mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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439 lines
16 KiB
C++
439 lines
16 KiB
C++
/*
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* Copyright (c) 2022-2024, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#pragma once
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#include "tensorrt_llm/kernels/trtllmGenKernels/batchedGemm/trtllmGen_bmm_export/trtllm/gen/DtypeDecl.h"
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#include <cuda.h>
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#include <cuda_runtime_api.h>
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#include <tensorrt_llm/common/cudaUtils.h>
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namespace moe::dev
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{
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace routing
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{
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace tg = batchedGemm::trtllm::gen;
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct Data
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{
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tg::Dtype mDtypeElt{tg::Dtype::Bfloat16};
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tg::Dtype mDtypeExpW{tg::Dtype::Bfloat16};
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bool mUsePdl{false};
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// note: at least one of the optional outputs below must be provided
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// note: if one of the indexes using "PermutedIdx" is provided,
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// then `mPtrExpertIdx` and `mPtrPermutedIdxSize` must be provided
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens, mTopK]
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int32_t* mPtrExpertIdx{nullptr};
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// optional: only used as an intermediate buffer when the number of tokens is large.
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// dim: [2*NumThreads] = [512]
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int32_t* mPtrExpertCounts{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [1]
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int32_t* mPtrPermutedIdxSize{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens * mTopK]
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int32_t* mPtrExpandedIdxToPermutedIdx{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens * mTopK + (mNumExperts << mPaddingLog2) - mNumExperts]
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int32_t* mPtrPermutedIdxToTokenIdx{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumLocalExperts * (2 ^ mLocalExpertsStrideLog2), mNumTokens]
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void* mPtrExpertWeightsFull{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens, mTopK]
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void* mPtrExpertWeights{nullptr};
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//
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// Grouped Gemm Launch Config Buffers
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//
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int32_t* mPtrCtaIdxXyToBatchIdx{nullptr};
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int32_t* mPtrCtaIdxXyToMnLimit{nullptr};
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int32_t* mPtrNumNonExitingCtas{nullptr};
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// mPtrPermutedIdxSize is ptrTotalNumPaddedTokens
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bool mAllToAllRouteAct{false};
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void const* mPtrRoutingWeights;
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void const* mPtrRoutingBias;
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void const* mPtrIn;
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float* mPtrScores;
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int32_t mNumTokens;
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int32_t mHiddenDim;
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int32_t mNumExperts;
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int32_t mNumExpertGroups;
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int32_t mNumLimitedGroups;
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int32_t mTopK;
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int32_t mPaddingLog2;
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int32_t mLocalExpertsStartIdx;
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int32_t mLocalExpertsStrideLog2;
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int32_t mNumLocalExperts;
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float mRouteScale;
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bool mUseRoutingSoftmax;
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int32_t* mPtrNumTokensPerExpert{nullptr};
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int32_t* mPtrPermutedIdxToExpandedIdx{nullptr};
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};
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template <typename Type_, typename TypeExpW_, bool UsePdl_>
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struct KernelParams
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{
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using Type = Type_;
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using TypeExpW = TypeExpW_;
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static constexpr bool UsePdl = UsePdl_;
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int32_t* mPtrExpertIdx;
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int32_t* mPtrExpertCounts;
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int32_t* mPtrPermutedIdxSize;
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int32_t* mPtrExpandedIdxToPermutedIdx;
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int32_t* mPtrPermutedIdxToTokenIdx;
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int32_t* mPtrPermutedIdxToExpandedIdx;
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int32_t* mPtrNumTokensPerExpert;
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int32_t* mPtrCtaIdxXyToBatchIdx;
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int32_t* mPtrCtaIdxXyToMnLimit;
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int32_t* mPtrNumNonExitingCtas;
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TypeExpW* mPtrExpertWeightsFull;
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TypeExpW* mPtrExpertWeights;
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TypeExpW const* mPtrRoutingWeights;
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TypeExpW const* mPtrRoutingBias;
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Type const* mPtrIn;
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float* mPtrScores;
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int32_t mHiddenDim;
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int32_t mNumExperts;
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int32_t mNumExpertGroups;
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int32_t mNumExpertsPerGroup;
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int32_t mNumLimitedGroups;
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int32_t mTopK;
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int32_t mPaddingLog2;
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int32_t mLocalExpertsStartIdx;
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int32_t mLocalExpertsStrideLog2;
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int32_t mNumLocalExperts;
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int32_t mNumTokens;
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float mRouteScale;
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bool mAllToAllRouteAct;
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static KernelParams setKernelParams(Data const& data)
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{
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KernelParams params;
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params.mPtrExpertIdx = data.mPtrExpertIdx;
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params.mPtrExpertCounts = data.mPtrExpertCounts;
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params.mPtrPermutedIdxSize = data.mPtrPermutedIdxSize;
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params.mPtrExpandedIdxToPermutedIdx = data.mPtrExpandedIdxToPermutedIdx;
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params.mPtrPermutedIdxToTokenIdx = data.mPtrPermutedIdxToTokenIdx;
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params.mPtrPermutedIdxToExpandedIdx = data.mPtrPermutedIdxToExpandedIdx;
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params.mPtrNumTokensPerExpert = data.mPtrNumTokensPerExpert;
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params.mPtrCtaIdxXyToBatchIdx = data.mPtrCtaIdxXyToBatchIdx;
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params.mPtrCtaIdxXyToMnLimit = data.mPtrCtaIdxXyToMnLimit;
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params.mPtrNumNonExitingCtas = data.mPtrNumNonExitingCtas;
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params.mPtrExpertWeightsFull = (TypeExpW*) data.mPtrExpertWeightsFull;
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params.mPtrExpertWeights = (TypeExpW*) data.mPtrExpertWeights;
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params.mPtrRoutingWeights = (TypeExpW*) data.mPtrRoutingWeights;
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params.mPtrRoutingBias = (TypeExpW*) data.mPtrRoutingBias;
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params.mPtrIn = (Type*) data.mPtrIn;
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params.mPtrScores = data.mPtrScores;
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params.mHiddenDim = data.mHiddenDim;
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params.mNumExperts = data.mNumExperts;
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params.mNumExpertGroups = data.mNumExpertGroups;
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params.mNumExpertsPerGroup = data.mNumExperts / data.mNumExpertGroups;
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params.mNumLimitedGroups = data.mNumLimitedGroups;
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params.mTopK = data.mTopK;
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params.mPaddingLog2 = data.mPaddingLog2;
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params.mLocalExpertsStartIdx = data.mLocalExpertsStartIdx;
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params.mLocalExpertsStrideLog2 = data.mLocalExpertsStrideLog2;
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params.mNumLocalExperts = data.mNumLocalExperts;
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params.mNumTokens = data.mNumTokens;
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params.mRouteScale = data.mRouteScale;
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params.mAllToAllRouteAct = data.mAllToAllRouteAct;
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return params;
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}
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};
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void run(Data const& data, void* stream);
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} // namespace routing
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace routingLlama4
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{
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace tg = batchedGemm::trtllm::gen;
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename TypeExpW>
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struct PackedScoreIdx
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{
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TypeExpW score;
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int16_t idx;
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct Data
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{
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tg::Dtype mDtypeExpW{tg::Dtype::Bfloat16};
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bool mUsePdl{false};
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// optional: if `nullptr`, `mPtrExpertIdx` must be provided.
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// If it is given, it represents the scores without sigmoid activation for
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// each token and expert.
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// note: if it is provided, we always re-compute the top1 scores
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// dim: [mNumTokens, mNumExperts]
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void const* mPtrScores{nullptr};
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// optional: if `nullptr`, scores are used directly as input.
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// If it is given, it must represent a packed value s.t. the most significant
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// 16/32 bits represent the score without sigmoid activation and
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// the least significant 16 bits represent the index of the chosen expert (unsigned).
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// note: this is required if the number of tokens is large.
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// dim: [mNumTokens, mTopK]
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void* mPtrExpertIdx{nullptr};
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// note: at least one of the optional outputs below must be provided
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// optional: only used as an intermediate buffer when the number of tokens is large.
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// dim: [2, mNumExperts]
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int32_t* mPtrExpertCounts{nullptr};
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// dim: [1]
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int32_t* mPtrPermutedIdxSize{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens * mTopK]
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int32_t* mPtrExpandedIdxToPermutedIdx{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens * mTopK + (mNumExperts << mPaddingLog2) - mNumExperts]
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int32_t* mPtrPermutedIdxToTokenIdx{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens, mTopK]
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void* mPtrExpertWeights{nullptr};
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//
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// Grouped Gemm Launch Config Buffers
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//
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int32_t* mPtrCtaIdxXyToBatchIdx{nullptr};
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int32_t* mPtrCtaIdxXyToMnLimit{nullptr};
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int32_t* mPtrNumNonExitingCtas{nullptr};
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int32_t mNumTokens;
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int32_t mNumExperts;
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int32_t mTopK;
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int32_t mPaddingLog2;
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int32_t mLocalExpertsStartIdx;
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int32_t mLocalExpertsStrideLog2;
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int32_t mNumLocalExperts;
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};
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template <typename Type_, typename TypeExpW_, bool UsePdl_>
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struct KernelParams
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{
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using Type = Type_;
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using TypeExpW = TypeExpW_;
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static constexpr bool UsePdl = UsePdl_;
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PackedScoreIdx<TypeExpW>* mPtrExpertIdx;
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TypeExpW const* mPtrScores;
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int32_t* mPtrExpertCounts;
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int32_t* mPtrPermutedIdxSize;
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int32_t* mPtrExpandedIdxToPermutedIdx;
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int32_t* mPtrPermutedIdxToTokenIdx;
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int32_t* mPtrCtaIdxXyToBatchIdx;
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int32_t* mPtrCtaIdxXyToMnLimit;
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int32_t* mPtrNumNonExitingCtas;
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TypeExpW* mPtrExpertWeights;
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int32_t mNumTokens;
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int32_t mNumExperts;
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int32_t mPaddingLog2;
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int32_t mLocalExpertsStartIdx;
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int32_t mLocalExpertsStrideLog2;
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int32_t mNumLocalExperts;
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static KernelParams setKernelParams(Data const& data)
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{
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KernelParams params;
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params.mPtrExpertIdx = (PackedScoreIdx<TypeExpW>*) data.mPtrExpertIdx;
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params.mPtrScores = (TypeExpW const*) data.mPtrScores;
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params.mPtrExpertCounts = data.mPtrExpertCounts;
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params.mPtrPermutedIdxSize = data.mPtrPermutedIdxSize;
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params.mPtrExpandedIdxToPermutedIdx = data.mPtrExpandedIdxToPermutedIdx;
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params.mPtrPermutedIdxToTokenIdx = data.mPtrPermutedIdxToTokenIdx;
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params.mPtrCtaIdxXyToBatchIdx = data.mPtrCtaIdxXyToBatchIdx;
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params.mPtrCtaIdxXyToMnLimit = data.mPtrCtaIdxXyToMnLimit;
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params.mPtrNumNonExitingCtas = data.mPtrNumNonExitingCtas;
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params.mPtrExpertWeights = (TypeExpW*) data.mPtrExpertWeights;
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params.mNumTokens = data.mNumTokens;
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params.mNumExperts = data.mNumExperts;
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params.mPaddingLog2 = data.mPaddingLog2;
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params.mLocalExpertsStartIdx = data.mLocalExpertsStartIdx;
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params.mLocalExpertsStrideLog2 = data.mLocalExpertsStrideLog2;
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params.mNumLocalExperts = data.mNumLocalExperts;
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return params;
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}
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};
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void run(Data const& data, void* stream);
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} // namespace routingLlama4
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace routingQwen3
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{
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace tg = batchedGemm::trtllm::gen;
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename TypeExpW>
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struct PackedScoreIdx
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{
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TypeExpW score;
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int16_t idx; // @TODO: Might use int8_t as the number of experts is 128
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct Data
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{
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tg::Dtype mDtypeExpW{tg::Dtype::Fp32};
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tg::Dtype mDtypeElt{tg::Dtype::Bfloat16};
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bool mUsePdl{false};
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bool mDoSoftmaxBeforeTopK{false};
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bool mNormTopkProb{true}; // Default value is true for Qwen3 model
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// optional: if `nullptr`, `mPtrExpertIdx` must be provided.
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// If it is given, it represents the scores without sigmoid activation for
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// each token and expert.
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// note: if it is provided, we always re-compute the top1 scores
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// dim: [mNumTokens, mNumExperts]
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void const* mPtrScores{nullptr};
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// optional: if `nullptr`, scores are used directly as input.
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// If it is given, it must represent a packed value s.t. the most significant
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// 16/32 bits represent the score without sigmoid activation and
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// the least significant 16 bits represent the index of the chosen expert (unsigned).
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// note: this is required if the number of tokens is large.
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// dim: [mNumTokens, mTopK]
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void* mPtrExpertIdx{nullptr};
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// note: at least one of the optional outputs below must be provided
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// optional: only used as an intermediate buffer when the number of tokens is large.
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// dim: [2, mNumExperts]
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int32_t* mPtrExpertCounts{nullptr};
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// dim: [1]
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int32_t* mPtrPermutedIdxSize{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens * mTopK]
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int32_t* mPtrExpandedIdxToPermutedIdx{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens * mTopK + (mNumExperts << mPaddingLog2) - mNumExperts]
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int32_t* mPtrPermutedIdxToTokenIdx{nullptr};
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// optional: if `nullptr`, it is not filled
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// dim: [mNumTokens, mTopK]
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void* mPtrExpertWeights{nullptr};
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//
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// Grouped Gemm Launch Config Buffers
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//
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int32_t* mPtrCtaIdxXyToBatchIdx{nullptr};
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int32_t* mPtrCtaIdxXyToMnLimit{nullptr};
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int32_t* mPtrNumNonExitingCtas{nullptr};
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int32_t mNumTokens;
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int32_t mNumExperts;
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int32_t mTopK;
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int32_t mPaddingLog2;
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int32_t mLocalExpertsStartIdx;
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int32_t mLocalExpertsStrideLog2;
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int32_t mNumLocalExperts;
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};
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template <typename Type_, typename TypeExpW_, bool UsePdl_>
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struct KernelParams
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{
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using Type = Type_;
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using TypeExpW = TypeExpW_;
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static constexpr bool UsePdl = UsePdl_;
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bool mNormTopkProb = true;
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PackedScoreIdx<TypeExpW>* mPtrExpertIdx;
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TypeExpW const* mPtrScores;
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int32_t* mPtrExpertCounts;
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int32_t* mPtrPermutedIdxSize;
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int32_t* mPtrExpandedIdxToPermutedIdx;
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int32_t* mPtrPermutedIdxToTokenIdx;
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int32_t* mPtrCtaIdxXyToBatchIdx;
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int32_t* mPtrCtaIdxXyToMnLimit;
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int32_t* mPtrNumNonExitingCtas;
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TypeExpW* mPtrExpertWeights;
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int32_t mNumTokens;
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int32_t mNumExperts;
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int32_t mPaddingLog2;
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int32_t mLocalExpertsStartIdx;
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int32_t mLocalExpertsStrideLog2;
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int32_t mNumLocalExperts;
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static KernelParams setKernelParams(Data const& data)
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{
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KernelParams params;
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params.mNormTopkProb = data.mNormTopkProb;
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params.mPtrExpertIdx = (PackedScoreIdx<TypeExpW>*) data.mPtrExpertIdx;
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params.mPtrScores = (TypeExpW const*) data.mPtrScores;
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params.mPtrExpertCounts = data.mPtrExpertCounts;
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params.mPtrPermutedIdxSize = data.mPtrPermutedIdxSize;
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params.mPtrExpandedIdxToPermutedIdx = data.mPtrExpandedIdxToPermutedIdx;
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params.mPtrPermutedIdxToTokenIdx = data.mPtrPermutedIdxToTokenIdx;
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params.mPtrCtaIdxXyToBatchIdx = data.mPtrCtaIdxXyToBatchIdx;
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params.mPtrCtaIdxXyToMnLimit = data.mPtrCtaIdxXyToMnLimit;
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params.mPtrNumNonExitingCtas = data.mPtrNumNonExitingCtas;
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params.mPtrExpertWeights = (TypeExpW*) data.mPtrExpertWeights;
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params.mNumTokens = data.mNumTokens;
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params.mNumExperts = data.mNumExperts;
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params.mPaddingLog2 = data.mPaddingLog2;
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params.mLocalExpertsStartIdx = data.mLocalExpertsStartIdx;
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params.mLocalExpertsStrideLog2 = data.mLocalExpertsStrideLog2;
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params.mNumLocalExperts = data.mNumLocalExperts;
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return params;
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}
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};
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void run(Data const& data, void* stream);
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} // namespace routingQwen3
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace moe::dev
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