mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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143 lines
4.5 KiB
C++
143 lines
4.5 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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#include <tensorrt_llm/executor/executor.h>
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#include <tensorrt_llm/executor/executorImpl.h>
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namespace tensorrt_llm::executor
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{
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Executor::Executor(std::filesystem::path const& modelPath, ModelType modelType, ExecutorConfig const& executorConfig)
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: mImpl(std::make_unique<Executor::Impl>(modelPath, std::nullopt, modelType, executorConfig))
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{
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}
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Executor::Executor(std::filesystem::path const& encoderModelPath, std::filesystem::path const& decoderModelPath,
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ModelType modelType, ExecutorConfig const& executorConfig)
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: mImpl(std::make_unique<Executor::Impl>(decoderModelPath, encoderModelPath, modelType, executorConfig))
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{
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}
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Executor::Executor(BufferView const& engineBuffer, std::string const& jsonConfigStr, ModelType modelType,
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ExecutorConfig const& executorConfig, std::optional<std::map<std::string, Tensor>> const& managedWeights)
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: mImpl(std::make_unique<Executor::Impl>(
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engineBuffer, jsonConfigStr, std::nullopt, std::nullopt, modelType, executorConfig, managedWeights))
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{
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}
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Executor::Executor(BufferView const& encoderEngineBuffer, std::string const& encoderJsonConfigStr,
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BufferView const& decoderEngineBuffer, std::string const& decoderJsonConfigStr, ModelType modelType,
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ExecutorConfig const& executorConfig)
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: mImpl(std::make_unique<Executor::Impl>(decoderEngineBuffer, decoderJsonConfigStr, encoderEngineBuffer,
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encoderJsonConfigStr, modelType, executorConfig, std::nullopt))
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{
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}
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Executor::Executor(std::shared_ptr<Model> model, ExecutorConfig const& executorConfig)
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: mImpl(std::make_unique<Executor::Impl>(std::move(model), std::nullopt, executorConfig))
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{
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}
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Executor::Executor(
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std::shared_ptr<Model> encoderModel, std::shared_ptr<Model> decoderModel, ExecutorConfig const& executorConfig)
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: mImpl(std::make_unique<Executor::Impl>(std::move(decoderModel), std::move(encoderModel), executorConfig))
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{
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}
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Executor::~Executor() = default;
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IdType Executor::enqueueRequest(Request const& llmRequest)
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{
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return mImpl->enqueueRequest(llmRequest);
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}
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std::vector<IdType> Executor::enqueueRequests(std::vector<Request> const& llmRequests)
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{
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return mImpl->enqueueRequests(llmRequests);
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}
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std::vector<Response> Executor::awaitResponses(std::optional<std::chrono::milliseconds> const& timeout)
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{
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return mImpl->awaitResponses(timeout);
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}
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std::vector<Response> Executor::awaitResponses(
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IdType const& requestId, std::optional<std::chrono::milliseconds> const& timeout)
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{
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return mImpl->awaitResponses(requestId, timeout);
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}
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std::vector<std::vector<Response>> Executor::awaitResponses(
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std::vector<IdType> const& requestIds, std::optional<std::chrono::milliseconds> const& timeout)
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{
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return mImpl->awaitResponses(requestIds, timeout);
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}
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SizeType32 Executor::getNumResponsesReady(std::optional<IdType> const& requestId) const
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{
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return mImpl->getNumResponsesReady(requestId);
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}
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void Executor::cancelRequest(IdType requestId)
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{
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return mImpl->cancelRequest(requestId);
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}
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void Executor::shutdown()
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{
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return mImpl->shutdown();
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}
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std::deque<IterationStats> Executor::getLatestIterationStats()
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{
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return mImpl->getLatestIterationStats();
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}
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std::deque<RequestStatsPerIteration> Executor::getLatestRequestStats()
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{
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return mImpl->getLatestRequestStats();
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}
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std::deque<DebugTensorsPerIteration> Executor::getLatestDebugTensors()
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{
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return mImpl->getLatestDebugTensors();
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}
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bool Executor::canEnqueueRequests() const
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{
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return mImpl->canEnqueueRequests();
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}
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bool Executor::isParticipant() const
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{
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return mImpl->isParticipant();
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}
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std::optional<std::shared_ptr<KVCacheEventManager>> Executor::getKVCacheEventManager() const
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{
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return mImpl->getKVCacheEventManager();
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}
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KVCacheEvent::KVCacheEvent(size_t eventId, KVCacheEventData data, SizeType32 windowSize)
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: eventId{eventId}
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, data{std::move(data)}
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, windowSize{windowSize}
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{
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}
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} // namespace tensorrt_llm::executor
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