mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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126 lines
4.5 KiB
C++
126 lines
4.5 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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/batch_manager/kvCacheManager.h"
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#include "tensorrt_llm/executor/executor.h"
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include <atomic>
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#include <condition_variable>
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#include <cstddef>
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#include <optional>
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#include <unordered_map>
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#include <vector>
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namespace tensorrt_llm::batch_manager::kv_cache_manager
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{
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class FabricMemory
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{
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public:
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explicit FabricMemory(size_t size);
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~FabricMemory();
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FabricMemory(FabricMemory const&) = delete;
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FabricMemory& operator=(FabricMemory const&) = delete;
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FabricMemory(FabricMemory&&) noexcept;
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FabricMemory& operator=(FabricMemory&&) noexcept;
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void* getPtr() const;
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size_t getSize() const;
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static size_t getAlignedSize(size_t size);
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static bool supportFbaricMemory();
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private:
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class Impl;
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std::unique_ptr<Impl> pImpl;
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};
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class CacheTransBufferManager
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{
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public:
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CacheTransBufferManager(KVCacheManager::BaseKVCacheManager* cacheManager,
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std::optional<size_t> maxNumTokens = std::nullopt, bool transferIndexerKCache = false);
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static size_t preAllocBufferSize(std::map<SizeType32, SizeType32> const& cacheSizeBytesPerTokenPerWindow,
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SizeType32 tokensPerBlock,
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std::optional<executor::CacheTransceiverConfig> const& cacheTransceiverConfig = std::nullopt);
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std::optional<int> assignBufferIndexForSend();
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void freeBufferIndexForSend(std::optional<int> bufferId);
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std::optional<int> assignBufferIndexForRecv();
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void freeBufferIndexForRecv(std::optional<int> bufferId);
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std::tuple<std::vector<runtime::ITensor::SharedPtr>, size_t, bool> getOrAllocateSendBuffers(
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std::optional<int> bufferId, int targetNum, std::vector<size_t> const& requestedNumberOfElements,
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runtime::BufferManager const& bufferManagerToUse);
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std::tuple<std::vector<runtime::ITensor::SharedPtr>, size_t, bool> getOrAllocateRecvBuffers(
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std::optional<int> bufferId, int targetNum, std::vector<size_t> const& requestedNumberOfElements,
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runtime::BufferManager const& bufferManagerToUse);
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runtime::ITensor::SharedPtr getSendBuffer(std::optional<int> bufferId);
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runtime::ITensor::SharedPtr getRecvBuffer(std::optional<int> bufferId);
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size_t getRecvBufferCount();
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size_t getSendBufferCount();
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std::optional<size_t> getMaxNumTokens()
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{
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return mMaxNumTokens;
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}
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private:
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struct ConcurrenceResource
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{
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std::unordered_map<int, runtime::ITensor::SharedPtr> mBuffers;
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std::vector<int> mBufferIndexFlag;
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std::mutex mBuffersMutex;
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std::condition_variable mBuffersCV;
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std::atomic<int> mConcurrence = 0;
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};
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std::tuple<std::vector<runtime::ITensor::SharedPtr>, size_t, bool> getOrAllocateBuffers(std::optional<int> bufferId,
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int targetNum, std::vector<size_t> const& requestedNumberOfElements,
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runtime::BufferManager const& bufferManagerToUse, ConcurrenceResource& concurrenceResource);
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void allocateBuffer();
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std::optional<int> assignBufferIndex(ConcurrenceResource& resource, size_t bufferCount, bool onlyUseDynamicBuffer);
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void freeBufferIndex(
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ConcurrenceResource& resource, std::optional<int> bufferId, size_t bufferCount, bool onlyUseDynamicBuffer);
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size_t mPreAllocBufferSize;
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size_t mRecvBufferCount;
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size_t mSendBufferCount;
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size_t mTransferBufferSize;
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bool mOnlyUseDynamicBuffer;
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bool mUseFabricMemory;
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size_t mNumberOfElements;
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nvinfer1::DataType mDataType;
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ConcurrenceResource mConcurrenceSendResource;
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ConcurrenceResource mConcurrenceRecvResource;
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KVCacheManager::BaseKVCacheManager* mCacheManager;
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runtime::BufferManager mBufferManager;
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std::vector<std::unique_ptr<FabricMemory>> mFabricMemory;
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std::optional<size_t> mMaxNumTokens;
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};
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} // namespace tensorrt_llm::batch_manager::kv_cache_manager
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