xc-llm-ascend/vllm_ascend/ops/common_fused_moe.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 Callable, Optional

import torch
import torch_npu
from vllm.config import CompilationLevel, get_current_vllm_config
from vllm.distributed import get_dp_group, get_ep_group, get_tp_group
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe.config import \
    FusedMoEParallelConfig  # isort: skip
from vllm.model_executor.layers.fused_moe.layer import (
    FusedMoE, UnquantizedFusedMoEMethod)

from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.ops.moe.moe_comm_method import (AllGatherCommImpl,
                                                 AlltoAllCommImpl, MC2CommImpl)
from vllm_ascend.ops.moe.token_dispatcher import setup_token_dispatchers
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_310p, vllm_version_is

original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__


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

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

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

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

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

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

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

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

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

    return final_hidden_states


def unquantized_fused_moe_init_func(self, *args, **kwargs):
    original_unquantized_fused_moe_init_func(self, *args, **kwargs)

    # NOTE: Currently, this self.use_aclgraph is only used in
    # UnquantizedFusedMoEMethod.forward_oot to decide whether to use in
    # ops/fused_moe.py:568 to circumvent torch.randint_like not supported issue.
    # Once torch.randint_like is supported or removed, this flag can be removed.
    vllm_config = get_current_vllm_config()
    ascend_config = get_ascend_config()
    if ascend_config.torchair_graph_config.enabled:
        self.use_aclgraph = False
    else:
        self.use_aclgraph = (vllm_config.compilation_config.level
                             == CompilationLevel.PIECEWISE
                             and not vllm_config.model_config.enforce_eager)


def forward_oot_v01011(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        use_grouped_topk: bool,
        top_k: int,
        router_logits: torch.Tensor,
        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: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:

    topk_weights, topk_ids, row_idx = 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,
        routed_scaling_factor=1.0,
        e_score_correction_bias=e_score_correction_bias,
        global_num_experts=global_num_experts)

    if topk_ids.shape[1] < top_k or is_310p():
        assert global_num_experts is not None
        return fused_experts_moge(
            hidden_states=x,
            w1=layer.w13_weight,
            w2=layer.w2_weight,
            moe_parallel_config=self.moe.moe_parallel_config,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            top_k=top_k,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            apply_router_weight_on_input=apply_router_weight_on_input)

    moe_comm_method = get_forward_context().moe_comm_method
    return moe_comm_method.fused_experts(hidden_states=x,
                                         w1=layer.w13_weight,
                                         w2=layer.w2_weight,
                                         topk_weights=topk_weights,
                                         topk_ids=topk_ids,
                                         row_idx=row_idx,
                                         global_num_experts=global_num_experts,
                                         expert_map=expert_map)


def forward_oot(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        use_grouped_topk: bool,
        top_k: int,
        router_logits: torch.Tensor,
        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: float = 1.0,
        e_score_correction_bias: Optional[torch.Tensor] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:

    topk_weights, topk_ids, row_idx = 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,
        routed_scaling_factor=routed_scaling_factor,
        e_score_correction_bias=e_score_correction_bias,
        global_num_experts=global_num_experts)

    if topk_ids.shape[1] < top_k or is_310p():
        assert global_num_experts is not None
        return fused_experts_moge(
            hidden_states=x,
            w1=layer.w13_weight,
            w2=layer.w2_weight,
            moe_parallel_config=self.moe.moe_parallel_config,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            top_k=top_k,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            apply_router_weight_on_input=apply_router_weight_on_input)

    moe_comm_method = get_forward_context().moe_comm_method
    return moe_comm_method.fused_experts(hidden_states=x,
                                         w1=layer.w13_weight,
                                         w2=layer.w2_weight,
                                         topk_weights=topk_weights,
                                         topk_ids=topk_ids,
                                         row_idx=row_idx,
                                         global_num_experts=global_num_experts,
                                         expert_map=expert_map)


def process_weights_after_loading(self, layer):
    super(UnquantizedFusedMoEMethod, self).process_weights_after_loading(layer)
    w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose(
        1, 2).contiguous()
    layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False)

    w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose(
        1, 2).contiguous()
    layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)

    if not is_310p():
        layer.w13_weight.data = torch_npu.npu_format_cast(
            layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
        layer.w2_weight.data = torch_npu.npu_format_cast(
            layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)


class AscendFusedMoE(FusedMoE):

    def __init__(
        self,
        num_experts,
        top_k,
        hidden_size,
        intermediate_size,
        params_dtype=None,
        reduce_results=False,
        renormalize=True,
        use_grouped_topk=False,
        num_expert_group=None,
        topk_group=None,
        quant_config=None,
        tp_size=None,
        ep_size=None,
        dp_size=None,
        prefix="",
        custom_routing_function=None,
        scoring_func="softmax",
        routed_scaling_fator: float = 1.0,
        e_score_correction_bias=None,
        apply_router_weight_on_input=False,
        activation="silu",
        enable_eplb=False,
        num_redundant_experts=0,
        has_bias=False,
    ):
        if vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1"):
            super().__init__(
                num_experts,
                top_k,
                hidden_size,
                intermediate_size,
                params_dtype,
                reduce_results,
                renormalize,
                use_grouped_topk,
                num_expert_group,
                topk_group,
                quant_config,
                tp_size,
                ep_size,
                dp_size,
                prefix,
                custom_routing_function,
                scoring_func,
                e_score_correction_bias,
                apply_router_weight_on_input,
                activation,
                enable_eplb,
                num_redundant_experts,
                has_bias,
            )
        else:
            super().__init__(
                num_experts,
                top_k,
                hidden_size,
                intermediate_size,
                params_dtype,
                reduce_results,
                renormalize,
                use_grouped_topk,
                num_expert_group,
                topk_group,
                quant_config,
                tp_size,
                ep_size,
                dp_size,
                prefix,
                custom_routing_function,
                scoring_func,
                routed_scaling_fator,
                e_score_correction_bias,
                apply_router_weight_on_input,
                activation,
                enable_eplb,
                num_redundant_experts,
                has_bias,
            )

        setup_token_dispatchers(self.moe_config.ep_size,
                                top_k=self.top_k,
                                num_experts=self.global_num_experts,
                                num_local_experts=self.local_num_experts)

        self.moe_config.tp_group = get_tp_group()
        self.moe_config.dp_group = get_dp_group()
        self.moe_config.ep_group = get_ep_group()
        self.moe_config.mc2_group = get_mc2_group()

        for method in {AllGatherCommImpl, AlltoAllCommImpl, MC2CommImpl}:
            setattr(
                self, method.__name__.lower(),
                method(moe_config=self.moe_config))  # type: ignore[abstract]

    def forward_impl(self, hidden_states: torch.Tensor,
                     router_logits: torch.Tensor):
        assert self.quant_method is not None

        forward_context = get_forward_context()
        moe_comm_method_name = forward_context.moe_comm_method_name

        forward_context.moe_comm_method = getattr(self, moe_comm_method_name)

        hidden_states, router_logits = forward_context.moe_comm_method.prepare(
            hidden_states=hidden_states, router_logits=router_logits)

        # Matrix multiply.
        final_hidden_states = self.quant_method.apply(
            layer=self,
            x=hidden_states,
            router_logits=router_logits,
            top_k=self.top_k,
            renormalize=self.renormalize,
            use_grouped_topk=self.use_grouped_topk,
            global_num_experts=self.global_num_experts,
            expert_map=self.expert_map,
            topk_group=self.topk_group,
            num_expert_group=self.num_expert_group,
            custom_routing_function=self.custom_routing_function,
            scoring_func=self.scoring_func,
            e_score_correction_bias=self.e_score_correction_bias,
            activation=self.activation,
            apply_router_weight_on_input=self.apply_router_weight_on_input,
            enable_eplb=self.enable_eplb,
            expert_load_view=self.expert_load_view,
            logical_to_physical_map=self.logical_to_physical_map,
            logical_replica_count=self.logical_replica_count,
        )

        final_hidden_states = forward_context.moe_comm_method.finalize(
            hidden_states=final_hidden_states,
            reduce_results=self.reduce_results)

        return final_hidden_states


UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
UnquantizedFusedMoEMethod.process_weights_after_loading = process_weights_after_loading

if vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1"):
    UnquantizedFusedMoEMethod.forward_oot = forward_oot_v01011
else:
    UnquantizedFusedMoEMethod.forward_oot = forward_oot