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https://github.com/NVIDIA/TensorRT-LLM.git
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* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Eddie-Wang1120 <wangjinheng1120@163.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
167 lines
6.1 KiB
Plaintext
167 lines
6.1 KiB
Plaintext
/*
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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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#include "tensorrt_llm/common/cudaUtils.h"
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#include "tensorrt_llm/common/reduceKernelUtils.cuh"
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#include "tensorrt_llm/kernels/stopCriteriaKernels.h"
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using namespace tensorrt_llm::common;
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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 stopWordsCriterion(int32_t const** outputIds, int32_t const** parentIds, int32_t const** stopWords,
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FinishedState* finished, int32_t const* sequenceLengths, int32_t const* batchSlots, int32_t const* stopWordsLens,
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int32_t batchSize, int32_t beamWidth, int32_t maxSeqLen)
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{
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int32_t const id = blockIdx.x * blockDim.x + threadIdx.x;
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int32_t const batchIdx = blockIdx.y / beamWidth;
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int32_t const beamIdx = blockIdx.y % beamWidth;
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auto const batchSlot = batchSlots != nullptr ? batchSlots[batchIdx] : batchIdx;
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auto const batchBeamIdx = batchSlot * beamWidth + beamIdx;
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auto const* baseStopWords = stopWords[batchSlot];
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auto const stopWordsLen = stopWordsLens[batchSlot];
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auto const* baseOffsets = baseStopWords + stopWordsLen;
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if (id >= stopWordsLen || baseOffsets[id] < 0)
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{
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return;
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}
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auto const itemEnd = baseOffsets[id];
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auto const itemStart = (id > 0) ? baseOffsets[id - 1] : 0;
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auto const itemSize = itemEnd - itemStart;
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// The single-token case unconditionally bans the token
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bool shouldStop = false;
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// Need to minus 1 because the sequenceLengths is updated in this step
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auto const currentStep = sequenceLengths[batchBeamIdx] - 1;
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// Enough previously generated tokens to look for a match
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if (currentStep + 1 >= itemSize)
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{
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shouldStop = true;
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auto parentId = beamIdx;
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bool const gatherBeam = beamWidth > 1;
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for (int32_t tokenIdx = itemSize - 1; tokenIdx >= 0; tokenIdx--)
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{
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auto const previousToken
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= outputIds[batchSlot][parentId * maxSeqLen + currentStep - (itemSize - 1) + tokenIdx];
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if (previousToken != baseStopWords[itemStart + tokenIdx])
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{
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shouldStop = false;
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break;
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}
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if (gatherBeam)
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{
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parentId = parentIds == nullptr
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? 0
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: parentIds[batchSlot][parentId * maxSeqLen + currentStep - (itemSize - 1) + tokenIdx];
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if (parentId < 0 || parentId >= beamWidth)
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{
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shouldStop = false;
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break;
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}
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}
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}
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}
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if (shouldStop)
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{
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finished[batchSlot * beamWidth + beamIdx].setFinishedStopWords();
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}
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}
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void invokeStopWordsCriterion(int32_t const** outputIds, int32_t const** parentIds, int32_t const** stopWords,
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FinishedState* finished, int32_t const* sequenceLengths, int32_t const* batchSlots, int32_t const* stopWordsLen,
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int32_t maxStopWordsLen, int32_t batchSize, int32_t beamWidth, int32_t maxSeqLen, cudaStream_t stream)
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{
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// Check if we have sampled a word from the stopWords list. If so, stop the sequence.
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dim3 block, grid;
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constexpr int32_t maxBlockSize{256};
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block.x = min(((maxStopWordsLen + 32 - 1) / 32) * 32, maxBlockSize);
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grid.x = (maxStopWordsLen + block.x - 1) / block.x;
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grid.y = batchSize * beamWidth;
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stopWordsCriterion<<<grid, block, 0, stream>>>(outputIds, parentIds, stopWords, finished, sequenceLengths,
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batchSlots, stopWordsLen, batchSize, beamWidth, maxSeqLen);
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sync_check_cuda_error();
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}
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__global__ void lengthCriterion(FinishedState* finished, int32_t* finishedSum, uint32_t const* sequenceLimitLength,
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int32_t* sequenceLengths, int32_t const* batchSlots, int32_t batchSize, int32_t beamWidth)
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{
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int32_t threadFinishedCount = 0;
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auto const batchIdx = blockIdx.x;
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auto const batchSlot = batchSlots != nullptr ? batchSlots[batchIdx] : batchIdx;
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for (int32_t beamIdx = threadIdx.x; beamIdx < beamWidth; beamIdx += blockDim.x)
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{
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auto const batchSlotBeamWidthIdx = batchSlot * beamWidth + beamIdx;
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auto finishState = finished[batchSlotBeamWidthIdx];
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if (sequenceLengths[batchSlotBeamWidthIdx] >= sequenceLimitLength[batchSlot])
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{
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finishState.setFinishedMaxLength();
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sequenceLengths[batchSlotBeamWidthIdx] = sequenceLimitLength[batchSlot];
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}
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threadFinishedCount += finishState.isFinished() ? 1 : 0;
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finished[batchSlotBeamWidthIdx] = finishState;
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}
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if (finishedSum)
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{
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int blockFinishedCount = 0;
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if (blockDim.x <= 32)
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{
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blockFinishedCount = warpReduceSum(threadFinishedCount);
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}
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else
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{
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blockFinishedCount = blockReduceSum(threadFinishedCount);
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}
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__syncthreads();
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if (threadIdx.x == 0)
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{
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finishedSum[batchSlot] = blockFinishedCount;
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}
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}
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}
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void invokeLengthCriterion(FinishedState* finished, int32_t* finishedSum, uint32_t const* sequenceLimitLength,
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int32_t* sequenceLengths, int32_t const* batchSlots, int32_t batchSize, int32_t beamWidth, cudaStream_t stream)
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{
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// Check if we have attained the sequence length limit. If so, stop the
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// sequence. In addition, check if all sequences are stopped and return the
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// result in shouldStop
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dim3 block{min(512, uint32_t(beamWidth))};
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dim3 grid{uint32_t(batchSize)};
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lengthCriterion<<<grid, block, 0, stream>>>(
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finished, finishedSum, sequenceLimitLength, sequenceLengths, batchSlots, batchSize, beamWidth);
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sync_check_cuda_error();
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}
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} // namespace kernels
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} // namespace tensorrt_llm
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