mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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257 lines
9.3 KiB
C++
257 lines
9.3 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/gptDecoderBatched.h"
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#include "common.h"
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#include "decoderState.h"
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#include "iBuffer.h"
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#include "tensorrt_llm/batch_manager/decoderBuffers.h"
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#include "tensorrt_llm/batch_manager/llmRequest.h"
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/executor/types.h"
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#include "tensorrt_llm/kernels/decodingKernels.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 <algorithm>
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#include <cassert>
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#include <memory>
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#include <vector>
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using namespace tensorrt_llm::runtime;
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namespace tb = tensorrt_llm::batch_manager;
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using TensorPtr = ITensor::SharedPtr;
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GptDecoderBatched::GptDecoderBatched(GptDecoderBatched::CudaStreamPtr stream)
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: mRuntimeStream{std::move(stream)}
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, mBufferManager{mRuntimeStream}
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{
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}
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void GptDecoderBatched::disableLookahead(RequestVector const& genRequests, TensorPtr const& batchSlots)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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std::vector<SamplingConfig> samplingConfigs;
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samplingConfigs.reserve(genRequests.size());
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auto batchSlotsRange = BufferRange<SizeType32>(*batchSlots);
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SizeType32 batchIdx = 0;
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for (auto const& llmReq : genRequests)
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{
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samplingConfigs.push_back(llmReq->mSamplingConfig);
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batchSlotsRange[batchIdx] = llmReq->mSeqSlot.value();
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batchIdx += 1;
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}
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auto const batchSize = batchIdx;
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std::optional<SamplingConfig> samplingConfig;
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if (batchSize > 0)
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{
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samplingConfig = SamplingConfig(samplingConfigs);
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}
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TensorPtr batchSlotsView = ITensor::slice(batchSlots, 0, batchSize);
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mDecoder->disableLookahead(samplingConfig, batchSize, batchSlots);
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CudaEvent event{};
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mDecoderStream->record(event);
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mRuntimeStream->wait(event);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatched::setup(executor::DecodingMode const& mode, SizeType32 maxNumSequences, SizeType32 maxBeamWidth,
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nvinfer1::DataType dtype, ModelConfig const& modelConfig, WorldConfig const& worldConfig)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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TLLM_CHECK(maxNumSequences > 0);
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TLLM_CHECK(maxBeamWidth > 0);
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std::shared_ptr<SpeculativeDecodingModule const> speculativeDecodingModulePtr = nullptr;
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if (modelConfig.getSpeculativeDecodingMode().predictsDraftTokens())
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{
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speculativeDecodingModulePtr = modelConfig.getSpeculativeDecodingModulePtr();
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}
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auto const device = mRuntimeStream->getDevice();
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mDecoderStream = std::make_shared<CudaStream>();
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TLLM_CHECK(mDecoderStream->getDevice() == device);
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auto const vocabSize = modelConfig.getVocabSize();
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auto const vocabSizePadded = modelConfig.getVocabSizePadded(worldConfig.getSize());
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mDecoder = IGptDecoder::create(mode, dtype, maxNumSequences, maxBeamWidth, vocabSize, vocabSizePadded,
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mDecoderStream, speculativeDecodingModulePtr);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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namespace
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{
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//! @brief Prepare Input and Output for decoder step.
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// TODO: produce new input and output objects
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void prepareForward(decoder::DecoderState const& decoderState, SizeType32 step, tb::DecoderInputBuffers const& input,
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BufferManager const& bufferManager)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto const speculativeDecodingMode = decoderState.getSpeculativeDecodingMode();
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auto& dInput = decoderState.getJointDecodingInput();
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auto& dOutput = decoderState.getJointDecodingOutput();
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dInput.batchSlots = input.forwardBatchSlots.at(step);
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dInput.batchSize = static_cast<SizeType32>(dInput.batchSlots->getSize());
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dInput.logitsVec = input.batchLogits.at(step);
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if (speculativeDecodingMode.isDraftTokensExternal())
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{
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dInput.externalDraftTokensInputs->step = step;
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// WAR: reset finished state for generation requests
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if (step == 0)
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{
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auto batchSlotsRange = BufferRange<SizeType32 const>(*dInput.batchSlots);
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for (auto batchSlot : batchSlotsRange)
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{
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::TensorPtr finishedStepsSlice = ITensor::slice(decoderState.getFinishReasons(), batchSlot, 1);
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bufferManager.setZero(*finishedStepsSlice);
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}
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}
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}
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dOutput.newTokens = ITensor::slice(dOutput.newTokensSteps, step, decoderState.getMaxDecodingDecoderTokens());
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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} // namespace
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void GptDecoderBatched::forwardDispatch(decoder::DecoderState const& decoderState, tb::DecoderInputBuffers const& input)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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for (SizeType32 step = 0; step < input.maxDecoderSteps; ++step)
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{
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BufferManager manager{mDecoderStream};
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prepareForward(decoderState, step, input, manager);
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if (decoderState.getJointDecodingInput().batchSize > 0)
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{
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mDecoder->forwardAsync(decoderState.getJointDecodingOutput(), decoderState.getJointDecodingInput());
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}
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}
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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CudaEvent GptDecoderBatched::forwardAsync(
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decoder::DecoderState const& decoderState, tb::DecoderInputBuffers const& input)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto eventStart = CudaEvent{};
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mRuntimeStream->record(eventStart);
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mDecoderStream->wait(eventStart.get());
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forwardDispatch(decoderState, input);
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CudaEvent event{};
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mDecoderStream->record(event);
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mRuntimeStream->wait(event);
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CudaEvent eventStop{};
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mRuntimeStream->record(eventStop);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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return eventStop;
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}
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void GptDecoderBatched::forward(decoder::DecoderState const& decoderState, tb::DecoderInputBuffers const& input)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto decoderFinishEvent = forwardAsync(decoderState, input);
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decoderFinishEvent.synchronize();
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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namespace
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{
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std::pair<DecodingInput, DecodingOutput> prepareGatherTree(
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decoder::DecoderState const& decoderState, SizeType32 batchSlot, bool streaming, CudaStream const& stream)
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{
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auto& dJointInput = decoderState.getJointDecodingInput();
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auto& dJointOutput = decoderState.getJointDecodingOutput();
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auto slice = [batchSlot](auto& a, auto const& b)
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{
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if (b && b->getShape().d[0] > 0)
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{
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a = ITensor::slice(b, batchSlot, 1);
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}
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};
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// Prepare a slice of dJointInput and dJointOutput for gatherTree
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DecodingInput dInput{dJointInput};
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slice(dInput.endIds, dJointInput.endIds);
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slice(dInput.lengths, dJointInput.lengths);
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DecodingOutput dOutput{
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ITensor::slice(dJointOutput.ids, batchSlot, 1), ITensor::slice(dJointOutput.gatheredIds, batchSlot, 1)};
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dOutput.beamHypotheses = dJointOutput.beamHypotheses.slice(batchSlot, 1);
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slice(dOutput.parentIds, dJointOutput.parentIds);
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slice(dOutput.cumLogProbs, dJointOutput.cumLogProbs);
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slice(dOutput.cacheIndirection, dJointOutput.cacheIndirection);
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slice(dOutput.lengths, dJointOutput.lengths);
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slice(dOutput.finishReasons, dJointOutput.finishReasons);
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slice(dOutput.logProbs, dJointOutput.logProbs);
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dOutput.newTokens = ITensor::view(dJointOutput.newTokens);
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TLLM_CHECK(dOutput.newTokens->getShape().d[0] == 1);
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dOutput.newTokens->squeeze(0);
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dOutput.newTokens = ITensor::slice(dOutput.newTokens, batchSlot, 1);
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dOutput.logProbsTiled = dJointOutput.logProbsTiled;
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if (streaming)
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{
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// in case of streaming we shouldn't overwrite the data in beamHypotheses, since the beam search kernels expect
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// ungathered data but the kernels in gatherTree write in-place.
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// Thus, we need to make a copy of the beamHypotheses
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auto const& beamSearchBuffers = decoderState.getBeamSearchBuffers();
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tensorrt_llm::kernels::invokeCopyBeamHypotheses(dOutput.beamHypotheses, beamSearchBuffers.mOutputBeamHypotheses,
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*dOutput.cumLogProbs, *beamSearchBuffers.mCumLogProbsTmp, stream, beamSearchBuffers.mNumSMs);
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dOutput.beamHypotheses = beamSearchBuffers.mOutputBeamHypotheses;
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dOutput.cumLogProbs = beamSearchBuffers.mCumLogProbsTmp;
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}
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return {(std::move(dInput)), (std::move(dOutput))};
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}
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} // namespace
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// TODO call this at the end of forward if mFinished[i] changes from false to true?
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CudaEvent GptDecoderBatched::finalize(decoder::DecoderState const& decoderState, SizeType32 batchSlot,
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SamplingConfig const& samplingConfig, bool streaming) const
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto [dInput, dOutput] = prepareGatherTree(decoderState, batchSlot, streaming, *mRuntimeStream);
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kernels::gatherTree(dOutput, dInput, samplingConfig, *mRuntimeStream);
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CudaEvent event{};
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mRuntimeStream->record(event);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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return event;
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}
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