mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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64 lines
2.2 KiB
C++
64 lines
2.2 KiB
C++
/*
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* Copyright (c) 2019-2024, NVIDIA CORPORATION. All rights reserved.
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* Copyright (c) 2021, NAVER Corp. Authored by CLOVA.
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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/layers/baseLayer.h"
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#include "tensorrt_llm/runtime/common.h"
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namespace tensorrt_llm::layers
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{
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//! \brief Layer to randomly sample tokens from TopK logits.
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//! When both TopK and TopP are specified, layer jointly samples using TopK and TopP.
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//! When no TopK param is specified, sampling is skipped for particular request.
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template <typename T>
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class TopKSamplingLayer : public BaseLayer
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{
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using Base = BaseLayer;
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public:
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TopKSamplingLayer(DecoderDomain const& decoderDomain, std::shared_ptr<runtime::BufferManager> bufferManager);
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void setup(runtime::SizeType32 batchSize, runtime::SizeType32 beamWidth, TensorConstPtr batchSlots,
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std::shared_ptr<BaseSetupParams> const& setupParams,
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std::shared_ptr<runtime::DecodingLayerWorkspace> const& workspace) override;
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void forwardAsync(std::shared_ptr<BaseDecodingOutputs> const& outputs,
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std::shared_ptr<BaseDecodingInputs> const& inputs,
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std::shared_ptr<runtime::DecodingLayerWorkspace> const& workspace) override;
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//! @returns workspace needed for this layer in bytes
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[[nodiscard]] size_t getWorkspaceSize() const noexcept override;
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protected:
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bool mNormalizeLogProbs{true};
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size_t mWorkspaceSize{0};
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size_t mSetupWorkspaceSize{0};
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TensorPtr mRuntimeTopKDevice;
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TensorPtr mRuntimeTopPDevice;
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TensorPtr mSkipDecodeDevice;
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TensorPtr mRuntimeTopKHost;
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TensorPtr mSkipDecodeHost;
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using Base::mDecoderDomain;
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private:
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void allocateBuffer(runtime::SizeType32 batchSize);
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};
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} // namespace tensorrt_llm::layers
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