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https://github.com/NVIDIA/TensorRT-LLM.git
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8681b3a4c0
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvanesh.sridharan@sprinklr.com> Co-authored-by: Qingquan Song <ustcsqq@gmail.com>
119 lines
3.3 KiB
C++
119 lines
3.3 KiB
C++
/*
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* Copyright (c) 2023-2024, 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/runtime/common.h"
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#include <cstdint>
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#include <list>
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#include <memory>
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#include <unordered_set>
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#include <utility>
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#include <vector>
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namespace tensorrt_llm::executor
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{
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class RequestWithId;
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}
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namespace tensorrt_llm::batch_manager
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{
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class LlmRequest;
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using RequestList = std::list<std::shared_ptr<LlmRequest>>;
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using RequestIdType = std::uint64_t;
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using RequestVector = std::vector<std::shared_ptr<LlmRequest>>;
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using ReqIdsSet = std::unordered_set<RequestIdType>;
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class ScheduledRequests
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{
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public:
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/// @brief context phase requests (for decoder-only models) or encoder phase requests (for encoder-decoder models
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/// and encoder-only models)
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RequestVector contextRequests;
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/// @brief generation phase requests (for decoder-only models) or empty for others
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RequestVector generationRequests;
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ScheduledRequests() = default;
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explicit ScheduledRequests(RequestVector contextRequests, RequestVector generationRequests)
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: contextRequests{std::move(contextRequests)}
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, generationRequests{std::move(generationRequests)}
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{
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}
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[[nodiscard]] bool empty() const
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{
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return contextRequests.empty() && generationRequests.empty();
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}
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[[nodiscard]] std::size_t size() const
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{
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return contextRequests.size() + generationRequests.size();
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}
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};
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class BatchState
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{
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public:
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BatchState() = default;
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BatchState(runtime::SizeType32 numCtxRequests, runtime::SizeType32 numGenRequests, runtime::SizeType32 numTokens,
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runtime::SizeType32 maxKvCacheLength)
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: mNumCtxRequests{numCtxRequests}
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, mNumGenRequests{numGenRequests}
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, mNumTokens{numTokens}
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, mMaxKvCacheLength{maxKvCacheLength}
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{
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}
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bool isAnyContext() const
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{
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return mNumCtxRequests > 0;
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}
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bool operator==(BatchState const& other) const
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{
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return mNumCtxRequests == other.mNumCtxRequests && mNumGenRequests == other.mNumGenRequests
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&& mNumTokens == other.mNumTokens && mMaxKvCacheLength == other.mMaxKvCacheLength;
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}
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size_t hash() const
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{
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size_t h1 = std::hash<runtime::SizeType32>{}(mNumCtxRequests);
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size_t h2 = std::hash<runtime::SizeType32>{}(mNumGenRequests);
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size_t h3 = std::hash<runtime::SizeType32>{}(mNumTokens);
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size_t h4 = std::hash<runtime::SizeType32>{}(mMaxKvCacheLength);
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return h1 ^ h2 ^ h3 ^ h4;
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}
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runtime::SizeType32 mNumCtxRequests;
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runtime::SizeType32 mNumGenRequests;
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runtime::SizeType32 mNumTokens;
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runtime::SizeType32 mMaxKvCacheLength;
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};
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struct BatchStateHash
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{
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size_t operator()(BatchState const& bs) const
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{
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return bs.hash();
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}
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};
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} // namespace tensorrt_llm::batch_manager
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