mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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109 lines
3.6 KiB
C++
109 lines
3.6 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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#pragma once
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#include "tensorrt_llm/common/tensor.h"
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#include "tensorrt_llm/layers/banWordsLayer.h"
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#include "tensorrt_llm/layers/baseLayer.h"
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#include "tensorrt_llm/layers/beamSearchLayer.h"
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#include "tensorrt_llm/layers/decodingLayer.h"
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#include "tensorrt_llm/layers/medusaDecodingLayer.h"
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#include "tensorrt_llm/layers/penaltyLayer.h"
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#include "tensorrt_llm/layers/samplingLayer.h"
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#include "tensorrt_llm/layers/stopCriteriaLayer.h"
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#include "tensorrt_llm/runtime/cudaStream.h"
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#include "tensorrt_llm/runtime/decodingMode.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include <optional>
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#include <string>
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#include <unordered_map>
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#include <utility>
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namespace tc = tensorrt_llm::common;
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namespace tensorrt_llm
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{
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namespace kernels
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{
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struct BeamHypotheses;
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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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class DynamicDecodeLayer : public BaseLayer
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{
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using Base = BaseLayer;
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public:
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DynamicDecodeLayer(runtime::DecodingMode const& mode, DecoderDomain const& decodingDomain, cudaStream_t stream,
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std::shared_ptr<tc::IAllocator> allocator);
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~DynamicDecodeLayer() override;
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void setup(runtime::SizeType32 batchSize, runtime::SizeType32 beamWidth, runtime::SizeType32 const* batchSlots,
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std::shared_ptr<BaseSetupParams> setupParams) override;
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void forward(std::shared_ptr<BaseOutputParams> outputs, std::shared_ptr<BaseInputParams> inputs) override;
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// Function is only used by test.
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// It is guaranteed by LayersFactory that the first layer is the Penalty layer.
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T* getRuntimeLogitsDevice()
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{
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return dynamic_cast<PenaltyLayer<T>*>(mLayers[0].get())->getRuntimeLogitsDevice();
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}
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private:
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void allocateBuffer();
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void freeBuffer();
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void initialize();
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void initializeLayers();
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void prepareIdsPtrs(std::shared_ptr<DynamicDecodeOutputParams> const& outputs,
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runtime::SizeType32 const* batchSlots, runtime::SizeType32 batchSize, runtime::SizeType32 beamWidth,
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runtime::SizeType32 maxSeqLen);
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static void 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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runtime::SizeType32 const* batchSlots, runtime::SizeType32 batchSize, runtime::SizeType32 maxBatchSize,
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runtime::SizeType32 beamWidth, runtime::SizeType32 maxSeqLen, runtime::SizeType32 maxTokensPerStep,
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runtime::SizeType32 cyclicStep, cudaStream_t stream);
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private:
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using Base::mAllocator;
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using Base::mStream;
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using Base::mDecoderDomain;
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std::vector<std::unique_ptr<BaseLayer>> mLayers;
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runtime::DecodingMode mDecodingMode;
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runtime::TokenIdType* mZeroParentIdsDevice{nullptr};
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runtime::ITensor::SharedPtr mIdsPtrHost;
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bool mHasDiffRuntimeArgs{false};
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runtime::SizeType32 mCyclicStep{0};
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runtime::SizeType32 mRuntimeMaxSeqLen{0};
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runtime::SizeType32 mConfiguredBeamWidth{-1};
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};
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} // namespace layers
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} // namespace tensorrt_llm
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