hunyuan_v1.py 38.6 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# coding=utf-8
# Copyright 2024 The HunYuan team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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."""
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import typing
from collections.abc import Callable, Iterable
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from itertools import islice
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import regex as re
import torch
from torch import nn
from transformers import PretrainedConfig

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from vllm.attention.backends.abstract import AttentionType
from vllm.attention.layer import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_reduce,
)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
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    ParallelLMHead,
    VocabParallelEmbedding,
)
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from vllm.model_executor.model_loader.weight_utils import (
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    default_weight_loader,
    maybe_remap_kv_scale_name,
)
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from vllm.sequence import IntermediateTensors

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from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
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from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    is_pp_missing_parameter,
    make_layers,
    maybe_prefix,
)
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def _is_moe(config: PretrainedConfig) -> bool:
    num_experts = getattr(config, "num_experts", None)
    if isinstance(num_experts, int):
        return num_experts > 1
    if isinstance(num_experts, list) and num_experts:
        # Ensure all elements are integers before calling max.
        if all(isinstance(e, int) for e in num_experts):
            return max(num_experts) > 1
        else:
            return False
    return False


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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,
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        quant_config: QuantizationConfig | None = None,
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        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":
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            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
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        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 HunYuanAttention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position_embeddings: int = 8192,
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        quant_config: QuantizationConfig | None = None,
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        bias: bool = False,
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        cache_config: CacheConfig | None = None,
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        prefix: str = "",
        layer_id: int = -1,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_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)
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        if hasattr(config, "head_dim") and config.head_dim:
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            self.head_dim = config.head_dim
        elif hasattr(config, "attention_head_dim"):
            self.head_dim = config.attention_head_dim
        else:
            self.head_dim = self.hidden_size // self.total_num_heads
        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.max_position_embeddings = max_position_embeddings
        self.use_qk_norm = getattr(config, "use_qk_norm", False)
        self.layer_id = layer_id

        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",
        )

        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",
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position_embeddings,
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            rope_parameters=config.rope_parameters,
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            is_neox_style=True,
        )
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

        if self.use_qk_norm:
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            self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
            self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
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        kv_states: tuple[torch.Tensor] | None = None,
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    ) -> torch.Tensor:
        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(
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                q.view(-1, self.num_heads, self.head_dim).contiguous()
            )
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            k = self.key_layernorm(
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                k.view(-1, self.num_kv_heads, self.head_dim).contiguous()
            )
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        attn_output = self.attn(q, k, v)
        # For o_proj
        attn_output = attn_output.view(q.shape[0], -1)
        output, _ = self.o_proj(attn_output)
        return output, (ori_k, v)


class HunYuanCrossAttention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position_embeddings: int = 8192,
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        quant_config: QuantizationConfig | None = None,
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        bias: bool = False,
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        cache_config: CacheConfig | None = None,
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        prefix: str = "",
        layer_id: int = -1,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_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
        if hasattr(config, "head_dim"):
            self.head_dim = config.head_dim
        elif hasattr(config, "attention_head_dim"):
            self.head_dim = config.attention_head_dim
        else:
            self.head_dim = self.hidden_size // self.total_num_heads
        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.max_position_embeddings = max_position_embeddings
        self.use_qk_norm = getattr(config, "use_qk_norm", False)
        self.layer_id = layer_id

        self.q_proj = ColumnParallelLinear(
            hidden_size,
            hidden_size,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.q_proj",
        )

        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",
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position_embeddings,
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            rope_parameters=config.rope_parameters,
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            is_neox_style=True,
        )
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
            attn_type=AttentionType.ENCODER_DECODER,
        )

        if self.use_qk_norm:
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            self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
            self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
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        kv_states: tuple[torch.Tensor] | None = None,
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    ) -> torch.Tensor:
        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(
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                q.view(-1, self.num_heads, self.head_dim).contiguous()
            )
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            k = self.key_layernorm(
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                k.view(-1, self.num_kv_heads, self.head_dim).contiguous()
            )
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        attn_output = self.attn(q, k, v)
        # For o_proj
        attn_output = attn_output.view(q.shape[0], -1)
        output, _ = self.o_proj(attn_output)
        return output, (ori_k, v)


class HunYuanSparseMoeBlock(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        layer_id: int = -1,
        prefix: str = "",
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        enable_eplb: bool = False,
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    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()

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        self.ep_group = get_ep_group().device_group
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        self.ep_rank = get_ep_group().rank_in_group
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        self.ep_size = self.ep_group.size()
        self.n_routed_experts = config.num_experts

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        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
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                f"the number of experts {config.num_experts}."
            )
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        # 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:
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            intermediate_size = (
                config.moe_intermediate_size
                if isinstance(config.moe_intermediate_size, int)
                else config.moe_intermediate_size[layer_id]
            )
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        # Load balancing settings.
        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
        self.enable_eplb = enable_eplb

        self.n_logical_experts = self.n_routed_experts
        self.n_redundant_experts = eplb_config.num_redundant_experts
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        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
        self.physical_expert_end = (
            self.physical_expert_start + self.n_local_physical_experts
        )
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        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.gate",
        )
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        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,
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                prefix=f"{prefix}.shared_mlp",
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            )
        else:
            self.shared_mlp = None

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        self.experts = SharedFusedMoE(
            shared_experts=self.shared_mlp,
            num_experts=self.n_routed_experts,
            top_k=top_k,
            hidden_size=config.hidden_size,
            intermediate_size=intermediate_size,
            reduce_results=False,
            renormalize=top_k > 1,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
        )

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    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)

        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)
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        final_hidden_states = self.experts(
            hidden_states=hidden_states, router_logits=router_logits
        )
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        if self.shared_mlp is not None:
            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]

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        if self.tp_size > 1:
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            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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        return final_hidden_states.view(orig_shape)


class HunYuanDecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
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        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
        layer_id: int = -1,
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        enable_eplb: bool = False,
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    ) -> None:
        super().__init__()
        assert layer_id >= 0
        self.layer_id = layer_id
        self.hidden_size = config.hidden_size
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        self.intermediate_size = (
            config.intermediate_size
            if isinstance(config.intermediate_size, int)
            else config.intermediate_size[layer_id]
        )
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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        attention_bias = getattr(config, "attention_bias", False) or getattr(
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            config, "bias", False
        )
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        cla_factor = _get_cla_factor(config)
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        attention_type = (
            AttentionType.ENCODER_DECODER
            if layer_id >= 0 and layer_id % cla_factor != 0
            else AttentionType.DECODER
        )
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        if attention_type == AttentionType.DECODER:
            self.self_attn = HunYuanAttention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
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                num_kv_heads=getattr(
                    config, "num_key_value_heads", config.num_attention_heads
                ),
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                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                bias=attention_bias,
                cache_config=cache_config,
                prefix=f"{prefix}.self_attn",
                layer_id=layer_id,
            )
        elif attention_type == AttentionType.ENCODER_DECODER:
            self.self_attn = HunYuanCrossAttention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
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                num_kv_heads=getattr(
                    config, "num_key_value_heads", config.num_attention_heads
                ),
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                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                bias=attention_bias,
                cache_config=cache_config,
                prefix=f"{prefix}.self_attn",
                layer_id=layer_id,
            )
        else:
            raise RuntimeError(f"Unsupported attention type: {attention_type}")

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        if _is_moe(config):
            self.mlp = HunYuanSparseMoeBlock(
                config=config,
                quant_config=quant_config,
                layer_id=layer_id,
                prefix=f"{prefix}.mlp",
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                enable_eplb=enable_eplb,
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            )
        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",
            )

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

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


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@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        # positions is of shape (xd, seq_len) if xdrope is enabled for hunyuan-vl,
        # otherwise (seq_len, ).
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
    }
)
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class HunYuanModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
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        eplb_config = vllm_config.parallel_config.eplb_config
        enable_eplb = vllm_config.parallel_config.enable_eplb
        self.num_redundant_experts = eplb_config.num_redundant_experts
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        self.config = config
        self.quant_config = quant_config
        self.padding_idx = config.pad_token_id
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        self.vocab_size = config.vocab_size

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        if get_pp_group().is_first_rank or (
            config.tie_word_embeddings and get_pp_group().is_last_rank
        ):
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            self.embed_tokens = VocabParallelEmbedding(
                self.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
            )
        else:
            self.embed_tokens = PPMissingLayer()
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: HunYuanDecoderLayer(
                config=config,
                layer_id=int(prefix.split(".")[-1]),
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=prefix,
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                enable_eplb=enable_eplb,
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            ),
            prefix=f"{prefix}.layers",
        )
        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()

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

    def forward(
        self,
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        input_ids: torch.Tensor | None,
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        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
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        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
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                hidden_states = self.embed_input_ids(input_ids)
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            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        cla_factor = _get_cla_factor(self.config)
        prev_kv_states = None
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        for i, layer in enumerate(
            islice(self.layers, self.start_layer, self.end_layer)
        ):
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            hidden_states, residual, kv_states = layer(
                positions,
                hidden_states,
                residual,
                prev_kv_states,
            )

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            if getattr(self.config, "use_cla", False) and i % cla_factor == 0:
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                prev_kv_states = kv_states
            else:
                prev_kv_states = None

        if not get_pp_group().is_last_rank:
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            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
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        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

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

        if hasattr(self.config, "head_dim"):
            attention_head_dim = self.config.head_dim
        elif hasattr(self.config, "attention_head_dim"):
            attention_head_dim = self.config.attention_head_dim
        else:
            attention_head_dim = self.config.hidden_size // num_attention_heads

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        qkv = qkv.reshape(
            num_kv_heads, num_key_value_groups + 2, attention_head_dim, hidden_size
        )
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        q, k, v = torch.split(qkv, (num_key_value_groups, 1, 1), dim=1)
        q = q.reshape(-1, hidden_size)
        k = k.reshape(-1, hidden_size)
        v = v.reshape(-1, hidden_size)
        return torch.concat((q, k, v))

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    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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        if _is_moe(self.config):
            # Params for weights, fp8 weight scales, fp8 activation scales
            # (param_name, weight_name, expert_id, shard_id)
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            return SharedFusedMoE.make_expert_params_mapping(
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                self,
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                ckpt_gate_proj_name="gate_proj",
                ckpt_down_proj_name="down_proj",
                ckpt_up_proj_name="up_proj",
                num_experts=self.config.num_experts,
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                num_redundant_experts=self.num_redundant_experts,
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            )
        else:
            return []
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    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
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        num_kv_heads = getattr(
            self.config, "num_key_value_heads", self.config.num_attention_heads
        )
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        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,
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                [("q", num_attention_heads), ("k", num_kv_heads), ("v", num_kv_heads)],
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                self._split_qkv_weight,
            ),
        ]

        params_dict = dict(self.named_parameters())
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        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
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        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")
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            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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                # 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
            if self.quant_config is not None and (
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                scale_name := self.quant_config.get_cache_scale(name)
            ):
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                # Loading kv cache scales for compressed-tensors quantization
                param = params_dict[scale_name]
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                weight_loader = getattr(param, "weight_loader", default_weight_loader)
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                loaded_weight = loaded_weight[0]
                weight_loader(param, loaded_weight)
                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

                if is_pp_missing_parameter(name, self):
                    continue

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
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                loaded_params.add(name)
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                is_found = True
                break
            if is_found:
                continue

            for (
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                param_name,
                weight_name,
                den,
                split_param,
                func,
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            ) 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

                if is_pp_missing_parameter(name, self):
                    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:
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                        weight_loader(
                            param, func(loaded_weight)[offset:new_offset], shard_id
                        )
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                    else:
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                        weight_loader(param, loaded_weight[offset:new_offset], shard_id)
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                    offset = new_offset

                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
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                is_expert_weight = False
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                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
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                    # this is an expert weight and should not be
                    # attempted to load as other weights later
                    is_expert_weight = True

                    # Do not modify `name` since the loop may continue here
                    # Instead, create a new variable
                    name_mapped = name.replace(weight_name, param_name)
                    if is_pp_missing_parameter(name_mapped, self):
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                        continue
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                    param = params_dict[name_mapped]
                    # We should ask the weight loader to return success or not
                    # here since otherwise we may skip experts with other
                    # available replicas.
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                    weight_loader = typing.cast(
                        Callable[..., bool], param.weight_loader
                    )
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                    success = weight_loader(
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                        param,
                        loaded_weight,
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                        name_mapped,
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                        shard_id=shard_id,
                        expert_id=expert_id,
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                        return_success=True,
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                    )
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                    if success:
                        name = name_mapped
                        break
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                else:
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                    if is_expert_weight:
                        # We've checked that this is an expert weight
                        # However it's not mapped locally to this rank
                        # So we simply skip it
                        continue
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                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue

                    if is_pp_missing_parameter(name, self):
                        continue

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

                    param = params_dict[name]
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                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
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                    weight_loader(param, loaded_weight)
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            loaded_params.add(name)
        return loaded_params


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class HunyuanV1ModelBase(nn.Module, SupportsLoRA, SupportsPP):
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    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config

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

            logit_scale = getattr(config, "logit_scale", 1.0)
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            self.logits_processor = LogitsProcessor(
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                config.vocab_size, scale=logit_scale
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            )
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        else:
            self.lm_head = PPMissingLayer()

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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
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        model_output = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
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        return model_output

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
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    ) -> torch.Tensor | None:
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        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def make_empty_intermediate_tensors(
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        self, batch_size: int, dtype: torch.dtype, device: torch.device
    ) -> IntermediateTensors:
        return IntermediateTensors(
            {
                "hidden_states": torch.zeros(
                    (batch_size, self.config.hidden_size), dtype=dtype, device=device
                ),
                "residual": torch.zeros(
                    (batch_size, self.config.hidden_size), dtype=dtype, device=device
                ),
            }
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        loader = AutoWeightsLoader(
            self,
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            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
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        )
        return loader.load_weights(weights)

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    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)
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class HunYuanMoEV1Base(HunyuanV1ModelBase, MixtureOfExperts):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)

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        # Set MoE hyperparameters
        self.expert_weights = []
        self.num_expert_groups = 1
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        self.moe_layers = []
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        example_layer = None
        for layer in self.model.layers:
            if isinstance(layer, PPMissingLayer):
                continue

            assert isinstance(layer, HunYuanDecoderLayer)
            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
                example_layer = layer.mlp
                self.moe_layers.append(layer.mlp.experts)

        if example_layer is None:
            raise RuntimeError("No HunYuanMoE layer found in model.layers.")

        self.num_moe_layers = len(self.moe_layers)
        self.num_logical_experts = example_layer.n_logical_experts
        self.num_physical_experts = example_layer.n_physical_experts
        self.num_local_physical_experts = example_layer.n_local_physical_experts
        self.num_routed_experts = example_layer.n_routed_experts
        self.num_redundant_experts = example_layer.n_redundant_experts

    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        assert self.num_local_physical_experts == num_local_physical_experts
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
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        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
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        for layer in self.model.layers:
            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
                moe = layer.mlp
                moe.n_local_physical_experts = num_local_physical_experts
                moe.n_physical_experts = num_physical_experts
                moe.n_redundant_experts = self.num_redundant_experts
                moe.experts.update_expert_map()

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    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()
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class HunYuanDenseV1Base(HunyuanV1ModelBase):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)
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class HunYuanDenseV1ForCausalLM(HunYuanDenseV1Base):
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    pass


1041
class HunYuanMoEV1ForCausalLM(HunYuanMoEV1Base):
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    pass