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https://github.com/NVIDIA/TensorRT-LLM.git
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9bd15f1937
* TensorRT-LLM Release 0.10.0 --------- Co-authored-by: Loki <lokravi@amazon.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
292 lines
11 KiB
C++
292 lines
11 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/layers/dynamicDecodeLayer.h"
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#include "tensorrt_llm/common/memoryUtils.h"
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#include "tensorrt_llm/kernels/decodingKernels.h"
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#include "tensorrt_llm/layers/beamSearchLayer.h"
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#include "tensorrt_llm/layers/defaultDecodingParams.h"
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#include "tensorrt_llm/layers/layerUtils.h"
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#include "tensorrt_llm/layers/layersFactory.h"
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/cudaStream.h"
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using namespace tensorrt_llm::common;
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using namespace tensorrt_llm::kernels;
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using namespace tensorrt_llm::runtime;
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namespace tensorrt_llm
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{
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namespace layers
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{
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template <typename T>
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DynamicDecodeLayer<T>::DynamicDecodeLayer(DecodingMode const& mode, DecoderDomain const& decoderDomain,
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cudaStream_t stream, std::shared_ptr<IAllocator> allocator)
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: BaseLayer(decoderDomain, stream, std::move(allocator))
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, mDecodingMode(mode)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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initialize();
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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DynamicDecodeLayer<T>::~DynamicDecodeLayer()
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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freeBuffer();
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::initialize()
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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mIdsPtrHost = runtime::BufferManager::pinned(ITensor::makeShape({}), runtime::TRTDataType<TokenIdType*>::value);
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allocateBuffer();
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mCyclicStep = 0;
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mRuntimeMaxSeqLen = 0;
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mConfiguredBeamWidth = -1;
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if (!mDecodingMode.isNone())
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{
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mConfiguredBeamWidth = mDecoderDomain.getMaxBeamWidth();
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initializeLayers();
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}
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::allocateBuffer()
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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mZeroParentIdsDevice = mAllocator->reMalloc(
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mZeroParentIdsDevice, sizeof(TokenIdType*) * 2 * mDecoderDomain.getMaxBatchSize(), false);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::freeBuffer()
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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mAllocator->free((void**) &mZeroParentIdsDevice);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::initializeLayers()
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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mLayers = createLayers<T>(mDecodingMode, mDecoderDomain, mStream, mAllocator);
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::setup(SizeType32 batchSize, SizeType32 beamWidth, SizeType32 const* batchSlots,
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std::shared_ptr<BaseSetupParams> baseSetupParams)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto setupParams = std::dynamic_pointer_cast<DynamicDecodeSetupParams>(baseSetupParams);
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if (mConfiguredBeamWidth == -1)
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{
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// This code is left only for Python runtime
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// In C++ runtime given maxBeamWidth should always be equal to the runtime beamWidth
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TLLM_CHECK(mDecodingMode.isNone());
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mConfiguredBeamWidth = beamWidth;
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mDecodingMode = mConfiguredBeamWidth == 1 ? DecodingMode::TopKTopP() : DecodingMode::BeamSearch();
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initializeLayers();
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}
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TLLM_CHECK_WITH_INFO((mConfiguredBeamWidth == 1 && beamWidth == 1)
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|| (mConfiguredBeamWidth > 1 && beamWidth > 1 && beamWidth <= mConfiguredBeamWidth),
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"Decoder is configured with beam width %d, but %d was given", mConfiguredBeamWidth, beamWidth);
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TLLM_CHECK_WITH_INFO(mConfiguredBeamWidth <= mDecoderDomain.getMaxBeamWidth(),
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"Decoder is created with max beam width %d, but %d was given", mDecoderDomain.getMaxBeamWidth(),
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mConfiguredBeamWidth);
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for (auto& layer : mLayers)
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{
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layer->setup(batchSize, beamWidth, batchSlots, baseSetupParams);
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}
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::forward(
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std::shared_ptr<BaseOutputParams> baseOutputs, std::shared_ptr<BaseInputParams> baseInputs)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto params = std::dynamic_pointer_cast<DynamicDecodeInputParams>(baseInputs);
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auto outputs = std::dynamic_pointer_cast<DynamicDecodeOutputParams>(baseOutputs);
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TLLM_CHECK_WITH_INFO(params->logits || params->logits_vec, "Either logits or logits_vec have to be specified.");
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TLLM_CHECK_WITH_INFO(
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outputs->sequence_length.has_value(), "sequence_length tensor is mandatory in DynamicDecoderLayer.");
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SizeType32 batchSize = 0;
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SizeType32 beamWidth = 0;
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SizeType32 vocabSize = 0;
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auto const maxSeqLen = outputs->output_ids.shape[outputs->output_ids.shape.size() - 1];
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if (params->logits)
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{
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auto const& logitsShape = params->logits->shape;
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TLLM_CHECK(logitsShape.size() == 3 || logitsShape.size() == 4);
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batchSize = logitsShape[0];
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auto const idxOffset = logitsShape.size() - 3;
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beamWidth = logitsShape[idxOffset + 1];
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vocabSize = logitsShape[idxOffset + 2];
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}
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else
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{
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TLLM_CHECK(params->logits_vec->size());
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auto const& logitsShape = params->logits_vec.value()[0].shape;
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TLLM_CHECK(logitsShape.size() == 3 || logitsShape.size() == 4);
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auto const idxOffset = logitsShape.size() - 3;
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batchSize = params->logits_vec->size();
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beamWidth = logitsShape[idxOffset + 1];
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vocabSize = logitsShape[idxOffset + 2];
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}
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TLLM_CHECK_WITH_INFO((mConfiguredBeamWidth == 1 && beamWidth == 1)
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|| (mConfiguredBeamWidth > 1 && beamWidth > 1 && beamWidth <= mConfiguredBeamWidth),
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"Decoder is configured with beam width %d, but %d was given", mConfiguredBeamWidth, beamWidth);
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if (!mIdsPtrHost->data())
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{
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mIdsPtrHost = runtime::BufferManager::pinnedPool(
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ITensor::makeShape(
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{static_cast<int32_t>(maxSeqLen), static_cast<int32_t>(2 * mDecoderDomain.getMaxBatchSize())}),
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runtime::TRTDataType<int32_t*>::value);
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mRuntimeMaxSeqLen = maxSeqLen;
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}
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std::vector<SizeType32> batchSlotsVec(batchSize);
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std::iota(batchSlotsVec.begin(), batchSlotsVec.end(), 0);
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auto batchSlotsHost
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= params->batch_slots ? params->batch_slots->template getPtr<SizeType32 const>() : batchSlotsVec.data();
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auto batchSlots = params->batch_slots ? params->batch_slots->template getPtr<SizeType32 const>() : nullptr;
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mCyclicStep = mCyclicStep % mRuntimeMaxSeqLen;
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prepareIdsPtrs(outputs, batchSlotsHost, batchSize, beamWidth, maxSeqLen);
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for (auto& layer : mLayers)
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{
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layer->forward(baseOutputs, baseInputs);
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}
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// Copy nextIds and transpose logits when needed
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prepareOutputData(outputs, params, mIdsPtrHost, batchSlots, batchSize, mDecoderDomain.getMaxBatchSize(), beamWidth,
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maxSeqLen, mDecoderDomain.getMaxTokensPerStep(), mCyclicStep, mStream);
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mCyclicStep += 1;
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sync_check_cuda_error();
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::prepareIdsPtrs(std::shared_ptr<DynamicDecodeOutputParams> const& outputs,
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SizeType32 const* batchSlots, SizeType32 batchSize, SizeType32 beamWidth, SizeType32 maxSeqLen)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto idsPtrHostSlice = ITensor::slice(mIdsPtrHost, mCyclicStep, 1);
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auto idsPtrHost = reinterpret_cast<TokenIdType**>(runtime::bufferCast<int64_t>(*idsPtrHostSlice));
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for (SizeType32 bi = 0; bi < batchSize; bi++)
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{
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auto const batchSlot = batchSlots[bi];
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idsPtrHost[batchSlot]
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= outputs->output_ids.template getPtrWithOffset<TokenIdType>(batchSlot * beamWidth * maxSeqLen);
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}
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for (SizeType32 bi = 0; bi < batchSize; bi++)
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{
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auto const batchSlot = batchSlots[bi];
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if (beamWidth > 1)
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{
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idsPtrHost[mDecoderDomain.getMaxBatchSize() + batchSlot]
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= outputs->parent_ids.value().template getPtrWithOffset<SizeType32>(bi * beamWidth * maxSeqLen);
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}
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else
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{
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idsPtrHost[mDecoderDomain.getMaxBatchSize() + batchSlot]
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= mZeroParentIdsDevice + bi * beamWidth * maxSeqLen;
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}
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}
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outputs->output_ids_ptr = Tensor(MEMORY_GPU, DataType::TYPE_INT32_PTR,
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{static_cast<size_t>(mDecoderDomain.getMaxBatchSize()), static_cast<size_t>(beamWidth),
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static_cast<size_t>(maxSeqLen)},
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idsPtrHost);
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outputs->parent_ids_ptr = Tensor(MEMORY_GPU, DataType::TYPE_INT32_PTR,
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{static_cast<size_t>(mDecoderDomain.getMaxBatchSize()), static_cast<size_t>(beamWidth),
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static_cast<size_t>(maxSeqLen)},
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idsPtrHost + mDecoderDomain.getMaxBatchSize());
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template <typename T>
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void DynamicDecodeLayer<T>::prepareOutputData(std::shared_ptr<DynamicDecodeOutputParams> const& outputs,
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std::shared_ptr<DynamicDecodeInputParams> const& params, runtime::ITensor::SharedPtr const& idsPtrsHost,
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SizeType32 const* batchSlots, SizeType32 batchSize, SizeType32 maxBatchSize, SizeType32 beamWidth,
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SizeType32 maxSeqLen, SizeType32 maxTokensPerStep, SizeType32 cyclicStep, cudaStream_t stream)
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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auto idsPtrHostSlice = ITensor::slice(idsPtrsHost, cyclicStep, 1);
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auto idsPtrHost = reinterpret_cast<TokenIdType**>(runtime::bufferCast<int64_t>(*idsPtrHostSlice));
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auto const numNewTokens = outputs->medusaOutputs
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? outputs->medusaOutputs->acceptedLengths.template getPtr<SizeType32 const>()
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: nullptr;
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invokeCopyNextStepIds(outputs->newTokens.template getPtr<TokenIdType>(), idsPtrHost,
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outputs->sequence_length->template getPtr<SizeType32>(), numNewTokens, batchSlots, batchSize, maxBatchSize,
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beamWidth, maxSeqLen, maxTokensPerStep, stream);
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// Transpose the output log probs from [maxSeqLen, bs, beamWidth] to [batchSize, beamWidth, maxSeqLen]
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if (outputs->output_log_probs_tiled)
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{
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auto logProbsMaxSeqLen = outputs->output_log_probs_tiled.value().shape[0];
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invokeTransposeLogProbs(outputs->output_log_probs.value().template getPtr<float>(),
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outputs->output_log_probs_tiled.value().template getPtr<float>(),
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outputs->sequence_length->template getPtr<SizeType32>(), batchSlots, batchSize, maxBatchSize, beamWidth,
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logProbsMaxSeqLen, stream);
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}
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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template class DynamicDecodeLayer<float>;
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template class DynamicDecodeLayer<half>;
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} // namespace layers
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} // namespace tensorrt_llm
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