大改

vllm_npu/ops/moe/moe_comm_method.py

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+# 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 __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional
+
+import torch
+from vllm.config import get_current_vllm_config
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe import FusedMoEConfig
+
+from vllm_npu.ascend_forward_context import MoECommType
+from vllm_npu.ops.moe.fused_moe_prepare_and_finalize import (
+    FusedMoEPrepareAndFinalizeWithAll2All,
+    FusedMoEPrepareAndFinalizeWithAllGather, FusedMoEPrepareAndFinalizeWithMC2,
+    FusedMoEPrepareAndFinalizeWithNaiveMulticast)
+from vllm_npu.ops.moe.moe_mlp import unified_apply_mlp
+from vllm_npu.ops.moe.token_dispatcher import (TokenDispatcherWithAll2AllV,
+                                                  TokenDispatcherWithAllGather,
+                                                  TokenDispatcherWithMC2,
+                                                  TokenDispatcherWithMoge)
+
+_MoECommMethods: Dict[Optional[MoECommType], MoECommMethod] = {}
+
+
+def get_moe_comm_method(
+        moe_comm_type: Optional[MoECommType]) -> Optional[MoECommMethod]:
+    return _MoECommMethods.get(moe_comm_type, None)
+
+
+def setup_moe_comm_method(moe_config):
+    _MoECommMethods[MoECommType.ALLTOALL] = AlltoAllCommImpl(moe_config)
+    _MoECommMethods[MoECommType.ALLGATHER] = AllGatherCommImpl(moe_config)
+    _MoECommMethods[MoECommType.MC2] = MC2CommImpl(moe_config)
+    _MoECommMethods[MoECommType.NAIVE_MULTICAST] = NaiveMulticastCommImpl(
+        moe_config)
+
+
+class MoECommMethod(ABC):
+    """Base class for MoE communication methods."""
+
+    def __init__(self, moe_config: FusedMoEConfig):
+        self.model_type = get_current_vllm_config(
+        ).model_config.hf_config.model_type
+        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,
+            replace_allreduce: bool = False
+    ) -> 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,
+            replace_allreduce)
+        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,
+            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,
+            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,
+            dynamic_eplb: bool = False):
+        # Check constraints
+        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,
+            expert_map=expert_map,
+            log2phy=log2phy,
+            global_redundant_expert_num=global_redundant_expert_num,
+            shared_experts=shared_experts,
+            quantized_x_for_share=quantized_x_for_share,
+            dynamic_scale_for_share=dynamic_scale_for_share,
+            mc2_mask=self.mc2_mask,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            with_quant=use_int8_w8a8 or use_int4_w4a8,
+            dynamic_eplb=dynamic_eplb)
+
+        permuted_hidden_states, expert_tokens, dynamic_scale, group_list_type, topk_scales = \
+            results["hidden_states"], results["group_list"], results.get("dynamic_scale"), results["group_list_type"], results.get("topk_scales")
+
+        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,
+                                       topk_scales=topk_scales,
+                                       with_quant=use_int8_w8a8
+                                       or use_int4_w4a8,
+                                       fusion=use_int8_w8a8,
+                                       need_trans=need_trans,
+                                       dynamic_eplb=dynamic_eplb)
+
+        final_hidden_states = self.token_dispatcher.token_combine(
+            hidden_states=mlp_output)
+
+        if dynamic_eplb:
+            return (final_hidden_states, group_list_type, expert_tokens)
+
+        return final_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):
+        if self.model_type == "PanguProMoE":
+            return TokenDispatcherWithMoge(
+                top_k=self.moe_config.experts_per_token,
+                num_experts=self.moe_config.num_experts,
+                num_local_experts=self.moe_config.num_local_experts)
+        else:
+            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 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)
+
+
+class NaiveMulticastCommImpl(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 FusedMoEPrepareAndFinalizeWithNaiveMulticast(self.moe_config)