[main] [refactor] refactor common_fused_moe.py (#2706)

### What this PR does / why we need it?
1. Move prepare/finalize operation from moe_comm_method to
/ops/moe/fused_moe_prepare_and_finalize
2. Adapt to token_dispatcher in moe_comm_method
3. Move
moe_comm_method/experts_selector/token_dispatcher/fused_moe_prepare_and_finalize
to /ops/moe
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut

- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55

Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>

vllm_ascend/ops/moe/experts_selector.py

@@ -0,0 +1,283 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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 typing import Callable, Optional
+
+import torch
+import torch_npu
+
+
+def return_row_idx(hidden_states, top_k):
+    num_tokens = hidden_states.shape[0]
+    row_idx_len = num_tokens * top_k
+    row_idx = (torch.arange(0,
+                            row_idx_len,
+                            dtype=torch.int32,
+                            device=hidden_states.device).view(
+                                top_k, -1).permute(1, 0).contiguous())
+    return row_idx
+
+
+def 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",
+                   routed_scaling_factor=1.0,
+                   e_score_correction_bias: Optional[torch.Tensor] = None,
+                   indices_type: Optional[torch.dtype] = None,
+                   global_num_experts: int = -1):
+    """
+    Fused experts with select experts.
+
+    Args:
+        router_logits: router logits of shape (num_tokens, hidden_size).
+        hidden_states: Hidden states of shape (num_tokens, hidden_size).
+        top_k: number of top k experts.
+        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.
+        indices_type: dtype of indices
+        global_num_experts: Global number of experts.
+
+    Returns:
+        topk_weights: router weights of shape (num_tokens, top_k).
+        topk_ids: selected expert IDs of shape (num_tokens, top_k).
+    """
+
+    topk_weights, topk_ids, row_idx = _select_experts_with_fusion_ops(
+        hidden_states=hidden_states,
+        router_logits=router_logits,
+        top_k=top_k,
+        use_grouped_topk=use_grouped_topk,
+        topk_group=topk_group,
+        renormalize=renormalize,
+        e_score_correction_bias=e_score_correction_bias,
+        num_expert_group=num_expert_group,
+        custom_routing_function=custom_routing_function,
+        scoring_func=scoring_func,
+        routed_scaling_factor=routed_scaling_factor,
+        global_num_experts=global_num_experts)
+
+    if topk_weights is None:
+        topk_weights, topk_ids = _native_select_experts(
+            hidden_states=hidden_states,
+            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,
+            global_num_experts=global_num_experts,
+        )
+    if row_idx is None:
+        row_idx = return_row_idx(hidden_states, top_k)
+    return topk_weights, topk_ids, row_idx
+
+
+def _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 _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
+
+
+def _select_expert_use_group_topk(
+        topk_weights: torch.Tensor, topk_group: Optional[int],
+        renormalize: bool, top_k: int, num_expert_group: Optional[int],
+        e_score_correction_bias: Optional[torch.Tensor]):
+    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 = _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
+
+
+def _select_experts_with_fusion_ops(
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        use_grouped_topk: bool,
+        renormalize: bool,
+        e_score_correction_bias: Optional[torch.Tensor],
+        topk_group: Optional[int],
+        num_expert_group: Optional[int],
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor=1.0,
+        global_num_experts: int = -1):
+
+    topk_weights, topk_ids, row_idx = None, None, None
+    # NOTE: now npu_moe_gating_top_k can only support 'group_count=256' pattern
+    is_deepseek_v3_r1 = global_num_experts == 256
+    if is_deepseek_v3_r1:
+        topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
+            router_logits,
+            k=top_k,  # topk currently 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))
+        row_idx = return_row_idx(hidden_states, top_k)
+    if not use_grouped_topk and custom_routing_function is None and scoring_func == "softmax":
+        topk_weights, topk_ids, row_idx = 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, row_idx
+
+
+def _native_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.
+    """
+
+    if scoring_func == "softmax":
+        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:
+        return _select_expert_use_group_topk(
+            topk_weights=topk_weights,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            e_score_correction_bias=e_score_correction_bias)
+
+    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

vllm_ascend/ops/moe/fused_moe_prepare_and_finalize.py

@@ -0,0 +1,240 @@
+# 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.
+
+from abc import ABC, abstractmethod
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from vllm.distributed import tensor_model_parallel_all_reduce
+from vllm.distributed.parallel_state import (
+    get_dp_group, get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size)
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe import FusedMoEConfig
+
+
+class FusedMoEPrepareAndFinalize(ABC):
+
+    def __init__(self, moe_config: FusedMoEConfig):
+        self.moe_config = moe_config
+
+    @abstractmethod
+    def prepare(self,
+                hidden_states: torch.Tensor,
+                router_logits: torch.Tensor,
+                enable_shared_expert_dp: bool = False,
+                rm_router_logits: bool = False,
+                replace_allreduce: bool = False,
+                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        raise NotImplementedError("Prepare not implemented.")
+
+    def finalize(self, hidden_states: torch.Tensor,
+                 reduce_results: bool) -> torch.Tensor:
+        raise NotImplementedError("Combine function not implemented.")
+
+
+class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
+
+    def __init__(self, moe_config: FusedMoEConfig):
+        super().__init__(moe_config)
+        self._restore_tp_across_dp()
+
+    def _restore_tp_across_dp(self):
+        # NOTE: Since vLLM flatten tp across dp, we need to restore the original
+        # tp_size and tp_rank.
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+    def prepare(self,
+                hidden_states: torch.Tensor,
+                router_logits: torch.Tensor,
+                enable_shared_expert_dp: bool = False,
+                rm_router_logits: bool = False,
+                replace_allreduce: bool = False,
+                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """The target_pad_length is calculated in forward_context, here we pad the
+        hidden states and router logits. And if TP size > 1, we also need to split
+        the tensors accordingly.
+        """
+        self.replace_allreduce = replace_allreduce
+        self.enable_shared_expert_dp = enable_shared_expert_dp
+
+        if not self.replace_allreduce:
+            self.num_tokens, _ = hidden_states.shape
+            forward_context = get_forward_context()
+            mc2_mask = forward_context.mc2_mask
+            target_pad_length = forward_context.padded_num_tokens
+            pad_size = target_pad_length - self.num_tokens
+
+            if pad_size > 0 and not self.enable_shared_expert_dp:
+                hidden_states = nn.functional.pad(hidden_states,
+                                                  (0, 0, 0, pad_size))
+                router_logits = nn.functional.pad(router_logits,
+                                                  (0, 0, 0, pad_size))
+
+            if self.tp_size > 1:
+                if not self.enable_shared_expert_dp:
+                    split_hidden_states = torch.tensor_split(hidden_states,
+                                                             self.tp_size,
+                                                             dim=0)
+                    split_router_logits = torch.tensor_split(router_logits,
+                                                             self.tp_size,
+                                                             dim=0)
+                    hidden_states = split_hidden_states[self.tp_rank]
+                    router_logits = split_router_logits[self.tp_rank]
+                    self.split_hidden_states = split_hidden_states
+
+                split_mc2_mask = torch.tensor_split(mc2_mask,
+                                                    self.tp_size,
+                                                    dim=0)
+                mc2_mask = split_mc2_mask[self.tp_rank]
+
+        return hidden_states, router_logits, mc2_mask
+
+    def finalize(self, hidden_states: torch.Tensor,
+                 reduce_results: bool) -> torch.Tensor:
+        """If TP size > 1, all-gather the hidden states to get the final output.
+        
+        Also, unpad the hidden states if needed.
+        """
+        if not (self.enable_shared_expert_dp or self.replace_allreduce):
+            if self.tp_size > 1:
+                dist.all_gather(list(self.split_hidden_states), hidden_states,
+                                self.moe_config.tp_group.device_group)
+                hidden_states = torch.cat(self.split_hidden_states, dim=0)
+
+            if self.num_tokens < hidden_states.shape[0]:
+                hidden_states = hidden_states[:self.num_tokens]
+
+        return hidden_states
+
+
+class FusedMoEPrepareAndFinalizeWithAll2All(FusedMoEPrepareAndFinalize):
+
+    def __init__(self, moe_config: FusedMoEConfig):
+        super().__init__(moe_config)
+        self._restore_tp_across_dp()
+
+    def _restore_tp_across_dp(self):
+        # NOTE: Since vLLM flatten tp across dp, we need to restore the original
+        # tp_size and tp_rank.
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+    def prepare(self,
+                hidden_states: torch.Tensor,
+                router_logits: torch.Tensor,
+                enable_shared_expert_dp: bool = False,
+                rm_router_logits: bool = False,
+                replace_allreduce: bool = False,
+                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        self.replace_allreduce = replace_allreduce
+        self.enable_shared_expert_dp = enable_shared_expert_dp
+
+        if not (self.replace_allreduce or self.enable_shared_expert_dp):
+            self.num_tokens, _ = hidden_states.shape
+            pad_size = self.tp_size - self.num_tokens
+
+            if pad_size > 0:
+                hidden_states = nn.functional.pad(hidden_states,
+                                                  (0, 0, 0, pad_size))
+                router_logits = nn.functional.pad(router_logits,
+                                                  (0, 0, 0, pad_size))
+
+            if self.tp_size > 1:
+                split_hidden_states = torch.tensor_split(hidden_states,
+                                                         self.tp_size,
+                                                         dim=0)
+                split_router_logits = torch.tensor_split(router_logits,
+                                                         self.tp_size,
+                                                         dim=0)
+                self.split_hidden_states = split_hidden_states
+
+                hidden_states = split_hidden_states[self.tp_rank]
+                router_logits = split_router_logits[self.tp_rank]
+
+        return hidden_states, router_logits, None
+
+    def finalize(self, hidden_states: torch.Tensor,
+                 reduce_results: bool) -> torch.Tensor:
+        """If TP size > 1, all-gather the hidden states to get the final output.
+
+        Also, unpad the hidden states if needed.
+        """
+        if not (self.enable_shared_expert_dp or self.replace_allreduce):
+            if self.tp_size > 1:
+                dist.all_gather(list(self.split_hidden_states), hidden_states,
+                                self.moe_config.tp_group.device_group)
+                hidden_states = torch.cat(self.split_hidden_states, dim=0)
+
+            if self.num_tokens < hidden_states.shape[0]:
+                hidden_states = hidden_states[:self.num_tokens]
+
+        return hidden_states
+
+
+class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
+
+    def prepare(self,
+                hidden_states: torch.Tensor,
+                router_logits: torch.Tensor,
+                enable_shared_expert_dp: bool = False,
+                rm_router_logits: bool = False,
+                replace_allreduce: bool = False,
+                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """When DP size > 1, pad the hidden states and router logits for communication."""
+        self.rm_router_logits = rm_router_logits
+        self.enable_shared_expert_dp = enable_shared_expert_dp
+
+        if self.moe_config.dp_size > 1:
+            forward_context = get_forward_context()
+            max_tokens_across_dp = forward_context.max_tokens_across_dp
+
+            self.num_tokens = hidden_states.shape[0]
+            pad_size = max_tokens_across_dp - self.num_tokens
+            if pad_size > 0:
+                hidden_states = nn.functional.pad(hidden_states,
+                                                  (0, 0, 0, pad_size))
+                if not self.rm_router_logits:
+                    router_logits = nn.functional.pad(router_logits,
+                                                      (0, 0, 0, pad_size))
+
+            hidden_states = self.moe_config.dp_group.all_gather(
+                hidden_states, 0)
+            if self.rm_router_logits:
+                router_logits, _ = gate(hidden_states)
+            else:
+                router_logits = self.moe_config.dp_group.all_gather(
+                    router_logits, 0)
+
+        return hidden_states, router_logits, None
+
+    def finalize(self, hidden_states: torch.Tensor,
+                 reduce_results: bool) -> torch.Tensor:
+        """When DP size > 1, reduce-scatter the hidden states to get the final output.
+
+        When TP size > 1, all-reduce the hidden states to get the final output.
+        """
+        if self.moe_config.dp_size > 1 and not self.enable_shared_expert_dp:
+            hidden_states = get_dp_group().reduce_scatter(hidden_states, 0)
+            hidden_states = hidden_states[:self.num_tokens]
+
+        if reduce_results and (self.moe_config.tp_size > 1
+                               or self.moe_config.ep_size > 1):
+            hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+
+        return hidden_states

vllm_ascend/ops/moe/moe_comm_method.py

@@ -0,0 +1,298 @@
+# 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.
+
+from abc import ABC, abstractmethod
+from typing import Any, Optional
+
+import torch
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe import FusedMoEConfig
+
+from vllm_ascend.ops.moe.fused_moe_prepare_and_finalize import (
+    FusedMoEPrepareAndFinalizeWithAll2All,
+    FusedMoEPrepareAndFinalizeWithAllGather, FusedMoEPrepareAndFinalizeWithMC2)
+from vllm_ascend.ops.moe.moe_mlp import unified_apply_mlp
+from vllm_ascend.ops.moe.token_dispatcher import (TokenDispatcherWithAll2AllV,
+                                                  TokenDispatcherWithAllGather,
+                                                  TokenDispatcherWithMC2)
+
+
+class MoECommMethod(ABC):
+    """Base class for MoE communication methods."""
+
+    def __init__(self, moe_config: FusedMoEConfig):
+        self.moe_config = moe_config
+        self.mc2_mask = None
+
+        self.token_dispatcher = self._get_token_dispatcher()
+        self.fused_moe_prepare_finalize = self._get_fused_moe_prepare_finalize(
+        )
+
+    def prepare(self,
+                hidden_states: torch.Tensor,
+                router_logits: torch.Tensor,
+                enable_shared_expert_dp: bool = False,
+                rm_router_logits: bool = False,
+                replace_allreduce: bool = False,
+                gate=None) -> tuple[torch.Tensor, torch.Tensor]:
+        hidden_states, router_logits, mc2_mask = self.fused_moe_prepare_finalize.prepare(
+            hidden_states, router_logits, enable_shared_expert_dp,
+            rm_router_logits, replace_allreduce, gate)
+        self.mc2_mask = mc2_mask
+        return hidden_states, router_logits
+
+    def finalize(self, hidden_states: torch.Tensor,
+                 reduce_results: bool) -> torch.Tensor:
+        hidden_states = self.fused_moe_prepare_finalize.finalize(
+            hidden_states, reduce_results)
+        return hidden_states
+
+    def fused_experts(
+            self,
+            hidden_states: torch.Tensor,
+            w1: torch.Tensor,
+            w2: torch.Tensor,
+            topk_weights: torch.Tensor,
+            topk_ids: torch.Tensor,
+            row_idx: torch.Tensor,
+            activation: str = "silu",
+            apply_router_weight_on_input: bool = False,
+            use_int8_w8a8: bool = False,
+            use_int4_w4a8: bool = False,
+            global_num_experts: Optional[int] = None,
+            expert_map: Optional[torch.Tensor] = None,
+            w1_scale: Optional[torch.Tensor] = None,
+            w2_scale: Optional[torch.Tensor] = None,
+            w1_scale_bias: torch.Tensor = None,
+            w2_scale_bias: torch.Tensor = None,
+            # For TorchAir graph
+            is_torchair: bool = False,
+            # For Cube/Vector parallel
+            shared_experts: Optional[Any] = None,
+            shared_gate_up: Optional[Any] = None,
+            shared_dequant_scale: Optional[Any] = None,
+            quantized_x_for_share: Optional[Any] = None,
+            dynamic_scale_for_share: Optional[Any] = None,
+            # For load balance
+            log2phy: torch.Tensor = None,
+            global_redundant_expert_num: int = 0,
+            need_trans: bool = False) -> torch.Tensor:
+        # Check constraints
+        assert hidden_states.shape[1] == w1.shape[1], (
+            f"Hidden size mismatch {hidden_states.shape[1]} != {w1.shape[1]}")
+        assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
+        assert hidden_states.is_contiguous(
+        ), "Hidden_states must be contiguous"
+        assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
+        assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
+        assert hidden_states.dtype in [
+            torch.float32, torch.float16, torch.bfloat16
+        ]
+
+        moe_comm_method = get_forward_context().moe_comm_method
+        assert moe_comm_method is not None, "Missing communication context"
+
+        results = self.token_dispatcher.token_dispatch(
+            hidden_states=hidden_states,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            row_idx=row_idx,
+            expert_map=expert_map,
+            log2phy=log2phy,
+            global_redundant_expert_num=global_redundant_expert_num,
+            shared_experts=shared_experts,
+            shared_gate_up=shared_gate_up,
+            shared_dequant_scale=shared_dequant_scale,
+            mc2_mask=self.mc2_mask,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            with_quant=use_int8_w8a8 or use_int4_w4a8)
+
+        permuted_hidden_states, expert_tokens, dynamic_scale, group_list_type = \
+            results["hidden_states"], results["group_list"], results.get("dynamic_scale"), results["group_list_type"]
+
+        mlp_output = unified_apply_mlp(hidden_states=permuted_hidden_states,
+                                       w1=w1,
+                                       w1_scale=w1_scale,
+                                       w2=w2,
+                                       w2_scale=w2_scale,
+                                       group_list=expert_tokens,
+                                       dynamic_scale=dynamic_scale,
+                                       group_list_type=group_list_type,
+                                       w1_scale_bias=w1_scale_bias,
+                                       w2_scale_bias=w2_scale_bias,
+                                       with_quant=use_int8_w8a8
+                                       or use_int4_w4a8,
+                                       need_trans=need_trans)
+
+        hidden_states[:] = self.token_dispatcher.token_combine(
+            hidden_states=mlp_output)
+
+        return hidden_states
+
+    @abstractmethod
+    def _get_token_dispatcher(self):
+        raise NotImplementedError(
+            "_get_token_dispatcher function not implemented.")
+
+    @abstractmethod
+    def _get_fused_moe_prepare_finalize(self):
+        raise NotImplementedError(
+            "_get_fused_moe_prepare_finalize function not implemented.")
+
+
+class AllGatherCommImpl(MoECommMethod):
+    """This implementation is the same as NativeAllGatherCommImpl,
+    but uses NPU-specific ops for better performance.
+
+    This implementation should be compatible with all scenarios, and
+    thus it is the default implementation for MoE communication methods.
+    It uses `torch_npu.npu_moe_init_routing_v2` for pre-processing
+    and `torch_npu.npu_moe_token_unpermute` for post-processing
+    to handle the token-to-expert mapping and communication efficiently.
+
+    NOTE(Yizhou): TBH, it is really weird that we were supposed to use
+    `torch_npu.npu_moe_init_routing_v2` and `torch_npu.npu_moe_finalize_routing`
+    or `torch_npu.npu_moe_token_permute` and `torch_npu.npu_moe_token_unpermute`
+    for pre-processing and post-processing, respectively.
+    But `npu_moe_finalize_routing` will lead to accuracy issues so we have to
+    use `torch_npu.npu_moe_token_unpermute` instead.
+    This is a workaround and should be removed after the issue is fixed.
+    """
+
+    def _get_token_dispatcher(self):
+        return TokenDispatcherWithAllGather(
+            top_k=self.moe_config.experts_per_token,
+            num_experts=self.moe_config.num_experts,
+            num_local_experts=self.moe_config.num_local_experts)
+
+    def _get_fused_moe_prepare_finalize(self):
+        return FusedMoEPrepareAndFinalizeWithAllGather(self.moe_config)
+
+
+class NativeAllGatherCommImpl(AllGatherCommImpl):
+    """This implementation should be compatible with all scenarios.
+
+    Note that this implementation purely consists of native PyTorch ops
+    and does not use any NPU-specific ops. So the performance may not be optimal.
+    But it is a good fallback for scenarios where NPU-specific ops are not available.
+    """
+
+    def permute(
+        self,
+        hidden_states: torch.Tensor,
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        expert_map: torch.Tensor,
+        num_experts: int,
+        apply_a8_quantization: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
+        num_tokens = hidden_states.shape[0]
+
+        # Generate token indices and flatten
+        token_indices = torch.arange(num_tokens,
+                                     device=hidden_states.device,
+                                     dtype=torch.int64)
+        token_indices = (token_indices.unsqueeze(1).expand(
+            -1, self.moe_config.experts_per_token).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]
+                              if expert_map is not None else experts_flat)
+
+        # Filter valid token-expert pairs
+        mask = local_experts_flat != -1
+        # FIXME: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
+        # So we need to filter out invalid tokens by zeroing their weights.
+        # This is a workaround and should be removed after the issue is fixed
+        filtered_weights = torch.where(mask, weights_flat,
+                                       torch.zeros_like(weights_flat)).to(
+                                           topk_weights.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))
+        self.sorted_token_indices = token_indices[sort_indices]
+        self.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=hidden_states.device,
+                                   dtype=torch.int64)
+        ones = torch.ones_like(filtered_experts, dtype=torch.int64)
+        token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
+        expert_tokens = token_counts[:num_experts]
+
+        # Rearrange hidden_states
+        permuted_hidden_states = hidden_states[self.sorted_token_indices]
+
+        group_list_type = 1  # `count` mode
+
+        return permuted_hidden_states, expert_tokens, None, group_list_type
+
+    def unpermute(self, mlp_output: torch.Tensor,
+                  hidden_states: torch.Tensor) -> None:
+        mlp_output = mlp_output * self.sorted_weights.unsqueeze(1)
+
+        final_hidden_states = torch.zeros_like(hidden_states)
+        final_hidden_states.index_add_(0, self.sorted_token_indices,
+                                       mlp_output)
+
+        hidden_states[:] = final_hidden_states
+
+
+class MC2CommImpl(MoECommMethod):
+    """This implementation is for the scenarios listed below:
+    1. `enable_expert_parallel=True`.
+    2. `npu_moe_distribute_dispatch` and `npu_moe_distribute_combine` are available.
+    3. `enable_expert_parallel=False` is not supported.
+    
+    This implementation uses the MC2 communication method, which is optimized for
+    Communication and Computation parallelism on Ascend devices.
+    """
+
+    def _get_token_dispatcher(self):
+        return TokenDispatcherWithMC2()
+
+    def _get_fused_moe_prepare_finalize(self):
+        return FusedMoEPrepareAndFinalizeWithMC2(self.moe_config)
+
+
+class AlltoAllCommImpl(MoECommMethod):
+    """This implementation is for the scenarios listed below:
+    1. `enable_expert_parallel=True`.
+    2. `npu_grouped_matmul` is available.
+
+    This implementation uses all-to-all communication to exchange tokens
+    between data parallel ranks before and after the MLP computation. It should
+    have better performance than AllGatherCommImpl when DP size > 1.
+    """
+
+    def _get_token_dispatcher(self):
+        return TokenDispatcherWithAll2AllV(
+            top_k=self.moe_config.experts_per_token,
+            num_experts=self.moe_config.num_experts,
+            num_local_experts=self.moe_config.num_local_experts)
+
+    def _get_fused_moe_prepare_finalize(self):
+        return FusedMoEPrepareAndFinalizeWithAll2All(self.moe_config)

vllm_ascend/ops/moe/moe_mlp.py

@@ -0,0 +1,252 @@
+# 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.
+
+from typing import Optional
+
+import torch
+import torch_npu
+from torch.nn.functional import pad
+from vllm.forward_context import get_forward_context
+
+from vllm_ascend.ascend_forward_context import FusedMoEState
+from vllm_ascend.utils import dispose_tensor, is_310p
+
+
+def cumsum_group_list(group_list: torch.Tensor,
+                      group_list_type: int,
+                      active_num: int = 0,
+                      expert_num: int = 0) -> torch.Tensor:
+    if group_list_type not in [0, 1, 2]:
+        raise ValueError(
+            f"group_list_type should be in [0, 1, 2], but received {group_list_type}"
+        )
+
+    if group_list_type == 0:
+        return group_list
+    if group_list_type == 1:
+        return group_list.cumsum(dim=0)
+
+    experts = pad(group_list[:, 0], (1, 0))
+    tokens = pad(group_list[:, 1].cumsum(dim=0), (1, 0))
+    cumsum_group_list = torch.full(size=(expert_num, ),
+                                   fill_value=active_num,
+                                   dtype=group_list.dtype,
+                                   device=group_list.device)
+
+    for i, (start, end) in enumerate(zip(experts[:-1], experts[1:])):
+        if end > start:
+            cumsum_group_list[start:end] = tokens[i]
+
+    return cumsum_group_list
+
+
+def quant_apply_mlp(hidden_states: torch.Tensor,
+                    w1: torch.Tensor,
+                    w1_scale: torch.Tensor,
+                    w2: torch.Tensor,
+                    w2_scale: torch.Tensor,
+                    group_list: torch.Tensor,
+                    group_list_type: int = 1,
+                    dynamic_scale: torch.Tensor = None,
+                    w1_scale_bias: torch.Tensor = None,
+                    w2_scale_bias: torch.Tensor = None,
+                    fusion: bool = False) -> torch.Tensor:
+    if dynamic_scale is None:
+        unquantized_hidden_states = hidden_states
+        hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
+            hidden_states)
+        # Dispose the original unquantized hidden states
+        # to save npu memory because they're no longer used.
+        dispose_tensor(unquantized_hidden_states)
+    else:
+        pertoken_scale = dynamic_scale
+
+    bias1, bias2 = None, None
+    _output_dtype = w2_scale.dtype
+
+    is_mc2 = get_forward_context().fused_moe_state == FusedMoEState.MC2
+    if w1_scale_bias is None and is_mc2:
+        if w1_scale.dtype != torch.float32:
+            w1_scale = w1_scale.to(torch.float32)
+        if fusion:
+            # gmm1: gate_up_proj & act_fn: swiglu
+            hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
+                x=hidden_states,
+                weight=w1,
+                group_list=cumsum_group_list(group_list, group_list_type),
+                weight_scale=w1_scale,
+                x_scale=pertoken_scale)
+        else:
+            # gmm1: gate_up_proj
+            hidden_states = torch_npu.npu_grouped_matmul(
+                x=[hidden_states],
+                weight=[w1],
+                split_item=3,
+                group_list_type=group_list_type,
+                group_type=0,
+                group_list=group_list,
+                output_dtype=torch.int32)[0]
+            # act_fn: swiglu
+            hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
+                x=hidden_states,
+                weight_scale=w1_scale,
+                activation_scale=pertoken_scale,
+                bias=None,
+                quant_scale=None,
+                quant_offset=None,
+                group_index=group_list,
+                activate_left=True,
+                quant_mode=1,
+            )
+        # gmm2: down_proj
+        hidden_states = torch_npu.npu_grouped_matmul(
+            x=[hidden_states],
+            weight=[w2],
+            scale=[w2_scale],
+            per_token_scale=[swiglu_out_scale],
+            split_item=2,
+            group_list_type=group_list_type,
+            group_type=0,
+            group_list=group_list,
+            output_dtype=w2_scale.dtype)[0]
+    else:
+        if w1_scale_bias is not None:
+            if group_list_type == 0:
+                group_list = torch.cat(
+                    [group_list[:1],
+                     torch.diff(group_list, dim=0)])
+                group_list_type = 1
+            bias1 = [w1_scale_bias] if not fusion else w1_scale_bias
+            bias2 = [w2_scale_bias]
+            # TODO w4a8 scene: dynamic acquisition of dtype in the future
+            _output_dtype = torch.bfloat16
+
+        if fusion:
+            # gmm1: gate_up_proj & act_fn: swiglu
+            hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
+                x=hidden_states,
+                weight=w1,
+                bias=bias1,
+                group_list=cumsum_group_list(group_list, group_list_type),
+                weight_scale=w1_scale,
+                x_scale=pertoken_scale)
+        else:
+            # gmm1: gate_up_proj
+            hidden_states = torch_npu.npu_grouped_matmul(
+                x=[hidden_states],
+                weight=[w1],
+                scale=[w1_scale.to(w2_scale.dtype)],
+                bias=bias1,
+                per_token_scale=[pertoken_scale],
+                split_item=2,
+                group_list_type=group_list_type,
+                group_type=0,
+                group_list=group_list,
+                output_dtype=_output_dtype)[0]
+            # act_fn: swiglu
+            hidden_states = torch_npu.npu_swiglu(hidden_states)
+            hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(
+                hidden_states)
+        # gmm2: down_proj
+        hidden_states = torch_npu.npu_grouped_matmul(
+            x=[hidden_states],
+            weight=[w2],
+            scale=[w2_scale],
+            bias=bias2,
+            per_token_scale=[swiglu_out_scale],
+            split_item=2,
+            group_list_type=group_list_type,
+            group_type=0,
+            group_list=group_list,
+            output_dtype=_output_dtype)[0]
+
+    return hidden_states
+
+
+def unquant_apply_mlp(hidden_states: torch.Tensor,
+                      w1: torch.Tensor,
+                      w2: torch.Tensor,
+                      group_list: torch.Tensor,
+                      group_list_type: int = 1,
+                      topk_scales: Optional[torch.Tensor] = None,
+                      need_trans: bool = True) -> torch.Tensor:
+
+    if need_trans:
+        w1 = w1.transpose(1, 2)
+        w2 = w2.transpose(1, 2)
+
+    gate_up_out = 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]
+    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)
+
+    if topk_scales is not None:
+        gate_up_out *= topk_scales
+
+    hidden_states = torch_npu.npu_grouped_matmul(
+        x=[gate_up_out],
+        weight=[w2],
+        split_item=2,
+        group_list_type=group_list_type,
+        group_type=0,
+        group_list=group_list,
+    )[0]
+    return hidden_states
+
+
+def unified_apply_mlp(hidden_states: torch.Tensor,
+                      w1: torch.Tensor,
+                      w1_scale: torch.Tensor,
+                      w2: torch.Tensor,
+                      w2_scale: torch.Tensor,
+                      group_list: torch.Tensor,
+                      dynamic_scale: torch.Tensor = None,
+                      group_list_type: int = 1,
+                      w1_scale_bias: torch.Tensor = None,
+                      w2_scale_bias: torch.Tensor = None,
+                      topk_scales: Optional[torch.Tensor] = None,
+                      with_quant: bool = False,
+                      fusion: bool = False,
+                      need_trans: bool = True) -> torch.Tensor:
+    if with_quant:
+        return quant_apply_mlp(hidden_states=hidden_states,
+                               w1=w1,
+                               w1_scale=w1_scale,
+                               w2=w2,
+                               w2_scale=w2_scale,
+                               group_list=group_list,
+                               dynamic_scale=dynamic_scale,
+                               group_list_type=group_list_type,
+                               w1_scale_bias=w1_scale_bias,
+                               w2_scale_bias=w2_scale_bias,
+                               fusion=fusion)
+    else:
+        return unquant_apply_mlp(hidden_states=hidden_states,
+                                 w1=w1,
+                                 w2=w2,
+                                 group_list=group_list,
+                                 group_list_type=group_list_type,
+                                 topk_scales=topk_scales,
+                                 need_trans=need_trans)

vllm_ascend/ops/moe/token_dispatcher.py

@@ -0,0 +1,726 @@
+# 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, Dict, Optional
+
+import torch
+import torch_npu
+from vllm.distributed.parallel_state import get_ep_group
+
+import vllm_ascend.envs as envs_ascend
+from vllm_ascend.distributed.parallel_state import get_mc2_group
+from vllm_ascend.distributed.tensor_parallel import \
+    gather_from_sequence_parallel_region
+from vllm_ascend.ops.comm_utils import async_all_to_all
+from vllm_ascend.utils import AscendSocVersion, get_ascend_soc_version
+
+_Dispatchers: Dict[str, Any] = {}
+
+
+def _register_token_dispatcher(dispatcher: Any):
+    _Dispatchers[dispatcher.__class__.__name__] = dispatcher
+
+
+def get_token_dispatcher(name: str):
+    return _Dispatchers.get(name)
+
+
+def setup_token_dispatchers(ep_size: int, **kwargs):
+    existing_dispatchers = set(_Dispatchers.keys())
+
+    if ep_size == 1 and "TokenDispatcherWithAllGather" not in existing_dispatchers:
+        _register_token_dispatcher(TokenDispatcherWithAllGather(**kwargs))
+    elif envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and ep_size > 1 \
+        and "TokenDispatcherWithAllGather" not in existing_dispatchers:
+        _register_token_dispatcher(TokenDispatcherWithAllGather(**kwargs))
+    elif ep_size < 16 and "TokenDispatcherWithAll2AllV" not in existing_dispatchers:
+        _register_token_dispatcher(TokenDispatcherWithAll2AllV(**kwargs))
+    elif ep_size >= 16:
+        if "TokenDispatcherWithAll2AllV" not in existing_dispatchers:
+            _register_token_dispatcher(TokenDispatcherWithAll2AllV(**kwargs))
+        if "TokenDispatcherWithMC2" not in existing_dispatchers:
+            _register_token_dispatcher(TokenDispatcherWithMC2(**kwargs))
+
+
+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,
+                       row_idx: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[torch.Tensor] = None,
+                       shared_gate_up: Optional[torch.Tensor] = None,
+                       shared_dequant_scale: Optional[torch.Tensor] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: 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
+        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
+
+    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,
+    ):
+        if self.with_quant:
+            quant_mode = 2
+            if (expert_map is not None):
+                moe_expert_num = len(expert_map) + global_redundant_expert_num
+            else:
+                moe_expert_num = global_redundant_expert_num
+        else:
+            quant_mode = 0
+            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": 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,
+            })
+
+        kwargs_mc2.update(stage1_kwargs)
+        return kwargs_mc2
+
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       row_idx: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[torch.Tensor] = None,
+                       shared_gate_up: Optional[torch.Tensor] = None,
+                       shared_dequant_scale: Optional[torch.Tensor] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False):
+        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.output[0:5]
+
+        if self.with_quant:
+            if shared_experts is not None:
+                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 = 1
+        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
+        moe_expert_num = len(self.expert_map)
+        # 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": 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,
+        }
+        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)
+        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)
+            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,
+                       row_idx: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[torch.Tensor] = None,
+                       shared_gate_up: Optional[torch.Tensor] = None,
+                       shared_dequant_scale: Optional[torch.Tensor] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: 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)
+            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, _ = (
+            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,
+            ))
+        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,
+        }
+
+    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=self.expanded_row_idx,
+            probs=self.topk_weights)
+        if len(self.original_shape) == 3:
+            final_hidden_states = final_hidden_states.view(self.original_shape)
+        return final_hidden_states
+
+
+# mypy: disable-error-code="override"
+class UnquantizedTokenDispatcherWithFusedExpertsMoge(MoETokenDispatcher):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.apply_router_weight_on_input = False
+        self.local_ep = 1
+        self.local_num_experts = self.num_experts // self.local_ep
+        self.local_num_group = self.top_k // self.local_ep
+        self.bsz = None
+
+    def token_dispatch(self,
+                       hidden_states: torch.Tensor,
+                       topk_weights: torch.Tensor,
+                       topk_ids: torch.Tensor,
+                       row_idx: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[torch.Tensor] = None,
+                       shared_gate_up: Optional[torch.Tensor] = None,
+                       shared_dequant_scale: Optional[torch.Tensor] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: bool = False):
+        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)
+
+        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.local_ep, -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.num_global_redundant_experts = kwargs.get(
+            "num_global_redundant_experts", 0)
+        self.num_experts = self.num_experts + self.num_global_redundant_experts
+
+        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,
+                       row_idx: torch.Tensor,
+                       expert_map: Optional[torch.Tensor] = None,
+                       log2phy: Optional[torch.Tensor] = None,
+                       global_redundant_expert_num: int = 0,
+                       shared_experts: Optional[torch.Tensor] = None,
+                       shared_gate_up: Optional[torch.Tensor] = None,
+                       shared_dequant_scale: Optional[torch.Tensor] = None,
+                       mc2_mask: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       with_quant: 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)
+
+        self.input_splits = None
+        self.output_splits = None
+        self.num_global_tokens_per_local_expert = 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())
+
+        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