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vllm_npu/ops/moe/token_dispatcher.py

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+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2024; NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from abc import ABC, abstractmethod
+from typing import Any, Optional
+
+import torch
+import torch_npu
+from vllm.distributed.parallel_state import get_ep_group
+
+from vllm_npu.distributed.parallel_state import get_mc2_group
+from vllm_npu.ops.moe.comm_utils import (
+    async_all_to_all, gather_from_sequence_parallel_region)
+from vllm_npu.utils import (AscendSocVersion, get_ascend_soc_version,
+                               is_hierarchical_communication_enabled)
+
+
+class MoETokenDispatcher(ABC):
+
+    def __init__(self, **kwargs) -> None:
+        """
+        Initialize the MoE Token Dispatcher.
+        """
+        self.top_k = kwargs.get("top_k", 0)
+        self.num_experts = kwargs.get("num_experts", 0)
+
+    @property
+    def ep_group(self):
+        """Get expert model parallel group."""
+        return get_ep_group().device_group
+
+    @property
+    def ep_rank(self):
+        return get_ep_group().rank_in_group
+
+    @property
+    def ep_size(self):
+        return get_ep_group().world_size
+
+    @abstractmethod
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[Any] = None,
+                       quantized_x_for_share: Optional[Any] = None,
+                       dynamic_scale_for_share: Optional[Any] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
+        raise NotImplementedError("Dispatch function not implemented.")
+
+    @abstractmethod
+    def token_combine(self,
+                      hidden_states: torch.Tensor,
+                      bias: torch.Tensor = None):
+        raise NotImplementedError("Combine function not implemented.")
+
+
+class TokenDispatcherWithMC2(MoETokenDispatcher):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        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)
+        self.ep_rank_id = get_mc2_group().rank_in_group
+        self.ep_world_size = get_mc2_group().world_size
+        self.enable_dispatch_v2 = hasattr(torch_npu,
+                                          "npu_moe_distribute_dispatch_v2")
+        self.need_extra_args = (
+            get_ascend_soc_version() == AscendSocVersion.A3)
+
+        # NOTE: Currently, when in A3, we need to pass in some extra param into dispatch & combine
+        self.a3_need_extra_args = \
+            get_ascend_soc_version() == AscendSocVersion.A3
+        # NOTE: When in A2, setting the environment variables HCCL_INTRA_PCIE_ENABLE=1 and
+        # HCCL_INTRA_ROCE_ENABLE=0 can reduce cross-machine communication traffic and significantly
+        # improve communication performance.
+        self.need_expert_scale = is_hierarchical_communication_enabled()
+        self.output = None
+        self.assist_info_for_combine = None
+        self.ep_recv_counts = None
+        self.shared_act = None
+        self.topk_ids = None
+        self.topk_weights = None
+        self.shared_experts = None
+        self.mc2_mask = None
+        self.with_quant = False
+        self.expand_scales = None
+
+    def get_dispatch_mc2_kwargs(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        expert_map: torch.Tensor,
+        global_redundant_expert_num: int = 0,
+    ):
+        quant_mode = 2 if self.with_quant else 0
+        self.moe_expert_num = len(expert_map) + global_redundant_expert_num
+        kwargs_mc2 = {
+            "x": hidden_states,
+            "expert_ids": topk_ids,
+            "expert_shard_type": 0,
+            "shared_expert_rank_num": 0,
+            "moe_expert_num": self.moe_expert_num,
+            "global_bs": 0,
+            "expert_token_nums_type": 0,
+        }
+
+        stage1_kwargs = {
+            "scales": None,
+            "quant_mode": quant_mode,
+            "group_ep": self.moe_all_to_all_group_name,
+            "ep_world_size": self.ep_world_size,
+            "ep_rank_id": self.ep_rank_id,
+        }
+        if self.need_extra_args:
+            stage1_kwargs.update({
+                "group_tp": self.moe_all_to_all_group_name,
+                "tp_world_size": 1,
+                "tp_rank_id": 0,
+            })
+        if self.a3_need_extra_args and self.enable_dispatch_v2:
+            stage1_kwargs.update({
+                "x_active_mask": self.mc2_mask,
+            })
+        if self.need_expert_scale:
+            stage1_kwargs.update({
+                "expert_scales":
+                topk_weights.to(torch.float32),
+            })
+
+        kwargs_mc2.update(stage1_kwargs)
+        return kwargs_mc2
+
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[Any] = None,
+                       quantized_x_for_share: Optional[Any] = None,
+                       dynamic_scale_for_share: Optional[Any] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
+        # Apply log2phy if needed
+        if log2phy is not None:
+            topk_ids = log2phy[topk_ids]
+
+        self.with_quant = with_quant
+        self.expert_map = expert_map
+        self.topk_ids = topk_ids
+        self.topk_weights = topk_weights
+        self.shared_experts = shared_experts
+        self.mc2_mask = mc2_mask
+
+        kwargs_mc2 = self.get_dispatch_mc2_kwargs(hidden_states, topk_weights,
+                                                  topk_ids, expert_map,
+                                                  global_redundant_expert_num)
+        self.output = torch_npu.npu_moe_distribute_dispatch_v2(
+            **kwargs_mc2
+        ) if self.enable_dispatch_v2 else torch_npu.npu_moe_distribute_dispatch(
+            **kwargs_mc2)
+        # comm_stream.wait_stream(torch.npu.current_stream())
+        expand_x, dynamic_scale, self.assist_info_for_combine, expert_token_nums, \
+            self.ep_recv_counts, _, self.expand_scales = self.output[0:7]
+
+        if self.with_quant:
+            if shared_experts is not None:
+                share_up_out, _ = shared_experts.gate_up_proj(
+                    (quantized_x_for_share, dynamic_scale_for_share))
+                shared_gate_up, shared_dequant_scale = share_up_out[
+                    0], share_up_out[1]
+
+                shared_act_out = shared_experts.act_fn(
+                    (shared_gate_up, shared_dequant_scale))
+                self.shared_act, self.swiglu_out_scale = \
+                    shared_act_out[0], shared_act_out[1]
+
+        else:
+            if shared_experts is not None:
+                shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states)
+                self.shared_act = shared_experts.act_fn(shared_gate_up)
+        group_list_type = 0
+        return {
+            "group_list_type": group_list_type,
+            "hidden_states": expand_x,
+            "group_list": expert_token_nums,
+            "dynamic_scale": dynamic_scale,
+        }
+
+    def get_combine_mc_kwargs(self, hidden_states: torch.Tensor):
+        assert self.expert_map is not None
+        assert self.topk_weights is not None
+        assert self.topk_ids is not None
+        assert self.output is not None
+        # moeCombine
+        kwargs_mc2 = {
+            "expand_x": hidden_states,
+            "expert_ids": self.topk_ids,
+            "expert_scales": self.topk_weights.to(torch.float32),
+            "expert_shard_type": 0,
+            "shared_expert_rank_num": 0,
+            "moe_expert_num": self.moe_expert_num,
+            "global_bs": 0,
+        }
+        if self.with_quant:
+            tp_recv_counts = torch.empty(1,
+                                         dtype=torch.int32,
+                                         device=hidden_states.device)
+        else:
+            tp_recv_counts = self.output[5]
+        stage3_kwargs = {
+            "ep_send_counts": self.ep_recv_counts,
+            "group_ep": self.moe_all_to_all_group_name,
+            "ep_world_size": self.ep_world_size,
+            "ep_rank_id": self.ep_rank_id,
+            "expand_scales": self.expand_scales,
+        }
+        if self.enable_dispatch_v2:
+            stage3_kwargs.update({
+                "assist_info_for_combine":
+                self.assist_info_for_combine,
+            })
+        else:
+            stage3_kwargs.update({
+                "expand_idx": self.assist_info_for_combine,
+            })
+        if self.need_extra_args:
+            stage3_kwargs.update({
+                "tp_send_counts": tp_recv_counts,
+                "group_tp": self.moe_all_to_all_group_name,
+                "tp_world_size": 1,
+                "tp_rank_id": 0,
+            })
+        if self.a3_need_extra_args and self.enable_dispatch_v2:
+            stage3_kwargs.update({
+                "x_active_mask": self.mc2_mask,
+            })
+        kwargs_mc2.update(stage3_kwargs)
+        return kwargs_mc2
+
+    def token_combine(self,
+                      hidden_states: torch.Tensor,
+                      bias: torch.Tensor = None):
+        kwargs_mc2 = self.get_combine_mc_kwargs(hidden_states)
+        hidden_states = torch_npu.npu_moe_distribute_combine_v2(
+            **kwargs_mc2
+        ) if self.enable_dispatch_v2 else torch_npu.npu_moe_distribute_combine(
+            **kwargs_mc2)
+
+        # these values are no longer used, so they need to be set to None for memory release.
+        self.output = None
+        self.assist_info_for_combine = None
+        self.ep_recv_counts = None
+        self.topk_ids = None
+        self.topk_weights = None
+        self.mc2_mask = None
+        self.expert_map = None
+        self.expand_scales = None
+
+        if self.shared_experts is None:
+            return hidden_states
+        else:
+            if self.with_quant:
+                shared_hidden_states, _ = self.shared_experts.down_proj(
+                    (self.shared_act, self.swiglu_out_scale))
+            else:
+                shared_hidden_states, _ = self.shared_experts.down_proj(
+                    self.shared_act)
+            self.shared_act = None
+            self.shared_experts = None
+            self.swiglu_out_scale = None
+            return hidden_states, shared_hidden_states
+
+
+class TokenDispatcherWithAllGather(MoETokenDispatcher):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.apply_router_weight_on_input = False
+        self.max_num_tokens = kwargs.get("max_num_tokens")
+        self.num_experts_local = kwargs.get("num_local_experts", 0)
+        self.sorted_weights = None
+        self.expanded_row_idx = None
+        self.sorted_token_indices = None
+        self.original_shape = None
+        self.mask = None
+        self.expert_map = None
+        self.topk_weights = None
+        self.topk_ids = None
+        self.with_quant = False
+
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[Any] = None,
+                       quantized_x_for_share: Optional[Any] = None,
+                       dynamic_scale_for_share: Optional[Any] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
+        self.with_quant = with_quant
+        self.original_shape = hidden_states.shape
+
+        num_tokens = hidden_states.shape[:-1].numel()
+        self.expert_map = expert_map
+        self.topk_weights = topk_weights
+        self.topk_ids = topk_ids
+        self.apply_router_weight_on_input = apply_router_weight_on_input
+        if self.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:
+            global_num_experts = len(expert_map) + global_redundant_expert_num
+            mask = (expert_map[topk_ids] != -1)
+            self.topk_weights = topk_weights * mask
+            first_expert_idx = get_ep_group(
+            ).rank_in_group * self.num_experts_local
+            last_expert_idx = first_expert_idx + self.num_experts_local
+        else:
+            first_expert_idx = 0
+            last_expert_idx = self.num_experts_local
+            global_num_experts = self.num_experts_local
+
+        sorted_hidden_states, self.expanded_row_idx, expert_tokens, pertoken_scale = (
+            torch_npu.npu_moe_init_routing_v2(
+                hidden_states,
+                topk_ids,
+                active_num=num_tokens * self.top_k,
+                expert_num=global_num_experts,
+                expert_tokens_num_type=1,
+                expert_tokens_num_flag=True,
+                active_expert_range=[first_expert_idx, last_expert_idx],
+                quant_mode=1 if self.with_quant else -1,
+            ))
+        expert_tokens = expert_tokens.to(torch.int64)
+        group_list_type = 1  # `count` mode
+        return {
+            "group_list_type": group_list_type,
+            "hidden_states": sorted_hidden_states,
+            "group_list": expert_tokens,
+            "dynamic_scale": pertoken_scale if self.with_quant else None,
+        }
+
+    def token_combine(self,
+                      hidden_states: torch.Tensor,
+                      bias: torch.Tensor = None):
+        assert self.original_shape is not None
+        final_hidden_states = torch_npu.npu_moe_token_unpermute(
+            permuted_tokens=hidden_states,
+            sorted_indices=torch.abs(self.expanded_row_idx),
+            probs=self.topk_weights)
+        if len(self.original_shape) == 3:
+            final_hidden_states = final_hidden_states.view(self.original_shape)
+
+        # these values are no longer used, so they need to be set to None for memory release.
+        self.expert_map = None
+        self.topk_weights = None
+        self.topk_ids = None
+        self.expanded_row_idx = None
+        return final_hidden_states
+
+
+# mypy: disable-error-code="override"
+class TokenDispatcherWithMoge(MoETokenDispatcher):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.apply_router_weight_on_input = False
+        self.local_num_experts = self.num_experts // self.ep_size
+        self.local_num_group = self.top_k // self.ep_size
+        self.bsz = None
+
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[Any] = None,
+                       quantized_x_for_share: Optional[Any] = None,
+                       dynamic_scale_for_share: Optional[Any] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
+        self.bsz, _ = hidden_states.shape
+        flatten_topk_ids = topk_ids.view(-1)
+        self.sorted_topk_ids = torch.argsort(flatten_topk_ids.float())
+        self.sorted_topk_ids = self.sorted_topk_ids.to(torch.int32)
+        sorted_hidden_states = hidden_states.index_select(
+            0, self.sorted_topk_ids // self.local_num_group)
+
+        experts_id = torch.arange(0,
+                                  self.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, self.sorted_topk_ids).unsqueeze(-1)
+        group_list = num_tokens_per_expert.cumsum(dim=0).to(torch.int64)
+        group_list_type = 0
+        return {
+            "group_list_type": group_list_type,
+            "hidden_states": sorted_hidden_states,
+            "group_list": group_list,
+            "topk_scales": topk_scales,
+        }
+
+    def token_combine(self,
+                      hidden_states: torch.Tensor,
+                      bias: torch.Tensor = None):
+        unsorted_topk_ids = torch.argsort(self.sorted_topk_ids.float()).to(
+            torch.int32)
+        unsorted_hidden_states = hidden_states.index_select(
+            0, unsorted_topk_ids)
+        final_hidden_states = unsorted_hidden_states.reshape(
+            self.bsz, self.top_k // self.ep_size, -1).sum(1)
+        return final_hidden_states
+
+
+class TokenDispatcherWithAll2AllV(MoETokenDispatcher):
+    """
+    The implementation of the AlltoAll-based token dispatcher, which handles token
+    dispatching on the sequence level instead of token level. The core of this implementation
+    lies in each device dispatching on the entire sequence, with the hidden state being partitioned.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.with_quant = False
+        self.num_local_experts = kwargs.get("num_local_experts", 0)
+
+        self.hidden_shape = None
+        self.topk_weights = None
+        self.input_splits = None
+        self.output_splits = None
+        self.hidden_shape_before_permute = None
+
+        # [tp_ep_size * ep_size, num_local_experts]. Represents the number of tokens sent
+        # to each local expert by all ranks.
+        self.num_global_tokens_per_local_expert = None
+
+        # cached intermediate tensors.
+        self.tokens_per_expert = None
+        self.global_input_tokens_local_experts_indices = None
+
+        assert self.num_local_experts > 0, "Expected at least one expert"
+        if self.num_local_experts > 1:
+            self.expert_ids_per_ep_rank = torch.tensor(
+                [i % self.num_local_experts for i in range(self.num_experts)],
+                dtype=torch.int32,
+                device=torch.npu.current_device(),
+            )
+
+        local_expert_indices_offset = (self.ep_rank * self.num_local_experts)
+
+        self.local_expert_indices = [
+            local_expert_indices_offset + i
+            for i in range(self.num_local_experts)
+        ]
+        assert (len(self.local_expert_indices) == self.num_local_experts
+                ), "Invalid local expert indices"
+        for i in range(len(self.local_expert_indices) - 1):
+            assert (self.local_expert_indices[i] ==
+                    self.local_expert_indices[i + 1] -
+                    1), "local_expert_indices must be continuous"
+
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[Any] = None,
+                       quantized_x_for_share: Optional[Any] = None,
+                       dynamic_scale_for_share: Optional[Any] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
+        self.with_quant = with_quant
+        self.hidden_shape = hidden_states.shape
+        self.topk_weights = topk_weights
+        assert topk_weights.dim() == 2, "Expected 2D tensor for topk_weights"
+        assert topk_ids.dim() == 2, "Expected 2D tensor for routing map"
+
+        if log2phy is not None:
+            topk_ids = log2phy[topk_ids]
+
+        permutated_local_input_tokens, reversed_local_input_permutation_mapping, tokens_per_expert = self._dispatch_preprocess(
+            hidden_states, topk_ids)
+        self.reversed_local_input_permutation_mapping = reversed_local_input_permutation_mapping
+
+        dynamic_scale_after_all2all = None
+        if self.with_quant:
+            permutated_local_input_tokens, dynamic_scale = torch_npu.npu_dynamic_quant(
+                permutated_local_input_tokens)
+
+            _, dynamic_scale_after_all2all, permute2_ep_all_to_all_handle = async_all_to_all(
+                dynamic_scale,
+                self.output_splits,
+                self.input_splits,
+                self.ep_group,
+            )
+            permute2_ep_all_to_all_handle.wait()
+            dynamic_scale.untyped_storage().resize_(0)
+
+        _, global_input_tokens, permute1_ep_all_to_all_handle = async_all_to_all(
+            permutated_local_input_tokens,
+            self.output_splits,
+            self.input_splits,
+            self.ep_group,
+        )
+        permute1_ep_all_to_all_handle.wait()
+        permutated_local_input_tokens.untyped_storage().resize_(0)
+
+        global_input_tokens, dynamic_scale = self._dispatch_postprocess(
+            global_input_tokens, dynamic_scale_after_all2all)
+        return {
+            "hidden_states": global_input_tokens,
+            "group_list": tokens_per_expert,
+            "dynamic_scale": dynamic_scale,
+            "group_list_type": 1
+        }
+
+    def token_combine(self,
+                      hidden_states: torch.Tensor,
+                      bias: torch.Tensor = None):
+        assert bias is None, "Bias is not supported in MoEAlltoAllvTokenDispatcher."
+
+        hidden_states = self._combine_preprocess(hidden_states)
+
+        # Perform expert parallel AlltoAll communication
+        # hidden_states: [SEQL, H] -> [SEQL, H/TP]
+        _, permutated_local_input_tokens, handle = async_all_to_all(
+            hidden_states, self.input_splits, self.output_splits,
+            self.ep_group)
+        handle.wait()
+        hidden_states.untyped_storage().resize_(0)
+
+        output = self._combine_postprocess(permutated_local_input_tokens)
+
+        # these values are no longer used, so they need to be set to None for memory release.
+        self.input_splits = None
+        self.output_splits = None
+        self.num_global_tokens_per_local_expert = None
+        self.topk_weights = None
+        self.reversed_local_input_permutation_mapping = None
+        self.reversed_global_input_permutation_mapping = None
+        self.global_input_tokens_local_experts_indices = None
+
+        return output
+
+    def _dispatch_preprocess(self, hidden_states, topk_ids):
+        assert self.hidden_shape is not None
+        hidden_states = hidden_states.view(-1, self.hidden_shape[-1])
+        tokens_per_expert = self._preprocess(topk_ids)
+
+        self.hidden_shape_before_permute = hidden_states.shape
+
+        permutated_local_input_tokens, reversed_local_input_permutation_mapping = torch_npu.npu_moe_token_permute(
+            tokens=hidden_states,
+            indices=topk_ids,
+            num_out_tokens=self.num_out_tokens,
+        )
+        return permutated_local_input_tokens, reversed_local_input_permutation_mapping, tokens_per_expert
+
+    def _preprocess(self, topk_ids: torch.Tensor) -> torch.Tensor:
+        num_local_tokens_per_expert = torch.histc(topk_ids,
+                                                  bins=self.num_experts,
+                                                  min=0,
+                                                  max=self.num_experts)
+
+        ep_size = self.ep_size
+
+        # Dropless
+        self.num_out_tokens = topk_ids.numel()
+
+        # ===================================================
+        # Calculate input_splits, output_splits for alltoall-v.
+        # ===================================================
+        self.input_splits = (num_local_tokens_per_expert.reshape(
+            ep_size,
+            self.num_local_experts).sum(axis=1).to(torch.device("cpu"),
+                                                   non_blocking=True).numpy())
+        num_global_tokens_per_expert = gather_from_sequence_parallel_region(
+            num_local_tokens_per_expert,
+            group=self.ep_group).reshape(ep_size, self.num_experts)
+        self.num_global_tokens_per_local_expert = num_global_tokens_per_expert[:, self.local_expert_indices[
+            0]:self.local_expert_indices[-1] + 1]
+        if self.num_global_tokens_per_local_expert is None:
+            raise ValueError(
+                "num_global_tokens_per_local_expert must be set before sum.")
+        self.output_splits = (self.num_global_tokens_per_local_expert.sum(
+            axis=-1).to(torch.device("cpu"), non_blocking=True).numpy())
+        num_tokens_per_local_expert = self.num_global_tokens_per_local_expert.sum(
+            axis=0)
+        # ===================================================
+        # num_global_tokens_per_expert: [ep_size, num_experts]
+        # num_global_tokens_per_local_expert: [ep_size, num_local_experts]
+        # num_tokens_per_local_expert: [num_local_experts]
+        # ===================================================
+
+        if self.num_local_experts > 1:
+            if self.num_global_tokens_per_local_expert is None:
+                raise ValueError(
+                    "num_global_tokens_per_local_expert must be set before operations."
+                )
+            self.global_input_tokens_local_experts_indices = torch.repeat_interleave(
+                self.expert_ids_per_ep_rank,
+                self.num_global_tokens_per_local_expert.ravel())
+        else:
+            # TODO: This full synchronization can be a performance bottleneck.
+            # A more granular sync (e.g., blocking D2H copies) should be investigated.
+            torch.npu.synchronize()
+
+        return num_tokens_per_local_expert
+
+    def _dispatch_postprocess(self, global_input_tokens, dynamic_scale=None):
+        # Early return if no local experts or no tokens
+        if self.num_local_experts <= 1:
+            return global_input_tokens, None
+
+        # Handle quantized case
+        if self.with_quant:
+            assert self.global_input_tokens_local_experts_indices is not None, \
+            "global_input_tokens_local_experts_indices must be initialized before calling _dispatch_postprocess"
+            expert_idx_2d = self.global_input_tokens_local_experts_indices.unsqueeze(
+                -1)
+            active_num = self.global_input_tokens_local_experts_indices.numel()
+
+            # Handle case with no active tokens
+            if active_num <= 0:
+                self.reversed_global_input_permutation_mapping = self.global_input_tokens_local_experts_indices
+                return global_input_tokens, dynamic_scale
+
+            # Process with active tokens
+            global_input_tokens, self.reversed_global_input_permutation_mapping, _, expanded_scale = torch_npu.npu_moe_init_routing_v2(
+                global_input_tokens,
+                expert_idx_2d,
+                scale=dynamic_scale,
+                active_num=active_num,
+                expert_capacity=0,
+                expert_num=self.num_local_experts,
+                expert_tokens_num_type=1,
+                expert_tokens_num_flag=True,
+                active_expert_range=[0, self.num_local_experts],
+                quant_mode=-1,
+                row_idx_type=0)
+            return global_input_tokens, expanded_scale
+
+        # Handle non-quantized case
+        global_input_tokens, self.reversed_global_input_permutation_mapping = torch_npu.npu_moe_token_permute(
+            global_input_tokens,
+            self.global_input_tokens_local_experts_indices)
+        return global_input_tokens, None
+
+    def _combine_preprocess(self, hidden_states):
+        # Unpermutation 2: expert output to AlltoAll input
+        if hidden_states.shape[0] > 0 and self.num_local_experts > 1:
+            hidden_states = torch_npu.npu_moe_token_unpermute(
+                hidden_states, self.reversed_global_input_permutation_mapping)
+
+        return hidden_states
+
+    def _combine_postprocess(self, permutated_local_input_tokens):
+        # Unpermutation 1: AlltoAll output to output
+        output = torch_npu.npu_moe_token_unpermute(
+            permuted_tokens=permutated_local_input_tokens,
+            sorted_indices=self.reversed_local_input_permutation_mapping.to(
+                torch.int32),
+            probs=self.topk_weights,
+            restore_shape=self.hidden_shape_before_permute)
+
+        # Reshape the output tensor
+        output = output.view(self.hidden_shape)
+        return output