mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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88 lines
3.8 KiB
C++
88 lines
3.8 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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#include "tensorrt_llm/runtime/decodingOutput.h"
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#include "tensorrt_llm/runtime/runtimeKernels.h"
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using namespace tensorrt_llm::runtime;
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void DecodingOutput::BeamHypotheses::empty(BufferManager& manager)
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{
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auto constexpr nvTokenIdType = TRTDataType<TokenIdType>::value;
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auto constexpr nvSizeType = TRTDataType<SizeType>::value;
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auto constexpr nvFloatType = TRTDataType<float>::value;
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auto constexpr nvBoolType = TRTDataType<bool>::value;
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outputIdsCBA = manager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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sequenceLengthsCBA = manager.emptyTensor(MemoryType::kGPU, nvSizeType);
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cumLogProbsCBA = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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normedScoresCBA = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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logProbsCBA = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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minNormedScoresCBA = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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numBeamsCBA = manager.emptyTensor(MemoryType::kGPU, nvSizeType);
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batchDones = manager.emptyTensor(MemoryType::kGPU, nvBoolType);
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}
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void DecodingOutput::BeamHypotheses::reshape(SizeType batchSize, SizeType beamWidth, SizeType maxSequenceLength)
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{
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outputIdsCBA->reshape(ITensor::makeShape({batchSize, 2 * beamWidth, maxSequenceLength}));
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sequenceLengthsCBA->reshape(ITensor::makeShape({batchSize, 2 * beamWidth}));
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cumLogProbsCBA->reshape(ITensor::makeShape({batchSize, 2 * beamWidth}));
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normedScoresCBA->reshape(ITensor::makeShape({batchSize, 2 * beamWidth}));
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logProbsCBA->reshape(ITensor::makeShape({batchSize, 2 * beamWidth, maxSequenceLength}));
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minNormedScoresCBA->reshape(ITensor::makeShape({batchSize}));
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numBeamsCBA->reshape(ITensor::makeShape({batchSize}));
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batchDones->reshape(ITensor::makeShape({batchSize}));
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}
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void DecodingOutput::BeamHypotheses::init(BufferManager& manager, TokenIdType endId)
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{
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kernels::invokeFill(*outputIdsCBA, endId, manager.getStream());
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manager.setZero(*sequenceLengthsCBA);
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manager.setZero(*cumLogProbsCBA);
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manager.setZero(*normedScoresCBA);
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manager.setZero(*logProbsCBA);
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manager.setZero(*minNormedScoresCBA);
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manager.setZero(*numBeamsCBA);
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manager.setZero(*batchDones);
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}
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DecodingOutput::BeamHypotheses DecodingOutput::BeamHypotheses::slice(SizeType batchIndex, SizeType size) const
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{
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DecodingOutput::BeamHypotheses bh{};
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bh.outputIdsCBA = ITensor::slice(outputIdsCBA, batchIndex, size);
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bh.sequenceLengthsCBA = ITensor::slice(sequenceLengthsCBA, batchIndex, size);
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bh.cumLogProbsCBA = ITensor::slice(cumLogProbsCBA, batchIndex, size);
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bh.normedScoresCBA = ITensor::slice(normedScoresCBA, batchIndex, size);
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bh.logProbsCBA = ITensor::slice(logProbsCBA, batchIndex, size);
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bh.minNormedScoresCBA = ITensor::slice(minNormedScoresCBA, batchIndex, size);
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bh.numBeamsCBA = ITensor::slice(numBeamsCBA, batchIndex, size);
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bh.batchDones = ITensor::slice(batchDones, batchIndex, size);
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return bh;
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}
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void DecodingOutput::BeamHypotheses::release()
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{
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outputIdsCBA->release();
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sequenceLengthsCBA->release();
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cumLogProbsCBA->release();
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normedScoresCBA->release();
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logProbsCBA->release();
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minNormedScoresCBA->release();
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numBeamsCBA->release();
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batchDones->release();
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}
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