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https://github.com/NVIDIA/TensorRT-LLM.git
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deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
88 lines
3.7 KiB
C++
88 lines
3.7 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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outputIdsTgt = manager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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sequenceLengthsTgt = manager.emptyTensor(MemoryType::kGPU, nvSizeType);
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cumLogProbs = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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normedScores = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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logProbs = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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minNormedScores = manager.emptyTensor(MemoryType::kGPU, nvFloatType);
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numBeams = manager.emptyTensor(MemoryType::kGPU, nvSizeType);
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isDone = 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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outputIdsTgt->reshape(ITensor::makeShape({batchSize, 2 * beamWidth, maxSequenceLength}));
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sequenceLengthsTgt->reshape(ITensor::makeShape({batchSize, 2 * beamWidth}));
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cumLogProbs->reshape(ITensor::makeShape({batchSize, 2 * beamWidth}));
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normedScores->reshape(ITensor::makeShape({batchSize, 2 * beamWidth}));
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logProbs->reshape(ITensor::makeShape({batchSize, 2 * beamWidth, maxSequenceLength}));
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minNormedScores->reshape(ITensor::makeShape({batchSize}));
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numBeams->reshape(ITensor::makeShape({batchSize}));
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isDone->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(*outputIdsTgt, endId, manager.getStream());
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manager.setZero(*sequenceLengthsTgt);
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manager.setZero(*cumLogProbs);
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manager.setZero(*normedScores);
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manager.setZero(*logProbs);
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manager.setZero(*minNormedScores);
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manager.setZero(*numBeams);
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manager.setZero(*isDone);
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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.outputIdsTgt = ITensor::slice(outputIdsTgt, batchIndex, size);
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bh.sequenceLengthsTgt = ITensor::slice(sequenceLengthsTgt, batchIndex, size);
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bh.cumLogProbs = ITensor::slice(cumLogProbs, batchIndex, size);
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bh.normedScores = ITensor::slice(normedScores, batchIndex, size);
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bh.logProbs = ITensor::slice(logProbs, batchIndex, size);
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bh.minNormedScores = ITensor::slice(minNormedScores, batchIndex, size);
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bh.numBeams = ITensor::slice(numBeams, batchIndex, size);
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bh.isDone = ITensor::slice(isDone, 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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outputIdsTgt->release();
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sequenceLengthsTgt->release();
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cumLogProbs->release();
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normedScores->release();
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logProbs->release();
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minNormedScores->release();
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numBeams->release();
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isDone->release();
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}
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