mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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120 lines
4.1 KiB
C++
120 lines
4.1 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/batch_manager/kvCacheManager.h"
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#include "tensorrt_llm/batch_manager/llmRequest.h"
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#include <algorithm>
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#include <map>
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#include <optional>
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namespace tensorrt_llm::batch_manager::kv_cache_manager
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{
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class NoEvictScheduledBlocksManager
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{
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using SizeType32 = tensorrt_llm::runtime::SizeType32;
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public:
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explicit NoEvictScheduledBlocksManager(BaseKVCacheManager const& kvCacheManager)
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: mKvCacheManager(kvCacheManager)
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, mAvailableBlocks(mKvCacheManager.getBlockManager().getNumFreeBlocksPerWindowSize())
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{
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}
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void decrementReservedBlocks(LlmRequest const& req)
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{
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for (auto& [windowSize, availableBlocks] : mAvailableBlocks)
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{
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availableBlocks -= mKvCacheManager.getRemainingBlocksToCompletion(req, windowSize);
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}
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}
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bool enoughAvailableBlocks(LlmRequest const& req)
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{
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return std::all_of(mAvailableBlocks.cbegin(), mAvailableBlocks.cend(),
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[this, &req](auto const& pair)
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{
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auto const& [windowSize, availableBlocks] = pair;
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auto const neededBlocks = mKvCacheManager.getRemainingBlocksToCompletion(req, windowSize);
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return neededBlocks <= availableBlocks;
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});
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}
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private:
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BaseKVCacheManager const& mKvCacheManager;
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std::map<SizeType32, SizeType32> mAvailableBlocks;
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};
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class MaxUtilizationScheduledBlocksManager
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{
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using SizeType32 = tensorrt_llm::runtime::SizeType32;
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public:
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MaxUtilizationScheduledBlocksManager(BaseKVCacheManager const& kvCacheManager, bool twoStepsLookAhead)
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: mKvCacheManager(kvCacheManager)
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, mTwoStepsLookAhead(twoStepsLookAhead)
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{
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auto const& windowSizes = mKvCacheManager.getBlockManager().getWindowSizesMetadata();
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for (auto const& [windowSize, _] : windowSizes)
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{
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mNumScheduledBlocks[windowSize] = 0;
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}
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}
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std::optional<std::map<SizeType32, SizeType32>> prepareNewNumberOfBlocksIfWeEndUpScheduling(LlmRequest const& req)
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{
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std::map<SizeType32, SizeType32> blocksIfScheduled;
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for (auto const& [windowSize, numScheduled] : mNumScheduledBlocks)
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{
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auto const required = mKvCacheManager.getNeededBlocksOneStep(req, mTwoStepsLookAhead, windowSize);
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TLLM_LOG_DEBUG("MaxUtilizationScheduler: request ID %lu required blocks %i for %i window size",
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req.mRequestId, required, windowSize);
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auto const scheduledTotal = numScheduled + required;
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bool const hasFreeBlocks
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= mKvCacheManager.getBlockManager().schedulingHasFreeBlocks(scheduledTotal, windowSize);
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if (!hasFreeBlocks)
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{
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return std::nullopt;
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}
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blocksIfScheduled[windowSize] = scheduledTotal;
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}
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return blocksIfScheduled;
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}
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void updateScheduledBlocks(std::map<SizeType32, SizeType32> const& numBlocksIfScheduled)
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{
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assert(numBlocksIfScheduled.size() == mNumScheduledBlocks.size());
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for (auto const& [windowSize, blocksIfScheduled] : numBlocksIfScheduled)
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{
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TLLM_LOG_DEBUG(
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"MaxUtilizationScheduler: scheduled blocks %i for window size %i", blocksIfScheduled, windowSize);
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mNumScheduledBlocks.at(windowSize) = blocksIfScheduled;
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}
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}
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private:
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BaseKVCacheManager const& mKvCacheManager;
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std::map<SizeType32, SizeType32> mNumScheduledBlocks;
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bool const mTwoStepsLookAhead;
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};
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} // namespace tensorrt_llm::batch_manager::kv_cache_manager
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