

        # If it is moe, moe_intermediate_size is preferred
        intermediate_size = config.intermediate_size
        if config.moe_intermediate_size is not None:
            intermediate_size = (
                config.moe_intermediate_size
                if isinstance(config.moe_intermediate_size, int)
                else config.moe_intermediate_size[layer_id]
            )

        self.topk = TopK(
            top_k=top_k,
            layer_id=layer_id,
            renormalize=True if top_k > 1 else False,
        )

        self.experts = FusedMoE(
            num_experts=config.num_experts,
            hidden_size=config.hidden_size,
            intermediate_size=intermediate_size,
            reduce_results=False,
            layer_id=layer_id,
            quant_config=quant_config,
        )

        self.gate = ReplicatedLinear(
            config.hidden_size, config.num_experts, bias=False, quant_config=None
        )
        if config.use_mixed_mlp_moe > 0:
            # Get layer_id num_shared_expert if config.num_shared_expert is a list
            if isinstance(config.num_shared_expert, list):
                assert layer_id >= 0
                assert len(config.num_shared_expert) > layer_id
                num_shared_expert = config.num_shared_expert[layer_id]
            else:
                num_shared_expert = config.num_shared_expert

            self.shared_mlp = HunYuanMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size * num_shared_expert,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
            )
        else:
            self.shared_mlp = None

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # NOTE: hidden_states can have either 1D or 2D shape.
        orig_shape = hidden_states.shape
        hidden_dim = hidden_states.shape[-1]
        hidden_states = hidden_states.view(-1, hidden_dim)
        shared_output = None
        if self.shared_mlp is not None:
            shared_output = self.shared_mlp(hidden_states)

        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)
        topk_output = self.topk(hidden_states, router_logits)
        final_hidden_states = self.experts(hidden_states, topk_output)
        if shared_output is not None:
            final_hidden_states = final_hidden_states + shared_output
        if self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)

        return final_hidden_states.view(orig_shape)


def get_head_dim(config):
    if hasattr(config, "head_dim"):
        return int(config.head_dim)
    if hasattr(config, "attention_head_dim"):
        return int(config.attention_head_dim)

    # since some hunyuan model don't follow the self.hidden_size // self.total_num_heads rule
    # wrong setting may cause runtime error, just throw error if this field is missing.
    raise ValueError("Missing head dim config, try set head_dim in config.json")


def check_head_dim(config):
    # Some models may lack `head_dim` and use `attention_head_dim` instead.
    # This attribute is also used by flashinfer_backend.py, so we check for
    # consistency and raise an error if it's not met to avoid silent failures.
    # Although we could adapt the HunYuan model to use `attention_head_dim`,
    # flashinfer expects `head_dim`, so we enforce its presence for correctness.
    calc_head_dim = config.hidden_size // config.num_attention_heads

    if hasattr(config, "attention_head_dim"):
        if calc_head_dim != config.attention_head_dim and not hasattr(
            config, "head_dim"
        ):
            # in this case, flash infer(and other components may calculate wrong value.)
            raise ValueError(
                f"HunYuan model config error: calculated head_dim {calc_head_dim} != attention_head_dim {config.attention_head_dim}"
                + f"\nPlease Add head_dim:{config.attention_head_dim} in config.json to make sure correctly inference."
            )

        if hasattr(config, "head_dim") and config.attention_head_dim != config.head_dim:
            raise ValueError(
                f"HunYuan model config error: head_dim({config.head_dim}) != attention_head_dim({config.attention_head_dim})"
                + f"\nPlease change head_dim:{config.attention_head_dim} in config.json to make sure correctly inference."
            )


class HunYuanAttention(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
        prefix: str = "",
        attention_type: str = "self",
        layer_id: int = -1,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_parallel().tp_size
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
        # Prioritize `head_dim` but fall back to `attention_head_dim` for Hunyuan models.
        self.head_dim = get_head_dim(config)

        check_head_dim(config)

        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings
        self.use_qk_norm = getattr(config, "use_qk_norm", False)
        self.attention_type = attention_type
        self.layer_id = layer_id

        if attention_type == "self":
            self.qkv_proj = QKVParallelLinear(
                hidden_size=hidden_size,
                head_size=self.head_dim,
                total_num_heads=self.total_num_heads,
                total_num_kv_heads=self.total_num_kv_heads,
                bias=bias,
                quant_config=quant_config,
                prefix=f"{prefix}.qkv_proj",
            )
        elif attention_type == "cross":
            self.q_proj = ColumnParallelLinear(
                hidden_size,
                hidden_size,
                bias=bias,
                quant_config=quant_config,
                prefix=f"{prefix}.q_proj",
            )
        else:
            raise RuntimeError("Not support attnention type")

        self.o_proj = RowParallelLinear(
            input_size=self.total_num_heads * self.head_dim,
            output_size=hidden_size,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        is_neox_style = True
        if quant_config is not None and quant_config.get_name() == "gguf":
            is_neox_style = False

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
            is_neox_style=is_neox_style,
        )
        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
            prefix=f"{prefix}.attn",
        )

        if self.use_qk_norm:
            self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
            self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        kv_states: Optional[Tuple[torch.Tensor]] = None,
    ) -> torch.Tensor:
        if self.attention_type == "self":
            qkv, _ = self.qkv_proj(hidden_states)
            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
            q, k = self.rotary_emb(positions, q, k)
            ori_k = k
            if self.use_qk_norm:
                # q = self.query_layernorm(q.view(-1, self.num_heads, self.head_dim).contiguous())
                # k = self.key_layernorm(k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
                q = self.query_layernorm(q.reshape(-1, self.head_dim).contiguous())
                k = self.key_layernorm(k.reshape(-1, self.head_dim).contiguous())
        elif self.attention_type == "cross":
            assert kv_states is not None
            ori_k, v = kv_states  # use last layer kv,
            k = ori_k
            q, _ = self.q_proj(hidden_states)
            k_tmp = torch.empty_like(k)  # Todo: reduant rotary embedding
            q, _ = self.rotary_emb(positions, q, k_tmp)
            if self.use_qk_norm:
                q = self.query_layernorm(
                    q.view(-1, self.num_heads, self.head_dim).contiguous()
                )
                k = self.key_layernorm(
                    k.view(-1, self.num_kv_heads, self.head_dim).contiguous()
                )
        else:
            raise RuntimeError("Not support attnention type")

        attn_output = self.attn(q, k, v, forward_batch)
        output, _ = self.o_proj(attn_output)
        return output, (ori_k, v)


class HunYuanDecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        layer_id: int = -1,
    ) -> None:
        super().__init__()
        assert layer_id >= 0
        self.layer_id = layer_id
        self.hidden_size = config.hidden_size
        self.intermediate_size = (
            config.intermediate_size
            if isinstance(config.intermediate_size, int)
            else config.intermediate_size[layer_id]
        )
        rope_theta, rope_scaling = get_rope_config(config)
        if rope_scaling is not None and getattr(
            config, "original_max_position_embeddings", None
        ):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings
            )
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        # Support abacusai/Smaug-72B-v0.1 with attention_bias
        # Support internlm/internlm-7b with bias
        attention_bias = getattr(config, "attention_bias", False) or getattr(
            config, "bias", False
        )
        cla_factor = _get_cla_factor(config)
        attention_type = (
            "cross" if layer_id >= 0 and layer_id % cla_factor != 0 else "self"
        )
        self.self_attn = HunYuanAttention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=getattr(
                config, "num_key_value_heads", config.num_attention_heads
            ),
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=attention_bias,
            prefix=f"{prefix}.self_attn",
            attention_type=attention_type,
            layer_id=layer_id,
        )
        if _is_moe(config):
            self.mlp = HunYuanSparseMoeBlock(
                config=config,
                quant_config=quant_config,
                layer_id=layer_id,
            )
        else:
            self.mlp = HunYuanMLP(
                hidden_size=self.hidden_size,
                intermediate_size=self.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                bias=getattr(config, "mlp_bias", False),
                prefix=f"{prefix}.mlp",
            )
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
        kv_states: Optional[Tuple[torch.Tensor]] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)
        hidden_states, ori_kv_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
            kv_states=kv_states,
        )

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual, ori_kv_states


class HunYuanModel(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.org_vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
        )

        self.layers = nn.ModuleList(
            [
                HunYuanDecoderLayer(
                    config=config,
                    layer_id=layer_id,
                    quant_config=quant_config,
                    # prefix=prefix
                )
                for layer_id in range(config.num_hidden_layers)
            ]
        )

        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: Optional[torch.Tensor],
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if input_embeds is not None:
            hidden_states = input_embeds
        else:
            hidden_states = self.get_input_embeddings(input_ids)
        residual = None

        for layer in self.layers:
            hidden_states, residual, _ = layer(
                positions,
                hidden_states,
                forward_batch,
                residual,
                None,
            )

        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class HunYuanMoEV1ForCausalLM(nn.Module):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]
    bitsandbytes_stacked_params_mapping = {
        # shard_name, weight_name, index
        "q_proj": ("qkv_proj", 0),
        "k_proj": ("qkv_proj", 1),
        "v_proj": ("qkv_proj", 2),
        "gate_proj": ("gate_up_proj", 0),
        "up_proj": ("gate_up_proj", 1),
    }

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.config = config

        self.model = HunYuanModel(config, quant_config, prefix="model")
        self.unpadded_vocab_size = config.vocab_size
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
        )
        if config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight

        self.hidden_size = config.hidden_size
        self.head_dim = get_head_dim(config)

        check_head_dim(config)

        logit_scale = getattr(config, "logit_scale", 1.0)
        self.logits_processor = LogitsProcessor(config, logit_scale=logit_scale)
        self.sampler = create_sampler()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch
        )

    def _split_qkv_weight(self, qkv: torch.Tensor):
        num_attention_heads = self.config.num_attention_heads
        num_kv_heads = getattr(
            self.config, "num_key_value_heads", self.config.num_attention_heads
        )
        num_key_value_groups = num_attention_heads // num_kv_heads

        qkv = qkv.reshape(
            num_kv_heads, num_key_value_groups + 2, self.head_dim, self.hidden_size
        )
        q, k, v = torch.split(qkv, (num_key_value_groups, 1, 1), dim=1)
        q = q.reshape(-1, self.hidden_size)
        k = k.reshape(-1, self.hidden_size)
        v = v.reshape(-1, self.hidden_size)
        return torch.concat((q, k, v))
        # return qkv.reshape((num_kv_heads, num_key_value_groups+2 , attention_head_dim, hidden_size)).permute((1,0,2,3)).reshape((-1, hidden_size)),

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        cla_factor = _get_cla_factor(self.config)
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]

        num_attention_heads = self.config.num_attention_heads
        num_kv_heads = getattr(
            self.config, "num_key_value_heads", self.config.num_attention_heads
        )
        split_params_mapping = [
            (".gate_up_proj", ".gate_and_up_proj", 2, [(1, 1), (0, 1)], None),
            (
                ".qkv_proj",
                ".qkv_proj",
                num_attention_heads + num_kv_heads * 2,
                [("q", num_attention_heads), ("k", num_kv_heads), ("v", num_kv_heads)],
                self._split_qkv_weight,
            ),
        ]

        if _is_moe(self.config):
            # Params for weights, fp8 weight scales, fp8 activation scales
            # (param_name, weight_name, expert_id, shard_id)
            expert_params_mapping = FusedMoE.make_expert_params_mapping(
                ckpt_gate_proj_name="gate_proj",
                ckpt_down_proj_name="down_proj",
                ckpt_up_proj_name="up_proj",
                num_experts=self.config.num_experts,
            )
        else:
            expert_params_mapping = {}

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if "gate_proj_bias" in name:
                name = name.replace("gate_proj_bias", "gate_proj.bias")
            if "up_proj_bias" in name:
                name = name.replace("up_proj_bias", "up_proj.bias")
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
            # With tie_word_embeddings, we can skip lm_head.weight
            # The weight might appear unnecessarily in the files if the model is
            # processed with quantization, LoRA, fine-tuning, etc.
            if self.config.tie_word_embeddings and "lm_head.weight" in name:
                continue

            is_found = False
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                if "mlp.experts" in name:
                    continue
                # cross layer only have q_proj, skip qkv pack
                if weight_name == ".q_proj":
                    match = re.search(r"layers\.\d+", name)
                    if match:
                        layer_id = int(match.group(0).split(".")[-1])
                        if cla_factor > 1 and layer_id % cla_factor != 0:
                            continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)

                is_found = True
                break
            if is_found:
                continue

            for param_name, weight_name, den, split_param, func in split_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue

                assert loaded_weight.shape[0] % den == 0
                units = loaded_weight.shape[0] // den

                param = params_dict[name]
                weight_loader = param.weight_loader
                offset = 0
                for shard_id, num in split_param:
                    new_offset = offset + num * units
                    if func:
                        weight_loader(
                            param, func(loaded_weight)[offset:new_offset], shard_id
                        )
                    else:
                        weight_loader(param, loaded_weight[offset:new_offset], shard_id)
                    offset = new_offset

                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    # Skip layers on other devices.
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue

                    if "mlp.gate.wg." in name:
                        name = name.replace("wg.", "")

                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)

    # If this function is called, it should always initialize KV cache scale
    # factors (or else raise an exception). Thus, handled exceptions should
    # make sure to leave KV cache scale factors in a known good (dummy) state
    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
        tp_size = get_parallel().tp_size
        tp_rank = get_parallel().tp_rank
        for layer_idx, scaling_factor in kv_cache_scales_loader(
            quantization_param_path,
            tp_rank,
            tp_size,
            self.config.num_hidden_layers,
            self.config.__class__.model_type,
        ):
            if not isinstance(self.model.layers[layer_idx], nn.Identity):
                layer_self_attn = self.model.layers[layer_idx].self_attn

            if is_hip():
                # The scaling factor convention we are assuming is
                # quantized_value * scaling_factor ~= true_value
                # which is consistent with the practice of setting
                # scaling_factor = tensor_amax / FPtype_max
                scaling_factor *= 2
            if hasattr(layer_self_attn, "kv_scale"):
                layer_self_attn.attn._kv_scale = scaling_factor
            else:
                raise RuntimeError(
                    "Self attention has no KV cache scaling " "factor attribute!"
                )


class HunYuanDenseV1ForCausalLM(HunYuanMoEV1ForCausalLM):
    pass


EntryClass = [HunYuanMoEV1ForCausalLM, HunYuanDenseV1ForCausalLM]