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https://github.com/NVIDIA/TensorRT-LLM.git
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2a115dae84
Co-authored-by: DreamGenX <x@dreamgen.com> Co-authored-by: Ace-RR <78812427+Ace-RR@users.noreply.github.com> Co-authored-by: bprus <39293131+bprus@users.noreply.github.com> Co-authored-by: janpetrov <janpetrov@icloud.com>
581 lines
22 KiB
Plaintext
581 lines
22 KiB
Plaintext
/*
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* Copyright (c) 2020-2024, 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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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/common/cudaTypeUtils.cuh"
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#include "tensorrt_llm/common/cudaUtils.h"
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#include "tensorrt_llm/common/memoryUtils.h"
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#include "tensorrt_llm/common/reduceKernelUtils.cuh"
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#include "tensorrt_llm/kernels/decodingKernels.h"
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#ifndef CUDART_VERSION
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#error CUDART_VERSION Undefined!
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#elif (CUDART_VERSION >= 11050)
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#include <cub/cub.cuh>
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#else
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#include "3rdparty/cub/cub.cuh"
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#endif
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using namespace tensorrt_llm::common;
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using namespace tensorrt_llm::runtime;
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namespace tensorrt_llm
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{
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namespace kernels
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{
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__global__ void gatherTree(gatherTreeParam param)
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{
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for (int batchbeamIdx = blockIdx.x * blockDim.x + threadIdx.x; batchbeamIdx < param.batchSize * param.beamWidth;
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batchbeamIdx += gridDim.x * blockDim.x)
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{
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int const batch = batchbeamIdx / param.beamWidth;
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int const beam = batchbeamIdx % param.beamWidth;
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int const inputLen = param.inputLengths == nullptr ? 0 : param.inputLengths[batchbeamIdx];
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int const* parentIds = param.parentIds;
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int const* stepIds = param.stepIds;
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// TODO optimize the reduce_max operation for large beamWidth
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int maxLen = -1;
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bool updateResponseInputLength = param.responseInputLengths != nullptr;
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// int selected_beam_index = 0;
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for (int beamIdx = 0; beamIdx < param.beamWidth; beamIdx++)
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{
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int tmpLen
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= param.sequenceLengths[batch * param.beamWidth + beamIdx] + param.maxSequenceLengthFinalStep - 1;
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param.sequenceLengths[batch * param.beamWidth + beamIdx] = tmpLen;
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if (updateResponseInputLength)
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{
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param.responseInputLengths[batch * param.beamWidth + beamIdx] = inputLen;
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}
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if (tmpLen > maxLen)
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{
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maxLen = tmpLen;
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}
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}
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int const maxSeqLenB = min(param.maxSeqLen, maxLen);
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if (maxSeqLenB <= 0)
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{
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continue;
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}
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int const initialTgtIx = batch * param.beamWidth * param.maxSeqLen + beam * param.maxSeqLen + maxSeqLenB - 1;
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int const initialParentIx = batch * param.beamWidth * param.maxSeqLen + beam * param.maxSeqLen + maxSeqLenB - 1;
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param.outputIds[initialTgtIx] = __ldg(stepIds + initialParentIx);
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int parent = parentIds == nullptr ? 0 : __ldg(parentIds + initialParentIx) % param.beamWidth;
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bool foundBad = false;
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for (int level = maxSeqLenB - 2; level >= 0; --level)
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{
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int const levelBeamIx = batch * param.beamWidth * param.maxSeqLen + beam * param.maxSeqLen + level;
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int const levelParentIx = batch * param.beamWidth * param.maxSeqLen + parent * param.maxSeqLen + level;
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if (parent < 0 || parent > param.beamWidth)
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{
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param.outputIds[levelBeamIx] = param.endTokens[batch];
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parent = -1;
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foundBad = true;
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}
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else
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{
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param.outputIds[levelBeamIx] = __ldg(stepIds + levelParentIx);
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parent = parentIds == nullptr ? 0 : __ldg(parentIds + levelParentIx) % param.beamWidth;
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}
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}
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// set the padded part as end_token
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// inputLen
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for (int index = maxLen; index < param.maxSeqLen; ++index)
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{
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param.outputIds[batch * param.beamWidth * param.maxSeqLen + beam * param.maxSeqLen + index]
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= param.endTokens[batch];
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}
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// Not necessary when using a BeamSearchDecoder, but necessary
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// when a user feeds in possibly broken trajectory (i.e., non-eos
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// entries in a beam following eos entries).
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if (!foundBad)
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{
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bool finished = false;
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// skip the step 0 because it is often the start token
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int startStep = 1;
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for (int time = startStep; time < maxSeqLenB; ++time)
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{
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int const levelBeamIx = batch * param.beamWidth * param.maxSeqLen + beam * param.maxSeqLen + time;
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if (finished)
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{
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param.outputIds[levelBeamIx] = param.endTokens[batch];
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}
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else if (param.outputIds[levelBeamIx] == param.endTokens[batch])
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{
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finished = true;
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}
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}
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}
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}
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}
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struct RankNorm
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{
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int rank;
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float norm;
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};
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inline __device__ RankNorm swap(RankNorm const& rankNorm, int mask, int dir)
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{
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// Exchange the rank and norm inside the warp.
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RankNorm other;
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other.rank = __shfl_xor_sync(unsigned(-1), rankNorm.rank, mask);
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other.norm = __shfl_xor_sync(unsigned(-1), rankNorm.norm, mask);
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// Update the sorted values.
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bool doSwap = (rankNorm.norm != other.norm) && ((rankNorm.norm > other.norm) == dir);
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RankNorm res;
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res.rank = doSwap ? other.rank : rankNorm.rank;
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res.norm = doSwap ? other.norm : rankNorm.norm;
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return res;
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}
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inline __device__ uint32_t bfe(uint32_t a, uint32_t start, uint32_t len = 1)
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{
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uint32_t d;
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asm volatile("bfe.u32 %0, %1, %2, %3;" : "=r"(d) : "r"(a), "r"(start), "r"(len));
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return d;
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}
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__global__ void finalized(gatherTreeParam param)
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{
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int const beamIdx = static_cast<int>(threadIdx.x);
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int const beamWidth{param.beamWidth};
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extern __shared__ char array[];
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int* sRank = (int*) (array);
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int* sLength = (int*) (sRank + beamWidth);
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float* sScores = (float*) (sLength + beamWidth);
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float* sNormedScores = (float*) (sScores + beamWidth);
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int* sIds = (int*) (sNormedScores + beamWidth);
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if (beamIdx < beamWidth)
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{
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int const idx = blockIdx.x * param.beamWidth + beamIdx;
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int const numGeneratedToken{param.sequenceLengths[idx] - param.inputLengths[idx]};
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sNormedScores[beamIdx] = applyLengthPenalty(param.cumLogProbs[idx], numGeneratedToken, param.lengthPenalty);
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sLength[beamIdx] = param.sequenceLengths[idx];
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sScores[beamIdx] = param.cumLogProbs[idx];
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}
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for (int idx = beamIdx; idx < beamWidth * param.maxSeqLen; idx += blockDim.x)
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{
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sIds[idx] = param.outputIds[blockIdx.x * param.beamWidth * param.maxSeqLen + idx];
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}
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__syncthreads();
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RankNorm rankNorm;
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rankNorm.rank = beamIdx;
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rankNorm.norm = beamIdx < beamWidth ? sNormedScores[beamIdx] : -FLT_MAX;
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if (beamWidth < 32)
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{
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int warpid = threadIdx.x / 32;
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int laneid = threadIdx.x % 32;
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if (warpid == 0 && beamWidth > 1)
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{
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 1) ^ bfe(laneid, 0)); // 2
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}
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if (warpid == 0 && beamWidth > 2)
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{
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 2) ^ bfe(laneid, 1)); // 3~4
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 2) ^ bfe(laneid, 0));
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}
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if (warpid == 0 && beamWidth > 4)
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{
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rankNorm = swap(rankNorm, 0x04, bfe(laneid, 3) ^ bfe(laneid, 2)); // 5~8
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 3) ^ bfe(laneid, 1));
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 3) ^ bfe(laneid, 0));
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}
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if (warpid == 0 && beamWidth > 8)
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{
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rankNorm = swap(rankNorm, 0x08, bfe(laneid, 4) ^ bfe(laneid, 3)); // 9~16
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rankNorm = swap(rankNorm, 0x04, bfe(laneid, 4) ^ bfe(laneid, 2));
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 4) ^ bfe(laneid, 1));
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 4) ^ bfe(laneid, 0));
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}
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if (warpid == 0 && beamWidth > 16)
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{
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rankNorm = swap(rankNorm, 0x10, bfe(laneid, 4) ^ bfe(laneid, 4)); // 17~32
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rankNorm = swap(rankNorm, 0x08, bfe(laneid, 4) ^ bfe(laneid, 3));
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rankNorm = swap(rankNorm, 0x04, bfe(laneid, 4) ^ bfe(laneid, 2));
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 4) ^ bfe(laneid, 1));
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 4) ^ bfe(laneid, 0));
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}
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}
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else
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{
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// Not supported! We must have a check before calling that kernel.
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}
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if (beamIdx < beamWidth)
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{
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sRank[beamIdx] = rankNorm.rank;
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}
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__syncthreads();
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if (beamIdx < beamWidth)
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{
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auto srcIdx{rankNorm.rank};
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auto tgtIdx{blockIdx.x * param.beamWidth + beamIdx};
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param.sequenceLengths[tgtIdx] = sLength[srcIdx];
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param.cumLogProbs[tgtIdx] = sScores[srcIdx];
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}
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for (int beamIdx = 0; beamIdx < beamWidth; beamIdx++)
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{
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for (int i = threadIdx.x; i < sLength[sRank[beamIdx]]; i += blockDim.x)
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{
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param.outputIds[blockIdx.x * beamWidth * param.maxSeqLen + beamIdx * param.maxSeqLen + i]
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= sIds[sRank[beamIdx] * param.maxSeqLen + i];
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}
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}
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}
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void invokeGatherTree(gatherTreeParam param)
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{
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int batchbeam = param.batchSize * param.beamWidth;
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dim3 grid(1), block(batchbeam);
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// though decoder do not support > 1024 for now
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if (batchbeam > 1024)
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{
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grid.x = ceil(param.batchSize * param.beamWidth / 1024.);
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block.x = 1024;
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}
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gatherTree<<<grid, block, 0, param.stream>>>(param);
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sync_check_cuda_error();
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if (param.beamWidth > 1)
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{
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TLLM_CHECK_WITH_INFO(param.beamWidth <= 32, "TRT-LLM does not support beam width > 32 now");
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// sort results by normalized cumLogProbs
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dim3 grid(param.batchSize);
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dim3 block(divUp(param.beamWidth, 32) * 32);
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auto shm_size = param.beamWidth * (sizeof(float) * 2 + sizeof(int) * 2 + sizeof(int) * param.maxSeqLen);
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finalized<<<grid, block, shm_size, param.stream>>>(param);
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}
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}
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__global__ void insertUnfinishedPathKernel(BeamHypotheses bh)
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{
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// Move ALL unfinished beams from bh.outputIdsUnfinish to bh.outputIdsCBA
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// So here might be more than `nBM` beams in bh.outputIdsCBA after the call
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// bh.outputIdsUnfinish -> bh.outputIdsCBA
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// bh.sequenceLengths -> bh.sequenceLengthsCBA
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// bh.cumLogProbs -> bh.cumLogProbsCBA
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// bh.logProbsTiled -> bh.logProbsCBA
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// update bh.normedScoresCBA
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// update bh.numBeamsCBA
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int const bid = blockIdx.x; // Index of Batch
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int const nBM{bh.nBeamWidth};
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int const nMBS{bh.nMaxBatchSize}; // Only for bh.logProbsTiled
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int const nMSL{bh.nMaxSeqLen};
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bool const bOutputLogProbs{bh.logProbsCBA != nullptr && bh.logProbsTiled != nullptr};
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int const indexDstStart{bh.numBeamsCBA[bid]};
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if (bh.batchDones[bid])
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{
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return;
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}
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for (int i = 0; i < nBM; ++i)
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{
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int const srcBeam = bid * nBM + i;
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int const dstBeam = bid * nBM * 2 + i + indexDstStart;
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int const step = bh.sequenceLengths[srcBeam] - 1;
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// The last token
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int const srcId = srcBeam * nMSL + step;
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int const dstId = dstBeam * nMSL + step;
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bh.outputIdsCBA[dstId] = bh.outputIdsUnfinish[srcId];
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if (bOutputLogProbs)
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{
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bh.logProbsCBA[dstId] = bh.logProbsTiled[step * nMBS * nBM + srcBeam];
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}
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// Previous tokens
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int prevId = bh.parentIdsUnfinish[srcId];
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for (int j = step - 1; j >= 0; --j)
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{
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int const index = bid * nBM * nMSL + prevId * nMSL + j;
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bh.outputIdsCBA[dstBeam * nMSL + j] = bh.outputIdsUnfinish[index];
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prevId = bh.parentIdsUnfinish[index];
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}
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if (bOutputLogProbs)
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{
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prevId = bh.parentIdsUnfinish[srcId];
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for (int j = step - 1; j >= 0; --j)
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{
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int const index = bid * nBM * nMSL + prevId * nMSL + j;
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bh.logProbsCBA[dstBeam * nMSL + j] = bh.logProbsTiled[j * nMBS * nBM + bid * nBM + prevId];
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prevId = bh.parentIdsUnfinish[index];
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}
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}
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// Other parameters
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bh.sequenceLengthsCBA[dstBeam] = bh.sequenceLengths[srcBeam];
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bh.normedScoresCBA[dstBeam]
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= applyLengthPenalty(bh.cumLogProbs[srcBeam], step - bh.inputLengths[srcBeam] + 1, bh.lengthPenalties[bid]);
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bh.cumLogProbsCBA[dstBeam] = bh.cumLogProbs[srcBeam];
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bh.numBeamsCBA[bid]++;
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}
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}
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void invokeInsertUnfinishedPath(BeamHypotheses& bh, cudaStream_t stream)
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{
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insertUnfinishedPathKernel<<<bh.nBatchSize, 1, 0, stream>>>(bh);
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}
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__global__ void finalizeKernel(BeamHypotheses bh)
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{
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// Do index sort on bh.normedScoresCBA, then move buffers from CBA to output by the order of index
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// bh.outputIdsCBA -> bh.outputIds
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// bh.sequenceLengthsCBA -> bh.sequenceLengths
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// bh.cumLogProbsCBA -> bh.cumLogProbs
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// bh.logProbsCBA -> bh.logProbs
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int const bid = blockIdx.x; // Index of Batch
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int const tid = threadIdx.x; // Index of Beam
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int const nBM{bh.nBeamWidth};
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int const nCBA{bh.numBeamsCBA[bid]}; // count of candidate beams in CBA, nBM <= nCBA <= 2*nBM
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int const nMSL{bh.nMaxSeqLen};
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extern __shared__ char smem[];
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int* smemRank = (int*) (smem); // [nBM]
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float* smemScore = (float*) (smemRank + nBM); // [2*nBM]
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int* smemSL = (int*) (smemScore + nBM * 2); // [nBM]
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// Sort
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if (tid < nCBA)
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{
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smemScore[tid] = bh.normedScoresCBA[bid * nBM * 2 + tid];
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}
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__syncthreads();
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if (nCBA < 32)
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{
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int const warpid = tid / 32;
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int const laneid = tid % 32;
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RankNorm rankNorm{tid, tid < nCBA ? smemScore[tid] : -FLT_MAX};
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if (warpid == 0 && nCBA > 1)
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{
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 1) ^ bfe(laneid, 0)); // 2
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}
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if (warpid == 0 && nCBA > 2)
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{
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 2) ^ bfe(laneid, 1)); // 3~4
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 2) ^ bfe(laneid, 0));
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}
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if (warpid == 0 && nCBA > 4)
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{
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rankNorm = swap(rankNorm, 0x04, bfe(laneid, 3) ^ bfe(laneid, 2)); // 5~8
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 3) ^ bfe(laneid, 1));
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 3) ^ bfe(laneid, 0));
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}
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if (warpid == 0 && nCBA > 8)
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{
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rankNorm = swap(rankNorm, 0x08, bfe(laneid, 4) ^ bfe(laneid, 3)); // 9~16
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rankNorm = swap(rankNorm, 0x04, bfe(laneid, 4) ^ bfe(laneid, 2));
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 4) ^ bfe(laneid, 1));
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 4) ^ bfe(laneid, 0));
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}
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if (warpid == 0 && nCBA > 16)
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{
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rankNorm = swap(rankNorm, 0x10, bfe(laneid, 4) ^ bfe(laneid, 4)); // 17~32
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rankNorm = swap(rankNorm, 0x08, bfe(laneid, 4) ^ bfe(laneid, 3));
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rankNorm = swap(rankNorm, 0x04, bfe(laneid, 4) ^ bfe(laneid, 2));
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rankNorm = swap(rankNorm, 0x02, bfe(laneid, 4) ^ bfe(laneid, 1));
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rankNorm = swap(rankNorm, 0x01, bfe(laneid, 4) ^ bfe(laneid, 0));
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}
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if (tid < nBM)
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{
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smemRank[tid] = rankNorm.rank;
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}
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__syncthreads();
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}
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else
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{
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for (int i = 0; i < nBM; ++i)
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{
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float const score = tid < bh.numBeamsCBA[bid] ? smemScore[tid] : -FLT_MAX;
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float const maxScore = blockReduceMax<float>(score);
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if (tid == 0)
|
|
{
|
|
for (int j = 0; j < nBM * 2; ++j)
|
|
{
|
|
if (smemScore[j] == maxScore)
|
|
{
|
|
smemRank[i] = j;
|
|
smemScore[j] = -FLT_MAX;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
|
|
// Move bh.sequenceLengths, bh.cumLogProbs
|
|
if (tid < nBM)
|
|
{
|
|
smemSL[tid] = bh.sequenceLengthsCBA[bid * nBM * 2 + smemRank[tid]];
|
|
bh.sequenceLengths[bid * nBM + tid] = smemSL[tid];
|
|
if (bh.cumLogProbs != nullptr)
|
|
{
|
|
bh.cumLogProbs[bid * nBM + tid] = bh.cumLogProbsCBA[bid * nBM * 2 + smemRank[tid]];
|
|
}
|
|
}
|
|
__syncthreads();
|
|
|
|
// Move bh.outputIds, bh.logProbs
|
|
for (int beamIdx = 0; beamIdx < nBM; beamIdx++)
|
|
{
|
|
for (int i = tid; i < smemSL[beamIdx]; i += blockDim.x)
|
|
{
|
|
int const dst = bid * nBM * nMSL + beamIdx * nMSL + i;
|
|
int const src = bid * nBM * 2 * nMSL + smemRank[beamIdx] * nMSL + i;
|
|
bh.outputIds[dst] = bh.outputIdsCBA[src];
|
|
}
|
|
if (bh.logProbs != nullptr)
|
|
{
|
|
for (int i = tid; i < smemSL[beamIdx]; i += blockDim.x)
|
|
{
|
|
if (int const inputLength = bh.inputLengths[bid * nBM + beamIdx]; i >= inputLength)
|
|
{
|
|
int const dst = bid * nBM * nMSL + beamIdx * nMSL + i;
|
|
int const src = bid * nBM * 2 * nMSL + smemRank[beamIdx] * nMSL + i;
|
|
bh.logProbs[dst - inputLength] = bh.logProbsCBA[src];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void invokeFinalize(BeamHypotheses& bh, cudaStream_t stream)
|
|
{
|
|
TLLM_LOG_DEBUG("%s %s start", __FILE__, __PRETTY_FUNCTION__);
|
|
|
|
int const nBM = bh.nBeamWidth;
|
|
size_t const smem_size = sizeof(int) * nBM * 2 + sizeof(float) * nBM * 2;
|
|
finalizeKernel<<<bh.nBatchSize, roundUp(nBM * 2, 32), smem_size, stream>>>(bh);
|
|
}
|
|
|
|
__global__ void initializeOutput(TokenIdType* finalOutputIds, TokenIdType const* endIds, SizeType32 const nMaxSeqLen)
|
|
{
|
|
for (int i = threadIdx.x; i < nMaxSeqLen; i += blockDim.x)
|
|
{
|
|
finalOutputIds[blockIdx.x * nMaxSeqLen + i] = endIds[blockIdx.x];
|
|
}
|
|
}
|
|
|
|
void invokeInitializeOutput(TokenIdType* finalOutputIds, TokenIdType const* endIds, SizeType32 const batchBeam,
|
|
SizeType32 const nMaxSeqLen, cudaStream_t stream)
|
|
{
|
|
initializeOutput<<<batchBeam, 256, 0, stream>>>(finalOutputIds, endIds, nMaxSeqLen);
|
|
}
|
|
|
|
__global__ void copyNextStepIds(TokenIdType* nextStepIds, TokenIdType const* const* outputIdsPtr,
|
|
SizeType32 const* sequenceLengths, SizeType32 const* numNewTokens, SizeType32 const* batchSlots,
|
|
SizeType32 batchSize, SizeType32 maxBatchSize, SizeType32 beamWidth, SizeType32 maxSeqLen,
|
|
SizeType32 maxTokensPerStep)
|
|
{
|
|
for (auto index = static_cast<SizeType32>(blockIdx.x * blockDim.x + threadIdx.x);
|
|
index < batchSize * beamWidth * maxTokensPerStep; index += static_cast<SizeType32>(blockDim.x * gridDim.x))
|
|
{
|
|
// batchSlots == nullptr both in Python/C++ workflow yet
|
|
// numNewTokens == nullptr when Medusa is disabled
|
|
auto const batchIdx{index / (beamWidth * maxTokensPerStep)};
|
|
auto const batchSlot{batchSlots != nullptr ? batchSlots[batchIdx] : batchIdx};
|
|
auto const remainder{index % (beamWidth * maxTokensPerStep)};
|
|
auto const beamIdx{remainder / maxTokensPerStep};
|
|
auto const tokenIdx{remainder % maxTokensPerStep};
|
|
auto const newTokens{numNewTokens == nullptr ? 1 : numNewTokens[batchSlot]};
|
|
auto const batchBeamIdx = batchSlot * beamWidth + beamIdx;
|
|
auto const tokenBatchBeamIdx = tokenIdx * maxBatchSize * beamWidth + batchSlot * beamWidth + beamIdx;
|
|
auto const indexSrc = sequenceLengths[batchBeamIdx] - newTokens + tokenIdx;
|
|
if (tokenIdx >= newTokens || indexSrc < 0)
|
|
{
|
|
continue;
|
|
}
|
|
nextStepIds[tokenBatchBeamIdx] = outputIdsPtr[batchSlot][beamIdx * maxSeqLen + indexSrc];
|
|
}
|
|
}
|
|
|
|
void invokeCopyNextStepIds(TokenIdType* nextStepIds, TokenIdType const* const* outputIdsPtr,
|
|
SizeType32 const* sequenceLengths, SizeType32 const* numNewTokens, SizeType32 const* batchSlots,
|
|
SizeType32 batchSize, SizeType32 maxBatchSize, SizeType32 beamWidth, SizeType32 maxSeqLen,
|
|
SizeType32 maxTokensPerStep, cudaStream_t stream)
|
|
{
|
|
int const numElems = batchSize * beamWidth * maxTokensPerStep;
|
|
dim3 block(min(256, numElems));
|
|
dim3 grid(divUp(numElems, block.x));
|
|
copyNextStepIds<<<grid, block, 0, stream>>>(nextStepIds, outputIdsPtr, sequenceLengths, numNewTokens, batchSlots,
|
|
batchSize, maxBatchSize, beamWidth, maxSeqLen, maxTokensPerStep);
|
|
}
|
|
|
|
__global__ void transposeLogProbs(float* outputLogProbs, float* outputLogProbsTiled, SizeType32 const* sequenceLengths,
|
|
SizeType32 const* batchSlots, SizeType32 batchSize, SizeType32 maxBatchSize, SizeType32 beamWidth,
|
|
SizeType32 maxSeqLen)
|
|
{
|
|
auto index = static_cast<SizeType32>(blockIdx.x * blockDim.x + threadIdx.x);
|
|
|
|
auto const batchIdx = index / (beamWidth * maxSeqLen);
|
|
auto const tmpIdx = index % (beamWidth * maxSeqLen);
|
|
auto const beamIdx = tmpIdx / maxSeqLen;
|
|
auto const pos = tmpIdx % maxSeqLen;
|
|
if (batchIdx >= batchSize)
|
|
{
|
|
return;
|
|
}
|
|
|
|
auto const batchSlot = batchSlots != nullptr ? batchSlots[batchIdx] : batchIdx;
|
|
if (pos < sequenceLengths[batchSlot])
|
|
{
|
|
auto const batchBeamIdx = batchSlot * beamWidth * maxSeqLen + beamIdx * maxSeqLen + pos;
|
|
outputLogProbs[batchBeamIdx]
|
|
= outputLogProbsTiled[pos * maxBatchSize * beamWidth + batchSlot * beamWidth + beamIdx];
|
|
}
|
|
}
|
|
|
|
void invokeTransposeLogProbs(float* outputLogProbs, float* outputLogProbsTiled, SizeType32 const* sequenceLengths,
|
|
SizeType32 const* batchSlots, SizeType32 batchSize, SizeType32 maxBatchSize, SizeType32 beamWidth,
|
|
SizeType32 maxSeqLen, cudaStream_t stream)
|
|
{
|
|
dim3 block(256);
|
|
dim3 grid(divUp(batchSize * beamWidth * maxSeqLen, block.x));
|
|
transposeLogProbs<<<grid, block, 0, stream>>>(outputLogProbs, outputLogProbsTiled, sequenceLengths, batchSlots,
|
|
batchSize, maxBatchSize, beamWidth, maxSeqLen);
|
|
}
|
|
|
|
} // namespace kernels
|
|
} // namespace tensorrt_llm
|