289 lines
9.0 KiB
C++
289 lines
9.0 KiB
C++
/*
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* Copyright (c) 2022-2025, 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 <cuda.h>
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#include <cuda_runtime_api.h>
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#include "IntFastDiv.h"
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#include "flashinfer/trtllm/batched_gemm/trtllmGen_bmm_export/trtllm/gen/DtypeDecl.h"
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#include "flashinfer/trtllm/common/cudaUtils.h"
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namespace moe::dev {
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namespace routing {
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namespace tg = batchedGemm::trtllm::gen;
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template <typename DataType>
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struct PackedScoreIdx {
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DataType score;
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int16_t idx;
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct DataBase {
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bool mUsePdl{false};
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// optional: only used as an intermediate buffer when the number of tokens is large.
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// dim: max([2*NumThreads] = [512], mNumExperts*2)
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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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// Note: this array (mPtrPermutedIdxToTokenIdx) is uninitialized
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// Any out-of-bounds values are undefined.
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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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// 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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// 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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//
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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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//
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// Metadata
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//
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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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/// For expert parallelization
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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 InputT_, typename OutputT_, bool UsePdl_>
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struct KernelParamsBase {
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using InputT = InputT_;
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using OutputT = OutputT_;
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static constexpr bool UsePdl = UsePdl_;
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// Public pointer members
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int32_t* mPtrExpertCounts = nullptr;
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int32_t* mPtrPermutedIdxSize = nullptr;
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int32_t* mPtrExpandedIdxToPermutedIdx = nullptr;
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int32_t* mPtrPermutedIdxToTokenIdx = nullptr;
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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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OutputT* mPtrExpertWeights = nullptr;
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InputT const* mPtrScores = nullptr;
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// Public scalar members
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int32_t mNumTokens = 0;
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int32_t mNumExperts = 0;
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int32_t mPaddingLog2 = 0;
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int32_t mLocalExpertsStartIdx = 0;
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int32_t mLocalExpertsStrideLog2 = 0;
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int32_t mNumLocalExperts = 0;
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// Public initialization function - make it a template to accept different Data types
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template <typename DataType>
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void setBaseParams(DataType const& data) {
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mPtrExpertCounts = data.mPtrExpertCounts;
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mPtrPermutedIdxSize = data.mPtrPermutedIdxSize;
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mPtrExpandedIdxToPermutedIdx = data.mPtrExpandedIdxToPermutedIdx;
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mPtrPermutedIdxToTokenIdx = data.mPtrPermutedIdxToTokenIdx;
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mPtrCtaIdxXyToBatchIdx = data.mPtrCtaIdxXyToBatchIdx;
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mPtrCtaIdxXyToMnLimit = data.mPtrCtaIdxXyToMnLimit;
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mPtrNumNonExitingCtas = data.mPtrNumNonExitingCtas;
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mPtrExpertWeights = static_cast<OutputT*>(data.mPtrExpertWeights);
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mPtrScores = (InputT const*)data.mPtrScores;
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mNumTokens = data.mNumTokens;
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mNumExperts = data.mNumExperts;
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mPaddingLog2 = data.mPaddingLog2;
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mLocalExpertsStartIdx = data.mLocalExpertsStartIdx;
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mLocalExpertsStrideLog2 = data.mLocalExpertsStrideLog2;
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mNumLocalExperts = data.mNumLocalExperts;
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}
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};
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namespace routingDeepSeek {
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct Data : public DataBase {
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tg::Dtype mDtypeExpW{tg::Dtype::Bfloat16};
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//
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// Grouped Gemm Launch Config Buffers
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//
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void const* mPtrRoutingBias;
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int32_t mHiddenDim; // not used
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int32_t mNumExpertGroups;
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int32_t mNumLimitedGroups;
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float mRouteScale;
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bool mUseRoutingSoftmax;
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};
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template <typename InputT_, typename OutputT_, bool UseGroups_, bool UsePdl_>
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struct KernelParams : public KernelParamsBase<InputT_, OutputT_, UsePdl_> {
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using InputT = InputT_;
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using OutputT = OutputT_;
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static constexpr bool UseGroups = UseGroups_;
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PackedScoreIdx<OutputT>* mPtrExpertIdx = nullptr;
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// OutputT* mPtrExpertWeightsFull = nullptr;
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// Note: this variable(mPtrExpertWeightsFull) might need to be added back for the low-latency
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// kernels for MoE in tllm-gen in the future
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OutputT const* mPtrRoutingBias = nullptr;
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int32_t mNumExpertGroups = 0;
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int32_t mNumExpertsPerGroup = 0;
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int32_t mNumLimitedGroups = 0;
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trtllm::dev::IntFastDiv mTopK;
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float mRouteScale = 0.f;
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static KernelParams setKernelParams(Data const& data) {
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KernelParams params;
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params.setBaseParams(data);
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params.mPtrExpertIdx = (PackedScoreIdx<OutputT>*)data.mPtrExpertIdx;
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// params.mPtrExpertWeightsFull = static_cast<OutputT*>(data.mPtrExpertWeightsFull);
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params.mPtrRoutingBias = static_cast<OutputT const*>(data.mPtrRoutingBias);
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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 = trtllm::dev::IntFastDiv(data.mTopK);
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params.mRouteScale = data.mRouteScale;
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return params;
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}
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};
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void run(Data& data, void* stream);
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} // namespace routingDeepSeek
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////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace routingLlama4 {
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct Data : public DataBase {
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tg::Dtype mDtypeExpW{tg::Dtype::Bfloat16};
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};
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template <typename InputT_, typename OutputT_, bool UsePdl_>
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struct KernelParams : public KernelParamsBase<InputT_, OutputT_, UsePdl_> {
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using InputT = InputT_;
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using OutputT = OutputT_;
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PackedScoreIdx<OutputT>* mPtrExpertIdx = nullptr;
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int32_t mTopK;
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static KernelParams setKernelParams(Data const& data) {
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KernelParams params;
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params.setBaseParams(data);
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params.mPtrExpertIdx = (PackedScoreIdx<OutputT>*)data.mPtrExpertIdx;
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params.mTopK = data.mTopK;
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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 routingRenormalize {
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct Data : public DataBase {
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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 mDoSoftmaxBeforeTopK{false};
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bool mNormTopkProb{true}; // Default value is true for Qwen3 model
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bool mApplySoftmaxAfterTopK{false};
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};
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template <typename InputT_, typename OutputT_, bool DoSoftmaxBeforeTopK_, bool UsePdl_>
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struct KernelParams : public KernelParamsBase<InputT_, OutputT_, UsePdl_> {
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using InputT = InputT_;
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using OutputT = OutputT_;
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static constexpr bool DoSoftmaxBeforeTopK = DoSoftmaxBeforeTopK_;
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PackedScoreIdx<OutputT>* mPtrExpertIdx = nullptr;
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int32_t mTopK = 0;
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bool mNormTopkProb = true;
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bool mApplySoftmaxAfterTopK = false;
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static KernelParams setKernelParams(Data const& data) {
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KernelParams params;
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params.setBaseParams(data);
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params.mPtrExpertIdx = (PackedScoreIdx<OutputT>*)data.mPtrExpertIdx;
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params.mNormTopkProb = data.mNormTopkProb;
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params.mApplySoftmaxAfterTopK = data.mApplySoftmaxAfterTopK;
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params.mTopK = data.mTopK;
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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 routingRenormalize
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace routing
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} // namespace moe::dev
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