mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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109 lines
4.8 KiB
C++
109 lines
4.8 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 "tensorrt_llm/runtime/gptJsonConfig.h"
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#include "tensorrt_llm/runtime/modelConfig.h"
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#include "tensorrt_llm/runtime/rawEngine.h"
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#include "tensorrt_llm/runtime/tllmLogger.h"
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#include "tensorrt_llm/runtime/worldConfig.h"
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#include "trtGptModelInflightBatching.h"
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#include "trtGptModelV1.h"
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#include <NvInferPlugin.h>
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#include <memory>
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#include <optional>
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namespace tensorrt_llm::batch_manager
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{
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class TrtGptModelFactory
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{
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public:
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using SizeType32 = tensorrt_llm::runtime::SizeType32;
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static std::shared_ptr<TrtGptModel> create(std::filesystem::path const& trtEnginePath, TrtGptModelType modelType,
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TrtGptModelOptionalParams const& optionalParams = TrtGptModelOptionalParams())
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{
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auto const jsonConfig = runtime::GptJsonConfig::parse(trtEnginePath / "config.json");
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auto worldConfig = getWorldConfig(jsonConfig, optionalParams.deviceIds);
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auto const enginePath = trtEnginePath / jsonConfig.engineFilename(worldConfig);
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auto const& modelConfig = jsonConfig.getModelConfig();
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return create(runtime::RawEngine(enginePath), modelConfig, worldConfig, modelType, optionalParams);
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}
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static std::shared_ptr<TrtGptModel> create(std::filesystem::path const& trtEnginePath, TrtGptModelType modelType,
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runtime::GptJsonConfig const& jsonConfig, runtime::WorldConfig const& worldConfig,
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TrtGptModelOptionalParams const& optionalParams = TrtGptModelOptionalParams())
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{
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auto const enginePath = trtEnginePath / jsonConfig.engineFilename(worldConfig);
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auto const& modelConfig = jsonConfig.getModelConfig();
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return create(runtime::RawEngine(enginePath), modelConfig, worldConfig, modelType, optionalParams);
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}
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static std::shared_ptr<TrtGptModel> create(runtime::RawEngine const& rawEngine,
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runtime::ModelConfig const& modelConfig, runtime::WorldConfig const& worldConfig, TrtGptModelType modelType,
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TrtGptModelOptionalParams const& optionalParams = TrtGptModelOptionalParams())
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{
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auto logger = std::make_shared<runtime::TllmLogger>();
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auto const device = worldConfig.getDevice();
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auto const rank = worldConfig.getRank();
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TLLM_LOG_INFO("Rank %d is using GPU %d", rank, device);
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TLLM_CUDA_CHECK(cudaSetDevice(device));
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if (modelType == TrtGptModelType::V1)
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{
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TLLM_LOG_WARNING(
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"TrtGptModelType::V1 is deprecated and will be removed in a future release."
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" Please use TrtGptModelType::InflightBatching or TrtGptModelType::InflightFusedBatching instead.");
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TrtGptModelOptionalParams const& fixedOptionalParams
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= TrtGptModelV1::optionalParamsAreValid(modelConfig, optionalParams)
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? optionalParams
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: TrtGptModelV1::fixOptionalParams(modelConfig, optionalParams);
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return std::make_shared<TrtGptModelV1>(logger, modelConfig, worldConfig, rawEngine, fixedOptionalParams);
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}
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else if ((modelType == TrtGptModelType::InflightBatching)
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|| (modelType == TrtGptModelType::InflightFusedBatching))
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{
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TrtGptModelOptionalParams const& fixedOptionalParams
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= TrtGptModelInflightBatching::optionalParamsAreValid(modelConfig, optionalParams)
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? optionalParams
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: TrtGptModelInflightBatching::fixOptionalParams(modelConfig, optionalParams);
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return std::make_shared<TrtGptModelInflightBatching>(logger, modelConfig, worldConfig, rawEngine,
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(modelType == TrtGptModelType::InflightFusedBatching), fixedOptionalParams);
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}
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else
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{
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throw std::runtime_error("Invalid modelType in trtGptModelFactory");
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}
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}
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private:
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static runtime::WorldConfig getWorldConfig(
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runtime::GptJsonConfig const& json, std::optional<std::vector<SizeType32>> const& deviceIds)
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{
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return runtime::WorldConfig::mpi(json.getGpusPerNode(), json.getTensorParallelism(),
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json.getPipelineParallelism(), json.getContextParallelism(), deviceIds);
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}
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};
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} // namespace tensorrt_llm::batch_manager
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