modeling_deepseekv3.py

# --------------------------------------------------
# Portions of this code were derived from DeepSeek‑V3:
#   https://github.com/deepseek-ai/DeepSeek-V3
#
# MIT License

# Copyright (c) 2023 DeepSeek

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# --------------------------------------------------

import copy
import math
import os
from typing import Dict, List, Optional, Tuple

import torch
import triton
import triton.language as tl
from torch import nn
from tqdm import tqdm
from transformers import PretrainedConfig

import tensorrt_llm.quantization.utils.fp4_utils as fp4_utils
from tensorrt_llm._ipc_utils import can_access_peer
from tensorrt_llm._torch.models.checkpoints.base_weight_loader import \
    ConsumableWeightsDict
from tensorrt_llm._utils import get_sm_version
from tensorrt_llm.functional import PositionEmbeddingType
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models.modeling_utils import QuantConfig
from tensorrt_llm.quantization.mode import QuantAlgo

from ..attention_backend import AttentionMetadata
from ..attention_backend.interface import PositionalEmbeddingParams, RopeParams
from ..distributed import (AllReduce, AllReduceFusionOp, AllReduceParams,
                           MoEAllReduce, MoEAllReduceParams, allgather,
                           cp_allgather)
from ..model_config import ModelConfig
from ..modules.attention import MLA
from ..modules.decoder_layer import DecoderLayer
from ..modules.embedding import Embedding
from ..modules.fused_moe import (DeepSeekV3MoeRoutingMethod, MoE,
                                 MoEWeightLoadingMode, create_moe)
from ..modules.fused_moe.fused_moe_wide_ep import WideEPMoE

# isort: off
from ..modules.fused_moe.routing import (Deepseekv3RoutingImpl,
                                         get_cached_perfect_router_logits,
                                         precompute_common_perfect_router_logits
                                         )
# isort: on
from ..modules.gated_mlp import GatedMLP
from ..modules.linear import Linear, TensorParallelMode, WeightsLoadingConfig
from ..modules.multi_stream_utils import maybe_execute_in_parallel
from ..modules.rms_norm import RMSNorm
from ..peft.lora.layer import LoraLayer
from ..speculative import SpecMetadata
from ..utils import (AuxStreamType, EventType, Fp4QuantizedTensor,
                     create_lm_head_tp_mapping)
from .modeling_speculative import SpecDecOneEngineForCausalLM
from .modeling_utils import (DecoderModel, EagerFusionConfig, filter_weights,
                             register_auto_model)


@triton.jit
def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
    """
    Dequantizes weights using the provided scaling factors and stores the result.

    Args:
        x_ptr (tl.pointer): Pointer to the quantized weights.
        s_ptr (tl.pointer): Pointer to the scaling factors.
        y_ptr (tl.pointer): Pointer to the output buffer for dequantized weights.
        M (int): Number of rows in the weight matrix.
        N (int): Number of columns in the weight matrix.
        BLOCK_SIZE (tl.constexpr): Size of the block for tiling.

    Returns:
        None
    """
    pid_m = tl.program_id(axis=0)
    pid_n = tl.program_id(axis=1)
    n = tl.cdiv(N, BLOCK_SIZE)
    offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    offs = offs_m[:, None] * N + offs_n[None, :]
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    x = tl.load(x_ptr + offs, mask=mask).to(tl.float32)
    s = tl.load(s_ptr + pid_m * n + pid_n)
    y = x * s
    tl.store(y_ptr + offs, y, mask=mask)


def weight_dequant(x: torch.Tensor,
                   s: torch.Tensor,
                   block_size: int = 128) -> torch.Tensor:
    """
    Dequantizes the given weight tensor using the provided scale tensor.

    Args:
        x (torch.Tensor): The quantized weight tensor of shape (M, N).
        s (torch.Tensor): The scale tensor of shape (M, N).
        block_size (int, optional): The block size to use for dequantization. Defaults to 128.

    Returns:
        torch.Tensor: The dequantized weight tensor of the same shape as `x`.

    Raises:
        AssertionError: If `x` or `s` are not contiguous or if their dimensions are not 2.
    """
    assert x.is_contiguous() and s.is_contiguous(
    ), 'Input tensors must be contiguous'
    assert x.dim() == 2 and s.dim() == 2, 'Input tensors must have 2 dimensions'
    M, N = x.size()
    y = torch.empty_like(x, dtype=torch.get_default_dtype())
    grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']),
                         triton.cdiv(N, meta['BLOCK_SIZE']))
    weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
    return y


@torch.compile(dynamic=True)
def moe_reduce_add_shared_output(routed_output, shared_output):
    routed_output = torch.sum(routed_output, dim=1, keepdim=False)
    return shared_output + routed_output


class DeepseekV3WeightLoader:

    def __init__(self, model, is_draft_model: bool = False):
        self.model = model
        self.config = model.config
        self.model_config = model.model_config
        self.is_draft_model = is_draft_model

    def load_weights(self,
                     weights: ConsumableWeightsDict,
                     skip_modules: List[str] = []):

        def requantize_weight_with_new_scale(weight, weight_scale, old_scale_2,
                                             new_scale_2, device):
            """
            Dequantize FP4 weights and requantize with a new scale.

            Args:
                weight: FP4 quantized weight tensor 2D [,]
                weight_scale: FP8 per-block scaling factors
                old_scale_2: original global scale (amax/(448*6))
                new_scale_2: new global scale (amax/(448*6))
                device: target device for computation

            Returns:
                (requantized_weight, new_weight_scale)
            """
            # Remember original dtype of weight_scale
            original_scale_dtype = weight_scale.dtype
            original_scale_shape = weight_scale.shape

            # Dequantize
            dequant_shape = (weight.shape[0], weight.shape[1] * 2)
            weight_dequant = torch.ops.tensorrt_llm.e2m1_and_ufp8sf_scale_to_float_v2(
                weight.contiguous(),
                weight_scale.flatten().view(
                    fp4_utils.float4_sf_dtype).contiguous(), old_scale_2, 16, 1,
                True).to(dtype=torch.bfloat16).reshape(dequant_shape)

            # Requantize using the new_scale_2
            weight_requant, weight_scale_requant = torch.ops.trtllm.fp4_quantize(
                weight_dequant.to(device),
                1.0 / new_scale_2.to(device),
                16,  # scaling_vector_size
                False)

            # Ensure the returned scale has the same dtype as the input scale
            return weight_requant.cpu(), weight_scale_requant.reshape(
                original_scale_shape).view(original_scale_dtype).cpu()

        def rename_moe_weight(weights: Dict, rename_rules: Dict):
            result = {}
            for key, value in weights.items():
                new_key = key
                for old, new in rename_rules.items():
                    new_key = new_key.replace(old, new)
                result[new_key] = value
            return result

        ## Prepare weights for TP
        def split(v, tp_size, idx, dim=0):
            if tp_size == 1:
                return v
            if len(v.shape) == 1:
                return torch.chunk(v, tp_size)[idx].contiguous()
            return torch.chunk(v, tp_size, dim=dim)[idx].contiguous()

        def split_matrix_tp(v, tensor_parallel, rank, dim):
            return split(v, tensor_parallel, rank, dim=dim)

        def load_kv_b_proj_and_k_b_proj_trans(module_name: str,
                                              is_scale: bool) -> torch.Tensor:
            weight_name = "weight" if not is_scale else "weight_scale_inv"
            local_qk_nope_head_dim = qk_nope_head_dim if not is_scale else qk_nope_head_dim // 128
            local_v_head_dim = v_head_dim if not is_scale else v_head_dim // 128
            local_kv_lora_rank = kv_lora_rank if not is_scale else kv_lora_rank // 128

            kv_b_proj = weights[f"{module_name}.{weight_name}"][:].unflatten(
                0,
                [
                    num_heads,
                    local_qk_nope_head_dim + local_v_head_dim,
                ],
            )

            if not self.model_config.mapping.enable_attention_dp:
                kv_b_proj = split_matrix_tp(kv_b_proj, tp_size, tp_rank, 0)
            k_nope_weight, v_weight = kv_b_proj.split(
                [local_qk_nope_head_dim, local_v_head_dim],
                dim=1,
            )
            weight_divisor = 1 if self.model_config.mapping.enable_attention_dp else tp_size
            local_num_heads = num_heads // weight_divisor

            k_nope_weight_trans = k_nope_weight.transpose(2, 1).contiguous()

            kv_b_proj = torch.concat([
                k_nope_weight.reshape(local_num_heads * local_qk_nope_head_dim,
                                      local_kv_lora_rank),
                v_weight.reshape(local_num_heads * local_v_head_dim,
                                 local_kv_lora_rank)
            ],
                                     dim=0)

            return kv_b_proj, k_nope_weight_trans

        def load_kv_b_proj_and_k_b_proj_trans_dequant(
                module_name: str) -> torch.Tensor:
            weight_name = "weight"
            local_qk_nope_head_dim = qk_nope_head_dim
            local_v_head_dim = v_head_dim
            local_kv_lora_rank = kv_lora_rank

            kv_b_proj = weights[f"{module_name}.{weight_name}"][:].cuda()

            weight_name = "weight_scale_inv"
            kv_b_proj_scale = weights[f"{module_name}.{weight_name}"][:].cuda()

            kv_b_proj = weight_dequant(kv_b_proj, kv_b_proj_scale)
            kv_b_proj = kv_b_proj.unflatten(
                0,
                [
                    num_heads,
                    local_qk_nope_head_dim + local_v_head_dim,
                ],
            )
            if not self.model_config.mapping.enable_attention_dp:
                kv_b_proj = split_matrix_tp(kv_b_proj, tp_size, tp_rank, 0)
            k_nope_weight, v_weight = kv_b_proj.split(
                [local_qk_nope_head_dim, local_v_head_dim],
                dim=1,
            )
            weight_divisor = 1 if self.model_config.mapping.enable_attention_dp else tp_size
            local_num_heads = num_heads // weight_divisor

            k_nope_weight_trans = k_nope_weight.transpose(2, 1).contiguous()

            kv_b_proj = torch.concat([
                k_nope_weight.reshape(local_num_heads * local_qk_nope_head_dim,
                                      local_kv_lora_rank),
                v_weight.reshape(local_num_heads * local_v_head_dim,
                                 local_kv_lora_rank)
            ],
                                     dim=0)

            return kv_b_proj, k_nope_weight_trans

        def split_kv_b_proj(kv_b_proj: torch.Tensor,
                            is_scale: bool) -> torch.Tensor:
            local_qk_nope_head_dim = qk_nope_head_dim if not is_scale else qk_nope_head_dim // 128
            local_v_head_dim = v_head_dim if not is_scale else v_head_dim // 128

            weight_divisor = 1 if self.model_config.mapping.enable_attention_dp else tp_size
            local_num_heads = num_heads // weight_divisor

            k_b_proj, v_b_proj = kv_b_proj.split([
                local_num_heads * local_qk_nope_head_dim,
                local_num_heads * local_v_head_dim
            ],
                                                 dim=0)
            k_b_proj = k_b_proj.view(
                [local_num_heads, local_qk_nope_head_dim, -1])
            v_b_proj = v_b_proj.view([local_num_heads, local_v_head_dim, -1])

            if cp_size > 1:
                local_cp_heads = local_num_heads // cp_size
                k_b_proj = k_b_proj[cp_rank * local_cp_heads:(cp_rank + 1) *
                                    local_cp_heads]
                v_b_proj = v_b_proj[cp_rank * local_cp_heads:(cp_rank + 1) *
                                    local_cp_heads]

            return k_b_proj, v_b_proj

        is_lite = self.config.q_lora_rank is None
        num_heads = self.config.num_attention_heads
        qk_nope_head_dim = self.config.qk_nope_head_dim
        v_head_dim = self.config.v_head_dim
        kv_lora_rank = self.config.kv_lora_rank

        tp_rank = self.model_config.mapping.tp_rank
        tp_size = self.model_config.mapping.tp_size
        cp_rank = self.model_config.mapping.cp_rank
        cp_size = self.model_config.mapping.cp_size

        params_map = {'gate_up_proj': ['gate_proj', 'up_proj']}
        all_named_modules = dict(self.model.named_modules())

        # Check if weights supports mark_consumed (ConsumableWeightsDict)
        can_mark_consumed = hasattr(weights, 'mark_consumed')

        for name, module in tqdm(all_named_modules.items(),
                                 desc="Loading weights"):
            if len(module._parameters) <= 0 or name.startswith("draft_model"):
                continue
            elif any(skip_module in name for skip_module in skip_modules):
                continue
            else:
                names = name.split('.')
                parent_module_name = '.'.join(names[:-1])
                if "model.layers" in name and int(
                        names[2]) >= self.config.num_hidden_layers:
                    mtp_layer_idx = int(
                        names[2]) - self.config.num_hidden_layers
                    names[2] = str(mtp_layer_idx %
                                   self.config.num_nextn_predict_layers +
                                   self.config.num_hidden_layers)
                    name = '.'.join(names)
                if names[-1] == "kv_b_proj":
                    # TODO: remove weight_dequant after enabling fp8_bmm
                    dequant_kv_b_proj = self.model_config.quant_config.is_module_excluded_from_quantization(
                        names[-1])
                    if dequant_kv_b_proj:
                        kv_b_proj, k_b_proj_trans = load_kv_b_proj_and_k_b_proj_trans_dequant(
                            name)
                    else:
                        kv_b_proj, k_b_proj_trans = load_kv_b_proj_and_k_b_proj_trans(
                            name, is_scale=False)
                    module.weight.data.copy_(
                        kv_b_proj.reshape(module.weight.shape))

                    attn_module = all_named_modules[parent_module_name]
                    _, v_b_proj = split_kv_b_proj(module.weight.data,
                                                  is_scale=False)
                    attn_module.v_b_proj = nn.Parameter(v_b_proj,
                                                        requires_grad=False)

                    attn_module.k_b_proj_trans.data.copy_(
                        k_b_proj_trans.reshape(
                            attn_module.k_b_proj_trans.shape))

                    if getattr(module, "weight_scale",
                               None) is not None and not dequant_kv_b_proj:
                        kv_b_proj_scale, k_b_proj_trans_scale = load_kv_b_proj_and_k_b_proj_trans(
                            name, is_scale=True)
                        module.weight_scale.copy_(
                            kv_b_proj_scale.reshape(module.weight_scale.shape))
                        attn_module.k_b_proj_trans_scale.copy_(
                            k_b_proj_trans_scale.reshape(
                                attn_module.k_b_proj_trans_scale.shape))

                        _, v_b_proj_scale = split_kv_b_proj(
                            module.weight_scale.data, is_scale=True)
                        attn_module.v_b_proj_scale = nn.Parameter(
                            v_b_proj_scale, requires_grad=False)

                        if attn_module.k_b_proj_trans_dequant is not None:
                            attn_module.k_b_proj_trans_dequant.data.copy_(
                                weight_dequant(
                                    k_b_proj_trans.view(
                                        -1, k_b_proj_trans.shape[-1]).cuda(),
                                    k_b_proj_trans_scale.view(
                                        -1,
                                        k_b_proj_trans_scale.shape[-1]).cuda(),
                                ).view(
                                    *attn_module.k_b_proj_trans_dequant.shape).
                                to(attn_module.k_b_proj_trans_dequant.dtype))
                        if attn_module.v_b_proj_dequant is not None:
                            attn_module.v_b_proj_dequant.data.copy_(
                                weight_dequant(
                                    v_b_proj.view(-1,
                                                  v_b_proj.shape[-1]).cuda(),
                                    v_b_proj_scale.view(
                                        -1, v_b_proj_scale.shape[-1]).cuda(),
                                ).view(*attn_module.v_b_proj_dequant.shape).to(
                                    attn_module.v_b_proj_dequant.dtype))
                    # Mark consumed kv_b_proj weights
                    if can_mark_consumed:
                        weights.mark_consumed(name)
                elif names[-1] == "kv_a_proj_with_mqa":
                    nvfp4_fused_a = self.model_config.get_quant_config(
                    ).layer_quant_mode.has_nvfp4() and weights[
                        f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight"].dtype == fp4_utils.float4_e2m1x2 and weights[
                            f"{'.'.join(names[:-1])}.q_a_proj.weight"].dtype == fp4_utils.float4_e2m1x2
                    if nvfp4_fused_a:
                        ########### input_scale
                        kv_a_proj_with_mqa_input_scale = weights[
                            f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.input_scale"]
                        if not is_lite:
                            q_a_proj_input_scale = weights[
                                f"{'.'.join(names[:-1])}.q_a_proj.input_scale"]
                            assert kv_a_proj_with_mqa_input_scale == q_a_proj_input_scale, "kv_a_proj_with_mqa.input_scale and q_a_proj.input_scale should be the same"
                        # modelopt ckpt stores amax/(448*6), convert to (448*6)/amax
                        shared_input_scale = kv_a_proj_with_mqa_input_scale
                        module.input_scale.data.copy_(1.0 / shared_input_scale)
                        E2M1_MAX = 6.0
                        module.inv_input_scale.data.copy_(module.input_scale /
                                                          E2M1_MAX)
                        ########### weight_scale_2
                        need_requant_kv_a_proj_with_mqa = False
                        need_requant_q_a_proj = False
                        kv_a_proj_with_mqa_scale_2 = weights[
                            f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight_scale_2"]
                        shared_weight_scale_2 = kv_a_proj_with_mqa_scale_2
                        if not is_lite:
                            q_a_proj_scale_2 = weights[
                                f"{'.'.join(names[:-1])}.q_a_proj.weight_scale_2"]
                            if kv_a_proj_with_mqa_scale_2 < q_a_proj_scale_2:
                                shared_weight_scale_2 = q_a_proj_scale_2
                                need_requant_kv_a_proj_with_mqa = True
                            elif q_a_proj_scale_2 < kv_a_proj_with_mqa_scale_2:
                                need_requant_q_a_proj = True

                        ########### alpha
                        alpha = shared_input_scale.float(
                        ) * shared_weight_scale_2.float()
                        module.alpha.data.copy_(alpha)
                        module.scalar_alpha = alpha.item()

                        ########### weights
                        kv_a_proj_with_mqa = weights[
                            f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight"][:]

                        if not is_lite:
                            q_a_proj = weights[
                                f"{'.'.join(names[:-1])}.q_a_proj.weight"][:]

                        ########### weight_scale
                        kv_a_proj_with_mqa_scale = weights[
                            f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight_scale"][:]
                        kv_a_proj_with_mqa_scale = torch.ops.trtllm.block_scale_interleave(
                            kv_a_proj_with_mqa_scale.view(
                                fp4_utils.float4_sf_dtype))
                        if not is_lite:
                            q_a_proj_scale = weights[
                                f"{'.'.join(names[:-1])}.q_a_proj.weight_scale"][:]
                            q_a_proj_scale = torch.ops.trtllm.block_scale_interleave(
                                q_a_proj_scale.view(fp4_utils.float4_sf_dtype))

                        ########### requantize
                        if need_requant_kv_a_proj_with_mqa:
                            # requant kv_a_proj_with_mqa
                            kv_a_proj_with_mqa, kv_a_proj_with_mqa_scale = requantize_weight_with_new_scale(
                                kv_a_proj_with_mqa,
                                kv_a_proj_with_mqa_scale,
                                kv_a_proj_with_mqa_scale_2,
                                shared_weight_scale_2,
                                device=module.weight.device,
                            )
                        if need_requant_q_a_proj:
                            # requant q_a_proj
                            q_a_proj, q_a_proj_scale = requantize_weight_with_new_scale(
                                q_a_proj,
                                q_a_proj_scale,
                                q_a_proj_scale_2,
                                shared_weight_scale_2,
                                device=module.weight.device)

                        ########### fuse and load weights
                        if not is_lite:
                            fused_a = torch.cat([q_a_proj, kv_a_proj_with_mqa],
                                                dim=0)
                        else:
                            fused_a = kv_a_proj_with_mqa

                        # For DeepseekV32: kv_a_proj_with_mqa is oversized
                        # to include indexer k weights, which is filled in post_load_weights.
                        module.weight.data[0:fused_a.shape[0]].copy_(fused_a)

                        ########### fuse weight_scale
                        if not is_lite:
                            fused_a_scale = torch.cat(
                                [q_a_proj_scale, kv_a_proj_with_mqa_scale],
                                dim=0)
                        else:
                            fused_a_scale = kv_a_proj_with_mqa_scale
                        # For DeepseekV32: kv_a_proj_with_mqa is oversized
                        # to include indexer k weights, which is filled in post_load_weights.
                        module.weight_scale.data[0:fused_a_scale.
                                                 shape[0]].copy_(fused_a_scale)
                    else:
                        fused_a = weights[
                            f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight"][:]
                        if not is_lite:
                            q_a_proj = weights[
                                f"{'.'.join(names[:-1])}.q_a_proj.weight"][:]
                            fused_a = torch.cat([q_a_proj, fused_a], dim=0)

                        if f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight_scale_inv" in weights:
                            fused_a_scale = weights[
                                f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight_scale_inv"]
                            if not is_lite:
                                q_a_proj_scale = weights[
                                    f"{'.'.join(names[:-1])}.q_a_proj.weight_scale_inv"][:]
                                fused_a_scale = torch.cat(
                                    [q_a_proj_scale, fused_a_scale], dim=0)

                            module.weight_scale.data[
                                0:fused_a_scale.shape[0]].copy_(fused_a_scale)
                        # For DeepseekV32: kv_a_proj_with_mqa is oversized
                        # to include indexer k weights, which is filled in post_load_weights.
                        module.weight.data[0:fused_a.shape[0]].copy_(fused_a)
                    # Mark consumed kv_a_proj_with_mqa and q_a_proj weights
                    if can_mark_consumed:
                        parent_prefix = '.'.join(names[:-1])
                        weights.mark_consumed(
                            f"{parent_prefix}.kv_a_proj_with_mqa")
                        if not is_lite:
                            weights.mark_consumed(f"{parent_prefix}.q_a_proj")
                elif names[-1] in params_map:
                    module_weights = []
                    for new_name in params_map[names[-1]]:
                        module_weights.append(
                            filter_weights('.'.join(names[:-1] + [new_name]),
                                           weights))
                    module.load_weights(weights=module_weights)
                    # Mark consumed source weights (e.g., gate_proj, up_proj)
                    if can_mark_consumed:
                        for src_name in params_map[names[-1]]:
                            weights.mark_consumed('.'.join(names[:-1] +
                                                           [src_name]))
                elif names[-1] == "experts":
                    module_weights = filter_weights(name, weights)
                    module_weights = rename_moe_weight(module_weights, {
                        "down_proj": "w2",
                        "up_proj": "w3",
                        "gate_proj": "w1",
                    })
                    module.load_weights(weights=[module_weights])
                    # Mark consumed experts weights
                    if can_mark_consumed:
                        weights.mark_consumed(name)
                elif names[-1] == "backend" and isinstance(module, MoE):
                    # Special case: ConfigurableMoE.backend (TRTLLMGenFusedMoE)
                    # Currently saved MoE weights don't include 'backend' in their names.
                    # After MoE refactoring, ConfigurableMoE now has a backend submodule,
                    # and weights loading is done in the backend, so module name includes '.backend'.
                    # We need to use parent module name (without .backend) to match saved weight names.
                    # After MoE refactoring is fully complete, all paths will follow this branch.
                    parent_name = '.'.join(names[:-1])
                    module_weights = filter_weights(parent_name, weights)
                    module_weights = rename_moe_weight(module_weights, {
                        "down_proj": "w2",
                        "up_proj": "w3",
                        "gate_proj": "w1",
                    })
                    module.load_weights(weights=[module_weights])
                    # Mark consumed MoE weights using parent name
                    if can_mark_consumed:
                        weights.mark_consumed(parent_name)
                elif names[-1] == "self_attn":
                    continue
                elif names[-1] == "next_layer_layernorm":
                    continue
                else:
                    module_weights = filter_weights(name, weights)
                    if hasattr(module, 'load_weights'):
                        module.load_weights(weights=[module_weights])
                    else:
                        for n, p in module.named_parameters():
                            p.data.copy_(module_weights[n][:])
                    # Mark consumed weights
                    if can_mark_consumed:
                        weights.mark_consumed(name)


class DeepseekV3MTPHead(nn.Module):

    def __init__(self, model_config: ModelConfig[PretrainedConfig]):
        super().__init__()
        config = model_config.pretrained_config
        self.model_config = model_config

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

        self.mapping_lm_head_tp = None

    @torch.compile(options={"max-autotune": True})
    def get_last_token_states(self, hidden_states, attn_metadata):
        last_tokens = torch.cumsum(
            attn_metadata.seq_lens_cuda,
            dim=0,
            dtype=torch.long,
        ) - 1
        return hidden_states[last_tokens]

    def forward(self,
                hidden_states: torch.Tensor,
                lm_head: Linear,
                attn_metadata: AttentionMetadata,
                return_context_logits: bool = False) -> torch.Tensor:
        if not return_context_logits:
            if attn_metadata is not None:
                hidden_states = self.get_last_token_states(
                    hidden_states, attn_metadata)
            else:
                hidden_states = hidden_states[-1].unsqueeze(0)

        enable_attention_dp = self.model_config.mapping.enable_attention_dp
        enable_lm_head_tp_in_adp = enable_attention_dp and self.model_config.mapping.enable_lm_head_tp_in_adp

        # Add pre-lm gather logic
        if enable_lm_head_tp_in_adp:
            # ADP + LM TP mode: perform All-Gather before LM_head
            self.mapping_lm_head_tp = create_lm_head_tp_mapping(
                self.model_config.mapping, hidden_states.shape[0])
            hidden_states = allgather(hidden_states,
                                      self.mapping_lm_head_tp,
                                      dim=0)

        # Temporarily disable gather_output when not in ADP mode or (in ADP mode and LM TP is enabled)
        if not enable_attention_dp or enable_lm_head_tp_in_adp:
            lm_head.gather_output = False
        logits = lm_head(hidden_states,
                         mapping_lm_head_tp=self.mapping_lm_head_tp,
                         is_spec_decoding_head=True)
        if not enable_attention_dp or enable_lm_head_tp_in_adp:
            lm_head.gather_output = True
        return logits


class DeepseekV3Linear(Linear):
    """
    A wrapper around Linear because we may optionally use min-latency kernels depending on input shapes.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = True,
        dtype: torch.dtype = None,
        mapping: Optional[Mapping] = None,
        tensor_parallel_mode: Optional[TensorParallelMode] = None,
        gather_output: bool = False,  # COLUMN parallel only
        quant_config: Optional[QuantConfig] = None,
        weights_loading_config: Optional[WeightsLoadingConfig] = None,
        reduce_output: bool = True,  # ROW parallel only
        skip_create_weights_in_init: bool = False,
        use_custom_cublas_mm: bool = False,
        use_cute_dsl_blockscaling_mm: bool = False,
        lora: Optional[LoraLayer] = None,
    ):
        super().__init__(
            in_features,
            out_features,
            bias,
            dtype,
            mapping,
            tensor_parallel_mode,
            gather_output,
            quant_config,
            weights_loading_config,
            reduce_output,
            skip_create_weights_in_init,
            use_custom_cublas_mm,
            lora,
            use_cute_dsl_blockscaling_mm=use_cute_dsl_blockscaling_mm,
        )

    def apply_linear(self,
                     input,
                     bias,
                     lora_params: Optional[dict] | None = None,
                     layer_idx: Optional[int] | None = None):
        num_tokens = input.shape[0]
        if (not self.has_any_quant and 1 <= num_tokens <= 16
                and get_sm_version() not in [120, 121]):
            output = torch.ops.trtllm.dsv3_fused_a_gemm_op(
                input, self.weight.t(), bias, None)
        else:
            output = super().apply_linear(input, bias, lora_params, layer_idx)
        return output


class DeepseekV3Attention(MLA):

    def __init__(
        self,
        model_config: ModelConfig[PretrainedConfig],
        layer_idx: Optional[int] = None,
        aux_stream: Optional[torch.cuda.Stream] = None,
        mapping_with_cp: Optional[Mapping] = None,
        reduce_output: bool = True,
    ):
        config = model_config.pretrained_config
        predicted_tokens_per_seq = model_config.spec_config.tokens_per_gen_step if model_config.spec_config is not None else 1
        super().__init__(hidden_size=config.hidden_size,
                         num_attention_heads=config.num_attention_heads,
                         num_key_value_heads=config.num_key_value_heads,
                         qk_rope_head_dim=config.qk_rope_head_dim,
                         qk_nope_head_dim=config.qk_nope_head_dim,
                         q_lora_rank=config.q_lora_rank,
                         kv_lora_rank=config.kv_lora_rank,
                         v_head_dim=config.v_head_dim,
                         predicted_tokens_per_seq=predicted_tokens_per_seq,
                         max_position_embeddings=config.max_position_embeddings,
                         bias=False,
                         pos_embd_params=PositionalEmbeddingParams(
                             type=PositionEmbeddingType.yarn,
                             rope=RopeParams.from_config(config),
                             is_neox=False,
                         ),
                         layer_idx=layer_idx,
                         dtype=config.torch_dtype,
                         config=model_config,
                         aux_stream=aux_stream,
                         mapping_with_cp=mapping_with_cp,
                         reduce_output=reduce_output)
        self.kv_a_proj_with_mqa = DeepseekV3Linear(
            config.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim +
            (self.q_lora_rank if not self.is_lite else 0),
            bias=False,
            dtype=config.torch_dtype,
            quant_config=model_config.get_quant_config(),
            skip_create_weights_in_init=model_config.
            skip_create_weights_in_init,
            use_custom_cublas_mm=True,
            use_cute_dsl_blockscaling_mm=model_config.
            use_cute_dsl_blockscaling_mm,
        )


class DeepseekV32Attention(MLA):

    def __init__(
        self,
        model_config: ModelConfig[PretrainedConfig],
        layer_idx: Optional[int] = None,
        aux_stream: Optional[torch.cuda.Stream] = None,
        reduce_output: bool = True,
    ):
        config = model_config.pretrained_config
        predicted_tokens_per_seq = model_config.spec_config.tokens_per_gen_step if model_config.spec_config is not None else 1

        super().__init__(hidden_size=config.hidden_size,
                         num_attention_heads=config.num_attention_heads,
                         num_key_value_heads=config.num_key_value_heads,
                         qk_rope_head_dim=config.qk_rope_head_dim,
                         qk_nope_head_dim=config.qk_nope_head_dim,
                         q_lora_rank=config.q_lora_rank,
                         kv_lora_rank=config.kv_lora_rank,
                         v_head_dim=config.v_head_dim,
                         predicted_tokens_per_seq=predicted_tokens_per_seq,
                         max_position_embeddings=config.max_position_embeddings,
                         bias=False,
                         pos_embd_params=PositionalEmbeddingParams(
                             type=PositionEmbeddingType.yarn,
                             rope=RopeParams.from_config(config),
                             is_neox=False,
                         ),
                         layer_idx=layer_idx,
                         dtype=config.torch_dtype,
                         config=model_config,
                         aux_stream=aux_stream,
                         reduce_output=reduce_output)

        self.indexer = self.mqa.indexer

        # For DeepseekV32, the kv_a_proj_with_mqa includes:
        # q_a_proj + kv_a_proj_with_mqa + indexer.wk
        self.kv_a_proj_with_mqa = DeepseekV3Linear(
            config.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim + self.q_lora_rank +
            self.indexer.head_dim,
            bias=False,
            dtype=config.torch_dtype,
            quant_config=model_config.get_quant_config(),
            skip_create_weights_in_init=model_config.
            skip_create_weights_in_init,
            use_custom_cublas_mm=True)

    def post_load_weights(self):
        """
        Concatenate indexer.wk weights into kv_a_proj_with_mqa's last dimension, to fuse indexer.wk projection with kv_a_proj_with_mqa GEMM.
        """
        assert self.kv_a_proj_with_mqa.weight.data.dtype == self.indexer.wk.weight.data.dtype, "all weights in kv_a_proj_with_mqa module must have matching dtype"
        # Copy indexer weights into the fused kv_a_proj_with_mqa module
        indexer_wk_weight = self.indexer.wk.weight.data
        offset = self.kv_lora_rank + self.qk_rope_head_dim + self.q_lora_rank
        self.kv_a_proj_with_mqa.weight.data[offset:offset +
                                            self.indexer.head_dim].copy_(
                                                indexer_wk_weight)

        # Copy indexer scale data if it exists
        if hasattr(self.indexer.wk,
                   'weight_scale') and self.indexer.wk.weight_scale is not None:
            indexer_wk_scale = self.indexer.wk.weight_scale.data
            assert self.kv_a_proj_with_mqa.weight_scale.dim(
            ) == 2, "weight_scale must be a 2D tensor"
            group_size = self.kv_a_proj_with_mqa.weight.shape[
                0] // self.kv_a_proj_with_mqa.weight_scale.shape[0]
            scale_offset = offset // group_size
            scale_size = indexer_wk_scale.shape[0]
            # Copy indexer scale to the corresponding position in the fused module
            self.kv_a_proj_with_mqa.weight_scale.data[
                scale_offset:scale_offset + scale_size].copy_(indexer_wk_scale)

        self.indexer.wk = None


class DeepseekV3Gate(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        num_experts: int,
        top_k: int,
        n_group: int,
        topk_group: int,
        routed_scaling_factor: float,
        dtype: Optional[torch.dtype] = None,
        fuse_routing_kernel: bool = True,
        apply_routing: bool = False,
        moe_backend: str = 'CUTLASS',
    ):
        super().__init__()
        self.weight = nn.Parameter(torch.empty((num_experts, hidden_size),
                                               dtype=dtype),
                                   requires_grad=False)
        self.moe_backend = moe_backend
        if moe_backend == 'TRTLLM':
            bias_dtype = torch.bfloat16
        else:
            bias_dtype = torch.float32

        self.e_score_correction_bias = nn.Parameter(torch.empty(
            (num_experts), dtype=bias_dtype),
                                                    requires_grad=False)

        assert not apply_routing, "DeepseekV3Gate routing is called inside MoE"

        # NOTE: e_score_correction_bias belongs in this gate class but is required by the routing impl.
        self.routing_impl = Deepseekv3RoutingImpl(
            top_k=top_k,
            n_group=n_group,
            topk_group=topk_group,
            routed_scaling_factor=routed_scaling_factor,
            is_fused=fuse_routing_kernel)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        logits = torch.ops.trtllm.dsv3_router_gemm_op(hidden_states,
                                                      self.weight.t(),
                                                      bias=None,
                                                      out_dtype=torch.float32)
        return logits

    def load_weights(self, weights: List[Dict]):
        assert len(weights) == 1

        self.weight.copy_(weights[0]["weight"][:])

        self.e_score_correction_bias.copy_(
            weights[0]["e_score_correction_bias"][:].to(
                self.e_score_correction_bias.dtype))

    @property
    def routing_method(self) -> DeepSeekV3MoeRoutingMethod:
        return DeepSeekV3MoeRoutingMethod(
            top_k=self.routing_impl.top_k,
            n_group=self.routing_impl.n_group,
            topk_group=self.routing_impl.topk_group,
            routed_scaling_factor=self.routing_impl.routed_scaling_factor,
            is_fused=self.routing_impl.is_fused,
            # Pass a callable to fetch the tensor from DeepseekV3Gate at runtime, ensuring it is on the correct device
            callable_e_score_correction_bias=lambda: self.
            e_score_correction_bias,
        )

    def apply(self, logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        # topk routing
        return self.routing_method.apply(logits)

    def get_experts_per_token(self):
        return self.routing_method.top_k


class Deepseekv3MoE(nn.Module):

    def __init__(self,
                 *,
                 num_experts: int,
                 top_k: int,
                 hidden_size: int,
                 intermediate_size: int,
                 shared_expert_intermediate_size: int,
                 aux_stream_dict: Dict[AuxStreamType, torch.cuda.Stream],
                 dtype: Optional[torch.dtype] = None,
                 model_config: ModelConfig = ModelConfig(),
                 override_quant_config: Optional[QuantConfig] = None,
                 layer_idx: Optional[int] = None):
        from ..distributed import AllReduce

        super().__init__()
        config = model_config.pretrained_config
        self.top_k = top_k
        self.use_dp = model_config.mapping.enable_attention_dp
        self.use_cute_dsl_blockscaling_mm = model_config.use_cute_dsl_blockscaling_mm
        gate_cls = DeepseekV3Gate
        if hasattr(model_config.pretrained_config, "gate_cls"):
            gate_cls = model_config.pretrained_config.gate_cls
        self.gate = gate_cls(hidden_size,
                             num_experts,
                             top_k=top_k,
                             n_group=config.n_group,
                             topk_group=config.topk_group,
                             routed_scaling_factor=config.routed_scaling_factor,
                             dtype=dtype,
                             fuse_routing_kernel=True,
                             apply_routing=False,
                             moe_backend=model_config.moe_backend)
        self.experts = create_moe(
            num_experts=num_experts,
            routing_method=self.gate.routing_method,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            dtype=dtype,
            reduce_results=
            False,  # In both low‑latency and attention‑DP modes, FusedMoE skips the in‑op all‑reduce.
            model_config=model_config,
            override_quant_config=override_quant_config,
            aux_stream_dict=aux_stream_dict,
            layer_idx=layer_idx,
            # DS-R1 W4A8 is only supported through custom quantization script from
            # examples/quantization/quantize_mixed_precision_moe.py
            weight_loading_mode=(
                MoEWeightLoadingMode.W4A8_CUSTOM
                if self._get_experts_quant_config(
                    model_config,
                    layer_idx).layer_quant_mode.is_int4_weight_only_per_group()
                else MoEWeightLoadingMode.VANILLA),
        )

        self.mapping = model_config.mapping

        # FIXME: incompatible with mixed quantization mode (including excluding modules from quantization)
        block_size = 1
        if model_config.quant_config and model_config.quant_config.group_size is not None:
            block_size = model_config.quant_config.group_size

        shared_tp_size, self.shared_output_scale = self._compute_shared_expert_tp_size(
            shared_expert_intermediate_size, block_size)

        self.shared_experts = GatedMLP(
            hidden_size=hidden_size,
            intermediate_size=shared_expert_intermediate_size,
            bias=False,
            dtype=dtype,
            config=model_config,
            overridden_tp_size=shared_tp_size,
            reduce_output=False,
            use_cute_dsl_blockscaling_mm=self.use_cute_dsl_blockscaling_mm,
        )

        self.allreduce = None
        if not self.use_dp and self.mapping.tp_size > 1:
            self.allreduce = AllReduce(mapping=model_config.mapping,
                                       strategy=model_config.allreduce_strategy)
        self.aux_stream = aux_stream_dict[AuxStreamType.MoeShared]
        self.event_dict = {
            key: torch.cuda.Event()
            for key in [EventType.Main, EventType.MoeShared]
        }

        # Store config values for perfect routing.
        self.model_config = model_config
        self.dtype = dtype

        # Perfect router caching - precompute common logits if enabled.
        if os.environ.get('ENABLE_PERFECT_ROUTER', '0') == '1':
            precompute_common_perfect_router_logits(
                num_experts=num_experts,
                experts_per_token=top_k,
                moe_ep_size=model_config.mapping.moe_ep_size,
                dtype=dtype)

    def _compute_shared_expert_tp_size(
            self, intermediate_size: int,
            block_size: int) -> tuple[int, float | None]:
        """
        In the case of Deepseek-R1, the TP size of MLP is capped by intermediate_size // block_size.
        For example, when the intermediate_size is 2048 and block scaling size is 128,
        TP sizes are limited to {1, 2, 4, 8, 16} because of 2048/128 = 16.

        Args:
            intermediate_size (int): MLP intermediate size.
            block_size (int): The quantization block scale size. In the case of Deepseek FP8 recipe,
                it's 128. For NVFP4, it's 16.

        Returns:
            tuple[int, float | None]: A tuple containing (shared_tp_size, shared_output_scale).
                - shared_tp_size: The computed TP size.
                - shared_output_scale: The output scale factor, or None if not needed.
        """

        assert intermediate_size % block_size == 0, "intermediate_size must be divisible by block_size."

        shared_output_scale = None
        # The block scale size is 128, which requires shared_expert_intermediate_size to be divisible by 128.
        if self.use_dp:
            # If using attention DP, the shared experts also use DP instead of TP.
            shared_tp_size = 1
        else:
            # Due to the restriction of block scale size (i.e., 128), the supported TP sizes only include 1, 2, 4, 8, and 16.
            # The math.gcd operation ensures that shared_tp_size falls in the supported TP sizes.
            shared_tp_size = math.gcd(
                intermediate_size // block_size,
                self.mapping.tp_size,
            )
            # If shared_tp_size has been overridden, the output of shared experts needs to be scaled down accordingly before all-reduce.
            if shared_tp_size != self.mapping.tp_size:
                shared_output_scale = shared_tp_size / self.mapping.tp_size

        return shared_tp_size, shared_output_scale

    @staticmethod
    def _get_experts_quant_config(model_config, layer_idx: int) -> QuantConfig:
        if getattr(model_config, "quant_config_dict", None) is None:
            return model_config.quant_config
        return model_config.quant_config_dict.get(
            f"model.layers.{layer_idx}.mlp.experts", model_config.quant_config)

    def _create_ideal_expert_load_balanced_logits(
            self, num_tokens: int, num_experts: int,
            device: torch.device) -> torch.Tensor:
        """
        Create ideal logits that produce GPU-aware load balanced expert assignment.
        This method uses the global cache to access precomputed logits to optimize performance.
        """
        # Use global cached logits.
        return get_cached_perfect_router_logits(
            num_tokens=num_tokens,
            num_experts=num_experts,
            experts_per_token=self.top_k,
            moe_ep_size=self.model_config.mapping.moe_ep_size,
            device=device,
            dtype=self.dtype)

    def compute_routed_output(self, hidden_states, hidden_states_fp4,
                              all_rank_num_tokens, do_finalize):
        # max-throughput
        use_dp_padding = False
        # Add DP padding on SM120 for context comm performance
        # TODO: Move this model-agonostic part to MoE
        if self.use_dp and self.mapping.tp_size > 1 and get_sm_version() == 120:
            use_dp_padding = True
            hidden_states = torch.nn.functional.pad(
                hidden_states,
                (0, 0, 0, max(all_rank_num_tokens) - hidden_states.shape[0]))

        router_logits = self.gate(hidden_states)

        # Use ideal load balanced logits if enabled, otherwise use gate output.
        if os.environ.get('ENABLE_PERFECT_ROUTER', '0') == '1':
            # WARNING: This discards the learned gate output and uses ideal logits for perfect load balancing.
            # Only use this for testing load balancing strategies, not for actual inference.
            # The gate is still computed to maintain realistic performance measurement.
            num_tokens, num_experts = router_logits.shape
            router_logits = self._create_ideal_expert_load_balanced_logits(
                num_tokens=num_tokens,
                num_experts=num_experts,
                device=hidden_states.device)

        routed_output = self.experts(
            hidden_states_fp4
            if hidden_states_fp4 is not None else hidden_states,
            router_logits,
            do_finalize=do_finalize,
            output_dtype=hidden_states.dtype,
            all_rank_num_tokens=all_rank_num_tokens,
            use_dp_padding=use_dp_padding,
            **({
                "alltoall_result_do_sum": False
            } if isinstance(self.experts, WideEPMoE) else {}),
        )

        return routed_output

    def forward(
        self,
        hidden_states: torch.Tensor,
        hidden_states_fp4: Optional[Fp4QuantizedTensor] = None,
        all_rank_num_tokens: Optional[list[int]] = None,
        final_all_reduce_params: Optional[AllReduceParams] = None,
        do_finalize: Optional[bool] = True,
    ) -> torch.Tensor:
        if not do_finalize:
            assert not self.use_dp

        def _compute_shared_output():
            shared_output = self.shared_experts(
                hidden_states_fp4
                if hidden_states_fp4 is not None else hidden_states)
            if self.shared_output_scale is not None:
                shared_output *= self.shared_output_scale
            return shared_output

        def _compute_routed_output():
            routed_output = self.compute_routed_output(hidden_states,
                                                       hidden_states_fp4,
                                                       all_rank_num_tokens,
                                                       do_finalize)
            return routed_output

        # NOTE: define compiled helpers at module scope to avoid defining decorators inside compiled frames

        routed_output, shared_output = maybe_execute_in_parallel(
            _compute_routed_output, _compute_shared_output,
            self.event_dict[EventType.Main],
            self.event_dict[EventType.MoeShared], self.aux_stream)

        if not do_finalize:
            return [shared_output, *routed_output]
        else:
            if routed_output.dim() == 3:
                assert shared_output.numel(
                ) * self.top_k == routed_output.numel(
                ), 'unmatched tensor shape'
                final_hidden_states = moe_reduce_add_shared_output(
                    routed_output, shared_output)
            else:
                assert shared_output.size() == routed_output.size(
                ), 'unmatched tensor shape'
                final_hidden_states = shared_output + routed_output

            if not self.use_dp and self.mapping.tp_size > 1:
                final_hidden_states = self.allreduce(
                    final_hidden_states,
                    all_reduce_params=final_all_reduce_params)

            return final_hidden_states


class DeepseekV3DecoderLayer(DecoderLayer):

    def __init__(self,
                 model_config: ModelConfig[PretrainedConfig],
                 layer_idx: int,
                 aux_stream_dict: Dict[AuxStreamType, torch.cuda.Stream],
                 is_separate_draft_engine: bool = False,
                 mapping_with_cp: Optional[Mapping] = None):
        super().__init__()
        self.model_config = model_config
        self.config = model_config.pretrained_config
        config = self.config

        self.hidden_size = config.hidden_size
        self.moe_intermediate_size = config.moe_intermediate_size
        self.num_experts = config.n_routed_experts
        self.num_shared_experts = config.n_shared_experts
        self.top_k = config.num_experts_per_tok

        self.mapping = model_config.mapping
        mapping = self.mapping
        self.enable_attention_dp = mapping.enable_attention_dp
        self.mlp_tp_size = mapping.tp_size
        self.is_p2p_supported = can_access_peer(mapping)

        layer_idx_for_attention = layer_idx
        if is_separate_draft_engine:
            #KVCacheManager only support 1 layer for separate draft engine
            layer_idx_for_attention = layer_idx - model_config.pretrained_config.num_hidden_layers

        if config.model_type == "deepseek_v32":
            self.self_attn = DeepseekV32Attention(
                model_config,
                layer_idx=layer_idx_for_attention,
                aux_stream=aux_stream_dict[AuxStreamType.Attention],
                reduce_output=not self.enable_attention_dp
                and self.mapping.tp_size > 1)
        else:
            # When enable_attention_dp is True, TP reduction is skipped since each DP rank
            # works on different batch elements. However, with CP > 1, attention is split
            # across CP ranks for the SAME batch element, so reduction is still needed
            # within the CP group.
            needs_tp_reduce = not self.enable_attention_dp and self.mapping.tp_size > 1
            needs_cp_reduce = mapping_with_cp is not None and mapping_with_cp.has_cp_helix(
            )
            self.self_attn = DeepseekV3Attention(
                model_config,
                layer_idx=layer_idx_for_attention,
                aux_stream=aux_stream_dict[AuxStreamType.Attention],
                mapping_with_cp=mapping_with_cp,
                reduce_output=needs_tp_reduce or needs_cp_reduce)

        self.fusion_config = EagerFusionConfig()
        self.enable_fusion = os.environ.get(
            "TRTLLM_DEEPSEEK_EAGER_FUSION_DISABLED", "0") == "0"
        self.enable_fusion &= not self.enable_attention_dp

        # FIXME: incompatible with mixed quantization mode
        quant_config = self._get_decoder_layer_quant_config(
            model_config, layer_idx)
        self.is_nvfp4 = quant_config.layer_quant_mode.has_nvfp4()
        assert (
            quant_config.quant_algo
            is not QuantAlgo.MIXED_PRECISION), "MIXED_PRECISION is ambiguous"

        self.allreduce = None
        self.moe_allreduce = None
        if not self.enable_attention_dp and self.mapping.tp_size > 1:
            self.allreduce = AllReduce(mapping=model_config.mapping,
                                       strategy=model_config.allreduce_strategy,
                                       dtype=config.torch_dtype)
            self.moe_allreduce = MoEAllReduce(self.mapping)

        has_tp = mapping.has_tp()
        if (config.n_routed_experts is not None
                and layer_idx >= config.first_k_dense_replace
                and layer_idx % config.moe_layer_freq == 0):

            self.fusion_config.PRE_MOE_FUSION = self.enable_fusion and has_tp
            self.fusion_config.POST_MOE_FUSION = self.fusion_config.PRE_MOE_FUSION

            self.mlp = Deepseekv3MoE(
                num_experts=self.num_experts,
                top_k=self.top_k,
                hidden_size=self.hidden_size,
                intermediate_size=self.moe_intermediate_size,
                shared_expert_intermediate_size=self.moe_intermediate_size *
                self.num_shared_experts,
                dtype=config.torch_dtype,
                model_config=model_config,
                override_quant_config=quant_config,
                aux_stream_dict=aux_stream_dict,
                layer_idx=layer_idx)
        else:
            block_size = 1
            if quant_config and quant_config.group_size is not None:
                block_size = quant_config.group_size
            self.mlp_tp_size = self._compute_mlp_tp_size(
                config.intermediate_size, block_size)

            has_mlp_tp = self.mlp_tp_size > 1
            self.fusion_config.PRE_MLP_FUSION = self.enable_fusion and has_mlp_tp and self.is_nvfp4
            self.fusion_config.POST_MLP_FUSION = self.enable_fusion and has_mlp_tp

            self.mlp = GatedMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                bias=False,
                dtype=config.torch_dtype,
                config=model_config,
                overridden_tp_size=self.mlp_tp_size,
                reduce_output=has_mlp_tp,
                use_cute_dsl_blockscaling_mm=model_config.
                use_cute_dsl_blockscaling_mm,
            )

        self.input_layernorm = RMSNorm(hidden_size=config.hidden_size,
                                       eps=config.rms_norm_eps,
                                       dtype=config.torch_dtype)

        # When enable_attention_dp is True, we normally skip attention all-reduce since each
        # DP rank works on different batch elements. However, with CP > 1, attention is split
        # across CP ranks for the SAME batch element, so all-reduce is still needed.
        has_cp = mapping_with_cp is not None and mapping_with_cp.cp_size > 1
        can_skip_for_attention_dp = self.enable_attention_dp and not has_cp
        self.disable_attn_allreduce = (self.fusion_config.PRE_MOE_FUSION
                                       or self.fusion_config.PRE_MLP_FUSION
                                       or self.mapping.tp_size == 1
                                       or can_skip_for_attention_dp)

        self.post_attention_layernorm = RMSNorm(hidden_size=config.hidden_size,
                                                eps=config.rms_norm_eps,
                                                dtype=config.torch_dtype)
        self.layer_idx = layer_idx
        self.next_layer_layernorm: RMSNorm = None

    def _get_decoder_layer_quant_config(
            self, model_config: ModelConfig[PretrainedConfig], layer_idx: int):
        """
        The MTP layer in the nvfp4 checkpoint is unquantized. Because the TRTLLM
        moe_backend only supports fp8/fp4 quantization, we need to override
        the quant_config for the MTP layer.
        """
        quant_config = model_config.quant_config

        layer_name = f"model.layers.{layer_idx}"
        if quant_config.is_module_excluded_from_quantization(layer_name):
            return QuantConfig(
                quant_algo=None,
                kv_cache_quant_algo=quant_config.kv_cache_quant_algo,
            )
        else:
            return model_config.quant_config

    def _compute_mlp_tp_size(self, intermediate_size: int,
                             block_size: int) -> int:
        """
        For DeepSeek‑R1, MLP TP size is limited by intermediate_size // block_size
        and must also be multiples of gpus_per_node to avoid expensive inter‑node allreduce.

        Args:
            intermediate_size (int): MLP intermediate size.
            block_size (int): The quantization block scale size. In the case of Deepseek FP8 recipe,
                it's 128. For NVFP4, it's 16.

        Returns:
            int: The computed tp_size.
        """

        assert intermediate_size % block_size == 0, "intermediate_size must be divisible by block_size."
        if self.enable_attention_dp:
            # If using attention DP, the MLP also uses DP instead of TP.
            mlp_tp_size = 1
        else:
            # The two math.gcd operations ensure that mlp_tp_size falls in the candidate TP sizes.
            tp = math.gcd(
                intermediate_size // block_size,
                self.mapping.tp_size,
            )

            if tp > self.mapping.gpus_per_node:
                mlp_tp_size = math.gcd(
                    tp,
                    self.mapping.gpus_per_node,
                )  # Avoid costly inter-node TP
            else:
                mlp_tp_size = tp
        return mlp_tp_size

    def forward(
        self,
        position_ids: torch.IntTensor,
        hidden_states: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: torch.Tensor,
        spec_metadata: Optional[SpecMetadata] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, residual = self.self_attn(
            position_ids=position_ids,
            hidden_states=hidden_states,
            attn_metadata=attn_metadata,
            all_reduce_params=AllReduceParams(
                enable_allreduce=not (self.disable_attn_allreduce)),
            residual=residual,
            **kwargs,
        )
        if isinstance(self.mlp, Deepseekv3MoE):
            if spec_metadata is not None and spec_metadata.is_layer_capture(
                    self.layer_idx):
                self.fusion_config.POST_MOE_FUSION = False
            return self.forward_MoE(
                hidden_states=hidden_states,
                attn_metadata=attn_metadata,
                residual=residual,
                spec_metadata=spec_metadata,
            )
        else:
            if spec_metadata is not None and spec_metadata.is_layer_capture(
                    self.layer_idx):
                self.fusion_config.POST_MLP_FUSION = False
            assert isinstance(self.mlp, GatedMLP)
            return self.forward_mlp(
                hidden_states=hidden_states,
                residual=residual,
                spec_metadata=spec_metadata,
            )

    def forward_MoE(
        self,
        hidden_states: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: torch.Tensor,
        spec_metadata: Optional[SpecMetadata] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        def _run_MoE(hidden_states, hidden_states_fp4, do_finalize):
            return self.mlp(
                hidden_states,
                hidden_states_fp4,
                all_rank_num_tokens=attn_metadata.all_rank_num_tokens,
                final_all_reduce_params=AllReduceParams(
                    enable_allreduce=not (self.fusion_config.POST_MOE_FUSION
                                          or self.mapping.tp_size == 1)),
                do_finalize=do_finalize,
            )

        if self.fusion_config.PRE_MOE_FUSION:
            # moe_backend can be either CUTLASS or TRTLLM here
            # TODO: unify the two min-latency MoE backends by enabling quant fusion
            hidden_states, residual = self.allreduce(
                hidden_states,
                all_reduce_params=AllReduceParams(
                    fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
                    residual=residual,
                    norm_weight=self.post_attention_layernorm.weight,
                    eps=self.post_attention_layernorm.variance_epsilon,
                    trigger_completion_at_end=False,
                ))
        else:
            # No fusion
            hidden_states, residual = self.post_attention_layernorm(
                hidden_states, residual)

        # Note: this fusion pattern is only supported for single-node TRTLLM-nvfp4 backend now
        do_finalize = self.mapping.is_multi_node() or (
            not (self.fusion_config.POST_MOE_FUSION
                 and hidden_states.shape[0] <= self.moe_allreduce.max_token
                 and self.model_config.moe_backend == "TRTLLM"
                 and self.mlp.experts.has_nvfp4 and self.is_p2p_supported))

        hidden_states = _run_MoE(hidden_states,
                                 hidden_states_fp4=None,
                                 do_finalize=do_finalize)

        if self.fusion_config.POST_MOE_FUSION:
            if do_finalize:
                hidden_states, residual = self.allreduce(
                    hidden_states,
                    all_reduce_params=AllReduceParams(
                        fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
                        residual=residual,
                        norm_weight=self.next_layer_layernorm.weight,
                        eps=self.next_layer_layernorm.variance_epsilon,
                        trigger_completion_at_end=False,
                    ))
            else:
                assert len(
                    hidden_states) == 4, "hidden_states must have 4 elements"

                shared_output = hidden_states[0]
                fc2_output = hidden_states[1]
                expert_scale_factor = hidden_states[2]
                expanded_idx_to_permuted_idx = hidden_states[3]

                moe_all_reduce_params = MoEAllReduceParams(
                    expanded_idx_to_permuted_idx=expanded_idx_to_permuted_idx,
                    expert_scale_factor=expert_scale_factor,
                    shared_expert_output=shared_output,
                    residual=residual,
                    norm_weight=self.next_layer_layernorm.weight,
                    eps=self.next_layer_layernorm.variance_epsilon,
                    is_cutlass_min_latency=False,
                )
                hidden_states, residual = self.moe_allreduce(
                    fc2_output, all_reduce_params=moe_all_reduce_params)
        else:
            if spec_metadata is not None and spec_metadata.is_layer_capture(
                    self.layer_idx):
                spec_metadata.maybe_capture_hidden_states(
                    self.layer_idx, hidden_states, residual)
            if self.next_layer_layernorm is not None:
                hidden_states, residual = self.next_layer_layernorm(
                    hidden_states, residual)

        return hidden_states, residual

    def forward_mlp(
        self,
        hidden_states: torch.Tensor,
        residual: torch.Tensor,
        spec_metadata: Optional[SpecMetadata] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        if self.fusion_config.PRE_MLP_FUSION:
            act_fp4, act_sf, residual = self.allreduce(
                hidden_states,
                all_reduce_params=AllReduceParams(
                    fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM_QUANT_NVFP4,
                    residual=residual,
                    norm_weight=self.post_attention_layernorm.weight,
                    scale=self.mlp.gate_up_proj.input_scale,
                    eps=self.post_attention_layernorm.variance_epsilon,
                ),
            )
            hidden_states = Fp4QuantizedTensor(act_fp4, act_sf)
        else:
            # No fusion
            # We need to add twoshot allreduce here to avoid modifying MLA logic
            hidden_states, residual = self.post_attention_layernorm(
                hidden_states, residual)

        hidden_states = self.mlp(
            hidden_states,
            final_all_reduce_params=AllReduceParams(enable_allreduce=not (
                self.fusion_config.POST_MLP_FUSION or self.mlp_tp_size == 1)),
        )

        if self.fusion_config.POST_MLP_FUSION:
            hidden_states, residual = self.allreduce(
                hidden_states,
                all_reduce_params=AllReduceParams(
                    fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
                    residual=residual,
                    norm_weight=self.next_layer_layernorm.weight,
                    eps=self.next_layer_layernorm.variance_epsilon,
                ),
            )
        else:
            if spec_metadata is not None and spec_metadata.is_layer_capture(
                    self.layer_idx):
                spec_metadata.maybe_capture_hidden_states(
                    self.layer_idx, hidden_states, residual)
            if self.next_layer_layernorm is not None:
                hidden_states, residual = self.next_layer_layernorm(
                    hidden_states, residual)

        return hidden_states, residual


class DeepseekV3MTP(DeepseekV3DecoderLayer):

    def __init__(self,
                 model_config: ModelConfig[PretrainedConfig],
                 layer_idx: int,
                 aux_stream_dict: Dict[AuxStreamType, torch.cuda.Stream],
                 is_separate_draft_engine: bool = False):
        super().__init__(model_config, layer_idx, aux_stream_dict,
                         is_separate_draft_engine)
        config = model_config.pretrained_config
        self.hidden_dim = config.hidden_size
        self.moe_intermediate_size = config.moe_intermediate_size
        self.num_experts = config.n_routed_experts
        self.num_shared_experts = config.n_shared_experts
        self.top_k = config.num_experts_per_tok

        self.aux_stream = aux_stream_dict[AuxStreamType.MoeShared]
        self.event_dict = {
            key: torch.cuda.Event()
            for key in [EventType.Main, EventType.MoeShared]
        }

        self.enorm = RMSNorm(hidden_size=config.hidden_size,
                             eps=config.rms_norm_eps,
                             dtype=config.torch_dtype)

        self.hnorm = RMSNorm(hidden_size=config.hidden_size,
                             eps=config.rms_norm_eps,
                             dtype=config.torch_dtype)
        if model_config.mapping.enable_attention_dp:
            self.eh_proj = Linear(
                config.hidden_size * 2,
                config.hidden_size,
                bias=False,
                dtype=config.torch_dtype,
                skip_create_weights_in_init=model_config.
                skip_create_weights_in_init,
                use_cute_dsl_blockscaling_mm=model_config.
                use_cute_dsl_blockscaling_mm,
            )
        else:
            self.eh_proj = Linear(
                config.hidden_size * 2,
                config.hidden_size,
                bias=False,
                dtype=config.torch_dtype,
                tensor_parallel_mode=TensorParallelMode.ROW,
                mapping=model_config.mapping,
                reduce_output=True,
                skip_create_weights_in_init=model_config.
                skip_create_weights_in_init,
                use_cute_dsl_blockscaling_mm=model_config.
                use_cute_dsl_blockscaling_mm,
            )

        self.shared_head = DeepseekV3MTPHead(model_config)

    def forward(
        self,
        input_ids: torch.IntTensor,
        position_ids: torch.IntTensor,
        hidden_states: torch.Tensor,
        embed_tokens: Embedding,
        attn_metadata: AttentionMetadata,
        all_rank_num_tokens: Optional[List[int]] = None,
        spec_metadata: Optional[SpecMetadata] = None,
        **kwargs,
    ) -> torch.Tensor:

        def norm_embeds():
            return self.enorm(embed_tokens(input_ids))  #emdedding

        def norm_hidden():
            return self.hnorm(hidden_states)

        inputs_embeds, hidden_states = maybe_execute_in_parallel(
            norm_embeds,
            norm_hidden,
            self.event_dict[EventType.Main],
            self.event_dict[EventType.MoeShared],
            self.aux_stream,
        )
        hidden_states = torch.concat([inputs_embeds, hidden_states], dim=-1)
        # Split hidden_states columnwise based on TP
        tp_size = self.model_config.mapping.tp_size
        tp_rank = self.model_config.mapping.tp_rank

        if tp_size > 1 and not (self.model_config.mapping.enable_attention_dp):
            hidden_states = torch.chunk(hidden_states, tp_size, dim=-1)[tp_rank]
        hidden_states = self.eh_proj(hidden_states)

        # Input layer norm
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, residual = self.self_attn(
            position_ids=position_ids,
            hidden_states=hidden_states,
            attn_metadata=attn_metadata,
            all_reduce_params=AllReduceParams(
                enable_allreduce=not (self.disable_attn_allreduce)),
            residual=residual,
            **kwargs,
        )

        # MTP Layer Must have sparse MOE
        if self.fusion_config.PRE_MOE_FUSION:
            hidden_states, residual = self.allreduce(
                hidden_states,
                all_reduce_params=AllReduceParams(
                    fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
                    residual=residual,
                    norm_weight=self.post_attention_layernorm.weight,
                    eps=self.post_attention_layernorm.variance_epsilon,
                ),
            )
        else:
            hidden_states, residual = self.post_attention_layernorm(
                hidden_states, residual)

        # MoE
        hidden_states = self.mlp(
            hidden_states,
            all_rank_num_tokens=all_rank_num_tokens,
            final_all_reduce_params=AllReduceParams(
                enable_allreduce=not (self.fusion_config.POST_MOE_FUSION
                                      or self.mapping.tp_size == 1)),
        )

        if self.fusion_config.POST_MOE_FUSION:
            hidden_states, residual = self.allreduce(
                hidden_states,
                all_reduce_params=AllReduceParams(
                    fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
                    residual=residual,
                    norm_weight=self.shared_head.norm.weight,
                    eps=self.shared_head.norm.variance_epsilon,
                ),
            )
        else:
            hidden_states, _ = self.shared_head.norm(hidden_states, residual)

        # It's for 2-model path, capture the hidden states
        if spec_metadata is not None:
            spec_metadata.maybe_capture_hidden_states(0, hidden_states, None)

        return hidden_states


class DeepseekV3Model(DecoderModel):

    def __init__(self,
                 model_config: ModelConfig[PretrainedConfig],
                 mapping_with_cp: Optional[Mapping] = None):
        super().__init__(model_config)
        config = model_config.pretrained_config
        self.vocab_size = config.vocab_size
        self.num_hidden_layers = config.num_hidden_layers
        self.mapping_with_cp = mapping_with_cp
        aux_stream_list = [torch.cuda.Stream() for _ in range(4)]
        self.aux_stream_dict = {
            AuxStreamType.Attention: aux_stream_list[0],
            AuxStreamType.MoeShared: aux_stream_list[0],
            AuxStreamType.MoeChunkingOverlap: aux_stream_list[1],
            AuxStreamType.MoeBalancer: aux_stream_list[2],
            AuxStreamType.MoeOutputMemset: aux_stream_list[3],
        }

        self.embed_tokens = Embedding(
            config.vocab_size,
            config.hidden_size,
            dtype=config.torch_dtype,
        )

        self.layers = nn.ModuleList([
            DeepseekV3DecoderLayer(model_config,
                                   layer_idx,
                                   self.aux_stream_dict,
                                   mapping_with_cp=mapping_with_cp)
            for layer_idx in range(config.num_hidden_layers)
        ])
        self.norm = RMSNorm(hidden_size=config.hidden_size,
                            eps=config.rms_norm_eps,
                            dtype=config.torch_dtype)

    def forward(
        self,
        attn_metadata: AttentionMetadata,
        input_ids: Optional[torch.IntTensor] = None,
        position_ids: Optional[torch.IntTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        spec_metadata: Optional[SpecMetadata] = None,
        **kwargs,
    ) -> torch.Tensor:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        hidden_states = inputs_embeds
        residual = None

        for idx, decoder_layer in enumerate(
                self.layers[:self.num_hidden_layers]):
            hidden_states, residual = decoder_layer(
                position_ids=position_ids,
                hidden_states=hidden_states,
                attn_metadata=attn_metadata,
                residual=residual,
                spec_metadata=spec_metadata,
            )

        # With CP helix, the last layer's reduce-scatter leaves each rank
        # with only its chunk of tokens.  AllGather restores the full token
        # count so the LM head (and norm) see every token.
        if (self.mapping_with_cp is not None
                and self.mapping_with_cp.has_cp_helix()
                and self.mapping_with_cp.enable_attention_dp):
            hidden_states = cp_allgather(hidden_states,
                                         self.mapping_with_cp,
                                         dim=0)
            hidden_states = hidden_states[:attn_metadata.num_tokens]

        return hidden_states


@register_auto_model("DeepseekV32ForCausalLM")
@register_auto_model("DeepseekV3ForCausalLM")
class DeepseekV3ForCausalLM(SpecDecOneEngineForCausalLM[DeepseekV3Model,
                                                        PretrainedConfig]):

    def __init__(self, model_config: ModelConfig[PretrainedConfig]):
        self.mapping_with_cp = None
        # Note: Currently the usage of mapping is all over the place making its usage brittle
        # in this file. As a temporary WAR, we hold on to an original copy of mapping when CP
        # is in action. This shall be passed on to attention which is the only layer that's
        # affected by CP. For other layers, CP ranks are repurposed to TP. This shall be undone
        # at the end of __init__.
        if model_config.mapping.has_cp_helix():
            print(
                f"[DeepseekV3ForCausalLM::__init__] Repurposing KVP ranks to TP while keeping other details the same."
            )
            self.mapping_with_cp = copy.deepcopy(model_config.mapping)
            # Repurpose KVP ranks to TP while keeping other details the same.
            model_config._frozen = False
            model_config.mapping = model_config.mapping.repurpose_helix_cp_to_tp(
            )
            model_config._frozen = True

        # Rename some keys of quant_config_dict to support legacy checkpoints
        if model_config.quant_config_dict is not None:
            model_config = copy.deepcopy(model_config)
            quant_config_dict = {}
            for key, val in model_config.quant_config_dict.items():
                key_split = key.split(".")
                if key_split[-1] == "fused_a":
                    key = ".".join(key_split[:-1] + ["kv_a_proj_with_mqa"])
                quant_config_dict[key] = val
            model_config._frozen = False
            model_config.quant_config_dict = quant_config_dict
            model_config._frozen = True

        super().__init__(model=DeepseekV3Model(
            model_config, mapping_with_cp=self.mapping_with_cp),
                         model_config=model_config)

        self.model_nextn = 0
        if model_config.spec_config is not None and model_config.spec_config.spec_dec_mode.is_mtp_one_model(
        ):
            model_nextn = model_config.spec_config.num_nextn_predict_layers
            ckpt_nextn = self.config.num_nextn_predict_layers
            self.num_hidden_layers = self.config.num_hidden_layers
            assert ckpt_nextn > 0, "There is not MTP modules in the checkpoint."
            if ckpt_nextn == 1 and not model_config.spec_config.use_mtp_vanilla:
                pass
            else:
                # modify the QuantConfig to support duplicated mtp layers
                if model_config.quant_config.exclude_modules is not None:
                    extend_exclude_modules = []
                    for model_mtp_idx in range(
                            self.num_hidden_layers,
                            self.num_hidden_layers + model_nextn):
                        ckpt_mtp_idx = (model_mtp_idx - self.num_hidden_layers
                                        ) % ckpt_nextn + self.num_hidden_layers
                        model_prefix = f"model.layers.{model_mtp_idx}"
                        ckpt_prefix = f"model.layers.{ckpt_mtp_idx}"
                        for exclude_module in model_config.quant_config.exclude_modules:
                            if ckpt_prefix in exclude_module and model_prefix not in exclude_module:
                                extend_exclude_modules.append(
                                    exclude_module.replace(
                                        ckpt_prefix, model_prefix))
                    self.model_config.quant_config.exclude_modules.extend(
                        extend_exclude_modules)
            self.model.layers.extend(self.draft_model.mtp_layers)

        # Undo any manipulations done to mapping.
        if self.mapping_with_cp is not None:
            print(
                f"[DeepseekV3ForCausalLM::__init__] Restoring original mapping."
            )
            model_config._frozen = False
            model_config.mapping = self.mapping_with_cp
            model_config._frozen = True

    def forward(
        self,
        attn_metadata: AttentionMetadata,
        input_ids: torch.IntTensor = None,
        position_ids: Optional[torch.IntTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        return_context_logits: bool = False,
        spec_metadata: Optional[SpecMetadata] = None,
        resource_manager=None,
        **kwargs,
    ) -> torch.Tensor:
        return super().forward(attn_metadata=attn_metadata,
                               input_ids=input_ids,
                               position_ids=position_ids,
                               inputs_embeds=inputs_embeds,
                               return_context_logits=return_context_logits,
                               spec_metadata=spec_metadata,
                               resource_manager=resource_manager,
                               **kwargs)

    def load_weights(self, weights: ConsumableWeightsDict):
        weight_loader = DeepseekV3WeightLoader(self)
        weight_loader.load_weights(weights)

    def post_load_weights(self):
        for idx, layer in enumerate(
                self.model.layers[:self.config.num_hidden_layers]):
            if idx == self.config.num_hidden_layers - 1:
                layer.next_layer_layernorm = self.model.norm
            else:
                layer.next_layer_layernorm = self.model.layers[
                    idx + 1].input_layernorm


@register_auto_model("KimiK25ForConditionalGeneration")
class KimiK25ForConditionalGeneration(DeepseekV3ForCausalLM):
    """Kimi-K2.5 multimodal model (text-only path).

    Extracts the DeepSeek-V3 text backbone from the composite config
    and strips the ``language_model.`` weight prefix so that the
    standard DeepseekV3ForCausalLM loading path works unchanged.

    NOTE: Kimi-K2.5's text backbone sets ``num_nextn_predict_layers = 0``,
    so MTP-based speculative decoding is not applicable to this model.
    """

    _LANG_PREFIX = "language_model."

    def __init__(self, model_config: ModelConfig[PretrainedConfig]):
        model_config = copy.copy(model_config)
        assert hasattr(model_config.pretrained_config, 'text_config'), \
            "Kimi K2.5 config must have text_config"
        model_config._frozen = False
        model_config.pretrained_config = model_config.pretrained_config.text_config
        if model_config.quant_config.exclude_modules:
            model_config.quant_config = copy.copy(model_config.quant_config)
            p = self._LANG_PREFIX
            mapped = []
            for m in model_config.quant_config.exclude_modules:
                if m.startswith(p):
                    rest = m[len(p):]
                    if rest.startswith('layers.'):
                        rest = 'model.' + rest
                    mapped.append(rest)
                else:
                    mapped.append(m)
            model_config.quant_config.exclude_modules = mapped
        model_config._frozen = True
        super().__init__(model_config)

    def load_weights(self, weights: ConsumableWeightsDict):
        has_prefix = any(k.startswith("language_model.") for k in weights)
        if has_prefix:
            weights = filter_weights("language_model", weights)
            weights = ConsumableWeightsDict(weights)
        super().load_weights(weights)