mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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e06f537e08
* Update TensorRT-LLM --------- Co-authored-by: erenup <ping.nie@pku.edu.cn> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
122 lines
4.3 KiB
C++
122 lines
4.3 KiB
C++
/*
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* Copyright (c) 2022-2024, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#pragma once
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#include "tensorrt_llm/common/cudaAllocator.h"
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/decodingInput.h"
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#include "tensorrt_llm/runtime/decodingOutput.h"
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#include "tensorrt_llm/runtime/samplingConfig.h"
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#include <curand_kernel.h>
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#include <cstdint>
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#include <memory>
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#include <NvInferRuntime.h>
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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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// Forward declaration
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template <typename T>
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class DynamicDecodeLayer;
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} // namespace layers
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namespace runtime
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{
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class IGptDecoder
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{
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public:
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virtual ~IGptDecoder() = default;
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virtual void setup(SamplingConfig const& samplingConfig, size_t batchSize, SizeType maxSequenceLength) = 0;
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virtual bool forward(DecodingOutput& output, DecodingInput const& input) = 0;
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virtual void forwardAsync(DecodingOutput& output, DecodingInput const& input) = 0;
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virtual void gatherTree(ITensor& finalOutputIds, DecodingOutput const& decodingOutput,
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DecodingInput const& decodingInput, BufferManager const& manager)
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= 0;
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virtual const SamplingConfig& getSamplingConfig() = 0;
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static void acceptDraftTokensByIds(const ITensor& targetTokenIds, const ITensor& draftTokenIds,
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const ITensor& contextLengths, const ITensor& numDraftTokens, ITensor& sequenceLengths,
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const ITensor& finishedVec, ITensor& finishedFinal, ITensor& finishedSum,
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BufferManager::CudaStreamPtr const& stream);
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static void acceptDraftTokensByLogits(ITensor& draftLogits, const ITensor& targetLogits, ITensor& draftProbs,
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ITensor& targetProbs, const ITensor& numDraftTokens, ITensor& finished, SizeType vocabSize,
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SizeType vocabSizePadded, bool useRandomAcceptThreshold, float randomAcceptThreshold,
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curandState_t* curandState, BufferManager::CudaStreamPtr const& stream);
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static std::unique_ptr<IGptDecoder> create(nvinfer1::DataType dtype, size_t maxBatchSize, size_t vocabSize,
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size_t vocabSizePadded, BufferManager::CudaStreamPtr const& stream);
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};
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template <typename T>
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class GptDecoder : public virtual IGptDecoder
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{
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public:
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using CudaStreamPtr = BufferManager::CudaStreamPtr;
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using TensorPtr = std::shared_ptr<ITensor>;
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GptDecoder(size_t maxBatchSize, size_t vocabSize, size_t vocabSizePadded, CudaStreamPtr const& stream);
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void setup(SamplingConfig const& samplingConfig, size_t batchSize, SizeType maxSequenceLength) override;
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bool forward(DecodingOutput& output, DecodingInput const& input) override;
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void forwardAsync(DecodingOutput& output, DecodingInput const& input) override;
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void gatherTree(ITensor& finalOutputIds, DecodingOutput const& decodingOutput, DecodingInput const& decodingInput,
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BufferManager const& manager) override;
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const SamplingConfig& getSamplingConfig() override
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{
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return mSamplingConfig;
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}
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private:
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BufferManager mManager;
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std::shared_ptr<tensorrt_llm::layers::DynamicDecodeLayer<T>> mDynamicDecodeLayer;
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TensorPtr mLogProbsTiled; // Buffer used to store the transpose of the logProbs. Needed because the kernels have
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// been written to use that shape.
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SamplingConfig mSamplingConfig;
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};
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inline std::unique_ptr<IGptDecoder> IGptDecoder::create(nvinfer1::DataType dtype, size_t maxBatchSize, size_t vocabSize,
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size_t vocabSizePadded, BufferManager::CudaStreamPtr const& stream)
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{
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switch (dtype)
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{
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case nvinfer1::DataType::kFLOAT:
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return std::make_unique<GptDecoder<float>>(maxBatchSize, vocabSize, vocabSizePadded, stream);
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case nvinfer1::DataType::kHALF:
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return std::make_unique<GptDecoder<half>>(maxBatchSize, vocabSize, vocabSizePadded, stream);
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default: return nullptr;
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}
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}
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} // namespace runtime
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} // namespace tensorrt_llm
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