mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
497 lines
22 KiB
C++
497 lines
22 KiB
C++
/*
|
|
* Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*/
|
|
#include "tensorrt_llm/runtime/gptDecoderBatch.h"
|
|
|
|
#include "tensorrt_llm/common/assert.h"
|
|
#include "tensorrt_llm/runtime/bufferManager.h"
|
|
#include "tensorrt_llm/runtime/cudaEvent.h"
|
|
#include "tensorrt_llm/runtime/runtimeKernels.h"
|
|
|
|
#include <algorithm>
|
|
#include <memory>
|
|
|
|
using namespace tensorrt_llm::runtime;
|
|
|
|
namespace tc = tensorrt_llm::common;
|
|
|
|
namespace
|
|
{
|
|
SamplingConfig extractSamplingConfig(SamplingConfig const& batchSamplingConfig, SizeType batchIdx)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
SamplingConfig samplingConfig{batchSamplingConfig.beamWidth};
|
|
|
|
auto extractOptional = [&batchIdx](auto& single, auto const& batch)
|
|
{
|
|
using T = typename std::remove_reference_t<decltype(batch)>::value_type;
|
|
if (batch)
|
|
{
|
|
if (batch->size() > 1)
|
|
single.emplace(T{batch->at(batchIdx)});
|
|
else
|
|
single.emplace(T{batch->at(0)});
|
|
}
|
|
};
|
|
|
|
extractOptional(samplingConfig.temperature, batchSamplingConfig.temperature);
|
|
extractOptional(samplingConfig.minLength, batchSamplingConfig.minLength);
|
|
extractOptional(samplingConfig.repetitionPenalty, batchSamplingConfig.repetitionPenalty);
|
|
extractOptional(samplingConfig.presencePenalty, batchSamplingConfig.presencePenalty);
|
|
// sampling layers
|
|
extractOptional(samplingConfig.topK, batchSamplingConfig.topK);
|
|
extractOptional(samplingConfig.topP, batchSamplingConfig.topP);
|
|
extractOptional(samplingConfig.randomSeed, batchSamplingConfig.randomSeed);
|
|
extractOptional(samplingConfig.topPDecay, batchSamplingConfig.topPDecay);
|
|
extractOptional(samplingConfig.topPMin, batchSamplingConfig.topPMin);
|
|
extractOptional(samplingConfig.topPResetIds, batchSamplingConfig.topPResetIds);
|
|
|
|
// beam search layer
|
|
samplingConfig.beamSearchDiversityRate = batchSamplingConfig.beamSearchDiversityRate;
|
|
samplingConfig.lengthPenalty = batchSamplingConfig.lengthPenalty;
|
|
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
return samplingConfig;
|
|
}
|
|
|
|
} // namespace
|
|
|
|
GptDecoderBatch::GptDecoderBatch(
|
|
std::size_t vocabSize, std::size_t vocabSizePadded, GptDecoderBatch::CudaStreamPtr stream)
|
|
: mVocabSize{vocabSize}
|
|
, mVocabSizePadded{vocabSizePadded}
|
|
, mStream{std::move(stream)}
|
|
, mBufferManager{mStream}
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto constexpr nvTokenIdType = TRTDataType<TokenIdType>::value;
|
|
auto constexpr nvSizeType = TRTDataType<SizeType>::value;
|
|
auto constexpr nvFloatType = TRTDataType<float>::value;
|
|
|
|
auto& dInput = mJointDecodingInput;
|
|
auto dummyLogits = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
|
|
auto endIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
|
|
dInput = std::make_unique<DecodingInput>(0, 0, std::move(dummyLogits), std::move(endIds));
|
|
|
|
dInput->sequenceLimitLength = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
|
|
dInput->lengths = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
|
|
|
|
auto& dOutput = mJointDecodingOutput;
|
|
auto outputIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
|
|
dOutput = std::make_unique<DecodingOutput>(std::move(outputIds));
|
|
|
|
dOutput->newTokens = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
|
|
dOutput->parentIds = mBufferManager.emptyTensor(MemoryType::kGPU, nvTokenIdType);
|
|
dOutput->finished = mBufferManager.emptyTensor(MemoryType::kGPU, TRTDataType<bool>::value);
|
|
// use batchSize many entries instead of the usual 1
|
|
dOutput->finishedSum = mBufferManager.emptyTensor(MemoryType::kPINNED, nvSizeType);
|
|
mFinishedSum = mBufferManager.pinned(ITensor::makeShape({1}), nvSizeType);
|
|
dOutput->lengths = mBufferManager.emptyTensor(MemoryType::kGPU, nvSizeType);
|
|
dOutput->cumLogProbs = mBufferManager.emptyTensor(MemoryType::kGPU, nvFloatType);
|
|
dOutput->beamHypotheses.empty(mBufferManager);
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::setup(
|
|
SizeType maxBatchSize, SizeType maxBeamWidth, SizeType maxSequenceLength, nvinfer1::DataType dtype)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
TLLM_CHECK(maxBatchSize > 0);
|
|
TLLM_CHECK(maxBeamWidth > 0);
|
|
TLLM_CHECK(maxSequenceLength > 0);
|
|
|
|
mActualBatchSize = maxBatchSize;
|
|
mMaxSequenceLength = maxSequenceLength;
|
|
|
|
auto const maxBatchSizeShape = ITensor::makeShape({maxBatchSize});
|
|
auto const maxBatchSizeXmaxBeamWidth = ITensor::makeShape({maxBatchSize, maxBeamWidth});
|
|
|
|
auto& dInput = *mJointDecodingInput;
|
|
const_cast<ITensor&>(*dInput.endIds).reshape(maxBatchSizeXmaxBeamWidth);
|
|
auto& sequenceLimitLength = const_cast<ITensor&>(*dInput.sequenceLimitLength);
|
|
sequenceLimitLength.reshape(maxBatchSizeShape);
|
|
kernels::invokeFill(sequenceLimitLength, mMaxSequenceLength, *mStream);
|
|
auto& inputLengths = const_cast<ITensor&>(*dInput.lengths);
|
|
inputLengths.reshape(maxBatchSizeXmaxBeamWidth);
|
|
mBufferManager.setZero(inputLengths);
|
|
|
|
auto const jointOutputIdsShape = ITensor::makeShape({maxBatchSize, maxBeamWidth, maxSequenceLength});
|
|
|
|
auto& dOutput = *mJointDecodingOutput;
|
|
dOutput.ids->reshape(jointOutputIdsShape);
|
|
dOutput.newTokens->reshape(maxBatchSizeXmaxBeamWidth);
|
|
mBufferManager.setZero(*dOutput.newTokens);
|
|
dOutput.parentIds->reshape(jointOutputIdsShape);
|
|
dOutput.lengths->reshape(maxBatchSizeXmaxBeamWidth);
|
|
mBufferManager.setZero(*dOutput.lengths);
|
|
dOutput.finished->reshape(maxBatchSizeXmaxBeamWidth);
|
|
mBufferManager.setZero(*dOutput.finished);
|
|
mBufferManager.setZero(*dOutput.finishedSum);
|
|
// use batchSize many entries instead of the usual 1
|
|
dOutput.finishedSum->reshape(maxBatchSizeShape);
|
|
mBufferManager.setZero(*dOutput.finishedSum);
|
|
|
|
if (maxBeamWidth > 1)
|
|
{
|
|
dOutput.cumLogProbs->reshape(maxBatchSizeXmaxBeamWidth);
|
|
mBufferManager.setZero(*dOutput.cumLogProbs);
|
|
dOutput.beamHypotheses.reshape(maxBatchSize, maxBeamWidth, mMaxSequenceLength);
|
|
}
|
|
else
|
|
{
|
|
dOutput.beamHypotheses.release();
|
|
}
|
|
|
|
mStreams.resize(maxBatchSize);
|
|
mDecoders.resize(maxBatchSize);
|
|
mDecodingInputs.resize(maxBatchSize);
|
|
mDecodingOutputs.resize(maxBatchSize);
|
|
mNbSteps.resize(maxBatchSize);
|
|
mFinished.resize(maxBatchSize);
|
|
mMaxNewTokens.resize(maxBatchSize);
|
|
mBeamWidths.resize(maxBatchSize);
|
|
auto const device = mStream->getDevice();
|
|
for (SizeType i = 0; i < maxBatchSize; ++i)
|
|
{
|
|
mStreams[i] = std::make_shared<CudaStream>();
|
|
TLLM_CHECK(mStreams[i]->getDevice() == device);
|
|
mDecoders[i] = IGptDecoder::create(dtype, mVocabSize, mVocabSizePadded, mStreams[i]);
|
|
mDecodingInputs[i].reset();
|
|
mDecodingOutputs[i].reset();
|
|
mNbSteps[i] = 0;
|
|
mFinished[i] = true;
|
|
mMaxNewTokens[i] = 0;
|
|
mBeamWidths[i] = 0;
|
|
}
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::newRequest(
|
|
SizeType batchIdx, decoder_batch::Request const& request, SamplingConfig const& samplingConfig)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
TLLM_CHECK(batchIdx >= 0);
|
|
auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
|
|
auto const batchSize = jointOutputIdsShape.d[0];
|
|
TLLM_CHECK(0 <= batchSize && batchIdx < 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 (%d) passed to decoder setup function.",
|
|
beamWidth, maxBeamWidth));
|
|
auto const& requestIds = request.ids;
|
|
auto const inputLength = requestIds->getShape().d[0];
|
|
auto const maxNewTokens = request.maxNewTokens.value_or(mMaxSequenceLength - inputLength);
|
|
TLLM_CHECK_WITH_INFO(inputLength + maxNewTokens <= mMaxSequenceLength,
|
|
tc::fmtstr("Input length (%d) + max new tokens (%d) must be less than max sequence length (%d).", inputLength,
|
|
maxNewTokens, mMaxSequenceLength));
|
|
TLLM_CHECK(requestIds->getDataType() == TRTDataType<TokenIdType>::value);
|
|
auto const endId = request.endId.value_or(mVocabSize - 1);
|
|
|
|
auto constexpr localBatchSize = 1;
|
|
|
|
auto& stream = mStreams[batchIdx];
|
|
BufferManager manager{stream};
|
|
|
|
// input
|
|
auto& dJointInput = *mJointDecodingInput;
|
|
auto& dInput = mDecodingInputs.at(batchIdx);
|
|
|
|
TensorPtr endIdTensorPtr{ITensor::slice(constPointerCast(dJointInput.endIds), batchIdx, localBatchSize)};
|
|
kernels::invokeFill(*endIdTensorPtr, endId, *stream);
|
|
dInput = std::make_unique<DecodingInput>(inputLength, localBatchSize, dJointInput.logits, endIdTensorPtr);
|
|
dInput->embeddingBias = request.embeddingBias;
|
|
dInput->badWordsList = request.badWordsList;
|
|
dInput->stopWordsList = request.stopWordsList;
|
|
TensorPtr sequenceLimitLength{
|
|
ITensor::slice(constPointerCast(dJointInput.sequenceLimitLength), batchIdx, localBatchSize)};
|
|
kernels::invokeFill(*sequenceLimitLength, inputLength + maxNewTokens, *stream);
|
|
dInput->sequenceLimitLength = std::move(sequenceLimitLength);
|
|
TensorPtr inputLengths{ITensor::slice(constPointerCast(dJointInput.lengths), batchIdx, localBatchSize)};
|
|
kernels::invokeFill(*inputLengths, inputLength, *stream);
|
|
dInput->lengths = inputLengths;
|
|
|
|
// output
|
|
auto& dJointOutput = *mJointDecodingOutput;
|
|
auto& dOutput = mDecodingOutputs.at(batchIdx);
|
|
auto const outputIdsShape = ITensor::makeShape({localBatchSize, beamWidth, mMaxSequenceLength});
|
|
|
|
TensorPtr outputIds = ITensor::slice(dJointOutput.ids, batchIdx, localBatchSize);
|
|
outputIds->reshape(outputIdsShape);
|
|
dOutput = std::make_unique<DecodingOutput>(outputIds);
|
|
|
|
dOutput->finished = ITensor::slice(dJointOutput.finished, batchIdx, localBatchSize);
|
|
manager.setZero(*dOutput->finished);
|
|
dOutput->finishedSum = ITensor::slice(dJointOutput.finishedSum, batchIdx, localBatchSize);
|
|
manager.setZero(*dOutput->finishedSum);
|
|
dOutput->lengths = ITensor::slice(dJointOutput.lengths, batchIdx, localBatchSize);
|
|
kernels::invokeFill(*dOutput->lengths, inputLength, *stream);
|
|
dOutput->newTokens = ITensor::slice(dJointOutput.newTokens, batchIdx, localBatchSize);
|
|
manager.setZero(*dOutput->newTokens);
|
|
|
|
if (beamWidth > 1)
|
|
{
|
|
dOutput->cumLogProbs = ITensor::slice(dJointOutput.cumLogProbs, batchIdx, localBatchSize);
|
|
manager.setZero(*IBuffer::slice(dOutput->cumLogProbs, 0, 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);
|
|
}
|
|
|
|
// remaining
|
|
mDecoders[batchIdx]->setup(samplingConfig, localBatchSize);
|
|
mBeamWidths[batchIdx] = beamWidth;
|
|
mNbSteps[batchIdx] = 0;
|
|
mFinished[batchIdx] = false;
|
|
mMaxNewTokens[batchIdx] = maxNewTokens;
|
|
|
|
// copy the request ids into outputIds
|
|
auto inputIdsView = ITensor::view(requestIds, ITensor::makeShape({localBatchSize, inputLength}));
|
|
auto outputIdsView = ITensor::view(outputIds, ITensor::makeShape({beamWidth, mMaxSequenceLength}));
|
|
kernels::invokeFill(*outputIdsView, endId, *stream);
|
|
kernels::tileTensor(*outputIdsView, *inputIdsView, beamWidth, *stream);
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
GptDecoderBatch::TokenPtr GptDecoderBatch::forwardAsync(
|
|
decoder_batch::Output& output, decoder_batch::Input const& input)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto& logits = input.logits;
|
|
auto const& logitsShape = logits->getShape();
|
|
|
|
TLLM_CHECK(logitsShape.d[0] == mActualBatchSize);
|
|
auto const& jointOutputIdsShape = mJointDecodingOutput->ids->getShape();
|
|
auto const maxBeamWidth = jointOutputIdsShape.d[1];
|
|
TLLM_CHECK(logitsShape.d[1] == maxBeamWidth);
|
|
TLLM_CHECK(static_cast<std::size_t>(logitsShape.d[2]) == mVocabSizePadded);
|
|
|
|
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);
|
|
|
|
// TODO(bhsueh) should remove this reshape and set shape to [batch_size, beam_width] outside
|
|
TensorPtr sequenceLengths = ITensor::view(output.sequenceLengths);
|
|
sequenceLengths->reshape(ITensor::makeShape({mActualBatchSize, maxBeamWidth}));
|
|
TLLM_CHECK(sequenceLengths);
|
|
auto constexpr singleRequest = 1;
|
|
|
|
CudaEvent eventStart{};
|
|
mStream->record(eventStart);
|
|
for (std::int32_t i = 0; i < mActualBatchSize; ++i)
|
|
{
|
|
if (mFinished[i] || !input.active.at(i))
|
|
continue;
|
|
|
|
auto& stream = mStreams[i];
|
|
stream->wait(eventStart.get());
|
|
auto& dInput = *mDecodingInputs[i];
|
|
auto& dOutput = *mDecodingOutputs[i];
|
|
auto logitsView = std::shared_ptr(ITensor::slice(logits, i, singleRequest));
|
|
dInput.logits
|
|
= ITensor::view(logitsView, ITensor::makeShape({singleRequest, mBeamWidths[i], logitsShape.d[2]}));
|
|
if (srcCacheIndirection && tgtCacheIndirection)
|
|
{
|
|
auto srcView = std::shared_ptr(ITensor::slice(srcCacheIndirection, i, singleRequest));
|
|
auto tgtView = std::shared_ptr(ITensor::slice(tgtCacheIndirection, i, singleRequest));
|
|
dInput.cacheIndirection
|
|
= ITensor::view(srcView, ITensor::makeShape({singleRequest, mBeamWidths[i], srcView->getShape().d[2]}));
|
|
dOutput.cacheIndirection
|
|
= ITensor::view(tgtView, ITensor::makeShape({singleRequest, mBeamWidths[i], tgtView->getShape().d[2]}));
|
|
}
|
|
auto sequenceLengthsView = std::shared_ptr(ITensor::slice(sequenceLengths, i, singleRequest));
|
|
dOutput.lengths = ITensor::view(sequenceLengthsView, ITensor::makeShape({singleRequest, mBeamWidths[i]}));
|
|
|
|
auto& decoder = *mDecoders[i];
|
|
decoder.forwardAsync(dOutput, dInput);
|
|
|
|
auto manager = BufferManager{stream};
|
|
|
|
auto jointOutputIdsView = ITensor::slice(mJointDecodingOutput->ids, i, singleRequest);
|
|
auto const& jointOutputShape = jointOutputIdsView->getShape();
|
|
// squeeze dim 0 and set beamWidth
|
|
jointOutputIdsView->reshape(ITensor::makeShape({mBeamWidths[i], jointOutputShape.d[2]}));
|
|
|
|
manager.copy(*dOutput.ids, *jointOutputIdsView);
|
|
|
|
auto jointSequenceLengthsView = ITensor::slice(mJointDecodingOutput->lengths, i, singleRequest);
|
|
jointSequenceLengthsView->reshape(ITensor::makeShape({1, mBeamWidths[i]}));
|
|
manager.copy(*dOutput.lengths, *jointSequenceLengthsView);
|
|
|
|
if (mBeamWidths[i] > 1)
|
|
{
|
|
auto jointOutputParentIdsView = ITensor::slice(mJointDecodingOutput->parentIds, i, singleRequest);
|
|
auto const& jointOutputParentIdsShape = jointOutputParentIdsView->getShape();
|
|
// squeeze dim 0 and set beamWidth
|
|
jointOutputParentIdsView->reshape(ITensor::makeShape({mBeamWidths[i], jointOutputParentIdsShape.d[2]}));
|
|
|
|
manager.copy(*dOutput.parentIds, *jointOutputParentIdsView);
|
|
}
|
|
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mStream->wait(event);
|
|
dInput.step += 1;
|
|
mNbSteps[i] += 1;
|
|
mFinished[i] = mNbSteps[i] >= mMaxNewTokens[i];
|
|
}
|
|
|
|
CudaEvent eventStop{};
|
|
mStream->record(eventStop);
|
|
TLLM_LOG_DEBUG("%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_DEBUG("%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) == static_cast<SizeType>(dOutput.finished->getSize());
|
|
}
|
|
}
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
// TODO (rkobus) call this at the end of forward if mFinished[i] changes from false to true?
|
|
CudaEvent GptDecoderBatch::postProcessRequest(SizeType batchIdx) const
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto& stream = mStreams[batchIdx];
|
|
auto manager = BufferManager{stream};
|
|
|
|
auto& dInput = *mDecodingInputs[batchIdx];
|
|
auto& dOutput = *mDecodingOutputs[batchIdx];
|
|
|
|
// TODO (rkobus) can we do this inplace?
|
|
auto& outputIds = dOutput.ids;
|
|
auto finalOutputIds = manager.gpu(outputIds->getShape(), outputIds->getDataType());
|
|
IGptDecoder::gatherTree(*finalOutputIds, dOutput, dInput, manager);
|
|
manager.copy(*finalOutputIds, *outputIds);
|
|
|
|
CudaEvent event{};
|
|
stream->record(event);
|
|
mStream->wait(event);
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
return event;
|
|
}
|
|
|
|
void GptDecoderBatch::newBatch(GenerationInput const& inputs, SamplingConfig const& samplingConfig)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
// split batch into single requests
|
|
auto const& inputLengths = inputs.lengths;
|
|
mActualBatchSize = inputLengths->getShape().d[0];
|
|
|
|
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);
|
|
inputIdsFlatView->reshape(ITensor::makeShape({inputIdsShape.d[1]}));
|
|
auto inputLengthsHost = mBufferManager.copyFrom(*inputLengths, MemoryType::kCPU);
|
|
auto inputLengthsPtr = bufferCast<SizeType>(*inputLengthsHost);
|
|
auto inputOffset = 0;
|
|
for (auto batchIdx = 0; batchIdx < mActualBatchSize; ++batchIdx)
|
|
{
|
|
auto const inputLength = inputLengthsPtr[batchIdx];
|
|
auto const inputShape = ITensor::makeShape({inputLength});
|
|
TensorPtr inputView;
|
|
if (inputs.packed)
|
|
{
|
|
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, std::nullopt, inputs.endId, inputs.padId};
|
|
request.embeddingBias = inputs.embeddingBiasOpt;
|
|
request.badWordsList = inputs.badWordsList;
|
|
request.stopWordsList = inputs.stopWordsList;
|
|
newRequest(batchIdx, request, extractSamplingConfig(samplingConfig, batchIdx));
|
|
}
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void GptDecoderBatch::forwardAsync(decoder::Output& output, decoder::Input const& input)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
decoder_batch::Input batchInput{input.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_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
bool GptDecoderBatch::isFinishedSync()
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
forwardSync(*mForwardToken);
|
|
auto const finished
|
|
= std::all_of(mFinished.begin(), mFinished.begin() + mActualBatchSize, [](bool x) { return x; });
|
|
// wait for mFinishedSum to be updated
|
|
mStream->wait(mForwardEvent);
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
return finished;
|
|
}
|
|
|
|
IStatefulGptDecoder::TensorPtr GptDecoderBatch::getFinalOutputIds() const
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
for (SizeType batchIdx = 0; batchIdx < mActualBatchSize; ++batchIdx)
|
|
{
|
|
postProcessRequest(batchIdx);
|
|
}
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
return getOutputIds();
|
|
}
|
|
|
|
std::tuple<CudaEvent, IStatefulGptDecoder::TensorPtr> GptDecoderBatch::getFinalOutputIds(SizeType batchIdx) const
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto event = postProcessRequest(batchIdx);
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
return {std::move(event), getOutputIds(batchIdx)};
|
|
}
|