mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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85 lines
3.4 KiB
C++
85 lines
3.4 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 "dataTransceiver.h"
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#include "tensorrt_llm/batch_manager/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/logger.h"
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#include "tensorrt_llm/executor/cache_transmission/cacheConcatenate.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/iTensor.h"
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#include <NvInferRuntimeBase.h>
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#include <condition_variable>
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#include <cstddef>
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#include <cstdint>
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#include <iterator>
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namespace tensorrt_llm::batch_manager::kv_cache_manager
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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 MLACacheFormatter final : public IOFormatter
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{
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public:
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using CacheState = executor::kv_cache::CacheState;
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MLACacheFormatter(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 formatOutput(LlmRequest const& llmRequest,
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std::vector<executor::kv_cache::Connection const*> const& connections, CacheState const& selfConfig,
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SizeType32 selfIdx, CacheState const& destConfig, runtime::BufferManager const& bufferManager) override;
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void formatInput(LlmRequest const& llmRequest,
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std::vector<executor::kv_cache::Connection const*> const& connections, CacheState const& selfConfig,
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SizeType32 selfIdx, CacheState const& destConfig, runtime::BufferManager const& bufferManager) 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
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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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static std::vector<executor::kv_cache::Connection const*> pickRecvConnections(
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std::vector<executor::kv_cache::Connection const*> const& connections, CacheState const& selfConfig,
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SizeType32 selfIdx, CacheState const& destConfig);
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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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} // namespace tensorrt_llm::batch_manager::kv_cache_manager
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