xc-llm-ascend/vllm_ascend/quantization/w8a8_dynamic.py

#
# 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 Any, Callable, Dict, Optional, Tuple, Union

import torch
import torch.distributed as dist
import torch_npu
from vllm.distributed import GroupCoordinator

from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.distributed.parallel_state import get_ep_group
from vllm_ascend.ops.fused_moe import select_experts
from vllm_ascend.utils import (FusedMoEState, dispose_tensor,
                               get_fused_moe_state, npu_stream_switch,
                               npu_wait_tensor)


def 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) -> torch.Tensor:
    """
    apply MLP: gate_up_proj -> swiglu -> down_proj

    Args:
        hidden_states: input hidden states with shape (num_tokens, hidden_size).
        w1: expert weights1 with shape
            (num_experts, hidden_size, intermediate_size * 2)
        w1_scale: weights1 scale with shape (num_experts, intermediate_size * 2)
        w2: expert weights2 with shape
            (num_experts, intermediate_size, hidden_size)
        w2_scale: weights2 scale with shape (num_experts, hidden_size)
        group_list: number of tokens for each expert, follow cumsum mode, and
            with shape (num_experts).
        transpose_weight:
            w1: (num_experts, intermediate_size * 2, hidden_size) ->
                    (num_experts, hidden_size, intermediate_size * 2)
            w2: (num_experts, hidden_size, intermediate_size) ->
                    (num_experts, intermediate_size, hidden_size)

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

    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

    # gmm1: gate_up_proj
    hidden_states = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w1],
        scale=[w1_scale],
        per_token_scale=[pertoken_scale],
        split_item=2,
        group_list_type=group_list_type,
        group_type=0,
        group_list=group_list,
        output_dtype=w2_scale.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],
        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]

    return hidden_states


def fused_experts_with_mc2(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    w1_scale: torch.Tensor,
    w2_scale: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    expert_map: torch.Tensor = None,
    moe_all_to_all_group_name: str = "",
    log2phy: torch.Tensor = None,
    global_redundant_expert_num: int = 0,
    shared_experts: Optional[Any] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    if log2phy:
        topk_ids = log2phy[topk_ids]
    global_bs = 0
    moe_expert_num = len(expert_map) + global_redundant_expert_num
    # hidden_states = hidden_states.bfloat16()
    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": global_bs,
    }

    rank = torch.distributed.get_rank()

    quant_mode = 2
    ep_group = get_ep_group().device_group
    local_rank = torch.distributed.get_rank(group=ep_group)
    all_to_all_group_size = torch.distributed.get_world_size(ep_group)

    world_szie = torch.distributed.get_world_size()
    tp_size = world_szie // all_to_all_group_size
    tp_rank = rank % tp_size

    stage1_kwargs = {
        "scales": None,
        "quant_mode": quant_mode,
        "group_ep": moe_all_to_all_group_name,
        "ep_world_size": all_to_all_group_size,
        "ep_rank_id": local_rank,
        # "group_tp": self.moe_rs_group_name,
        "group_tp": moe_all_to_all_group_name,
        "tp_world_size": tp_size,
        "tp_rank_id": tp_rank,
    }
    kwargs_mc2.update(stage1_kwargs)

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

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

    # `expand_x` will be disposed in the `apply_mlp` function
    down_out_list = apply_mlp(expand_x,
                              w1,
                              w1_scale,
                              w2,
                              w2_scale,
                              expert_token_nums,
                              dynamic_scale=dynamic_scale)

    # moeCombine
    kwargs_mc2 = {
        "expand_x": down_out_list,
        "expert_ids": topk_ids,
        "expand_idx": expand_idx,
        "expert_scales": topk_weights.to(torch.float32),
        "expert_shard_type": 0,
        "shared_expert_rank_num": 0,
        "moe_expert_num": moe_expert_num,
        "global_bs": 0,
    }
    tp_recv_counts = torch.empty(1,
                                 dtype=torch.int32,
                                 device=hidden_states.device)
    stage3_kwargs = {
        "ep_send_counts": ep_recv_counts,
        "group_ep": moe_all_to_all_group_name,
        "ep_world_size": all_to_all_group_size,
        "ep_rank_id": local_rank,
        "tp_send_counts": tp_recv_counts,
        # "group_tp": self.moe_rs_group_name,
        "group_tp": moe_all_to_all_group_name,
        "tp_world_size": tp_size,
        "tp_rank_id": tp_rank,
    }
    kwargs_mc2.update(stage3_kwargs)

    hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs_mc2)

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


# currently expert parallelism implemented with all2all
# is under-optimized.
def fused_experts_with_all2all(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w1_scale: torch.Tensor,
    w2: torch.Tensor,
    w2_scale: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    expert_map: torch.Tensor = None,
    ep_group: GroupCoordinator = None,
    log2phy: torch.Tensor = None,
    global_redundant_expert_num: int = 0,
):
    if log2phy:
        topk_ids = log2phy[topk_ids]
    original_shape = hidden_states.shape
    if len(original_shape) == 3:
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])

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

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

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

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

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

        sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx)

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

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

        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
            expanded_expert_idx, num_experts)
        expert_tokens = expert_tokens.to(torch.int64)
        group_list_type = 0

    # `hidden_states` will be disposed in the `apply_mlp` function
    hidden_states = apply_mlp(
        hidden_states,
        w1,
        w1_scale,  #17
        w2,
        w2_scale,
        expert_tokens,  #16
        group_list_type=group_list_type)

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

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


def fused_experts(hidden_states: torch.Tensor,
                  w1: torch.Tensor,
                  w1_scale: torch.Tensor,
                  w2: torch.Tensor,
                  w2_scale: torch.Tensor,
                  topk_weights: torch.Tensor,
                  topk_ids: torch.Tensor,
                  top_k: int,
                  expert_map: torch.Tensor = None):
    original_shape = hidden_states.shape
    if len(original_shape) == 3:
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])

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

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

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

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

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

        # Compute token counts with minlength of num_experts
        # This is equivalent to but faster than:
        # >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1]
        token_counts = torch.zeros(num_experts + 1,
                                   device=device,
                                   dtype=torch.int64)
        ones = torch.ones_like(filtered_experts, dtype=torch.int64)
        token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
        expert_tokens = token_counts[:num_experts]
        # Rearrange hidden_states
        hidden_states = hidden_states[sorted_token_indices]
        group_list_type = 1
    else:
        row_idx_len = num_tokens * top_k
        row_idx = torch.arange(0,
                               row_idx_len,
                               dtype=torch.int32,
                               device=topk_weights.device).view(
                                   top_k, -1).permute(1, 0).contiguous()
        hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
            hidden_states,
            row_idx=row_idx,
            expert_idx=topk_ids,
            active_num=num_tokens)

        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
            expanded_expert_idx, num_experts)
        expert_tokens = expert_tokens.to(torch.int64)
        group_list_type = 0

    # `hidden_states` will be disposed in the `apply_mlp` function
    hidden_states = apply_mlp(hidden_states,
                              w1,
                              w1_scale,
                              w2,
                              w2_scale,
                              expert_tokens,
                              group_list_type=group_list_type)

    if expert_map is not None:
        hidden_states.mul_(sorted_weights.unsqueeze(1))
        final_hidden_states = torch.zeros(*original_shape,
                                          device=device,
                                          dtype=dtype)

        num_valid_tokens = mask.sum()
        valid_token_mask = torch.arange(
            0, sorted_token_indices.shape[0],
            device=device).unsqueeze(1) < num_valid_tokens
        hidden_states = hidden_states.masked_fill_(~valid_token_mask,
                                                   0).to(dtype)
        final_hidden_states.index_add_(0, sorted_token_indices, hidden_states)
    else:
        # TODO: Reorder device memory 2 times here, replace the current
        # implementation here when suitable operators become available.
        final_hidden_states = torch_npu.npu_moe_finalize_routing(
            hidden_states,
            skip1=None,
            skip2=None,
            bias=None,
            scales=topk_weights,
            expanded_src_to_dst_row=expanded_row_idx,
            export_for_source_row=topk_ids,
        )

    if len(original_shape) == 3:
        final_hidden_states = final_hidden_states.view(original_shape)
    return final_hidden_states


class AscendW8A8DynamicLinearMethod:
    """Linear method for Ascend W8A8_DYNAMIC.
    """

    def __init__(self):
        self.transpose_weight = True

    @staticmethod
    def get_weight(input_size: int, output_size: int,
                   params_dtype: torch.dtype) -> Dict[str, Any]:
        params_dict = {
            "weight": torch.empty(output_size, input_size, dtype=torch.int8)
        }
        return params_dict

    @staticmethod
    def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
        return {}

    @staticmethod
    def get_perchannel_param(
        output_size: int,
        params_dtype: torch.dtype,
    ) -> Dict[str, Any]:
        params_dict = {}
        params_dict["weight_scale"] = torch.empty(output_size,
                                                  1,
                                                  dtype=params_dtype)
        params_dict["weight_offset"] = torch.empty(output_size,
                                                   1,
                                                   dtype=params_dtype)
        return params_dict

    @staticmethod
    def apply(
        layer: torch.nn.Module,
        x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        bias: Optional[torch.Tensor] = None,
        tp_rank: Optional[int] = 0,
    ) -> torch.Tensor:
        config = getattr(layer, "_ascend_quant_config", {})
        if not isinstance(x, tuple):
            output_dtype = config.get("output_dtype", x.dtype)
            quantized_x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
        else:
            assert "output_dtype" in config.keys(), (
                f"DynamicLinearMethod needs explicitly specified `output_dtype`"
                f"for pre-quantized input, got config [{config}]")
            output_dtype = config["output_dtype"]
            quantized_x, dynamic_scale = x
        pertoken_scale = (dynamic_scale
                          if config.get("pertoken_scale", True) else None)

        output = torch_npu.npu_quant_matmul(
            quantized_x,
            layer.weight,
            layer.weight_scale,
            pertoken_scale=pertoken_scale,
            bias=bias,
            output_dtype=output_dtype,
        )
        return ((output, dynamic_scale)
                if config.get("return_scale", False) else output)

    def process_weights_after_loading(self, layer):
        if self.transpose_weight:
            layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
        # cast quantized weight tensors in NZ format (29) for higher inference speed
        layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, 29)
        layer.weight_scale.data = layer.weight_scale.data.flatten()
        layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
        layer.weight_offset.data = layer.weight_offset.data.flatten()


class AscendW8A8DynamicFusedMoEMethod:
    """FusedMoe method for Ascend W8A8_DYNAMIC.
    """

    def __init__(self):
        self.transpose_weight = True

        self.ep_group = get_ep_group()

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

        try:
            device_group = self.ep_group.device_group
            # TODO: Try local_rank = ep_group.rank_in_group
            local_rank = torch.distributed.get_rank(group=device_group)
            backend = device_group._get_backend(torch.device("npu"))
            self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
                local_rank)
        except AttributeError:
            self.moe_all_to_all_group_name = ""

    @staticmethod
    def get_weight(num_experts: int, intermediate_size_per_partition: int,
                   hidden_sizes: int,
                   params_dtype: torch.dtype) -> Dict[str, Any]:
        param_dict = {}
        param_dict["w13_weight"] = torch.empty(num_experts,
                                               2 *
                                               intermediate_size_per_partition,
                                               hidden_sizes,
                                               dtype=torch.int8)
        param_dict["w2_weight"] = torch.empty(num_experts,
                                              hidden_sizes,
                                              intermediate_size_per_partition,
                                              dtype=torch.int8)
        return param_dict

    @staticmethod
    def get_dynamic_quant_param(num_experts: int,
                                intermediate_size_per_partition: int,
                                hidden_sizes: int,
                                params_dtype: torch.dtype) -> Dict[str, Any]:
        param_dict = {}
        param_dict["w13_weight_scale"] = torch.empty(
            num_experts,
            2 * intermediate_size_per_partition,
            1,
            dtype=params_dtype)
        param_dict["w13_weight_offset"] = torch.empty(
            num_experts,
            2 * intermediate_size_per_partition,
            1,
            dtype=params_dtype)
        param_dict["w2_weight_scale"] = torch.empty(num_experts,
                                                    hidden_sizes,
                                                    1,
                                                    dtype=params_dtype)
        param_dict["w2_weight_offset"] = torch.empty(num_experts,
                                                     hidden_sizes,
                                                     1,
                                                     dtype=params_dtype)
        return param_dict

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        is_prefill: bool = True,
        enable_force_load_balance: bool = True,
        log2phy: torch.Tensor = None,
        global_redundant_expert_num: int = 0,
        shared_experts: Optional[Any] = None,
        **kwargs,
    ) -> torch.Tensor:
        assert router_logits.shape[
            1] == global_num_experts, "Number of global experts mismatch"

        # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
        if global_num_experts == 256:
            topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
                router_logits,
                k=top_k,  # topk当前写8
                bias=e_score_correction_bias,
                k_group=topk_group,  # fix: 4
                group_count=num_expert_group,  # fix 8
                group_select_mode=1,  # 0: group中的最大; 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; 第三个输出是否输出
                # y2_flag=False, # old api; 第三个输出是否输出
                routed_scaling_factor=1,
                eps=float(1e-20))
        else:
            topk_weights, topk_ids = select_experts(
                hidden_states=x,
                router_logits=router_logits,
                top_k=top_k,
                use_grouped_topk=use_grouped_topk,
                renormalize=renormalize,
                topk_group=topk_group,
                num_expert_group=num_expert_group,
                custom_routing_function=custom_routing_function,
                scoring_func=scoring_func,
                e_score_correction_bias=e_score_correction_bias,
            )

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

        topk_weights = topk_weights.to(x.dtype)

        fused_moe_state = get_fused_moe_state(self.ep_group.world_size,
                                              is_prefill)
        if fused_moe_state == FusedMoEState.MC2:
            return fused_experts_with_mc2(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                top_k=top_k,
                expert_map=expert_map,
                moe_all_to_all_group_name=self.moe_all_to_all_group_name,
                log2phy=log2phy,
                global_redundant_expert_num=global_redundant_expert_num,
                shared_experts=shared_experts)
        elif fused_moe_state == FusedMoEState.AllGather:
            return fused_experts(hidden_states=x,
                                 w1=layer.w13_weight,
                                 w1_scale=layer.w13_weight_scale,
                                 w2=layer.w2_weight,
                                 w2_scale=layer.w2_weight_scale,
                                 topk_weights=topk_weights,
                                 topk_ids=topk_ids,
                                 top_k=top_k,
                                 expert_map=expert_map)
        else:
            # The current implementation of deepseek moe splits hidden_states
            # according to tp_size before they are feed into fused_moe module.
            # Therefore, all2all is needed no matter how dp/tp is set so as to
            # dispatch/combine tokens.
            return fused_experts_with_all2all(
                hidden_states=x,
                w1=layer.w13_weight,
                w1_scale=layer.w13_weight_scale,
                w2=layer.w2_weight,
                w2_scale=layer.w2_weight_scale,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                top_k=top_k,
                expert_map=expert_map,
                ep_group=self.ep_group,
                log2phy=log2phy,
                global_redundant_expert_num=global_redundant_expert_num,
            )

    def process_weights_after_loading(self, layer):
        if self.transpose_weight:
            layer.w13_weight.data = layer.w13_weight.data.transpose(
                1, 2).contiguous()
            layer.w2_weight.data = layer.w2_weight.data.transpose(
                1, 2).contiguous()
        layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
            layer.w13_weight_scale.data.shape[0], -1)
        layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(
            layer.w13_weight_offset.data.shape[0], -1)
        layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(
            layer.w2_weight_scale.data.shape[0], -1)
        layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(
            layer.w2_weight_offset.data.shape[0], -1)