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728cc0044b
* Update TensorRT-LLM --------- Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
920 lines
41 KiB
C++
920 lines
41 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/gptDecoderBatch.h"
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/kernels/decodingCommon.h"
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/cudaEvent.h"
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#include "tensorrt_llm/runtime/runtimeKernels.h"
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#include <algorithm>
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#include <cassert>
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#include <memory>
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using namespace tensorrt_llm::runtime;
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namespace tc = tensorrt_llm::common;
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namespace tk = tensorrt_llm::kernels;
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namespace
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{
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SamplingConfig extractSamplingConfig(SamplingConfig const& batchSamplingConfig, SizeType batchIdx)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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SamplingConfig samplingConfig{batchSamplingConfig.beamWidth};
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auto extractOptional = [&batchIdx](auto& single, auto const& batch)
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{
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using T = typename std::remove_reference_t<decltype(batch)>::value_type;
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if (batch)
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{
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if (batch->size() > 1)
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single.emplace(T{batch->at(batchIdx)});
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else
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single.emplace(T{batch->at(0)});
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}
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};
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extractOptional(samplingConfig.temperature, batchSamplingConfig.temperature);
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extractOptional(samplingConfig.minLength, batchSamplingConfig.minLength);
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extractOptional(samplingConfig.repetitionPenalty, batchSamplingConfig.repetitionPenalty);
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extractOptional(samplingConfig.presencePenalty, batchSamplingConfig.presencePenalty);
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extractOptional(samplingConfig.frequencyPenalty, batchSamplingConfig.frequencyPenalty);
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// sampling layers
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extractOptional(samplingConfig.topK, batchSamplingConfig.topK);
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extractOptional(samplingConfig.topP, batchSamplingConfig.topP);
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extractOptional(samplingConfig.randomSeed, batchSamplingConfig.randomSeed);
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extractOptional(samplingConfig.topPDecay, batchSamplingConfig.topPDecay);
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extractOptional(samplingConfig.topPMin, batchSamplingConfig.topPMin);
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extractOptional(samplingConfig.topPResetIds, batchSamplingConfig.topPResetIds);
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// beam search layer
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samplingConfig.beamSearchDiversityRate = batchSamplingConfig.beamSearchDiversityRate;
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samplingConfig.lengthPenalty = batchSamplingConfig.lengthPenalty;
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samplingConfig.earlyStopping = batchSamplingConfig.earlyStopping;
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samplingConfig.normalizeLogProbs = batchSamplingConfig.normalizeLogProbs;
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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return samplingConfig;
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}
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} // namespace
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GptDecoderBatch::GptDecoderBatch(
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std::size_t vocabSize, std::size_t vocabSizePadded, GptDecoderBatch::CudaStreamPtr stream)
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: mVocabSize{vocabSize}
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, mVocabSizePadded{vocabSizePadded}
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, mStream{std::move(stream)}
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, mBufferManager{mStream}
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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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& dInput = mJointDecodingInput;
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auto dummyLogits = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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auto endIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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dInput = std::make_unique<DecodingInput>(0, 0, 0, 0, std::move(dummyLogits), std::move(endIds));
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dInput->sequenceLimitLength = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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dInput->lengths = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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auto& dOutput = mJointDecodingOutput;
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auto outputIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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dOutput = std::make_unique<DecodingOutput>(std::move(outputIds));
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dOutput->newTokensSteps = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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dOutput->parentIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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mFinishedSteps
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= mBufferManager.emptyTensor(MemoryType::kGPU, TRTDataType<tk::FinishedState::UnderlyingType>::value);
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mDraftProbs = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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mTargetProbs = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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mBatchSlotsSetup = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<SizeType>::value);
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mBatchSlotsDecoder = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<SizeType>::value);
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mBatchSlotsAcceptTokens = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<SizeType>::value);
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mBatchSlotsAcceptLogits = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<SizeType>::value);
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// use batchSize many entries instead of the usual 1
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dOutput->finishedSum = mBufferManager.emptyTensor(MemoryType::kPINNED, nvSizeType);
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mFinishedSum = mBufferManager.pinned(ITensor::makeShape({1}), nvSizeType);
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// we don't need dOutput->lengths because lengths are passed from outside
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dOutput->cumLogProbs = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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dOutput->logProbs = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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dOutput->beamHypotheses.empty(mBufferManager);
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mNumDraftTokens = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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mCurandStates = mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT8);
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mDraftTokenIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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mDraftLogits = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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mTargetLogitsPtrs = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<float*>::value);
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dInput->stopWordsPtrs = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<int32_t*>::value);
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dInput->stopWordsLens = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<SizeType>::value);
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dInput->badWordsPtrs = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<int32_t*>::value);
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dInput->badWordsLens = mBufferManager.emptyTensor(MemoryType::kPINNED, TRTDataType<SizeType>::value);
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dInput->embeddingBias = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatch::setup(DecodingMode const& mode, SizeType maxBatchSize, SizeType maxBeamWidth,
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SizeType maxAttentionWindow, SizeType sinkTokenLength, SizeType maxSequenceLength, SizeType maxTokensPerStep,
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bool fusedDecoder, nvinfer1::DataType dtype)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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TLLM_CHECK(maxBatchSize > 0);
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TLLM_CHECK(maxBeamWidth > 0);
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TLLM_CHECK(maxTokensPerStep > 0);
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TLLM_CHECK(maxSequenceLength > 0);
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mActualBatchSize = maxBatchSize;
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mGeneratedTokensPerStep.resize(maxBatchSize);
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mMaxSequenceLength = maxSequenceLength;
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mMaxAttentionWindow = maxAttentionWindow;
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mSinkTokenLength = sinkTokenLength;
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mMaxTokensPerStep = maxTokensPerStep;
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mFusedDecoder = fusedDecoder;
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auto const maxBatchSizeShape = ITensor::makeShape({maxBatchSize});
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auto const maxBatchSizeXmaxBeamWidth = ITensor::makeShape({maxBatchSize, maxBeamWidth});
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auto const maxTokensPerStepXmaxBatchSizeXmaxBeamWidth
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= ITensor::makeShape({maxTokensPerStep, maxBatchSize, maxBeamWidth});
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auto const maxBatchSizeXmaxTokensPerStep = ITensor::makeShape({maxBatchSize, maxTokensPerStep});
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auto& dInput = *mJointDecodingInput;
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dInput.maxLength = mMaxSequenceLength;
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dInput.maxAttentionWindow = mMaxAttentionWindow;
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dInput.sinkTokenLength = mSinkTokenLength;
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const_cast<ITensor&>(*dInput.endIds).reshape(maxBatchSizeXmaxBeamWidth);
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auto& sequenceLimitLength = const_cast<ITensor&>(*dInput.sequenceLimitLength);
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sequenceLimitLength.reshape(maxBatchSizeShape);
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kernels::invokeFill(sequenceLimitLength, mMaxSequenceLength, *mStream);
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auto& inputLengths = const_cast<ITensor&>(*dInput.lengths);
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inputLengths.reshape(maxBatchSizeXmaxBeamWidth);
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mBufferManager.setZero(inputLengths);
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auto const jointOutputIdsShape = ITensor::makeShape({maxBatchSize, maxBeamWidth, maxSequenceLength});
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auto& dOutput = *mJointDecodingOutput;
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dOutput.ids->reshape(jointOutputIdsShape);
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dOutput.newTokensSteps->reshape(maxTokensPerStepXmaxBatchSizeXmaxBeamWidth);
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mBufferManager.setZero(*dOutput.newTokensSteps);
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mFinishedSteps->reshape(maxTokensPerStepXmaxBatchSizeXmaxBeamWidth);
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mBufferManager.setZero(*mFinishedSteps);
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if (mFusedDecoder)
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{
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mBatchSlotsSetup->reshape(ITensor::makeShape({maxBatchSize}));
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mBatchSlotsDecoder->reshape(ITensor::makeShape({maxTokensPerStep, maxBatchSize}));
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mBatchSlotsAcceptTokens->reshape(ITensor::makeShape({maxTokensPerStep, maxBatchSize}));
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mBatchSlotsAcceptLogits->reshape(ITensor::makeShape({maxTokensPerStep, maxBatchSize}));
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}
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if (mMaxTokensPerStep > 1)
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{
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mDraftProbs->reshape(ITensor::makeShape(
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{maxBatchSize, maxTokensPerStep, maxBeamWidth, static_cast<SizeType>(mVocabSizePadded)}));
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mTargetProbs->reshape(ITensor::makeShape(
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{maxBatchSize, maxTokensPerStep, maxBeamWidth, static_cast<SizeType>(mVocabSizePadded)}));
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}
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dOutput.parentIds->reshape(jointOutputIdsShape);
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// use batchSize many entries instead of the usual 1
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dOutput.finishedSum->reshape(maxBatchSizeShape);
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mBufferManager.setZero(*dOutput.finishedSum);
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dOutput.cumLogProbs->reshape(maxBatchSizeXmaxBeamWidth);
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mBufferManager.setZero(*dOutput.cumLogProbs);
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dOutput.logProbs->reshape(ITensor::makeShape({maxBatchSize, maxBeamWidth, mMaxSequenceLength}));
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mBufferManager.setZero(*dOutput.logProbs);
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if (maxBeamWidth > 1)
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{
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dOutput.beamHypotheses.reshape(maxBatchSize, maxBeamWidth, mMaxSequenceLength);
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}
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else
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{
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dOutput.beamHypotheses.release();
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}
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// speculative decoding only works for beam width == 1
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mDraftTokenIds->reshape(maxBatchSizeXmaxTokensPerStep);
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mDraftLogits->reshape(
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ITensor::makeShape({maxBatchSize, maxTokensPerStep, static_cast<SizeType>(mVocabSizePadded)}));
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mAcceptByLogits.resize(maxBatchSize);
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mNumDraftTokens->reshape(ITensor::makeShape({maxBatchSize, 1}));
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mCurandStates->reshape(ITensor::makeShape({maxBatchSize, sizeof(curandState_t)}));
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mTargetLogitsPtrs->reshape(ITensor::makeShape({maxTokensPerStep, maxBatchSize}));
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const_cast<ITensor&>(*dInput.embeddingBias)
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.reshape(ITensor::makeShape({maxBatchSize, static_cast<SizeType>(mVocabSizePadded)}));
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const_cast<ITensor&>(*dInput.badWordsPtrs).reshape(ITensor::makeShape({maxBatchSize}));
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const_cast<ITensor&>(*dInput.badWordsLens).reshape(ITensor::makeShape({maxBatchSize}));
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const_cast<ITensor&>(*dInput.stopWordsPtrs).reshape(ITensor::makeShape({maxBatchSize}));
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const_cast<ITensor&>(*dInput.stopWordsLens).reshape(ITensor::makeShape({maxBatchSize}));
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auto const numOfDecoders = fusedDecoder ? 1 : maxBatchSize;
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mStreams.resize(maxBatchSize);
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mDecoders.resize(numOfDecoders);
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mDecodingInputs.resize(maxBatchSize);
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mDecodingOutputs.resize(maxBatchSize);
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mNbSteps.resize(maxBatchSize);
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mFinished.resize(maxBatchSize);
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mMaxNewTokens.resize(maxBatchSize);
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mBeamWidths.resize(maxBatchSize);
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auto const device = mStream->getDevice();
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for (SizeType i = 0; i < maxBatchSize; ++i)
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{
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mStreams[i] = std::make_shared<CudaStream>();
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TLLM_CHECK(mStreams[i]->getDevice() == device);
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if (i < numOfDecoders)
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{
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auto maxBatchSizePerDecoder = fusedDecoder ? maxBatchSize : 1;
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mDecoders[i] = IGptDecoder::create(mode, dtype, maxBatchSizePerDecoder, maxBeamWidth, mVocabSize,
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mVocabSizePadded, mMaxSequenceLength, mStreams[i]);
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}
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mDecodingInputs[i].reset();
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mDecodingOutputs[i].reset();
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mNbSteps[i] = 0;
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mFinished[i] = true;
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mMaxNewTokens[i] = 0;
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mBeamWidths[i] = 0;
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mGeneratedTokensPerStep[i] = 0;
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}
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatch::newRequest(
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SizeType batchIdx, decoder_batch::Request const& request, SamplingConfig const& samplingConfig)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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TLLM_CHECK(batchIdx >= 0);
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auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
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auto const batchSize = jointOutputIdsShape.d[0];
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TLLM_CHECK(0 <= batchSize && batchIdx < batchSize);
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auto const maxBeamWidth = jointOutputIdsShape.d[1];
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auto const beamWidth = samplingConfig.beamWidth;
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TLLM_CHECK_WITH_INFO(beamWidth <= maxBeamWidth,
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tc::fmtstr("Beam width (%d) must be smaller than maxBeamWidth (%d) passed to decoder setup function.",
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beamWidth, maxBeamWidth));
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auto const& requestIds = request.ids;
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auto const inputLength = request.inputLen;
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auto const maxNewTokens = request.maxNewTokens.value_or(mMaxSequenceLength - inputLength);
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TLLM_CHECK_WITH_INFO(inputLength + maxNewTokens <= mMaxSequenceLength,
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tc::fmtstr("Input length (%d) + max new tokens (%d) must be less than max sequence length (%d).", inputLength,
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maxNewTokens, mMaxSequenceLength));
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TLLM_CHECK(requestIds->getDataType() == TRTDataType<TokenIdType>::value);
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auto const endId = request.endId.value_or(-1);
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auto constexpr localBatchSize = 1;
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auto const decoderIdx = mFusedDecoder ? 0 : batchIdx;
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auto& stream = mStreams[decoderIdx];
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BufferManager manager{stream};
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// input
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auto& dJointInput = *mJointDecodingInput;
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auto& dInput = mDecodingInputs.at(batchIdx);
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TensorPtr endIdTensorPtr{ITensor::slice(constPointerCast(dJointInput.endIds), batchIdx, localBatchSize)};
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kernels::invokeFill(*endIdTensorPtr, endId, *stream);
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dInput = std::make_unique<DecodingInput>(
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inputLength, mMaxAttentionWindow, mSinkTokenLength, localBatchSize, dJointInput.logits, endIdTensorPtr);
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TensorPtr embeddingBiasSlice
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= ITensor::slice(constPointerCast(dJointInput.embeddingBias), batchIdx, localBatchSize);
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if (request.embeddingBias)
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{
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TLLM_CHECK(request.embeddingBias->getShape().nbDims == 2);
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TLLM_CHECK(request.embeddingBias->getShape().d[0] == 1);
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TLLM_CHECK_WITH_INFO(request.embeddingBias->getShape().d[1] == static_cast<SizeType>(mVocabSize),
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"The embedding bias shape is not as expected. Expected last dimension to be same as vocab size: %lu.",
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mVocabSize);
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manager.copy(*request.embeddingBias, *embeddingBiasSlice);
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dInput->embeddingBias = embeddingBiasSlice;
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}
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else
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{
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manager.setZero(*embeddingBiasSlice);
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}
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auto setupWords = [fusedDecoder = mFusedDecoder](SharedConstPtr& inputWordsList, TensorPtr const& requestWordsList,
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SharedConstPtr& jointWordsPtrs, SharedConstPtr& jointWordsLens, SharedConstPtr& wordsPtrs,
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SharedConstPtr& wordsLens, SizeType& inputMaxStopWordsLen, SizeType& maxWordsLen,
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SizeType localBatchSize, SizeType batchIdx)
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{
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if (requestWordsList)
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{
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auto const wordsLen = requestWordsList->getShape().d[1];
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BufferRange<int32_t*>(*constPointerCast(jointWordsPtrs))[batchIdx]
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= bufferCast<SizeType>(*requestWordsList);
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bufferCast<SizeType>(*constPointerCast(jointWordsLens))[batchIdx] = wordsLen;
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// FIXME(nkorobov): this is monotonically growing size
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maxWordsLen = std::max(wordsLen, maxWordsLen);
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if (!fusedDecoder)
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{
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wordsPtrs = ITensor::slice(jointWordsPtrs, batchIdx, localBatchSize);
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wordsLens = ITensor::slice(jointWordsLens, batchIdx, localBatchSize);
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inputMaxStopWordsLen = wordsLen;
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}
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// NOTE(nkorobov): dInput-><name>WordsList is not used in gptDecoder, but required to keep <name>WordsList's
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// memory allocated
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inputWordsList = requestWordsList;
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}
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else
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{
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bufferCast<SizeType>(*constPointerCast(jointWordsLens))[batchIdx] = 0;
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inputMaxStopWordsLen = 0;
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}
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};
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setupWords(dInput->stopWordsList, request.stopWordsList, dJointInput.stopWordsPtrs, dJointInput.stopWordsLens,
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dInput->stopWordsPtrs, dInput->stopWordsLens, dInput->maxStopWordsLen, mMaxStopWordsLen, localBatchSize,
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batchIdx);
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dJointInput.maxStopWordsLen = mMaxStopWordsLen;
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setupWords(dInput->badWordsList, request.badWordsList, dJointInput.badWordsPtrs, dJointInput.badWordsLens,
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dInput->badWordsPtrs, dInput->badWordsLens, dInput->maxBadWordsLen, mMaxBadWordsLen, localBatchSize, batchIdx);
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dJointInput.maxBadWordsLen = mMaxBadWordsLen;
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TensorPtr sequenceLimitLength{
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ITensor::slice(constPointerCast(dJointInput.sequenceLimitLength), batchIdx, localBatchSize)};
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kernels::invokeFill(*sequenceLimitLength, inputLength + maxNewTokens, *stream);
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dInput->sequenceLimitLength = std::move(sequenceLimitLength);
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TensorPtr inputLengths{ITensor::slice(constPointerCast(dJointInput.lengths), batchIdx, localBatchSize)};
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kernels::invokeFill(*inputLengths, inputLength, *stream);
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dInput->lengths = inputLengths;
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// output
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auto& dJointOutput = *mJointDecodingOutput;
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auto& dOutput = mDecodingOutputs.at(batchIdx);
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auto const outputIdsShape = ITensor::makeShape({localBatchSize, beamWidth, mMaxSequenceLength});
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TensorPtr outputIds = ITensor::slice(dJointOutput.ids, batchIdx, localBatchSize);
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outputIds->reshape(outputIdsShape);
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dOutput = std::make_unique<DecodingOutput>(outputIds);
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dOutput->finishedSum = ITensor::slice(dJointOutput.finishedSum, batchIdx, localBatchSize);
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manager.setZero(*dOutput->finishedSum);
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dOutput->newTokensVec.resize(mMaxTokensPerStep);
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for (SizeType ti = 0; ti < mMaxTokensPerStep; ++ti)
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{
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TensorPtr newTokensStepView = ITensor::slice(dJointOutput.newTokensSteps, ti, localBatchSize);
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newTokensStepView->squeeze(0);
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dOutput->newTokensVec[ti] = ITensor::slice(newTokensStepView, batchIdx, localBatchSize);
|
|
manager.setZero(*dOutput->newTokensVec[ti]);
|
|
}
|
|
|
|
// FIXME(nkorobov): we call setZero mMaxTokensPerStep times for only 1 element
|
|
for (SizeType ti = 0; ti < mMaxTokensPerStep; ++ti)
|
|
{
|
|
TensorPtr finishedStepsView = std::move(ITensor::slice(mFinishedSteps, ti, 1));
|
|
finishedStepsView->squeeze(0);
|
|
TensorPtr finishedSteps = std::move(ITensor::slice(finishedStepsView, batchIdx, localBatchSize));
|
|
manager.setZero(*finishedSteps);
|
|
}
|
|
|
|
// cumLogProb is mandatory for beamWidth > 1
|
|
dOutput->cumLogProbs = nullptr;
|
|
if (request.computeCumLogProbs || beamWidth > 1)
|
|
{
|
|
dOutput->cumLogProbs = ITensor::slice(dJointOutput.cumLogProbs, batchIdx, localBatchSize);
|
|
manager.setZero(*dOutput->cumLogProbs);
|
|
}
|
|
|
|
dOutput->logProbs = nullptr;
|
|
if (request.computeLogProbs)
|
|
{
|
|
dOutput->logProbs = ITensor::slice(dJointOutput.logProbs, batchIdx, localBatchSize);
|
|
manager.setZero(*dOutput->logProbs);
|
|
}
|
|
|
|
if (beamWidth > 1)
|
|
{
|
|
kernels::invokeFill(
|
|
*IBuffer::slice(dOutput->cumLogProbs, 1, beamWidth - 1), DecodingOutput::kNegativeInfinity, *stream);
|
|
dOutput->parentIds = ITensor::slice(dJointOutput.parentIds, batchIdx, localBatchSize);
|
|
dOutput->parentIds->reshape(outputIdsShape);
|
|
manager.setZero(*dOutput->parentIds);
|
|
dOutput->beamHypotheses = dJointOutput.beamHypotheses.slice(batchIdx, localBatchSize);
|
|
dOutput->beamHypotheses.init(manager, endId);
|
|
}
|
|
|
|
auto generatedTokensPerStep = request.generatedTokensPerStep();
|
|
if (generatedTokensPerStep > 1)
|
|
{
|
|
TLLM_CHECK(beamWidth == 1);
|
|
auto numDraftTokens = generatedTokensPerStep - 1;
|
|
TensorPtr draftTokensReqBatchSlice = std::move(ITensor::slice(mDraftTokenIds, batchIdx, 1));
|
|
draftTokensReqBatchSlice->squeeze(0);
|
|
TensorPtr draftTokensReqTokensSlice = ITensor::slice(draftTokensReqBatchSlice, 0, numDraftTokens);
|
|
TensorPtr draftTokensView = ITensor::view(request.draftTokens, ITensor::makeShape({numDraftTokens}));
|
|
manager.copy(*draftTokensView, *draftTokensReqTokensSlice);
|
|
mAcceptByLogits[batchIdx] = false;
|
|
if (request.draftLogits.has_value())
|
|
{
|
|
TensorPtr draftLogitsView = ITensor::view(request.draftLogits.value());
|
|
mAcceptByLogits[batchIdx] = true;
|
|
|
|
TensorPtr draftLogitsReqBatchSlice = std::move(ITensor::slice(mDraftLogits, batchIdx, 1));
|
|
draftLogitsReqBatchSlice->squeeze(0);
|
|
TensorPtr draftLogitsReqTokensSlice = ITensor::slice(draftLogitsReqBatchSlice, 0, numDraftTokens);
|
|
manager.copy(*draftLogitsView, *draftLogitsReqTokensSlice);
|
|
}
|
|
|
|
auto numDraftTokensView = ITensor::slice(mNumDraftTokens, batchIdx, localBatchSize);
|
|
kernels::invokeFill(*numDraftTokensView, numDraftTokens, *stream);
|
|
|
|
auto const curandStatesView = ITensor::slice(mCurandStates, batchIdx, localBatchSize);
|
|
auto curandState = reinterpret_cast<curandState_t*>(bufferCast<int8_t>(*curandStatesView));
|
|
if (samplingConfig.randomSeed.has_value())
|
|
{
|
|
tk::invokeCurandInitialize(
|
|
curandState, nullptr, localBatchSize, samplingConfig.randomSeed.value()[0], stream->get());
|
|
}
|
|
else
|
|
{
|
|
tk::invokeCurandInitialize(curandState, nullptr, localBatchSize, 0, stream->get());
|
|
}
|
|
}
|
|
|
|
// remaining
|
|
if (!mFusedDecoder)
|
|
{
|
|
mDecoders[decoderIdx]->setup(samplingConfig, localBatchSize, mMaxSequenceLength);
|
|
}
|
|
TLLM_CHECK_WITH_INFO(!mFusedDecoder || beamWidth == 1, "Fused decoder is not supported for beam search yet.");
|
|
mBeamWidths[batchIdx] = beamWidth;
|
|
mNbSteps[batchIdx] = 0;
|
|
mFinished[batchIdx] = false;
|
|
mMaxNewTokens[batchIdx] = maxNewTokens;
|
|
mGeneratedTokensPerStep[batchIdx] = generatedTokensPerStep;
|
|
|
|
// copy the request ids into outputIds
|
|
auto const requestIdsShape = requestIds->getShape();
|
|
auto inputIdsView = ITensor::view(requestIds, ITensor::makeShape({localBatchSize, requestIdsShape.d[0]}));
|
|
auto outputIdsView = ITensor::view(outputIds, ITensor::makeShape({beamWidth, mMaxSequenceLength}));
|
|
kernels::invokeFill(*outputIdsView, endId, *stream);
|
|
kernels::tileTensor(*outputIdsView, *inputIdsView, beamWidth, *stream);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::newRequests(std::vector<SizeType> const& seqSlots,
|
|
std::vector<decoder_batch::Request> const& requests, std::vector<SamplingConfig> const& samplingConfigs)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto batchSlotsPtr = bufferCast<SizeType>(*mBatchSlotsSetup);
|
|
SizeType const localBatchSize = seqSlots.size();
|
|
for (SizeType bi = 0; bi < localBatchSize; ++bi)
|
|
{
|
|
newRequest(seqSlots[bi], requests[bi], samplingConfigs[bi]);
|
|
if (mFusedDecoder)
|
|
{
|
|
batchSlotsPtr[bi] = seqSlots[bi];
|
|
}
|
|
}
|
|
if (mFusedDecoder)
|
|
{
|
|
TensorPtr batchSlotsView = std::move(ITensor::slice(mBatchSlotsSetup, 0, localBatchSize));
|
|
auto fusedSamplingConfig = SamplingConfig(samplingConfigs);
|
|
mDecoders[0]->setup(fusedSamplingConfig, localBatchSize, mMaxSequenceLength, {batchSlotsView});
|
|
}
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
GptDecoderBatch::TokenPtr GptDecoderBatch::forwardAsync(
|
|
decoder_batch::Output& output, decoder_batch::Input const& input)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
auto& allTargetLogits = input.logits;
|
|
|
|
// TODO(nkorobov): check logits shape considering draft tokens
|
|
auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
|
|
auto const maxBeamWidth = jointOutputIdsShape.d[1];
|
|
|
|
auto& srcCacheIndirection = input.cacheIndirection;
|
|
auto& tgtCacheIndirection = output.cacheIndirection;
|
|
TLLM_CHECK_WITH_INFO((srcCacheIndirection && tgtCacheIndirection) || (!srcCacheIndirection && !tgtCacheIndirection),
|
|
"Specify both srcCacheIndirection and tgtCacheIndirection or neither.");
|
|
TLLM_CHECK(!srcCacheIndirection || srcCacheIndirection->getDataType() == TRTDataType<SizeType>::value);
|
|
TLLM_CHECK(!tgtCacheIndirection || tgtCacheIndirection->getDataType() == TRTDataType<SizeType>::value);
|
|
|
|
TLLM_CHECK(static_cast<SizeType>(output.sequenceLengths->getSize()) == mActualBatchSize * maxBeamWidth);
|
|
// TODO should remove this reshape and set shape to [batch_size, beam_width] outside
|
|
TensorPtr sequenceLengths
|
|
= ITensor::view(output.sequenceLengths, ITensor::makeShape({mActualBatchSize, maxBeamWidth}));
|
|
auto batchSlotsDecoderPtr = bufferCast<SizeType>(*mBatchSlotsDecoder);
|
|
auto batchSlotsAcceptTokensPtr = bufferCast<SizeType>(*mBatchSlotsAcceptTokens);
|
|
auto batchSlotsAcceptLogitsPtr = bufferCast<SizeType>(*mBatchSlotsAcceptLogits);
|
|
TLLM_CHECK(sequenceLengths);
|
|
auto constexpr singleRequest = 1;
|
|
|
|
CudaEvent eventStart{};
|
|
mStream->record(eventStart);
|
|
|
|
auto const maxGeneratedTokensPerStep
|
|
= *std::max_element(std::begin(mGeneratedTokensPerStep), std::end(mGeneratedTokensPerStep));
|
|
|
|
for (SizeType si = 0; si < maxGeneratedTokensPerStep; ++si)
|
|
{
|
|
SizeType localBatchDecoderIdx = 0;
|
|
SizeType localBatchAcceptTokensIdx = 0;
|
|
SizeType localBatchAcceptLogitsIdx = 0;
|
|
for (SizeType bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || si >= mGeneratedTokensPerStep[bi])
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (mFusedDecoder)
|
|
{
|
|
if (!mAcceptByLogits[bi] && mGeneratedTokensPerStep[bi] > 1 && si == mGeneratedTokensPerStep[bi] - 1)
|
|
{
|
|
batchSlotsAcceptTokensPtr[si * mActualBatchSize + localBatchAcceptTokensIdx] = bi;
|
|
localBatchAcceptTokensIdx++;
|
|
}
|
|
else if (mAcceptByLogits[bi] && mGeneratedTokensPerStep[bi] > 1 && si == 0)
|
|
{
|
|
batchSlotsAcceptLogitsPtr[si * mActualBatchSize + localBatchAcceptLogitsIdx] = bi;
|
|
localBatchAcceptLogitsIdx++;
|
|
}
|
|
batchSlotsDecoderPtr[si * mActualBatchSize + localBatchDecoderIdx] = bi;
|
|
localBatchDecoderIdx++;
|
|
}
|
|
}
|
|
|
|
if (!mFusedDecoder)
|
|
{
|
|
for (SizeType bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || si >= mGeneratedTokensPerStep[bi])
|
|
{
|
|
continue;
|
|
}
|
|
|
|
auto& stream = mStreams[bi];
|
|
stream->wait(eventStart.get());
|
|
|
|
auto& targetLogits = allTargetLogits[bi];
|
|
auto& dInput = *mDecodingInputs[bi];
|
|
auto& dOutput = *mDecodingOutputs[bi];
|
|
auto& decoder = *mDecoders[bi];
|
|
|
|
TensorPtr finishedStepsInput = ITensor::slice(mFinishedSteps, si, 1);
|
|
TensorPtr finishedStepsOutput
|
|
= ITensor::slice(mFinishedSteps, std::min(si + 1, mGeneratedTokensPerStep[bi] - 1), 1);
|
|
finishedStepsInput->squeeze(0);
|
|
finishedStepsOutput->squeeze(0);
|
|
|
|
if (srcCacheIndirection && tgtCacheIndirection)
|
|
{
|
|
auto srcView = std::shared_ptr(ITensor::slice(srcCacheIndirection, bi, singleRequest));
|
|
auto tgtView = std::shared_ptr(ITensor::slice(tgtCacheIndirection, bi, singleRequest));
|
|
dInput.cacheIndirection = ITensor::view(
|
|
srcView, ITensor::makeShape({singleRequest, mBeamWidths[bi], srcView->getShape().d[2]}));
|
|
dOutput.cacheIndirection = ITensor::view(
|
|
tgtView, ITensor::makeShape({singleRequest, mBeamWidths[bi], tgtView->getShape().d[2]}));
|
|
}
|
|
|
|
auto sequenceLengthsView = std::shared_ptr(ITensor::slice(sequenceLengths, bi, singleRequest));
|
|
dOutput.lengths
|
|
= ITensor::view(sequenceLengthsView, ITensor::makeShape({singleRequest, mBeamWidths[bi]}));
|
|
|
|
{
|
|
dInput.logits = ITensor::slice(targetLogits, si, singleRequest);
|
|
dOutput.newTokens = ITensor::view(dOutput.newTokensVec[si]);
|
|
dInput.finished = ITensor::slice(finishedStepsInput, bi, singleRequest);
|
|
dOutput.finished = ITensor::slice(finishedStepsOutput, bi, singleRequest);
|
|
|
|
decoder.forwardAsync(dOutput, dInput);
|
|
|
|
mNbSteps[bi] += 1;
|
|
mFinished[bi] = mNbSteps[bi] >= mMaxNewTokens[bi];
|
|
dInput.step += 1;
|
|
}
|
|
|
|
if (si == mGeneratedTokensPerStep[bi] - 1)
|
|
{
|
|
auto& stream = mStreams[bi];
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mStream->wait(event);
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
auto& dInput = *mJointDecodingInput;
|
|
auto& dOutput = *mJointDecodingOutput;
|
|
auto& decoder = *mDecoders[0];
|
|
auto& stream = mStreams[0];
|
|
|
|
stream->wait(eventStart.get());
|
|
|
|
BufferManager manager{stream};
|
|
|
|
std::vector<SharedConstPtr> logitsVec;
|
|
auto targetLogitsPtrsSlice = ITensor::slice(mTargetLogitsPtrs, si, 1);
|
|
auto targetLogitsPtrsSlicePtr = reinterpret_cast<void const**>(bufferCast<int64_t>(*targetLogitsPtrsSlice));
|
|
SizeType targetLogitsIdx = 0;
|
|
for (SizeType bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || si >= mGeneratedTokensPerStep[bi])
|
|
{
|
|
continue;
|
|
}
|
|
auto& targetLogits = allTargetLogits[bi];
|
|
SharedConstPtr logitsSlice = std::move(ITensor::slice(targetLogits, si, singleRequest));
|
|
logitsVec.push_back(logitsSlice);
|
|
targetLogitsPtrsSlicePtr[targetLogitsIdx++] = logitsSlice->data();
|
|
}
|
|
|
|
if (localBatchAcceptLogitsIdx > 0)
|
|
{
|
|
// These params are only used for testing. Thus, can be per batch instead of per request
|
|
auto const& samplingConfig = decoder.getSamplingConfig();
|
|
const bool useRandomAcceptanceThreshold = !samplingConfig.draftAcceptanceThreshold.has_value();
|
|
const float randomAcceptanceThreshold
|
|
= useRandomAcceptanceThreshold ? 0 : samplingConfig.draftAcceptanceThreshold.value()[0];
|
|
|
|
TensorPtr batchSlotsAcceptLogitsStepSlice = std::move(ITensor::slice(mBatchSlotsAcceptLogits, si, 1));
|
|
batchSlotsAcceptLogitsStepSlice->squeeze(0);
|
|
TensorPtr batchSlotsAcceptLogitsSlice
|
|
= std::move(ITensor::slice(batchSlotsAcceptLogitsStepSlice, 0, localBatchAcceptLogitsIdx));
|
|
|
|
IGptDecoder::acceptDraftTokensByLogits(
|
|
/* [max_bs, max_tokens_per_step, vocabPadded] */ *mDraftLogits,
|
|
/* [max_bs][max_tokens_per_step, vocabPadded] */ *targetLogitsPtrsSlice,
|
|
/* [max_bs, max_tokens_per_step, vocabPadded] */ *mDraftProbs,
|
|
/* [max_bs, max_tokens_per_step, vocabPadded] */ *mTargetProbs,
|
|
/* [max_bs] */ *mNumDraftTokens,
|
|
/* [max_tokens_per_step, max_bs] */ *mFinishedSteps,
|
|
/* [bs] */ *batchSlotsAcceptLogitsSlice, static_cast<SizeType>(mVocabSize),
|
|
static_cast<SizeType>(mVocabSizePadded), useRandomAcceptanceThreshold, randomAcceptanceThreshold,
|
|
reinterpret_cast<curandState_t*>(bufferCast<int8_t>(*mCurandStates)), stream);
|
|
}
|
|
|
|
TensorPtr finishedStepsInput = ITensor::slice(mFinishedSteps, si, 1);
|
|
TensorPtr finishedStepsOutput
|
|
= ITensor::slice(mFinishedSteps, std::min(maxGeneratedTokensPerStep - 1, si + 1), 1);
|
|
finishedStepsInput->squeeze(0);
|
|
finishedStepsOutput->squeeze(0);
|
|
TensorPtr newTokensStepView = std::move(ITensor::slice(dOutput.newTokensSteps, si, 1));
|
|
newTokensStepView->squeeze(0);
|
|
|
|
dInput.logitsVec = logitsVec;
|
|
dInput.finished = finishedStepsInput;
|
|
TensorPtr batchSlotsDecoderSlice = std::move(ITensor::slice(mBatchSlotsDecoder, si, 1));
|
|
batchSlotsDecoderSlice->squeeze(0);
|
|
dInput.batchSlots = batchSlotsDecoderSlice;
|
|
dInput.maxBatchSize = localBatchDecoderIdx;
|
|
|
|
dOutput.newTokens = newTokensStepView;
|
|
dOutput.finished = finishedStepsOutput;
|
|
dOutput.lengths = sequenceLengths;
|
|
|
|
if (localBatchDecoderIdx > 0)
|
|
{
|
|
decoder.forwardAsync(dOutput, dInput);
|
|
}
|
|
|
|
for (SizeType bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || si >= mGeneratedTokensPerStep[bi])
|
|
{
|
|
continue;
|
|
}
|
|
mNbSteps[bi] += 1;
|
|
mFinished[bi] = mNbSteps[bi] >= mMaxNewTokens[bi];
|
|
}
|
|
if (localBatchAcceptTokensIdx > 0)
|
|
{
|
|
TensorPtr batchSlotsAcceptTokensStepSlice = std::move(ITensor::slice(mBatchSlotsAcceptTokens, si, 1));
|
|
batchSlotsAcceptTokensStepSlice->squeeze(0);
|
|
auto batchSlotsAcceptTokensSlice
|
|
= ITensor::slice(batchSlotsAcceptTokensStepSlice, 0, localBatchAcceptTokensIdx);
|
|
|
|
// Update finished state for 0th step
|
|
auto finishedFinal = ITensor::slice(mFinishedSteps, si, 1);
|
|
IGptDecoder::acceptDraftTokensByIds(
|
|
/* [max_bs, max_seq_len] */ *dOutput.ids,
|
|
/* [max_bs, max_draft_tokens] */ *mDraftTokenIds,
|
|
/* [max_bs] */ *dInput.lengths,
|
|
/* [max_bs] */ *mNumDraftTokens,
|
|
/* [max_bs] */ *dOutput.lengths,
|
|
/* [max_tokens_per_step, max_bs] */ *mFinishedSteps,
|
|
/* [max_bs] */ *finishedFinal,
|
|
/* [max_bs] */ *dOutput.finishedSum,
|
|
/* [bs] */ *batchSlotsAcceptTokensSlice, stream);
|
|
}
|
|
|
|
if (si == maxGeneratedTokensPerStep - 1)
|
|
{
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mStream->wait(event);
|
|
}
|
|
}
|
|
}
|
|
|
|
CudaEvent eventStop{};
|
|
mStream->record(eventStop);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
return std::make_unique<decoder_batch::Token>(std::move(eventStop), input.active);
|
|
}
|
|
|
|
void GptDecoderBatch::forwardSync(decoder_batch::Token const& token)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
token.event.synchronize();
|
|
|
|
for (std::int32_t i = 0; i < mActualBatchSize; ++i)
|
|
{
|
|
if (token.active[i] && !mFinished[i])
|
|
{
|
|
auto& dOutput = *mDecodingOutputs[i];
|
|
mFinished[i] = mFinished[i]
|
|
// This condition requires the synchronization above
|
|
|| bufferCast<SizeType>(*dOutput.finishedSum)[0] == static_cast<SizeType>(mBeamWidths[i]);
|
|
}
|
|
}
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
// TODO call this at the end of forward if mFinished[i] changes from false to true?
|
|
CudaEvent GptDecoderBatch::postProcessRequest(SizeType batchIdx) const
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
auto& stream = mStreams[batchIdx];
|
|
auto manager = BufferManager{stream};
|
|
auto& decoder = *mDecoders[batchIdx];
|
|
|
|
auto& dInput = *mDecodingInputs[batchIdx];
|
|
auto& dOutput = *mDecodingOutputs[batchIdx];
|
|
|
|
// TODO can we do this inplace?
|
|
auto& outputIds = dOutput.ids;
|
|
auto finalOutputIds = manager.gpu(outputIds->getShape(), outputIds->getDataType());
|
|
decoder.gatherTree(*finalOutputIds, dOutput, dInput, manager);
|
|
manager.copy(*finalOutputIds, *outputIds);
|
|
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mStream->wait(event);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
return event;
|
|
}
|
|
|
|
void GptDecoderBatch::newBatch(
|
|
GenerationInput const& inputs, GenerationOutput const& outputs, SamplingConfig const& samplingConfig)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
// split batch into single requests
|
|
auto const& inputLengths = inputs.lengths;
|
|
mActualBatchSize = inputLengths->getShape().d[0];
|
|
mGeneratedTokensPerStep.resize(mActualBatchSize);
|
|
|
|
auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
|
|
auto const maxBatchSize = jointOutputIdsShape.d[0];
|
|
TLLM_CHECK(mActualBatchSize <= maxBatchSize);
|
|
auto const maxBeamWidth = jointOutputIdsShape.d[1];
|
|
TLLM_CHECK(samplingConfig.beamWidth <= maxBeamWidth);
|
|
|
|
auto const inputIdsShape = inputs.ids->getShape();
|
|
TensorPtr inputIdsFlatView = ITensor::view(inputs.ids);
|
|
if (inputs.packed && inputIdsShape.nbDims == 2)
|
|
{ // For users still pass inputs.ids with shape [1, num_tokens], do squeeze for them.
|
|
inputIdsFlatView->squeeze(0);
|
|
}
|
|
auto inputLengthsHost = mBufferManager.copyFrom(*inputLengths, MemoryType::kCPU);
|
|
auto inputLengthsPtr = bufferCast<SizeType>(*inputLengthsHost);
|
|
auto inputOffset = 0;
|
|
for (auto batchIdx = 0; batchIdx < mActualBatchSize; ++batchIdx)
|
|
{
|
|
mGeneratedTokensPerStep[batchIdx] = 1;
|
|
auto const inputLength = inputLengthsPtr[batchIdx];
|
|
auto const inputShape = ITensor::makeShape({inputLength});
|
|
TensorPtr inputView;
|
|
if (inputs.packed)
|
|
{
|
|
TLLM_CHECK(inputIdsFlatView->getShape().nbDims == 1);
|
|
inputView = ITensor::slice(inputIdsFlatView, inputOffset, inputLength);
|
|
inputOffset += inputLength;
|
|
}
|
|
else
|
|
{
|
|
inputView = ITensor::slice(inputs.ids, batchIdx, 1);
|
|
inputView->reshape(inputShape);
|
|
}
|
|
auto request = decoder_batch::Request{inputView, inputLength, inputs.maxNewTokens, inputs.endId};
|
|
request.computeCumLogProbs = (outputs.cumLogProbs != nullptr);
|
|
request.computeLogProbs = (outputs.logProbs != nullptr);
|
|
|
|
if (inputs.embeddingBias)
|
|
{
|
|
TLLM_THROW("newBatch doesn't support embeddingBias yet.");
|
|
}
|
|
if (inputs.badWordsList)
|
|
{
|
|
auto const& shape = inputs.badWordsList->getShape();
|
|
if (shape.nbDims == 2)
|
|
{
|
|
request.badWordsList = inputs.badWordsList;
|
|
}
|
|
else
|
|
{
|
|
assert(shape.nbDims == 3);
|
|
TensorPtr badWordsListView = ITensor::slice(inputs.badWordsList, batchIdx, 1);
|
|
badWordsListView->squeeze(0);
|
|
request.badWordsList = badWordsListView;
|
|
}
|
|
}
|
|
if (inputs.stopWordsList)
|
|
{
|
|
TensorPtr stopWordsListView = ITensor::slice(inputs.stopWordsList, batchIdx, 1);
|
|
stopWordsListView->squeeze(0);
|
|
request.stopWordsList = stopWordsListView;
|
|
}
|
|
newRequest(batchIdx, request, extractSamplingConfig(samplingConfig, batchIdx));
|
|
}
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::forwardAsync(decoder::Output& output, decoder::Input const& input)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto const& logitsShape = input.logits->getShape();
|
|
auto const batchSize = logitsShape.d[0];
|
|
auto constexpr singleRequest = 1;
|
|
std::vector<ITensor::SharedConstPtr> logits;
|
|
logits.reserve(batchSize);
|
|
for (auto batchIdx = 0; batchIdx < batchSize; ++batchIdx)
|
|
{
|
|
auto logitsSlice = std::shared_ptr(ITensor::slice(input.logits, batchIdx, singleRequest));
|
|
logits.emplace_back(
|
|
ITensor::view(logitsSlice, ITensor::makeShape({singleRequest, mBeamWidths[batchIdx], logitsShape.d[2]})));
|
|
}
|
|
|
|
decoder_batch::Input batchInput{logits};
|
|
batchInput.cacheIndirection = input.cacheIndirection;
|
|
|
|
decoder_batch::Output batchOutput;
|
|
batchOutput.cacheIndirection = output.cacheIndirection;
|
|
batchOutput.sequenceLengths = output.sequenceLengths;
|
|
|
|
mForwardToken = forwardAsync(batchOutput, batchInput);
|
|
mBufferManager.setZero(*mFinishedSum);
|
|
kernels::reduce(*mFinishedSum, *ITensor::slice(mJointDecodingOutput->finishedSum, 0, mActualBatchSize), *mStream);
|
|
mStream->record(mForwardEvent);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::forwardSync()
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
forwardSync(*mForwardToken);
|
|
// wait for mFinishedSum to be updated
|
|
mForwardEvent.synchronize();
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::finalize() const
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
for (SizeType batchIdx = 0; batchIdx < mActualBatchSize; ++batchIdx)
|
|
{
|
|
auto event = postProcessRequest(batchIdx);
|
|
}
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
CudaEvent GptDecoderBatch::finalize(SizeType batchIdx) const
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
auto event = postProcessRequest(batchIdx);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
return event;
|
|
}
|