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b777bd6475
* Update TensorRT-LLM --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: Tlntin <TlntinDeng01@Gmail.com> Co-authored-by: ZHENG, Zhen <zhengzhen.z@qq.com> Co-authored-by: Pham Van Ngoan <ngoanpham1196@gmail.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Tushar Goel <tushar.goel.ml@gmail.com> Co-authored-by: Mati <132419219+matichon-vultureprime@users.noreply.github.com>
85 lines
3.1 KiB
C++
85 lines
3.1 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2022-2024 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/executor/executor.h"
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#include "tensorrt_llm/runtime/common.h"
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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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enum class CacheType
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{
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kSELF = 0,
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kCROSS = 1,
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};
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//! @brief Encapsulates parameters to configure paged KV cache.
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class KvCacheConfig
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{
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public:
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using SizeType32 = tensorrt_llm::runtime::SizeType32;
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explicit KvCacheConfig(std::optional<SizeType32> maxTokens = std::nullopt,
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std::optional<SizeType32> maxAttentionWindow = std::nullopt,
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std::optional<SizeType32> sinkTokenLength = std::nullopt,
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std::optional<float> freeGpuMemoryFraction = std::nullopt, bool enableBlockReuse = false, bool useUvm = false,
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std::optional<size_t> hostCacheSize = std::nullopt, bool onboardBlocks = true)
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: maxTokens{maxTokens}
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, maxAttentionWindow{maxAttentionWindow}
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, sinkTokenLength{sinkTokenLength}
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, freeGpuMemoryFraction{freeGpuMemoryFraction}
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, enableBlockReuse(enableBlockReuse)
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, useUvm(useUvm)
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, hostCacheSize(hostCacheSize)
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, onboardBlocks(onboardBlocks)
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{
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}
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explicit KvCacheConfig(executor::KvCacheConfig const& kvCacheConfig)
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: KvCacheConfig(kvCacheConfig.getMaxTokens(), kvCacheConfig.getMaxAttentionWindow(),
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kvCacheConfig.getSinkTokenLength(), kvCacheConfig.getFreeGpuMemoryFraction(),
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kvCacheConfig.getEnableBlockReuse(), false, kvCacheConfig.getHostCacheSize(),
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kvCacheConfig.getOnboardBlocks())
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{
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}
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bool operator==(KvCacheConfig const& other) const
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{
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return maxTokens == other.maxTokens && maxAttentionWindow == other.maxAttentionWindow
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&& sinkTokenLength == other.sinkTokenLength && freeGpuMemoryFraction == other.freeGpuMemoryFraction
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&& enableBlockReuse == other.enableBlockReuse && useUvm == other.useUvm
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&& hostCacheSize == other.hostCacheSize && onboardBlocks == other.onboardBlocks;
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}
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friend std::ostream& operator<<(std::ostream& os, KvCacheConfig const& self);
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std::optional<SizeType32> maxTokens;
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std::optional<SizeType32> maxAttentionWindow;
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std::optional<SizeType32> sinkTokenLength;
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std::optional<float> freeGpuMemoryFraction;
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bool enableBlockReuse;
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static constexpr auto kDefaultGpuMemFraction = 0.9f;
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bool useUvm;
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std::optional<size_t> hostCacheSize;
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bool onboardBlocks;
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};
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} // namespace tensorrt_llm::batch_manager::kv_cache_manager
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