mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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140 lines
5.8 KiB
C++
140 lines
5.8 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 "cacheTransBuffer.h"
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#include "tensorrt_llm/batch_manager/kvCacheManager.h"
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#include "tensorrt_llm/batch_manager/kvCacheUtils.h"
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/common/envUtils.h"
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#include "tensorrt_llm/common/logger.h"
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#include "tensorrt_llm/executor/cacheCommunicator.h"
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#include "tensorrt_llm/executor/cache_transmission/cacheSplitConcat.h"
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#include "tensorrt_llm/executor/dataTransceiverState.h"
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/utils/mpiUtils.h"
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#include <NvInferRuntimeBase.h>
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#include <cstddef>
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#include <cstdint>
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#include <fstream>
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#include <vector>
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// Forward declare TransferSession in the correct global namespace scope
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namespace tensorrt_llm::batch_manager
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{
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class TransferSession;
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}
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namespace tensorrt_llm::batch_manager::kv_cache_manager
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{
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BlockRange getBlockRangeForSending(BaseKVCacheManager* cacheManager, LlmRequest const& llmRequest,
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BlockKey const& lastBlockKey, SizeType32 indexFromEnd, bool recvSideHasCP = false);
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using DataContext = tensorrt_llm::executor::kv_cache::DataContext;
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using Connection = tensorrt_llm::executor::kv_cache::Connection;
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using SizeType32 = tensorrt_llm::runtime::SizeType32;
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using BaseKVCacheManager = kv_cache_manager::BaseKVCacheManager;
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using CacheTransBufferManager = kv_cache_manager::CacheTransBufferManager;
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using BlockRange = kv_cache_manager::BlockRange;
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BlockRange getBlockRangeForReceiving(BaseKVCacheManager* cacheManager, LlmRequest const& llmRequest,
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bool srcEnableBlockReuse, bool recvSideHasCP = false);
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// Used to support the cache transmission with different layouts and different protocols.
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class BaseCacheFormatter
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{
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public:
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using CacheState = executor::kv_cache::CacheState;
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/// @brief Format the cache data into bytes for sending.
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/// @param session The transfer session.
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virtual void format(tensorrt_llm::batch_manager::TransferSession& session) = 0;
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/// @brief Unformat the cache data from received bytes.
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/// @param session The transfer session.
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virtual void unformat(tensorrt_llm::batch_manager::TransferSession& session) = 0;
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/// @brief Determine whether the sender is applicable to the source and target.
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/// @param selfConfig Source data arrangement.
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/// @param destConfig Target data arrangement.
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/// @return Whether the sender is applicable to the source and target.
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[[nodiscard]] virtual bool inquireSupport(CacheState const& selfConfig, CacheState const& destConfig) const = 0;
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/// @brief Obtain the indies of the counterparts that need to be actually communicated with.
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/// @param selfConfig Source data arrangement.
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/// @param selfIdx The sequential index of the current executor process within the entire parallel group.
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/// @param destConfig Target data arrangement.
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/// @return The indies of the counterparts.
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[[nodiscard]] virtual std::vector<SizeType32> getCounterparts(
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CacheState const& selfConfig, SizeType32 selfIdx, CacheState const& destConfig) const
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= 0;
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[[nodiscard]] virtual BaseKVCacheManager* getCacheManager() const noexcept = 0;
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[[nodiscard]] virtual std::vector<size_t> pickRecvConnections(
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size_t numConnections, CacheState const& selfConfig, SizeType32 selfIdx, CacheState const& destConfig) const
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= 0;
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/// @brief Destructor.
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virtual ~BaseCacheFormatter() = default;
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};
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// Simple cache block copy. Because it does not involve data splitting or merging, it performs best when the
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// parallel topology is completely identical, making it the preferred method.
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class CacheFormatter final : public BaseCacheFormatter
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{
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public:
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CacheFormatter(BaseKVCacheManager* cacheManager, CacheTransBufferManager* cacheTransBufferManager)
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: mCacheManager{cacheManager}
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, mCacheTransBufferManager{cacheTransBufferManager}
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{
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TLLM_CHECK(mCacheManager);
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TLLM_CHECK(mCacheTransBufferManager);
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}
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void format(tensorrt_llm::batch_manager::TransferSession& session) override;
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void unformat(tensorrt_llm::batch_manager::TransferSession& session) override;
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[[nodiscard]] bool inquireSupport(CacheState const& selfConfig, CacheState const& destConfig) const override;
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[[nodiscard]] std::vector<SizeType32> getCounterparts(
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CacheState const& selfConfig, SizeType32 selfIdx, CacheState const& destConfig) const override
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{
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return executor::kv_cache::targetIRanks(destConfig, selfConfig, selfIdx).mIRanks;
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}
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[[nodiscard]] BaseKVCacheManager* getCacheManager() const noexcept override
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{
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return mCacheManager;
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}
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static bool needSendCache(CacheState const& selfConfig, CacheState const& destConfig, runtime::SizeType32 selfIdx);
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std::vector<size_t> pickRecvConnections(size_t numConnections, CacheState const& selfConfig, SizeType32 selfIdx,
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CacheState const& destConfig) const override;
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private:
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BaseKVCacheManager* mCacheManager;
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CacheTransBufferManager* mCacheTransBufferManager;
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};
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std::unique_ptr<BaseCacheFormatter> createCacheFormatter(BaseKVCacheManager* cacheManager,
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std::vector<CacheTransBufferManager*> const& cacheTransBufferManagers, bool isMLA = false);
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} // namespace tensorrt_llm::batch_manager::kv_cache_manager
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