xc-llm-ascend/vllm_ascend/torchair/ops/torchair_fused_moe.py

# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/kernels/test_moe.py

import os
from typing import Any, Callable, Optional, Tuple, Union

import torch
import torch.distributed as dist
import torch_npu
from torch import nn
from vllm.config import get_current_vllm_config
from vllm.distributed import (GroupCoordinator, get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              tensor_model_parallel_all_reduce)
from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
                                             get_tp_group)
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe.config import \
    FusedMoEConfig  # isort: skip
from vllm.model_executor.layers.fused_moe.config import \
    FusedMoEParallelConfig  # isort: skip
from vllm.model_executor.layers.fused_moe.layer import (
    FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map)
from vllm.model_executor.layers.quantization.base_config import \
    QuantizationConfig

from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import FusedMoEState
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.eplb.core.eplb_utils import (determine_default_expert_map,
                                              determine_default_log2phy_map)
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.quantization.quant_config import AscendFusedMoEMethod
from vllm_ascend.torchair.ops.sequence_parallel import MetadataForPadding
from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
from vllm_ascend.utils import (AscendSocVersion, dispose_tensor,
                               get_all_reduce_merge_state,
                               get_ascend_soc_version,
                               get_rm_router_logits_state, is_310p)


def torchair_fused_experts_with_mc2(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    moe_parallel_config: FusedMoEParallelConfig,
    expert_map: torch.Tensor = None,
    moe_all_to_all_group_name: Optional[str] = None,
    shared_experts: Optional[Any] = None,
    is_torchair: bool = False,
    mc2_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    quant_mode = 0
    ep_rank_id = moe_parallel_config.ep_rank
    ep_world_size = moe_parallel_config.ep_size

    # NOTE: Currently, when in A3 or in torchair graph, we need to pass in some extra param into dispatch & combine
    need_extra_args = (get_ascend_soc_version() == AscendSocVersion.A3
                       or is_torchair)

    # NOTE: Currently, when in A3, we need to pass in some extra param into dispatch & combine
    a3_need_extra_args = get_ascend_soc_version() == AscendSocVersion.A3

    enable_dispatch_v2 = hasattr(torch_npu, "npu_moe_distribute_dispatch_v2")

    moe_expert_num = len(expert_map)
    kwargs_mc2 = {
        "x": hidden_states,
        "expert_ids": topk_ids,
        "expert_shard_type": 0,
        "shared_expert_rank_num": 0,
        "moe_expert_num": moe_expert_num,
        "global_bs": 0,
    }

    stage1_kwargs = {
        "scales": None,
        "quant_mode": quant_mode,
        "group_ep": moe_all_to_all_group_name,
        "ep_world_size": ep_world_size,
        "ep_rank_id": ep_rank_id,
    }
    if need_extra_args:
        stage1_kwargs.update({
            "group_tp": moe_all_to_all_group_name,
            "tp_world_size": 1,
            "tp_rank_id": 0,
        })
    if a3_need_extra_args and enable_dispatch_v2:
        stage1_kwargs.update({
            "x_active_mask": mc2_mask,
        })

    kwargs_mc2.update(stage1_kwargs)

    output = torch_npu.npu_moe_distribute_dispatch_v2(
        **kwargs_mc2
    ) if enable_dispatch_v2 else torch_npu.npu_moe_distribute_dispatch(
        **kwargs_mc2)
    # comm_stream.wait_stream(torch.npu.current_stream())
    expand_x, dynamic_scale, assist_info_for_combine, expert_token_nums, ep_recv_counts = output[
        0:5]

    if shared_experts is not None:
        with npu_stream_switch("moe_secondary", 0):
            npu_wait_tensor(hidden_states, topk_weights)
            shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states)
            npu_wait_tensor(shared_gate_up, expand_x)
            shared_act = shared_experts.act_fn(shared_gate_up)

    w1 = w1.transpose(1, 2)

    group_list = expert_token_nums.to(torch.int64)
    gate_up_out_list = torch_npu.npu_grouped_matmul(
        x=[expand_x],
        weight=[w1],
        split_item=2,
        # 1 means count mode, to avoid cumulative operation of the group list
        group_list_type=1,
        group_type=0,
        group_list=group_list,
    )[0]

    gate_up_out = torch_npu.npu_swiglu(gate_up_out_list)

    w2 = w2.transpose(1, 2)
    down_out_list = torch_npu.npu_grouped_matmul(
        x=[gate_up_out],
        weight=[w2],
        split_item=2,
        group_list_type=1,
        group_type=0,
        group_list=group_list,
    )[0]

    # moeCombine
    kwargs_mc2 = {
        "expand_x": down_out_list,
        "expert_ids": topk_ids,
        "expert_scales": topk_weights.to(torch.float32),
        "expert_shard_type": 0,
        "shared_expert_rank_num": 0,
        "moe_expert_num": moe_expert_num,
        "global_bs": 0,
    }
    tp_recv_counts = output[5]
    stage3_kwargs = {
        "ep_send_counts": ep_recv_counts,
        "group_ep": moe_all_to_all_group_name,
        "ep_world_size": ep_world_size,
        "ep_rank_id": ep_rank_id,
    }
    if enable_dispatch_v2:
        stage3_kwargs.update({
            "assist_info_for_combine":
            assist_info_for_combine,
        })
    else:
        stage3_kwargs.update({
            "expand_idx": assist_info_for_combine,
        })
    if need_extra_args:
        stage3_kwargs.update({
            "tp_send_counts": tp_recv_counts,
            "group_tp": moe_all_to_all_group_name,
            "tp_world_size": 1,
            "tp_rank_id": 0,
        })
    if a3_need_extra_args and enable_dispatch_v2:
        stage3_kwargs.update({
            "x_active_mask": mc2_mask,
        })
    kwargs_mc2.update(stage3_kwargs)

    hidden_states = torch_npu.npu_moe_distribute_combine_v2(
        **kwargs_mc2
    ) if enable_dispatch_v2 else torch_npu.npu_moe_distribute_combine(
        **kwargs_mc2)

    if shared_experts is None:
        return hidden_states
    else:
        with npu_stream_switch("moe_secondary", 0):
            npu_wait_tensor(shared_act, down_out_list)
            shared_hidden_states, _ = shared_experts.down_proj(shared_act)
        return hidden_states, shared_hidden_states


def torchair_apply_mlp(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    group_list: torch.Tensor,
    group_list_type: int = 1,
) -> torch.Tensor:
    """
    apply MLP: gate_up_proj -> swiglu -> down_proj

    Args:
        hidden_states_wrapper: wrapper of input hidden states with shape (num_tokens, hidden_size).
        w1: expert weights1 with shape
            (num_experts, hidden_size, intermediate_size * 2)
        w2: expert weights2 with shape
            (num_experts, intermediate_size, hidden_size)
        group_list: number of tokens for each expert, follow cumsum mode, and
            with shape (num_experts).
        transpose_weight:
            w1: (num_experts, intermediate_size * 2, hidden_size) ->
                    (num_experts, hidden_size, intermediate_size * 2)
            w2: (num_experts, hidden_size, intermediate_size) ->
                    (num_experts, intermediate_size, hidden_size)

    Returns:
        hidden_states: output hidden states after MLP.
    """

    w1 = w1.transpose(1, 2)
    hidden_states = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w1],
        split_item=2,
        group_list_type=group_list_type,
        group_type=0,
        group_list=group_list,
    )[0]

    hidden_states = torch_npu.npu_swiglu(hidden_states)

    w2 = w2.transpose(1, 2)
    hidden_states = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w2],
        split_item=2,
        group_list_type=group_list_type,
        group_type=0,
        group_list=group_list,
    )[0]

    return hidden_states


# currently expert parallelism implemented with all2all
# is under-optimized.
def torchair_fused_experts_with_all2all(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    expert_map: torch.Tensor = None,
    ep_group: GroupCoordinator = None,
):
    original_shape = hidden_states.shape
    if len(original_shape) == 3:
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])

    num_tokens, _ = hidden_states.shape
    num_experts = w1.shape[0]
    device = hidden_states.device

    if expert_map is not None:
        global_num_experts = len(expert_map)
        local_num_experts = global_num_experts // ep_group.world_size
        row_idx_len = num_tokens * top_k
        row_idx = (torch.arange(0,
                                row_idx_len,
                                dtype=torch.int32,
                                device=device).view(top_k, -1).permute(
                                    1, 0).contiguous())
        hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
            hidden_states,
            row_idx=row_idx,
            expert_idx=topk_ids,
            active_num=num_tokens)

        global_expert_tokens = torch.bincount(expanded_expert_idx,
                                              minlength=global_num_experts)
        scatter_sizes = global_expert_tokens.view(ep_group.world_size,
                                                  -1).sum(-1)

        gather_sizes = torch.empty_like(scatter_sizes)
        dist.all_to_all_single(gather_sizes,
                               scatter_sizes,
                               group=ep_group.device_group)
        scatter_size_list = scatter_sizes.cpu().tolist()
        gather_size_list = gather_sizes.cpu().tolist()

        expanded_expert_idx = expanded_expert_idx % local_num_experts
        hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
                                            scatter_size_list,
                                            gather_size_list)
        local_expert_idx = ep_group.all_to_all(expanded_expert_idx, 0, 0,
                                               scatter_size_list,
                                               gather_size_list)

        sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx)

        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
            sorted_local_expert_idx, local_num_experts).to(torch.int64)

        hidden_states = hidden_states[sorted_idx]
    else:
        row_idx_len = num_tokens * top_k
        row_idx = torch.arange(0,
                               row_idx_len,
                               dtype=torch.int32,
                               device=topk_weights.device).view(
                                   top_k, -1).permute(1, 0).contiguous()
        hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
            hidden_states,
            row_idx=row_idx,
            expert_idx=topk_ids,
            active_num=num_tokens)

        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
            expanded_expert_idx, num_experts)
        expert_tokens = expert_tokens.to(torch.int64)

    w1 = w1.transpose(1, 2)
    gate_up_out_list = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w1],
        split_item=2,
        group_list_type=0,
        group_type=0,
        group_list=expert_tokens,
    )[0]

    hidden_states = torch_npu.npu_swiglu(gate_up_out_list)

    w2 = w2.transpose(1, 2)
    hidden_states = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w2],
        split_item=2,
        group_list_type=0,
        group_type=0,
        group_list=expert_tokens,
    )[0]

    if expert_map is not None:
        resorted_idx = torch.argsort(sorted_idx)
        hidden_states = hidden_states[resorted_idx]
        hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
                                            gather_size_list,
                                            scatter_size_list)

        final_hidden_states = torch_npu.npu_moe_finalize_routing(
            hidden_states,
            skip1=None,
            skip2=None,
            bias=None,
            scales=topk_weights,
            expanded_src_to_dst_row=expanded_row_idx,
            export_for_source_row=topk_ids,
        )
    else:
        # TODO: Reorder device memory 2 times here, replace the current
        # implementation here when suitable operators become available.
        final_hidden_states = torch_npu.npu_moe_finalize_routing(
            hidden_states,
            skip1=None,
            skip2=None,
            bias=None,
            scales=topk_weights,
            expanded_src_to_dst_row=expanded_row_idx,
            export_for_source_row=topk_ids,
        )
    if len(original_shape) == 3:
        final_hidden_states = final_hidden_states.view(original_shape)
    return final_hidden_states


def torchair_fused_experts_moge(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    moe_parallel_config: FusedMoEParallelConfig,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    global_num_experts: int,
    expert_map: torch.Tensor = None,
    apply_router_weight_on_input: bool = False,
) -> torch.Tensor:
    """

    Args:
        hidden_states: Hidden states of shape (num_tokens, hidden_size).
        w1: Expert weights1 of shape (num_experts, intermediate_size * 2, hidden_size).
        w2: Expert weights2 of shape (num_experts, hidden_size, intermediate_size).
        topk_weights: Routing weights of shape (num_tokens, top_k).
        topk_ids: Selected expert IDs of shape (num_tokens, top_k).
        top_k: Number of experts to select.
        expert_map: Expert mapping of shape (num_experts,).

    Returns:
        hidden_states: Hidden states after routing.
    """
    ep_size = moe_parallel_config.ep_size
    local_num_experts = global_num_experts // ep_size
    local_num_group = top_k // ep_size

    if apply_router_weight_on_input:
        assert (topk_weights.dim() == 2
                ), "`topk_weights` should be in shape (num_tokens, topk)"
        _, topk = topk_weights.shape
        assert (
            topk == 1
        ), "Only support topk=1 when `apply_router_weight_on_input` is True"
        hidden_states = hidden_states * topk_weights.to(hidden_states.dtype)

    bsz, _ = hidden_states.shape
    flatten_topk_ids = topk_ids.view(-1)
    sorted_topk_ids = torch.argsort(flatten_topk_ids.float())
    sorted_topk_ids = sorted_topk_ids.to(torch.int32)
    sorted_hidden_states = hidden_states.index_select(
        0, sorted_topk_ids // local_num_group)

    experts_id = torch.arange(0,
                              local_num_experts,
                              dtype=topk_ids.dtype,
                              device=topk_ids.device)
    num_tokens_per_expert = (flatten_topk_ids.unsqueeze(-1) == experts_id).to(
        torch.float32).sum(0)
    topk_scales = topk_weights.view(-1).index_select(
        0, sorted_topk_ids).unsqueeze(-1)
    group_list = num_tokens_per_expert.cumsum(dim=0).to(torch.int64)

    w1 = w1.transpose(1, 2)
    gate_up_out = torch_npu.npu_grouped_matmul(
        x=[sorted_hidden_states],
        weight=[w1],
        split_item=2,
        group_list_type=0,
        group_type=0,
        group_list=group_list,
    )[0]

    if is_310p():
        gate_up_out = torch_npu.npu_swiglu(gate_up_out.to(torch.float32)).to(
            torch.float16)
    else:
        gate_up_out = torch_npu.npu_swiglu(gate_up_out)
    gate_up_out *= topk_scales

    w2 = w2.transpose(1, 2)
    down_out_list = torch_npu.npu_grouped_matmul(
        x=[gate_up_out],
        weight=[w2],
        split_item=2,
        group_list_type=0,
        group_type=0,
        group_list=group_list,
    )[0]

    unsorted_topk_ids = torch.argsort(sorted_topk_ids.float()).to(torch.int32)
    unsorted_hidden_states = down_out_list.index_select(0, unsorted_topk_ids)
    final_hidden_states = unsorted_hidden_states.reshape(
        bsz, top_k // ep_size, -1).sum(1)

    return final_hidden_states


def torchair_fused_experts(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    expert_map: torch.Tensor = None,
    apply_router_weight_on_input: bool = False,
    max_num_tokens: Optional[int] = None,
) -> torch.Tensor:
    """
    Fused experts with top-k routing.

    Args:
        hidden_states: Hidden states of shape (num_tokens, hidden_size).
        w1: Expert weights1 of shape (num_experts, intermediate_size * 2, hidden_size).
        w2: Expert weights2 of shape (num_experts, hidden_size, intermediate_size).
        topk_weights: Routing weights of shape (num_tokens, top_k).
        topk_ids: Selected expert IDs of shape (num_tokens, top_k).
        top_k: Number of experts to select.
        expert_map: Expert mapping of shape (num_experts,).

    Returns:
        hidden_states: Hidden states after routing.
    """
    """
    # Check constraints.
    assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
    """
    # if torch.distributed.get_rank() == 0:
    #     print(w1.shape)
    #     print(hidden_states.shape)

    original_shape = hidden_states.shape
    # assert len(original_shape) == 2

    num_tokens = hidden_states.shape[:-1].numel()
    num_experts = w1.shape[0]
    dtype = hidden_states.dtype
    device = hidden_states.device
    # assert dtype in [torch.float32, torch.float16, torch.bfloat16
    #                  ], "Only float32, float16, and bfloat16 are supported"

    if apply_router_weight_on_input:
        assert (topk_weights.dim() == 2
                ), "`topk_weights` should be in shape (num_tokens, topk)"
        _, topk = topk_weights.shape
        assert (
            topk == 1
        ), "Only support topk=1 when `apply_router_weight_on_input` is True"
        hidden_states = hidden_states * topk_weights.to(hidden_states.dtype)

    if expert_map is not None:
        # Generate token indices and flatten
        token_indices = (torch.arange(num_tokens,
                                      device=device,
                                      dtype=torch.int64).unsqueeze(1).expand(
                                          -1, top_k).reshape(-1))

        # Flatten token-to-expert mappings and map to local experts
        weights_flat = topk_weights.view(-1)
        experts_flat = topk_ids.view(-1)
        local_experts_flat = expert_map[experts_flat]

        # Filter valid token-expert pairs
        mask = local_experts_flat != -1
        filtered_weights = torch.where(
            mask, weights_flat, torch.zeros_like(weights_flat)).to(dtype)
        filtered_experts = torch.where(
            mask, local_experts_flat,
            torch.full_like(local_experts_flat,
                            num_experts)).to(topk_ids.dtype)

        # Sort by local expert IDs
        sort_indices = torch.argsort(filtered_experts.view(torch.float32))
        sorted_token_indices = token_indices[sort_indices]
        sorted_weights = filtered_weights[sort_indices]

        # Compute token counts with minlength of num_experts
        # This is equivalent to but faster than:
        # >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1]
        token_counts = torch.zeros(num_experts + 1,
                                   device=device,
                                   dtype=torch.int64)
        ones = torch.ones_like(filtered_experts, dtype=torch.int64)
        token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
        token_counts = token_counts[:num_experts]
        expert_tokens = torch.cumsum(token_counts, dim=0, dtype=torch.int64)

        # Rearrange hidden_states
        sorted_hidden_states = hidden_states[sorted_token_indices]
    else:
        row_idx_len = num_tokens * top_k
        row_idx = (torch.arange(0,
                                row_idx_len,
                                dtype=torch.int32,
                                device=device).view(top_k, -1).permute(
                                    1, 0).contiguous())
        active_num = max_num_tokens if max_num_tokens is not None else num_tokens
        sorted_hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
            hidden_states,
            row_idx=row_idx,
            expert_idx=topk_ids,
            active_num=active_num)

        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
            expanded_expert_idx, num_experts)
        expert_tokens = expert_tokens.to(torch.int64)

    w1 = w1.transpose(1, 2)
    gate_up_out_list = torch_npu.npu_grouped_matmul(
        x=[sorted_hidden_states],
        weight=[w1],
        split_item=2,
        group_list_type=0,
        group_type=0,
        group_list=expert_tokens,
    )[0]

    gate_up_out = torch_npu.npu_swiglu(gate_up_out_list)

    w2 = w2.transpose(1, 2)
    down_out_list = torch_npu.npu_grouped_matmul(
        x=[gate_up_out],
        weight=[w2],
        split_item=2,
        group_list_type=0,
        group_type=0,
        group_list=expert_tokens,
    )[0]

    if expert_map is not None:
        weighted_down_out = down_out_list * sorted_weights.unsqueeze(1)

        final_hidden_states = torch.zeros(*original_shape,
                                          device=hidden_states.device,
                                          dtype=dtype)

        # TODO: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
        # This created multiple NaN and index_add_ will mix them up which harms accuracy
        # remove this mask and filter after it being fixed
        num_valid_tokens = mask.sum()
        valid_token_mask = torch.arange(
            0, sorted_token_indices.shape[0],
            device=device).unsqueeze(1) < num_valid_tokens
        valid_output = torch.where(
            valid_token_mask, weighted_down_out,
            torch.zeros_like(weighted_down_out)).to(dtype)
        final_hidden_states.index_add_(0, sorted_token_indices, valid_output)
    else:
        scales = torch.ones_like(
            topk_weights) if apply_router_weight_on_input else topk_weights
        # TODO: Reorder device memory 2 times here, replace the current
        # implementation here when suitable operators become available.
        final_hidden_states = torch_npu.npu_moe_finalize_routing(
            down_out_list,
            skip1=None,
            skip2=None,
            bias=None,
            scales=scales,
            expanded_src_to_dst_row=expanded_row_idx,
            export_for_source_row=topk_ids,
        )

    return final_hidden_states


def torchair_native_grouped_topk(
    topk_weights: torch.Tensor,
    num_expert_group: Optional[int],
    topk_group: Optional[int],
):
    topk_group = 0 if topk_group is None else topk_group
    num_expert_group = 0 if num_expert_group is None else num_expert_group

    num_token = topk_weights.shape[0]
    grouped_weights = topk_weights.view(num_token, num_expert_group,
                                        -1).max(dim=-1).values
    topk_group_indices = torch.topk(grouped_weights.to(torch.float32),
                                    k=topk_group,
                                    dim=-1,
                                    sorted=False)[1]
    topk_group_mask = torch.zeros_like(grouped_weights)
    topk_group_mask.scatter_(1, topk_group_indices, 1)
    topk_weight_mask = (topk_group_mask.unsqueeze(-1).expand(
        num_token, num_expert_group,
        topk_weights.shape[-1] // num_expert_group).reshape(num_token, -1))
    topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0)

    return topk_weights


def torchair_select_experts(
    hidden_states: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    use_grouped_topk: bool,
    renormalize: bool,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[torch.Tensor] = None,
    global_num_experts: Optional[torch.Tensor] = None
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Select top-k experts based on router logits.

    Args:
        hidden_states: Hidden states of shape (num_tokens, hidden_size).
        router_logits: Router logits of shape (num_tokens, num_experts).
        top_k: Number of experts to select.
        use_grouped_topk: Whether to group experts before selecting top-k.
        renormalize: Whether to renormalize the routing weights.
        topk_group: Number of expert groups to select from.
        num_expert_group: Number of experts in each group.
        custom_routing_function: Custom routing function.
        scoring_func: Scoring function to use.
        e_score_correction_bias: Correction bias to apply to expert scores.

    Returns:
        topk_weights: Routing weights of shape (num_tokens, top_k).
        topk_ids: Selected expert IDs of shape (num_tokens, top_k).

    Raises:
        ValueError: If an unsupported scoring function is provided.
    """

    def _renormalize_topk_weights(
        topk_weights: torch.Tensor,
        renormalize: bool,
    ):
        if renormalize:
            topk_weights = topk_weights / topk_weights.sum(dim=-1,
                                                           keepdim=True)
        return topk_weights

    if scoring_func == "softmax":
        # NOTE: vLLM use dtype=torch.float here
        if not use_grouped_topk and custom_routing_function is None:
            topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k_softmax(
                x=router_logits, finished=None, k=top_k)
            topk_ids = topk_ids.to(torch.int32)
            topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
            return topk_weights, topk_ids

        topk_weights = router_logits.softmax(dim=-1)
    elif scoring_func == "sigmoid":
        topk_weights = router_logits.sigmoid()
    else:
        raise ValueError(f"Unsupported scoring function: {scoring_func}")

    if use_grouped_topk:
        assert topk_group is not None
        assert num_expert_group is not None

        if e_score_correction_bias is not None:
            # Store original scores before applying correction bias. We use biased
            # scores for expert selection but original scores for routing weights
            original_weights = topk_weights
            topk_weights = topk_weights + e_score_correction_bias.unsqueeze(0)

        # TODO: Change to npu_group_topk when the latest CANN and NNAL is available
        # >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group)
        topk_weights = torchair_native_grouped_topk(topk_weights,
                                                    num_expert_group,
                                                    topk_group)
        # TODO bfloat16 is not supported in torch.topk with ge graph.
        if e_score_correction_bias is not None:
            topk_ids = torch.topk(topk_weights.to(torch.float32),
                                  k=top_k,
                                  dim=-1,
                                  sorted=False)[1]
            # Use original unbiased scores for the routing weights
            topk_weights = original_weights.gather(1, topk_ids)
        else:
            topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32),
                                                k=top_k,
                                                dim=-1,
                                                sorted=False)
        topk_ids = topk_ids.to(torch.int32)
        topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
        return topk_weights, topk_ids

    if custom_routing_function is not None:
        topk_weights, topk_ids = custom_routing_function(
            hidden_states=hidden_states,
            gating_output=router_logits,
            topk=top_k,
            renormalize=renormalize,
            global_num_experts=global_num_experts)
        # Required by npu_moe_init_routing
        topk_ids = topk_ids.to(torch.int32)
        return topk_weights, topk_ids

    topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1)
    topk_weights = topk_weights.to(hidden_states.dtype)

    # Required by npu_moe_init_routing
    topk_ids = topk_ids.to(torch.int32)
    topk_weights = _renormalize_topk_weights(topk_weights, renormalize)

    return topk_weights, topk_ids


class TorchairAscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):

    def __init__(self, moe: FusedMoEConfig = None):

        super().__init__(moe=moe)
        vllm_config = get_current_vllm_config()

        self.global_batch_size = vllm_config.scheduler_config.max_num_seqs
        self.max_model_len = vllm_config.model_config.max_model_len

        ascend_config = get_ascend_config()
        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
        self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp

        try:
            device_group = get_mc2_group().device_group
            # TODO: Try local_rank = ep_group.rank_in_group
            local_rank = torch.distributed.get_rank(group=device_group)
            backend = device_group._get_backend(torch.device("npu"))
            self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
                local_rank)
        except AttributeError:
            self.moe_all_to_all_group_name = None

    def process_weights_after_loading(self, layer):
        super(UnquantizedFusedMoEMethod,
              self).process_weights_after_loading(layer)
        layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
            layer.w13_weight.data),
                                              requires_grad=False)
        layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
            layer.w2_weight.data),
                                             requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        is_prefill: bool = False,
        enable_force_load_balance: bool = False,
        shared_experts: Optional[Any] = None,
        **kwargs,
    ) -> torch.Tensor:

        is_deepseek_v3_r1 = global_num_experts == 256
        # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
        if is_deepseek_v3_r1:
            topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
                router_logits,
                k=top_k,  # topk currently is 8
                bias=e_score_correction_bias,
                k_group=topk_group,  # fix: 4
                group_count=num_expert_group,  # fix 8
                group_select_mode=
                1,  # 0: the maximum in the group; 1: topk2.sum(fix)
                renorm=0,  # 0: softmax->topk(fix); 1: topk->softmax
                norm_type=1,  # 0: softmax; 1: sigmoid(fix)
                # out_flag=False, # todo new api; should the third output be output
                # y2_flag=False, # old api; should the third output be output
                routed_scaling_factor=1,
                eps=float(1e-20))
        else:
            topk_weights, topk_ids = torchair_select_experts(
                hidden_states=x,
                router_logits=router_logits,
                top_k=top_k,
                use_grouped_topk=use_grouped_topk,
                renormalize=renormalize,
                topk_group=topk_group,
                num_expert_group=num_expert_group,
                custom_routing_function=custom_routing_function,
                scoring_func=scoring_func,
                e_score_correction_bias=e_score_correction_bias,
            )

        topk_weights = topk_weights.to(x.dtype)
        # this is a naive implementation for experts load balance so as
        # to avoid accumulating too much tokens on a single rank.
        # currently it is only activated when doing profile runs.
        if enable_force_load_balance:
            topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)

        fused_moe_state = get_forward_context().fused_moe_state
        if self.enable_shared_expert_dp and fused_moe_state == FusedMoEState.MC2:
            fused_moe_state = FusedMoEState.All2All

        if fused_moe_state == FusedMoEState.MC2:
            return torchair_fused_experts_with_mc2(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                moe_parallel_config=self.moe.moe_parallel_config,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                top_k=top_k,
                expert_map=expert_map,
                moe_all_to_all_group_name=self.moe_all_to_all_group_name,
                shared_experts=shared_experts,
                is_torchair=self.torchair_graph_enabled,
                mc2_mask=kwargs.get("mc2_mask", None))
        elif fused_moe_state in [
                FusedMoEState.AllGather, FusedMoEState.NaiveMulticast
        ]:
            return torchair_fused_experts(hidden_states=x,
                                          w1=layer.w13_weight,
                                          w2=layer.w2_weight,
                                          topk_weights=topk_weights,
                                          topk_ids=topk_ids,
                                          top_k=top_k,
                                          expert_map=expert_map)
        else:
            return torchair_fused_experts_with_all2all(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                top_k=top_k,
                expert_map=expert_map,
                ep_group=get_ep_group())


class TorchairAscendFusedMoE(FusedMoE):

    # The moe_counter parameter is required during the initialization of EPLB
    # to identify the current layer index within the MOE model.
    moe_counter = -1

    def __init__(
        self,
        num_experts: int,  # Global number of experts
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: Optional[torch.dtype] = None,
        reduce_results: bool = False,
        renormalize: bool = True,
        use_grouped_topk: bool = False,
        num_expert_group: Optional[int] = None,
        topk_group: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
        tp_size: Optional[int] = None,
        ep_size: Optional[int] = None,
        dp_size: Optional[int] = None,
        prefix: str = "",
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        activation: str = "silu",
        apply_router_weight_on_input: bool = False,
    ):
        # TODO: This could not initialize FusedMoE baseclass,
        # fixme and make __init__() of AscendFusedMoE more clear
        super().__init__(
            num_experts=num_experts,
            top_k=top_k,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            params_dtype=params_dtype,
            reduce_results=reduce_results,
            renormalize=renormalize,
            use_grouped_topk=use_grouped_topk,
            num_expert_group=num_expert_group,
            topk_group=topk_group,
            quant_config=quant_config,
            tp_size=tp_size,
            ep_size=ep_size,
            dp_size=dp_size,
            prefix=prefix,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
            activation=activation,
        )
        TorchairAscendFusedMoE.moe_counter += 1
        self.moe_instance_id = TorchairAscendFusedMoE.moe_counter

        if params_dtype is None:
            params_dtype = torch.get_default_dtype()

        vllm_config = get_current_vllm_config()

        self.moe_parallel_config = FusedMoEParallelConfig.make(
            tp_size_=(tp_size if tp_size is not None else
                      get_tensor_model_parallel_world_size()),
            dp_size_=(dp_size
                      if dp_size is not None else get_dp_group().world_size),
            vllm_parallel_config=vllm_config.parallel_config)

        self.top_k = top_k
        self.num_experts = num_experts
        self.global_num_experts = num_experts
        assert intermediate_size % self.tp_size == 0
        self.intermediate_size_per_partition = intermediate_size // self.tp_size
        self.reduce_results = reduce_results
        self.renormalize = renormalize
        self.use_grouped_topk = use_grouped_topk
        if self.use_grouped_topk:
            assert num_expert_group is not None and topk_group is not None
        self.num_expert_group = num_expert_group
        self.topk_group = topk_group
        self.custom_routing_function = custom_routing_function
        self.scoring_func = scoring_func
        self.e_score_correction_bias = e_score_correction_bias
        self.expert_map = None
        self.activation = activation
        self.log2phy = None
        self.global_redundant_expert_num = 0

        is_deepseek_v3_r1 = self.global_num_experts == 256
        self.rm_router_logits = get_rm_router_logits_state(
            self.moe_parallel_config.ep_size, self.dp_size, is_deepseek_v3_r1)
        self.all_reduce_merge = get_all_reduce_merge_state(
            self.moe_parallel_config.ep_size, is_deepseek_v3_r1)

        ascend_config = get_ascend_config()
        self.dynamic_eplb = ascend_config.dynamic_eplb
        self.expert_map_path = ascend_config.expert_map_path
        self.global_redundant_expert_num = ascend_config.init_redundancy_expert
        self.global_num_experts = num_experts + self.global_redundant_expert_num
        # static eplb initializing with expert_map_path
        if self.expert_map_path and os.path.exists(
                self.expert_map_path) and os.access(self.expert_map_path,
                                                    os.R_OK):
            self.expert_load_balancer = ExpertLoadBalancer(
                self.expert_map_path, self.global_num_experts)
            self.local_num_experts, self.expert_map = (
                self.expert_load_balancer.get_rank_placement_map(
                    self.moe_instance_id, self.ep_rank))
            self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
                self.moe_instance_id, self.ep_rank).npu()
            self.global_redundant_expert_num = (
                self.expert_load_balancer.get_global_redundant_expert_num())
        else:
            # init moe.
            self.local_num_experts, self.expert_map = determine_expert_map(
                self.ep_size, self.ep_rank, self.global_num_experts)
            # dynamic eplb initializing with not expert_map_path
            if self.dynamic_eplb:
                self.global_redundant_expert_num = ascend_config.init_redundancy_expert
                self.local_num_experts, self.expert_map = determine_default_expert_map(
                    self.global_num_experts, self.ep_size, self.ep_rank,
                    self.global_redundant_expert_num)
                self.log2phy = determine_default_log2phy_map(
                    self.global_num_experts, self.ep_size, self.ep_rank,
                    self.global_redundant_expert_num).npu()
        local_num_experts = (torch.sum(self.expert_map != -1)
                             if self.expert_map is not None else num_experts)
        if self.dynamic_eplb:
            self.moe_load = torch.zeros(local_num_experts, dtype=torch.int64)

        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
        self.multistream_overlap_shared_expert = \
            ascend_config.multistream_overlap_shared_expert and \
            self.torchair_graph_enabled
        self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp

        if self.scoring_func != "softmax" and not self.use_grouped_topk:
            raise ValueError("Only softmax scoring function is supported for "
                             "non-grouped topk.")
        self.moe = FusedMoEConfig(
            num_experts=self.global_num_experts,
            experts_per_token=top_k,
            hidden_dim=hidden_size,
            num_local_experts=self.local_num_experts,
            moe_parallel_config=self.moe_parallel_config,
            in_dtype=params_dtype,
        )
        if quant_config is None:
            self.quant_method = TorchairAscendUnquantizedFusedMoEMethod(
                self.moe)
        else:
            if quant_config.is_layer_skipped_ascend(
                    prefix, quant_config.packed_modules_mapping):
                self.quant_method = TorchairAscendUnquantizedFusedMoEMethod(
                    self.moe)
            else:
                self.quant_method = AscendFusedMoEMethod(
                    quant_config, prefix, quant_config.packed_modules_mapping)

        assert self.quant_method is not None

        self.moe_load = None
        local_num_experts = (torch.sum(self.expert_map != -1)
                             if self.expert_map is not None else num_experts)
        if self.dynamic_eplb:
            self.moe_load = torch.zeros(local_num_experts, dtype=torch.int64)

        moe_quant_params = {
            "num_experts": local_num_experts,
            "hidden_size": hidden_size,
            "intermediate_size_per_partition":
            self.intermediate_size_per_partition,
            "params_dtype": params_dtype,
            "weight_loader": self.weight_loader,
        }
        # need full intermediate size pre-sharding for WNA16 act order
        if (self.quant_method.__class__.__name__
                in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
            moe_quant_params["intermediate_size_full"] = intermediate_size

        self.ep_group = get_ep_group()
        # NOTE: self.tp_group is not expert_tp_group
        self.tp_group = get_tp_group().device_group
        self.quant_method.create_weights(layer=self, **moe_quant_params)

    def naive_multicast(self, x: torch.Tensor,
                        cu_tokens_across_dp_cpu: torch.Tensor):
        assert (len(x.shape) == 2)
        buffer = torch.empty((cu_tokens_across_dp_cpu[-1], x.size(1)),
                             device=x.device,
                             dtype=x.dtype)
        start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
            self.dp_rank - 1]
        end = cu_tokens_across_dp_cpu[self.dp_rank]
        buffer[start:end, :].copy_(x)
        for idx in range(self.dp_size):
            start = 0 if idx == 0 else cu_tokens_across_dp_cpu[idx - 1]
            end = cu_tokens_across_dp_cpu[idx]
            get_dp_group().broadcast(buffer[start:end, :], idx)
        return buffer

    def forward(self,
                hidden_states: torch.Tensor,
                router_logits: torch.Tensor,
                is_prefill: bool,
                enable_force_load_balance: bool = False,
                top_k: Optional[int] = None,
                shared_experts: Optional[Any] = None,
                gate=None,
                replace_allreduce: bool = False,
                _metadata_for_padding: Optional[MetadataForPadding] = None):

        assert self.quant_method is not None

        if top_k:
            real_top_k = top_k
        else:
            real_top_k = self.top_k

        num_tokens, hidden_size = hidden_states.shape

        forward_context = get_forward_context()
        fused_moe_state = forward_context.fused_moe_state
        mc2_mask = forward_context.mc2_mask
        if self.enable_shared_expert_dp and fused_moe_state == FusedMoEState.MC2:
            fused_moe_state = FusedMoEState.All2All
        # For w8a8 dynamic we can do npu_dynamic_quant and gate in parallel.
        quantized_x_for_share, dynamic_scale_for_share = None, None
        from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import \
            TorchairAscendW8A8DynamicFusedMoEMethod
        if self.multistream_overlap_shared_expert:
            if not self.rm_router_logits:
                router_logits, _ = gate(hidden_states)
            if hasattr(self.quant_method, "quant_method") and \
               isinstance(self.quant_method.quant_method,
                          TorchairAscendW8A8DynamicFusedMoEMethod
                          ) and fused_moe_state == FusedMoEState.MC2:
                with npu_stream_switch("moe_secondary", 0):
                    quantized_x_for_share, dynamic_scale_for_share = torch_npu.npu_dynamic_quant(
                        hidden_states)

        if shared_experts:
            if not self.multistream_overlap_shared_expert or fused_moe_state != FusedMoEState.MC2:
                # When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
                shared_hidden_states = shared_experts(hidden_states)

        mc2_mask = forward_context.mc2_mask

        enable_sp = _metadata_for_padding is not None and _metadata_for_padding.not_dummy_and_is_prefill
        tp_size = get_tensor_model_parallel_world_size()
        if enable_sp:
            tp_rank = get_tensor_model_parallel_rank()
            mc2_mask_sp = _metadata_for_padding.mc2_mask if _metadata_for_padding is not None else forward_context.mc2_mask
            chunk_mc2_mask = torch.tensor_split(mc2_mask_sp, tp_size, dim=0)
            mc2_mask = chunk_mc2_mask[tp_rank]
            replace_allreduce = True

        if (fused_moe_state not in [
                FusedMoEState.AllGather, FusedMoEState.AllGatherEP,
                FusedMoEState.NaiveMulticast
        ]):
            if tp_size > 1:
                tp_rank = get_tensor_model_parallel_rank()
                chunk_mc2_mask = torch.tensor_split(mc2_mask, tp_size, dim=0)
                mc2_mask = chunk_mc2_mask[tp_rank]
            if not replace_allreduce:
                if fused_moe_state in {FusedMoEState.MC2}:
                    padding_size = forward_context.padded_num_tokens
                else:
                    # TODO: Determine if we can remove the padding
                    padding_size = tp_size
                if num_tokens < padding_size and not self.enable_shared_expert_dp:
                    hidden_states = nn.functional.pad(
                        hidden_states, (0, 0, 0, padding_size - num_tokens))
                    router_logits = nn.functional.pad(
                        router_logits, (0, 0, 0, padding_size - num_tokens))
                if tp_size > 1:
                    tp_rank = get_tensor_model_parallel_rank()
                    if not self.enable_shared_expert_dp:
                        chunk_hidden_states = torch.tensor_split(hidden_states,
                                                                 tp_size,
                                                                 dim=0)
                        chunk_router_logits = torch.tensor_split(router_logits,
                                                                 tp_size,
                                                                 dim=0)
                        hidden_states = chunk_hidden_states[tp_rank]
                        router_logits = chunk_router_logits[tp_rank]

        if self.dp_size > 1:
            if fused_moe_state == FusedMoEState.AllGather:
                # NOTE: When in torchair graph, it has been padded in model_runner_v1
                if not self.torchair_graph_enabled:
                    max_tokens_across_dp = forward_context.max_tokens_across_dp
                    if num_tokens < max_tokens_across_dp:
                        hidden_states = nn.functional.pad(
                            hidden_states,
                            (0, 0, 0, max_tokens_across_dp - num_tokens))
                        if not self.rm_router_logits:
                            router_logits = nn.functional.pad(
                                router_logits,
                                (0, 0, 0, max_tokens_across_dp - num_tokens))
                hidden_states = get_dp_group().all_gather(hidden_states, 0)
                if self.rm_router_logits:
                    router_logits, _ = gate(hidden_states)
                else:
                    router_logits = get_dp_group().all_gather(router_logits, 0)

            elif fused_moe_state == FusedMoEState.NaiveMulticast:
                cu_tokens_across_dp_cpu = get_forward_context(
                ).dp_metadata.cu_tokens_across_sp(1)
                hidden_states = self.naive_multicast(hidden_states,
                                                     cu_tokens_across_dp_cpu)
                if self.rm_router_logits:
                    router_logits, _ = gate(hidden_states)
                else:
                    router_logits = self.naive_multicast(
                        router_logits, cu_tokens_across_dp_cpu)

        # Matrix multiply.
        e_hidden_states = self.quant_method.apply(
            layer=self,
            x=hidden_states,
            router_logits=router_logits,
            top_k=real_top_k,
            renormalize=self.renormalize,
            use_grouped_topk=self.use_grouped_topk,
            global_num_experts=self.global_num_experts,
            expert_map=self.expert_map,
            topk_group=self.topk_group,
            num_expert_group=self.num_expert_group,
            custom_routing_function=self.custom_routing_function,
            scoring_func=self.scoring_func,
            e_score_correction_bias=self.e_score_correction_bias,
            is_prefill=is_prefill,
            enable_force_load_balance=enable_force_load_balance,
            log2phy=self.log2phy,
            global_redundant_expert_num=self.global_redundant_expert_num,
            shared_experts=shared_experts if self.torchair_graph_enabled
            and self.multistream_overlap_shared_expert and not is_prefill else
            None,
            mc2_mask=mc2_mask,
            quantized_x_for_share=quantized_x_for_share,
            dynamic_scale_for_share=dynamic_scale_for_share,
        )

        if shared_experts:
            if isinstance(e_hidden_states, tuple):
                e_hidden_states, shared_hidden_states = e_hidden_states

        if self.dynamic_eplb and isinstance(
                e_hidden_states, tuple) and len(e_hidden_states) == 3:
            self.moe_load += e_hidden_states[2] if e_hidden_states[1] == 0 else \
                torch.cat(e_hidden_states[2][:1], e_hidden_states[2][1:] - e_hidden_states[2][:-1])

        if (fused_moe_state not in [
                FusedMoEState.AllGather, FusedMoEState.AllGatherEP,
                FusedMoEState.NaiveMulticast
        ] and not replace_allreduce and not self.enable_shared_expert_dp):
            if tp_size > 1:
                dist.all_gather(list(chunk_hidden_states), e_hidden_states,
                                self.tp_group)
                final_hidden_states = torch.cat(chunk_hidden_states, dim=0)
                dispose_tensor(e_hidden_states)
            else:
                final_hidden_states = e_hidden_states
            if num_tokens < padding_size:
                final_hidden_states = final_hidden_states[:num_tokens]
        elif self.dp_size > 1 and not self.enable_shared_expert_dp:
            if fused_moe_state == FusedMoEState.NaiveMulticast:
                start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
                    self.dp_rank - 1]
                end = cu_tokens_across_dp_cpu[self.dp_rank]
                final_hidden_states = get_dp_group().all_reduce(
                    e_hidden_states)
                final_hidden_states = final_hidden_states[start:end, :]
                dispose_tensor(e_hidden_states)
            elif fused_moe_state == FusedMoEState.AllGather:
                final_hidden_states = get_dp_group().reduce_scatter(
                    e_hidden_states, 0)
                final_hidden_states = final_hidden_states[:num_tokens]
                dispose_tensor(e_hidden_states)
            else:
                final_hidden_states = e_hidden_states
        else:
            final_hidden_states = e_hidden_states

        if tp_size > 1 and not self.all_reduce_merge and fused_moe_state in [
                FusedMoEState.AllGather, FusedMoEState.AllGatherEP,
                FusedMoEState.NaiveMulticast
        ]:
            final_hidden_states = tensor_model_parallel_all_reduce(
                final_hidden_states)

        if shared_experts:
            return final_hidden_states, shared_hidden_states
        else:
            return final_hidden_states

    def update_expert_map(self, new_expert_map):
        self.expert_map = new_expert_map

    def get_map(self):
        return self.expert_map

    def get_log2phy_map(self):
        return self.logical_to_physical_map

    def clear_moe_load(self):
        if self.moe_load is not None:
            self.moe_load.zero_()

    # ----------------------------------------- TBO-related --------------------------------------------

    def _forward_ms_fused_moe_comp(
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
        is_prefill: bool,
        real_top_k,
        enable_force_load_balance: bool = False,
    ):
        hidden_states = self.quant_method.apply(
            layer=self,
            x=hidden_states,
            router_logits=router_logits,
            top_k=real_top_k,
            renormalize=self.renormalize,
            use_grouped_topk=self.use_grouped_topk,
            global_num_experts=self.global_num_experts,
            expert_map=self.expert_map,
            topk_group=self.topk_group,
            num_expert_group=self.num_expert_group,
            custom_routing_function=self.custom_routing_function,
            scoring_func=self.scoring_func,
            e_score_correction_bias=self.e_score_correction_bias,
            is_prefill=is_prefill,
            enable_force_load_balance=enable_force_load_balance,
        )

        return hidden_states