mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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8681b3a4c0
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvanesh.sridharan@sprinklr.com> Co-authored-by: Qingquan Song <ustcsqq@gmail.com>
1213 lines
52 KiB
C++
1213 lines
52 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 "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/kernels/decodingCommon.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 "tensorrt_llm/runtime/memoryCounters.h"
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#include "tensorrt_llm/runtime/runtimeBuffers.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, SizeType32 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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extractOptional(samplingConfig.noRepeatNgramSize, batchSamplingConfig.noRepeatNgramSize);
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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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extractOptional(samplingConfig.beamSearchDiversityRate, batchSamplingConfig.beamSearchDiversityRate);
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extractOptional(samplingConfig.lengthPenalty, batchSamplingConfig.lengthPenalty);
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extractOptional(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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GptDecoderBatched::GptDecoderBatched(std::size_t vocabSize, std::size_t vocabSizePadded,
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GptDecoderBatched::CudaStreamPtr stream, SpeculativeDecodingMode const& speculativeDecodingMode,
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nvinfer1::DataType dtype)
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: mVocabSize{vocabSize}
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, mVocabSizePadded{vocabSizePadded}
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, mRuntimeStream{std::move(stream)}
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, mBufferManager{mRuntimeStream}
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, mSpeculativeDecodingMode{speculativeDecodingMode}
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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<SizeType32>::value;
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auto constexpr nvFloatType = TRTDataType<float>::value;
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auto& dInput = mJointDecodingInput;
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{ // prevent reusing these vars after std::move
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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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auto batchSlots = mBufferManager.emptyTensor(MemoryType::kPINNED, nvSizeType);
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dInput = std::make_unique<DecodingInput>(
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0, 0, 0, 0, std::move(dummyLogits), std::move(endIds), std::move(batchSlots));
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}
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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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{ // prevent reusing these vars after std::move
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auto outputIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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auto gatheredOutputIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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dOutput = std::make_unique<DecodingOutput>(std::move(outputIds), std::move(gatheredOutputIds));
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}
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dOutput->newTokensSteps = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
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dOutput->parentIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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mFinishedSteps
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= mBufferManager.emptyTensor(MemoryType::kGPU, TRTDataType<tk::FinishedState::UnderlyingType>::value);
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mBatchSlotsSetup = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, nvSizeType);
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mBatchSlotsDecoder = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, nvSizeType);
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// use batchSize many entries instead of the usual 1
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dOutput->finishedSum = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, nvSizeType);
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mFinishedSum = BufferManager::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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dOutput->finishReasons
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= mBufferManager.emptyTensor(MemoryType::kGPU, TRTDataType<tk::FinishedState::UnderlyingType>::value);
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dOutput->logProbsTiled = mBufferManager.emptyTensor(MemoryType::kGPU, TRTDataType<float>::value);
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dInput->stopWordsPtrs = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, TRTDataType<int32_t*>::value);
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dInput->stopWordsLens = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, nvSizeType);
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dInput->badWordsPtrs = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, TRTDataType<int32_t*>::value);
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dInput->badWordsLens = mBufferManager.emptyTensor(MemoryType::kPINNEDPOOL, nvSizeType);
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dInput->embeddingBias = mBufferManager.emptyTensor(MemoryType::kGPU, dtype);
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int device;
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cudaGetDevice(&device);
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cudaDeviceProp deviceProp;
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cudaGetDeviceProperties(&deviceProp, device);
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mNumSMs = deviceProp.multiProcessorCount;
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if (!mSpeculativeDecodingMode.isNone())
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{
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allocateSpeculativeDecodingBuffers(dtype);
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}
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatched::allocateSpeculativeDecodingBuffers(nvinfer1::DataType dtype)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto constexpr nvSizeType = TRTDataType<SizeType32>::value;
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auto& dInput = mJointDecodingInput;
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auto& dOutput = mJointDecodingOutput;
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if (mSpeculativeDecodingMode.isMedusa())
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{
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DecodingInput::MedusaInputs medusaInputs;
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medusaInputs.medusaPaths = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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medusaInputs.medusaTreeIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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medusaInputs.medusaCurTokensPerStep = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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medusaInputs.medusaTargetTokensPerStep = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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dInput->medusaInputs = medusaInputs;
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}
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DecodingOutput::SpeculativeDecodingOutputs speculativeDecodingOutputs;
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if (mSpeculativeDecodingMode.predictsDraftTokens())
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{
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speculativeDecodingOutputs.nextDraftTokens
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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if (mSpeculativeDecodingMode.variableDraftLength())
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{
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speculativeDecodingOutputs.nextDraftTokensLen
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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speculativeDecodingOutputs.prevDraftTokensLen
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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}
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}
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if (mSpeculativeDecodingMode.isLookaheadDecoding())
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{
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dInput->lookaheadInputs = DecodingInput::LookaheadInputs();
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}
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if (mSpeculativeDecodingMode.needsKVCacheRewind())
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{
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speculativeDecodingOutputs.acceptedTokensLen
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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speculativeDecodingOutputs.acceptedLengthsCumSum
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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speculativeDecodingOutputs.pathsOffsets
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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}
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dOutput->speculativeDecodingOutputs = speculativeDecodingOutputs;
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if (mSpeculativeDecodingMode.isDraftTokensExternal())
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{
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DecodingInput::ExternalDraftTokensInputs externalDraftTokensInputs;
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externalDraftTokensInputs.draftLogits = mBufferManager.emptyTensor(MemoryType::kGPU, dtype);
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externalDraftTokensInputs.draftProbs = mBufferManager.emptyTensor(MemoryType::kGPU, dtype);
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externalDraftTokensInputs.targetProbs = mBufferManager.emptyTensor(MemoryType::kGPU, dtype);
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externalDraftTokensInputs.numDraftTokens = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
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externalDraftTokensInputs.useDraftLogits
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= mBufferManager.emptyTensor(MemoryType::kGPU, TRTDataType<bool>::value);
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externalDraftTokensInputs.draftTokenIds
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= mBufferManager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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dInput->externalDraftTokensInputs = externalDraftTokensInputs;
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}
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatched::setupExplicitDraftTokens(ExplicitDraftTokensBuffers::Inputs explicitDraftTokensBuffers)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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TLLM_CHECK(mSpeculativeDecodingMode.isExplicitDraftTokens());
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mJointDecodingOutput->explicitDraftTokensBuffers = std::move(explicitDraftTokensBuffers);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatched::setupLookahead(LookaheadDecodingBuffers lookaheadDecodingBuffers)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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TLLM_CHECK(mSpeculativeDecodingMode.isLookaheadDecoding());
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mJointDecodingOutput->lookaheadOutputs = std::move(lookaheadDecodingBuffers);
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mJointDecodingInput->lookaheadInputs->tokensPerStep = mJointDecodingOutput->lookaheadOutputs->generationLengths;
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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 maxBatchSize, SizeType32 maxBeamWidth,
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SizeType32 maxAttentionWindow, SizeType32 sinkTokenLength, SizeType32 maxSequenceLength,
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SizeType32 maxTokensPerEngineStep, nvinfer1::DataType dtype, ModelConfig const& modelConfig)
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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(maxTokensPerEngineStep > 0);
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TLLM_CHECK(maxSequenceLength > 0);
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mActualBatchSize = maxBatchSize;
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mMaxSequenceLength = maxSequenceLength;
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mMaxAttentionWindow = maxAttentionWindow;
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mSinkTokenLength = sinkTokenLength;
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mMaxDecodingEngineTokens = maxTokensPerEngineStep;
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mDecodingMode = mode;
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TLLM_CHECK_WITH_INFO((mMaxDecodingEngineTokens == 1 && mSpeculativeDecodingMode.isNone())
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|| (mMaxDecodingEngineTokens > 1 && !mSpeculativeDecodingMode.isNone()),
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"Max tokens per engine step must be equal to 1 when no speculative decoding is configured, "
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"or > 1 for any speculative decoding mode");
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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({maxTokensPerEngineStep, maxBatchSize, maxBeamWidth});
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auto const maxBatchSizeXmaxTokensPerStep = ITensor::makeShape({maxBatchSize, maxTokensPerEngineStep});
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auto const jointOutputIdsShape = ITensor::makeShape({maxBatchSize, maxBeamWidth, maxSequenceLength});
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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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dInput.stopWordsLists.resize(maxBatchSize);
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dInput.badWordsLists.resize(maxBatchSize);
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const_cast<ITensor&>(*dInput.endIds).reshape(maxBatchSizeShape);
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const_cast<ITensor&>(*dInput.batchSlots).reshape(maxBatchSizeShape);
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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, *mRuntimeStream);
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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& dOutput = *mJointDecodingOutput;
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dOutput.ids->reshape(jointOutputIdsShape);
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if (maxBeamWidth > 1)
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{
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dOutput.gatheredIds->reshape(jointOutputIdsShape);
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mOutputBeamHypotheses = std::make_shared<DecodingOutput::BeamHypotheses>();
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mOutputBeamHypotheses->empty(mBufferManager);
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mOutputBeamHypotheses->reshape(1, maxBeamWidth, mMaxSequenceLength);
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mCumLogProbsTmp = mBufferManager.gpu(ITensor::makeShape({1, maxBeamWidth}), nvinfer1::DataType::kFLOAT);
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}
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else
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{
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dOutput.gatheredIds = dOutput.ids;
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}
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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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dOutput.finishReasons->reshape(maxBatchSizeXmaxBeamWidth);
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mBufferManager.setZero(*dOutput.finishReasons);
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mBatchSlotsSetup->reshape(ITensor::makeShape({maxBatchSize}));
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mBatchSlotsDecoder->reshape(ITensor::makeShape({maxTokensPerEngineStep, maxBatchSize}));
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if (mSpeculativeDecodingMode.isDraftTokensExternal())
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{
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dInput.externalDraftTokensInputs->draftProbs->reshape(ITensor::makeShape(
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{maxBatchSize, maxTokensPerEngineStep, maxBeamWidth, static_cast<SizeType32>(mVocabSizePadded)}));
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dInput.externalDraftTokensInputs->targetProbs->reshape(ITensor::makeShape(
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{maxBatchSize, maxTokensPerEngineStep, maxBeamWidth, static_cast<SizeType32>(mVocabSizePadded)}));
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dInput.externalDraftTokensInputs->draftLogits->reshape(
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ITensor::makeShape({maxBatchSize, maxTokensPerEngineStep, static_cast<SizeType32>(mVocabSizePadded)}));
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dInput.externalDraftTokensInputs->draftTokenIds->reshape(maxBatchSizeXmaxTokensPerStep);
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dInput.externalDraftTokensInputs->numDraftTokens->reshape(ITensor::makeShape({maxBatchSize, 1}));
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dInput.externalDraftTokensInputs->useDraftLogits->reshape(ITensor::makeShape({maxBatchSize, 1}));
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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.newTokensSteps->reshape(ITensor::makeShape({maxTokensPerEngineStep, maxBatchSize, maxBeamWidth}));
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dOutput.cumLogProbs->reshape(maxBatchSizeXmaxBeamWidth);
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mBufferManager.setZero(*dOutput.cumLogProbs);
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dOutput.logProbs->reshape(jointOutputIdsShape);
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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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dOutput.logProbsTiled->reshape(ITensor::makeShape({maxSequenceLength, maxBatchSize, maxBeamWidth}));
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mBufferManager.setZero(*dOutput.logProbsTiled);
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const_cast<ITensor&>(*dInput.embeddingBias)
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.reshape(ITensor::makeShape({maxBatchSize, static_cast<SizeType32>(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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std::shared_ptr<SpeculativeDecodingModule const> speculativeDecodingModulePtr = nullptr;
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if (mSpeculativeDecodingMode.predictsDraftTokens())
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{
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speculativeDecodingModulePtr = modelConfig.getSpeculativeDecodingModulePtr();
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setupSpeculativeDecoding(modelConfig);
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}
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else
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{
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mMaxDecodingDecoderTokens = 1;
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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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mDecoder = IGptDecoder::create(mode, dtype, maxBatchSize, maxBeamWidth, mVocabSize, mVocabSizePadded,
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mMaxSequenceLength, mDecoderStream, speculativeDecodingModulePtr);
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mNbSteps.clear();
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mNbSteps.resize(maxBatchSize, 0);
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mFinished.clear();
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mFinished.resize(maxBatchSize, true);
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mMaxNewTokens.clear();
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mMaxNewTokens.resize(maxBatchSize, 0);
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mBeamWidths.clear();
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mBeamWidths.resize(maxBatchSize, 0);
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mNumDecodingEngineTokens.clear();
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mNumDecodingEngineTokens.resize(maxBatchSize, 0);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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void GptDecoderBatched::setupSpeculativeDecoding(ModelConfig const& modelConfig)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto& dInput = *mJointDecodingInput;
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auto& dOutput = *mJointDecodingOutput;
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auto const speculativeDecodingModule = modelConfig.getSpeculativeDecodingModulePtr();
|
|
if (mSpeculativeDecodingMode.isMedusa())
|
|
{
|
|
auto& medusaPaths = const_cast<ITensor&>(*dInput.medusaInputs->medusaPaths);
|
|
medusaPaths.reshape(ITensor::makeShape({mActualBatchSize, speculativeDecodingModule->getMaxDecodingTokens(),
|
|
speculativeDecodingModule->getMaxPathLen()}));
|
|
mBufferManager.setMem(medusaPaths, -1);
|
|
|
|
auto& medusaTreeIds = const_cast<ITensor&>(*dInput.medusaInputs->medusaTreeIds);
|
|
medusaTreeIds.reshape(
|
|
ITensor::makeShape({mActualBatchSize, speculativeDecodingModule->getMaxDecodingDraftTokens()}));
|
|
mBufferManager.setZero(medusaTreeIds);
|
|
auto& curTokensPerStep = const_cast<ITensor&>(*dInput.medusaInputs->medusaCurTokensPerStep);
|
|
auto& targetTokensPerStep = const_cast<ITensor&>(*dInput.medusaInputs->medusaTargetTokensPerStep);
|
|
curTokensPerStep.reshape(ITensor::makeShape({mActualBatchSize}));
|
|
targetTokensPerStep.reshape(ITensor::makeShape({mActualBatchSize}));
|
|
mBufferManager.setZero(curTokensPerStep);
|
|
mBufferManager.setZero(targetTokensPerStep);
|
|
}
|
|
|
|
if (mSpeculativeDecodingMode.predictsDraftTokens())
|
|
{
|
|
dOutput.speculativeDecodingOutputs->nextDraftTokens->reshape(
|
|
ITensor::makeShape({mActualBatchSize, mMaxDecodingEngineTokens - 1}));
|
|
if (mSpeculativeDecodingMode.variableDraftLength())
|
|
{
|
|
dOutput.speculativeDecodingOutputs->nextDraftTokensLen->reshape(ITensor::makeShape({mActualBatchSize}));
|
|
dOutput.speculativeDecodingOutputs->prevDraftTokensLen->reshape(ITensor::makeShape({mActualBatchSize}));
|
|
}
|
|
}
|
|
if (mSpeculativeDecodingMode.needsKVCacheRewind())
|
|
{
|
|
dOutput.speculativeDecodingOutputs->acceptedTokensLen->reshape(ITensor::makeShape({mActualBatchSize}));
|
|
dOutput.speculativeDecodingOutputs->acceptedLengthsCumSum->reshape(ITensor::makeShape({mActualBatchSize + 1}));
|
|
dOutput.speculativeDecodingOutputs->pathsOffsets->reshape(
|
|
ITensor::makeShape({mActualBatchSize * speculativeDecodingModule->getMaxDraftPathLen()}));
|
|
}
|
|
|
|
mMaxDecodingDecoderTokens = mMaxDecodingEngineTokens;
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::newRequest(
|
|
SizeType32 batchSlot, decoder_batch::Request const& request, SamplingConfig const& samplingConfig)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
TLLM_CHECK(batchSlot >= 0);
|
|
auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
|
|
auto const batchSize = jointOutputIdsShape.d[0];
|
|
TLLM_CHECK(0 <= batchSize && batchSlot < batchSize);
|
|
auto const maxBeamWidth = jointOutputIdsShape.d[1];
|
|
auto const beamWidth = samplingConfig.beamWidth;
|
|
TLLM_CHECK_WITH_INFO(beamWidth <= maxBeamWidth,
|
|
tc::fmtstr("Beam width (%d) must be smaller than maxBeamWidth (" FMT_DIM ") passed to decoder setup function.",
|
|
beamWidth, maxBeamWidth));
|
|
auto const& requestIds = request.ids;
|
|
auto const inputLength = request.inputLen;
|
|
auto const numDecodingEngineTokens = request.generatedTokensPerEngineStep;
|
|
auto const numDecodingDraftEngineTokens = numDecodingEngineTokens - 1;
|
|
auto const maxNewTokens
|
|
= request.maxNewTokens.value_or(mMaxSequenceLength - inputLength - numDecodingDraftEngineTokens);
|
|
|
|
TLLM_CHECK_WITH_INFO(inputLength + maxNewTokens + numDecodingDraftEngineTokens <= mMaxSequenceLength,
|
|
tc::fmtstr(
|
|
"Input length (%d) + max new tokens (%d) + draft tokens (%d) must be less than max sequence length (%d).",
|
|
inputLength, maxNewTokens, numDecodingDraftEngineTokens, mMaxSequenceLength));
|
|
TLLM_CHECK(requestIds->getDataType() == TRTDataType<TokenIdType>::value);
|
|
auto const endId = request.endId.value_or(-1);
|
|
|
|
auto const& stream = mDecoderStream;
|
|
BufferManager manager{stream};
|
|
|
|
// input
|
|
auto& dJointInput = *mJointDecodingInput;
|
|
|
|
TensorPtr endIdTensorPtr{ITensor::slice(constPointerCast(dJointInput.endIds), batchSlot, 1)};
|
|
kernels::invokeFill(*endIdTensorPtr, endId, *stream);
|
|
|
|
TensorPtr embeddingBiasSlice = ITensor::slice(constPointerCast(dJointInput.embeddingBias), batchSlot, 1);
|
|
if (request.embeddingBias)
|
|
{
|
|
TLLM_CHECK(request.embeddingBias->getShape().nbDims == 2);
|
|
TLLM_CHECK(request.embeddingBias->getShape().d[0] == 1);
|
|
TLLM_CHECK_WITH_INFO(request.embeddingBias->getShape().d[1] == static_cast<SizeType32>(mVocabSize),
|
|
"The embedding bias shape is not as expected. Expected last dimension to be same as vocab size: %lu.",
|
|
mVocabSize);
|
|
manager.copy(*request.embeddingBias, *embeddingBiasSlice);
|
|
}
|
|
else
|
|
{
|
|
manager.setZero(*embeddingBiasSlice);
|
|
}
|
|
|
|
auto setupWords = [](std::vector<runtime::ITensor::SharedPtr>& jointWordsLists, TensorPtr const& requestWordsList,
|
|
SharedConstPtr& jointWordsPtrs, SharedConstPtr& jointWordsLens, SizeType32& jointMaxWordsLen,
|
|
SizeType32 batchSlot)
|
|
{
|
|
if (requestWordsList)
|
|
{
|
|
auto const wordsLen = requestWordsList->getShape().d[1];
|
|
BufferRange<int32_t*>(*constPointerCast(jointWordsPtrs))[batchSlot]
|
|
= bufferCast<TokenIdType>(*requestWordsList);
|
|
bufferCast<SizeType32>(*constPointerCast(jointWordsLens))[batchSlot] = wordsLen;
|
|
// FIXME(nkorobov): this is monotonically growing size
|
|
jointMaxWordsLen = std::max(static_cast<SizeType32>(wordsLen), jointMaxWordsLen);
|
|
|
|
// NOTE(nkorobov): jointWordsList is not used in gptDecoder, but required to keep <name>WordsList's
|
|
// memory allocated
|
|
jointWordsLists[batchSlot] = requestWordsList;
|
|
}
|
|
else
|
|
{
|
|
bufferCast<SizeType32>(*constPointerCast(jointWordsLens))[batchSlot] = 0;
|
|
}
|
|
};
|
|
|
|
setupWords(dJointInput.stopWordsLists, request.stopWordsList, dJointInput.stopWordsPtrs, dJointInput.stopWordsLens,
|
|
dJointInput.maxStopWordsLen, batchSlot);
|
|
|
|
setupWords(dJointInput.badWordsLists, request.badWordsList, dJointInput.badWordsPtrs, dJointInput.badWordsLens,
|
|
dJointInput.maxBadWordsLen, batchSlot);
|
|
|
|
TensorPtr sequenceLimitLength{ITensor::slice(constPointerCast(dJointInput.sequenceLimitLength), batchSlot, 1)};
|
|
kernels::invokeFill(*sequenceLimitLength, inputLength + maxNewTokens, *stream);
|
|
|
|
TensorPtr inputLengths{ITensor::slice(constPointerCast(dJointInput.lengths), batchSlot, 1)};
|
|
kernels::invokeFill(*inputLengths, inputLength, *stream);
|
|
|
|
// output
|
|
auto& dJointOutput = *mJointDecodingOutput;
|
|
auto const outputIdsShape = ITensor::makeShape({1, beamWidth, mMaxSequenceLength});
|
|
|
|
auto finishedSum = ITensor::slice(dJointOutput.finishedSum, batchSlot, 1);
|
|
manager.setZero(*finishedSum);
|
|
|
|
for (SizeType32 ti = 0; ti < mMaxDecodingEngineTokens; ++ti)
|
|
{
|
|
TensorPtr newTokensStepView = ITensor::slice(dJointOutput.newTokensSteps, ti, 1);
|
|
newTokensStepView->squeeze(0);
|
|
auto newTokensVec = ITensor::slice(newTokensStepView, batchSlot, 1);
|
|
manager.setZero(*newTokensVec);
|
|
}
|
|
|
|
// FIXME(nkorobov): we call setZero mMaxDecodingEngineTokens times for only 1 element
|
|
for (SizeType32 ti = 0; ti < mMaxDecodingEngineTokens; ++ti)
|
|
{
|
|
TensorPtr finishedStepsView = ITensor::slice(mFinishedSteps, ti, 1);
|
|
finishedStepsView->squeeze(0);
|
|
TensorPtr finishedSteps = ITensor::slice(finishedStepsView, batchSlot, 1);
|
|
manager.setZero(*finishedSteps);
|
|
}
|
|
|
|
// cumLogProb is mandatory for beamWidth > 1
|
|
if ((samplingConfig.cumLogProbs.has_value() && samplingConfig.cumLogProbs->at(0)) || beamWidth > 1)
|
|
{
|
|
auto cumLogProbs = ITensor::slice(dJointOutput.cumLogProbs, batchSlot, 1);
|
|
manager.setZero(*cumLogProbs);
|
|
}
|
|
|
|
if (samplingConfig.outputLogProbs.has_value() && samplingConfig.outputLogProbs->at(0))
|
|
{
|
|
auto logProbs = ITensor::slice(dJointOutput.logProbs, batchSlot, 1);
|
|
manager.setZero(*logProbs);
|
|
}
|
|
|
|
if (beamWidth > 1)
|
|
{
|
|
TensorPtr cumLogProbs = ITensor::slice(dJointOutput.cumLogProbs, batchSlot, 1);
|
|
kernels::invokeFill(*IBuffer::slice(cumLogProbs, 1, beamWidth - 1), DecodingOutput::kNegativeInfinity, *stream);
|
|
|
|
auto parentIds = ITensor::slice(dJointOutput.parentIds, batchSlot, 1);
|
|
parentIds->reshape(outputIdsShape);
|
|
manager.setZero(*parentIds);
|
|
|
|
auto beamHypotheses = dJointOutput.beamHypotheses.slice(batchSlot, 1);
|
|
beamHypotheses.init(manager, endId);
|
|
}
|
|
|
|
// Speculative execution
|
|
if (numDecodingEngineTokens > 1)
|
|
{
|
|
TLLM_CHECK(beamWidth == 1);
|
|
newRequestSpeculativeDecoding(batchSlot, request, samplingConfig);
|
|
}
|
|
|
|
// remaining
|
|
mBeamWidths[batchSlot] = beamWidth;
|
|
mNbSteps[batchSlot] = 0;
|
|
mFinished[batchSlot] = false;
|
|
mMaxNewTokens[batchSlot] = maxNewTokens;
|
|
mNumDecodingEngineTokens[batchSlot] = numDecodingEngineTokens;
|
|
|
|
// copy the request ids into outputIds
|
|
auto const requestIdsShape = requestIds->getShape();
|
|
auto inputIdsView = ITensor::view(requestIds, ITensor::makeShape({1, requestIdsShape.d[0]}));
|
|
TensorPtr outputIds = ITensor::slice(dJointOutput.ids, batchSlot, 1);
|
|
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 GptDecoderBatched::newRequestSpeculativeDecoding(
|
|
SizeType32 batchIdx, decoder_batch::Request const& request, SamplingConfig const& samplingConfig)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
if (mSpeculativeDecodingMode.predictsDraftTokens())
|
|
{
|
|
auto const& stream = mDecoderStream;
|
|
BufferManager manager{stream};
|
|
|
|
auto& dJointOutput = *mJointDecodingOutput;
|
|
|
|
TensorPtr nextDraftTokens
|
|
= ITensor::slice(dJointOutput.speculativeDecodingOutputs->nextDraftTokens, batchIdx, 1);
|
|
// FIXME(nkorobov): can we skip this?
|
|
manager.setZero(*nextDraftTokens);
|
|
if (mSpeculativeDecodingMode.variableDraftLength())
|
|
{
|
|
TensorPtr nextDraftTokensLen
|
|
= ITensor::slice(dJointOutput.speculativeDecodingOutputs->nextDraftTokensLen, batchIdx, 1);
|
|
manager.setZero(*nextDraftTokensLen);
|
|
}
|
|
}
|
|
|
|
if (mSpeculativeDecodingMode.isDraftTokensExternal())
|
|
{
|
|
newRequestDraftTokensExternal(batchIdx, request, samplingConfig);
|
|
}
|
|
else if (mSpeculativeDecodingMode.isMedusa())
|
|
{
|
|
newRequestMedusa(batchIdx, request);
|
|
}
|
|
else if (mSpeculativeDecodingMode.isLookaheadDecoding())
|
|
{
|
|
newRequestLookahead(batchIdx, request);
|
|
}
|
|
else if (mSpeculativeDecodingMode.isExplicitDraftTokens())
|
|
{
|
|
newRequestExplicitDraftTokens(batchIdx, request);
|
|
}
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::newRequestDraftTokensExternal(
|
|
SizeType32 batchIdx, decoder_batch::Request const& request, SamplingConfig const& samplingConfig)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto const& stream = mDecoderStream;
|
|
BufferManager manager{stream};
|
|
|
|
auto& dJointInput = *mJointDecodingInput;
|
|
auto useDraftLogits = false;
|
|
|
|
auto const numDraftTokens = request.generatedTokensPerEngineStep - 1;
|
|
if (request.draftLogits.has_value())
|
|
{
|
|
TensorPtr draftLogitsView = ITensor::view(request.draftLogits.value());
|
|
useDraftLogits = true;
|
|
|
|
TensorPtr draftLogitsReqBatchSlice
|
|
= ITensor::slice(dJointInput.externalDraftTokensInputs->draftLogits, batchIdx, 1);
|
|
draftLogitsReqBatchSlice->squeeze(0);
|
|
TensorPtr draftLogitsReqTokensSlice = ITensor::slice(draftLogitsReqBatchSlice, 0, numDraftTokens);
|
|
manager.copy(*draftLogitsView, *draftLogitsReqTokensSlice);
|
|
}
|
|
auto useDraftLogitsView = ITensor::slice(dJointInput.externalDraftTokensInputs->useDraftLogits, batchIdx, 1);
|
|
kernels::invokeFill(*useDraftLogitsView, useDraftLogits, *stream);
|
|
|
|
TensorPtr draftTokensReqBatchSlice
|
|
= ITensor::slice(dJointInput.externalDraftTokensInputs->draftTokenIds, 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);
|
|
|
|
auto numDraftTokensView = ITensor::slice(dJointInput.externalDraftTokensInputs->numDraftTokens, batchIdx, 1);
|
|
kernels::invokeFill(*numDraftTokensView, numDraftTokens, *stream);
|
|
|
|
bool const useRandomAcceptanceThreshold = !samplingConfig.draftAcceptanceThreshold.has_value();
|
|
float const constantThreshold
|
|
= useRandomAcceptanceThreshold ? 0 : samplingConfig.draftAcceptanceThreshold.value()[0];
|
|
|
|
dJointInput.externalDraftTokensInputs->useRandomAcceptanceThreshold = useRandomAcceptanceThreshold;
|
|
dJointInput.externalDraftTokensInputs->constantThreshold = constantThreshold;
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::newRequestMedusa(SizeType32 batchIdx, decoder_batch::Request const& request)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto const& stream = mDecoderStream;
|
|
BufferManager manager{stream};
|
|
|
|
auto& dJointInput = *mJointDecodingInput;
|
|
|
|
TensorPtr curTokensPerStepSlice
|
|
= ITensor::slice(constPointerCast(dJointInput.medusaInputs->medusaCurTokensPerStep), batchIdx, 1);
|
|
// Context phase Medusa processes 1 token only, new value from targetTokensPerStep will be filled at the end
|
|
// of first decoder
|
|
kernels::invokeFill(*curTokensPerStepSlice, 1, *stream);
|
|
TensorPtr targetTokensPerStepSlice
|
|
= ITensor::slice(constPointerCast(dJointInput.medusaInputs->medusaTargetTokensPerStep), batchIdx, 1);
|
|
auto const generatedTokensPerEngineStep = request.generatedTokensPerEngineStep;
|
|
TLLM_CHECK_WITH_INFO(generatedTokensPerEngineStep <= mMaxDecodingEngineTokens,
|
|
"Tokens per step for (%d) is larger than maximum tokens per step (%d)", generatedTokensPerEngineStep,
|
|
mMaxDecodingEngineTokens);
|
|
kernels::invokeFill(*targetTokensPerStepSlice, generatedTokensPerEngineStep, *stream);
|
|
|
|
TensorPtr pathsSlice = ITensor::slice(constPointerCast(dJointInput.medusaInputs->medusaPaths), batchIdx, 1);
|
|
manager.copy(*request.medusaPaths, *pathsSlice);
|
|
|
|
TensorPtr treeIdsSlice = ITensor::slice(constPointerCast(dJointInput.medusaInputs->medusaTreeIds), batchIdx, 1);
|
|
manager.copy(*request.medusaTreeIds, *treeIdsSlice);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::newRequestLookahead(SizeType32 batchIdx, decoder_batch::Request const& request)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
TLLM_CHECK(mJointDecodingOutput->lookaheadOutputs);
|
|
|
|
auto& stream = mRuntimeStream;
|
|
|
|
// The first generation step only generate 1 token.
|
|
TensorPtr curTokensPerStepSlice
|
|
= ITensor::slice(constPointerCast(mJointDecodingInput->lookaheadInputs->tokensPerStep), batchIdx, 1);
|
|
kernels::invokeFill(*curTokensPerStepSlice, 1, *stream);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::newRequestExplicitDraftTokens(SizeType32 batchIdx, decoder_batch::Request const& request)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
TLLM_CHECK(mJointDecodingOutput->explicitDraftTokensBuffers);
|
|
|
|
auto& stream = mRuntimeStream;
|
|
|
|
TensorPtr positionIdsBaseSlice
|
|
= ITensor::slice(mJointDecodingOutput->explicitDraftTokensBuffers->positionIdsBase, batchIdx, 1);
|
|
kernels::invokeFill(*positionIdsBaseSlice, request.inputLen, *stream);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::setExplicitDraftTokensInputs(decoder_batch::Input const& input)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto explicitDraftTokensInputs = DecodingInput::ExplicitDraftTokensInputs();
|
|
TLLM_CHECK(input.explicitDraftTokensInputs.has_value());
|
|
TLLM_CHECK(input.explicitDraftTokensLastInputs.has_value());
|
|
|
|
explicitDraftTokensInputs.nextDraftTokens = input.explicitDraftTokensInputs->nextDraftTokens;
|
|
explicitDraftTokensInputs.nextFlatTokens = input.explicitDraftTokensInputs->nextFlatTokens;
|
|
explicitDraftTokensInputs.nextDraftIndices = input.explicitDraftTokensInputs->nextDraftIndices;
|
|
explicitDraftTokensInputs.nextDraftProbs = input.explicitDraftTokensInputs->nextDraftProbs;
|
|
explicitDraftTokensInputs.lastDraftTokens = input.explicitDraftTokensLastInputs->draftTokens;
|
|
explicitDraftTokensInputs.lastDraftIndices = input.explicitDraftTokensLastInputs->draftIndices;
|
|
explicitDraftTokensInputs.lastPositionIdsBase = input.explicitDraftTokensLastInputs->positionIdsBase;
|
|
explicitDraftTokensInputs.masks = input.explicitDraftTokensInputs->masks;
|
|
explicitDraftTokensInputs.packedPositionIds = input.explicitDraftTokensInputs->packedPositionIds;
|
|
explicitDraftTokensInputs.bestPathLengths = input.explicitDraftTokensInputs->bestPathLengths;
|
|
explicitDraftTokensInputs.bestPathIndices = input.explicitDraftTokensInputs->bestPathIndices;
|
|
explicitDraftTokensInputs.nextGenerationLengths = input.explicitDraftTokensInputs->nextGenerationLengths;
|
|
explicitDraftTokensInputs.lastGenerationLengths = input.explicitDraftTokensLastInputs->generationLengths;
|
|
explicitDraftTokensInputs.maxGenLengthDevice = input.explicitDraftTokensInputs->maxGenToken;
|
|
explicitDraftTokensInputs.seqSlots = input.seqSlots;
|
|
mJointDecodingInput->explicitDraftTokensInputs = explicitDraftTokensInputs;
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::newRequests(std::vector<SizeType32> const& seqSlots,
|
|
std::vector<decoder_batch::Request> const& requests, std::vector<SamplingConfig> const& samplingConfigs)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto batchSlotsPtr = bufferCast<SizeType32>(*mBatchSlotsSetup);
|
|
SizeType32 const localBatchSize = seqSlots.size();
|
|
for (SizeType32 bi = 0; bi < localBatchSize; ++bi)
|
|
{
|
|
newRequest(seqSlots[bi], requests[bi], samplingConfigs[bi]);
|
|
batchSlotsPtr[bi] = seqSlots[bi];
|
|
}
|
|
|
|
TensorPtr batchSlotsView = ITensor::slice(mBatchSlotsSetup, 0, localBatchSize);
|
|
auto samplingConfig = SamplingConfig(samplingConfigs);
|
|
mDecoder->setup(samplingConfig, localBatchSize, batchSlotsView, {*mJointDecodingOutput}, {requests});
|
|
|
|
auto const& stream = mDecoderStream;
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mRuntimeStream->wait(event);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::forwardDispatch(
|
|
decoder_batch::Output& output, decoder_batch::Input const& input, ForwardType forwardType)
|
|
{
|
|
auto const maxDecodingEngineTokens
|
|
= *std::max_element(std::begin(mNumDecodingEngineTokens), std::end(mNumDecodingEngineTokens));
|
|
|
|
for (SizeType32 si = 0; si < maxDecodingEngineTokens; si += mMaxDecodingDecoderTokens)
|
|
{
|
|
forwardDecoder(si, output, input, forwardType);
|
|
}
|
|
}
|
|
|
|
GptDecoderBatched::DecoderFinishedEventPtr GptDecoderBatched::forwardAsync(
|
|
decoder_batch::Output& output, decoder_batch::Input const& input)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
forwardDispatch(output, input, ForwardType::kASYNC);
|
|
|
|
CudaEvent eventStop{};
|
|
mRuntimeStream->record(eventStop);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
return std::make_unique<decoder_batch::DecoderFinishedEvent>(std::move(eventStop), input.active);
|
|
}
|
|
|
|
void GptDecoderBatched::forwardDecoder(
|
|
SizeType32 step, decoder_batch::Output& output, decoder_batch::Input const& input, ForwardType forwardType)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto eventStart = CudaEvent{};
|
|
mRuntimeStream->record(eventStart);
|
|
|
|
auto& allTargetLogits = input.logits;
|
|
auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
|
|
auto const maxBeamWidth = jointOutputIdsShape.d[1];
|
|
|
|
auto constexpr singleRequest = 1;
|
|
|
|
TLLM_CHECK(static_cast<SizeType32>(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}));
|
|
TLLM_CHECK(sequenceLengths);
|
|
|
|
auto batchSlotsDecoderPtr = maxBeamWidth > 1 && input.seqSlots ? bufferCast<SizeType32>(*input.seqSlots)
|
|
: bufferCast<SizeType32>(*mBatchSlotsDecoder);
|
|
auto& dInput = *mJointDecodingInput;
|
|
auto& dOutput = *mJointDecodingOutput;
|
|
auto& decoder = *mDecoder;
|
|
auto const& stream = mDecoderStream;
|
|
|
|
if (maxBeamWidth > 1)
|
|
{
|
|
dInput.cacheIndirection = input.cacheIndirection;
|
|
dOutput.cacheIndirection = output.cacheIndirection;
|
|
}
|
|
|
|
if (mSpeculativeDecodingMode.isExplicitDraftTokens())
|
|
{
|
|
setExplicitDraftTokensInputs(input);
|
|
}
|
|
|
|
bool const async = forwardType == ForwardType::kASYNC;
|
|
|
|
if (async)
|
|
{
|
|
stream->wait(eventStart.get());
|
|
}
|
|
|
|
SizeType32 localBatchDecoderIdx = 0;
|
|
for (SizeType32 bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || step >= mNumDecodingEngineTokens[bi])
|
|
{
|
|
continue;
|
|
}
|
|
batchSlotsDecoderPtr[step * mActualBatchSize + localBatchDecoderIdx] = bi;
|
|
localBatchDecoderIdx++;
|
|
}
|
|
|
|
auto const maxDecodingEngineTokens
|
|
= *std::max_element(std::begin(mNumDecodingEngineTokens), std::end(mNumDecodingEngineTokens));
|
|
|
|
std::vector<SharedConstPtr> logitsVec;
|
|
for (SizeType32 bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || step >= mNumDecodingEngineTokens[bi])
|
|
{
|
|
continue;
|
|
}
|
|
auto const& targetLogits = allTargetLogits[bi];
|
|
TensorPtr logitsSlice = ITensor::slice(targetLogits, step, singleRequest);
|
|
logitsVec.push_back(logitsSlice);
|
|
}
|
|
|
|
TensorPtr finishedStepsInput = ITensor::slice(mFinishedSteps, step, 1);
|
|
TensorPtr finishedStepsOutput = ITensor::slice(mFinishedSteps, std::min(maxDecodingEngineTokens - 1, step + 1), 1);
|
|
finishedStepsInput->squeeze(0);
|
|
finishedStepsOutput->squeeze(0);
|
|
TensorPtr newTokensStepView = ITensor::slice(dOutput.newTokensSteps, step, mMaxDecodingDecoderTokens);
|
|
|
|
dInput.logitsVec = logitsVec;
|
|
dInput.finishReasons = finishedStepsInput;
|
|
|
|
if (maxBeamWidth > 1 && input.seqSlots)
|
|
{
|
|
dInput.batchSlots = input.seqSlots;
|
|
}
|
|
else
|
|
{
|
|
TensorPtr batchSlotsDecoderSlice = ITensor::slice(mBatchSlotsDecoder, step, 1);
|
|
batchSlotsDecoderSlice->squeeze(0);
|
|
dInput.batchSlots = batchSlotsDecoderSlice;
|
|
}
|
|
|
|
dInput.batchSize = localBatchDecoderIdx;
|
|
if (mSpeculativeDecodingMode.isMedusa())
|
|
{
|
|
dInput.medusaInputs->medusaLogits = input.predictedDraftLogits;
|
|
}
|
|
|
|
if (mSpeculativeDecodingMode.isDraftTokensExternal())
|
|
{
|
|
dInput.externalDraftTokensInputs->step = step;
|
|
}
|
|
|
|
dOutput.newTokens = newTokensStepView;
|
|
dOutput.finishReasons = finishedStepsOutput;
|
|
dOutput.lengths = sequenceLengths;
|
|
|
|
if (localBatchDecoderIdx > 0)
|
|
{
|
|
if (forwardType == ForwardType::kASYNC)
|
|
{
|
|
decoder.forwardAsync(dOutput, dInput);
|
|
}
|
|
else if (forwardType == ForwardType::kSYNC)
|
|
{
|
|
decoder.forwardSync(dOutput, dInput);
|
|
}
|
|
else
|
|
{
|
|
TLLM_THROW("Unknown ForwardType");
|
|
}
|
|
}
|
|
|
|
for (SizeType32 bi = 0; bi < mActualBatchSize; ++bi)
|
|
{
|
|
if (mFinished[bi] || !input.active.at(bi) || step >= mNumDecodingEngineTokens[bi])
|
|
{
|
|
continue;
|
|
}
|
|
mNbSteps[bi] += 1;
|
|
mFinished[bi] = mNbSteps[bi] >= mMaxNewTokens[bi];
|
|
}
|
|
|
|
// If last iteration
|
|
if (async && step == maxDecodingEngineTokens - mMaxDecodingDecoderTokens)
|
|
{
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mRuntimeStream->wait(event);
|
|
}
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::updateFinished(decoder_batch::DecoderFinishedEvent const& decoderFinishEvent)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
for (std::int32_t i = 0; i < mActualBatchSize; ++i)
|
|
{
|
|
if (decoderFinishEvent.active[i] && !mFinished[i])
|
|
{
|
|
auto finishedSum = ITensor::slice(mJointDecodingOutput->finishedSum, i, 1);
|
|
mFinished[i] = mFinished[i]
|
|
// This condition requires the synchronization above
|
|
|| bufferCast<SizeType32>(*finishedSum)[0] == static_cast<SizeType32>(mBeamWidths[i]);
|
|
}
|
|
}
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::forwardSync(decoder_batch::DecoderFinishedEvent const& decoderFinishEvent)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
decoderFinishEvent.event.synchronize();
|
|
|
|
updateFinished(decoderFinishEvent);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::forwardSync(decoder_batch::DecoderFinishedEvent const& decoderFinishEvent,
|
|
decoder_batch::Output& output, decoder_batch::Input const& input)
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
decoderFinishEvent.event.synchronize();
|
|
|
|
forwardDispatch(output, input, ForwardType::kSYNC);
|
|
|
|
updateFinished(decoderFinishEvent);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
// TODO call this at the end of forward if mFinished[i] changes from false to true?
|
|
CudaEvent GptDecoderBatched::postProcessRequest(
|
|
SizeType32 batchSlot, SamplingConfig const& samplingConfig, bool streaming) const
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
|
|
auto& stream = mRuntimeStream;
|
|
auto manager = BufferManager{stream};
|
|
|
|
auto& dJointInput = *mJointDecodingInput;
|
|
auto& dJointOutput = *mJointDecodingOutput;
|
|
|
|
auto slice = [batchSlot](auto& a, auto const& b)
|
|
{
|
|
if (b && b->getShape().d[0] > 0)
|
|
{
|
|
a = ITensor::slice(b, batchSlot, 1);
|
|
}
|
|
};
|
|
|
|
// Prepare a slice of dJointInput and dJointOutput for gatherTree
|
|
DecodingInput dInput{dJointInput};
|
|
slice(dInput.endIds, dJointInput.endIds);
|
|
slice(dInput.lengths, dJointInput.lengths);
|
|
|
|
DecodingOutput dOutput{
|
|
ITensor::slice(dJointOutput.ids, batchSlot, 1), ITensor::slice(dJointOutput.gatheredIds, batchSlot, 1)};
|
|
dOutput.beamHypotheses = dJointOutput.beamHypotheses.slice(batchSlot, 1);
|
|
slice(dOutput.parentIds, dJointOutput.parentIds);
|
|
slice(dOutput.cumLogProbs, dJointOutput.cumLogProbs);
|
|
slice(dOutput.cacheIndirection, dJointOutput.cacheIndirection);
|
|
slice(dOutput.lengths, dJointOutput.lengths);
|
|
slice(dOutput.finishReasons, dJointOutput.finishReasons);
|
|
slice(dOutput.logProbs, dJointOutput.logProbs);
|
|
|
|
dOutput.newTokens = ITensor::view(dJointOutput.newTokens);
|
|
TLLM_CHECK(dOutput.newTokens->getShape().d[0] == 1);
|
|
dOutput.newTokens->squeeze(0);
|
|
dOutput.newTokens = ITensor::slice(dOutput.newTokens, batchSlot, 1);
|
|
dOutput.logProbsTiled = dJointOutput.logProbsTiled;
|
|
if (streaming)
|
|
{
|
|
// in case of streaming we shouldn't overwrite the data in beamHypotheses, since the beam search kernels expect
|
|
// ungathered data but the kernels in gatherTree write in-place.
|
|
// Thus, we need to make a copy of the beamHypotheses
|
|
tensorrt_llm::kernels::invokeCopyBeamHypotheses(
|
|
dOutput.beamHypotheses, *mOutputBeamHypotheses, *dOutput.cumLogProbs, *mCumLogProbsTmp, *stream, mNumSMs);
|
|
dOutput.beamHypotheses = *mOutputBeamHypotheses;
|
|
dOutput.cumLogProbs = mCumLogProbsTmp;
|
|
}
|
|
|
|
kernels::gatherTree(dOutput, dInput, manager, samplingConfig);
|
|
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mRuntimeStream->wait(event);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
return event;
|
|
}
|
|
|
|
void GptDecoderBatched::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];
|
|
mNumDecodingEngineTokens.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);
|
|
|
|
TensorPtr batchSlotsView = ITensor::slice(mBatchSlotsSetup, 0, mActualBatchSize);
|
|
auto batchSlots = BufferRange<SizeType32>(*batchSlotsView);
|
|
std::iota(batchSlots.begin(), batchSlots.end(), 0);
|
|
|
|
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<SizeType32>(*inputLengthsHost);
|
|
auto inputOffset = 0;
|
|
std::vector<SamplingConfig> samplingConfigs;
|
|
for (auto batchIdx = 0; batchIdx < mActualBatchSize; ++batchIdx)
|
|
{
|
|
mNumDecodingEngineTokens[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};
|
|
|
|
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;
|
|
}
|
|
auto requestSamplingConfig = extractSamplingConfig(samplingConfig, batchIdx);
|
|
requestSamplingConfig.cumLogProbs = {{outputs.cumLogProbs != nullptr}};
|
|
requestSamplingConfig.outputLogProbs = {{outputs.logProbs != nullptr}};
|
|
newRequest(batchIdx, request, requestSamplingConfig);
|
|
samplingConfigs.push_back(requestSamplingConfig);
|
|
}
|
|
|
|
auto fusedSamplingConfig = samplingConfig;
|
|
fusedSamplingConfig.cumLogProbs = std::vector<bool>(mActualBatchSize, outputs.cumLogProbs != nullptr);
|
|
fusedSamplingConfig.outputLogProbs = std::vector<bool>(mActualBatchSize, outputs.logProbs != nullptr);
|
|
|
|
mDecoder->setup(fusedSamplingConfig, mActualBatchSize, batchSlotsView, {*mJointDecodingOutput});
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::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::SharedPtr> 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;
|
|
|
|
mDecoderFinishEvent = forwardAsync(batchOutput, batchInput);
|
|
mBufferManager.setZero(*mFinishedSum);
|
|
kernels::reduce(
|
|
*mFinishedSum, *ITensor::slice(mJointDecodingOutput->finishedSum, 0, mActualBatchSize), *mRuntimeStream);
|
|
mRuntimeStream->record(mForwardEvent);
|
|
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::forwardSync()
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
forwardSync(*mDecoderFinishEvent);
|
|
// wait for mFinishedSum to be updated
|
|
mForwardEvent.synchronize();
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatched::finalize(SamplingConfig const& samplingConfig) const
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
auto batchSlots = bufferCast<SizeType32>(*mBatchSlotsSetup);
|
|
for (SizeType32 batchIdx = 0; batchIdx < mActualBatchSize; ++batchIdx)
|
|
{
|
|
auto slot = batchSlots[batchIdx];
|
|
auto requestSamplingConfig = extractSamplingConfig(samplingConfig, slot);
|
|
auto event = postProcessRequest(slot, requestSamplingConfig, /*streaming*/ false);
|
|
}
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
CudaEvent GptDecoderBatched::finalize(SizeType32 batchSlot, SamplingConfig const& samplingConfig, bool streaming) const
|
|
{
|
|
TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
|
|
auto event = postProcessRequest(batchSlot, samplingConfig, streaming);
|
|
TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
|
|
return event;
|
|
}
|