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| // Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | ||
| // | ||
| // Redistribution and use in source and binary forms, with or without | ||
| // modification, are permitted provided that the following conditions | ||
| // are met: | ||
| // * Redistributions of source code must retain the above copyright | ||
| // notice, this list of conditions and the following disclaimer. | ||
| // * Redistributions in binary form must reproduce the above copyright | ||
| // notice, this list of conditions and the following disclaimer in the | ||
| // documentation and/or other materials provided with the distribution. | ||
| // * Neither the name of NVIDIA CORPORATION nor the names of its | ||
| // contributors may be used to endorse or promote products derived | ||
| // from this software without specific prior written permission. | ||
| // | ||
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY | ||
| // EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR | ||
| // PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR | ||
| // CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, | ||
| // EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, | ||
| // PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR | ||
| // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY | ||
| // OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
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| #include "src/servables/tensorrt/autofill.h" | ||
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| #include <NvInfer.h> | ||
| #include "src/core/constants.h" | ||
| #include "src/core/logging.h" | ||
| #include "src/core/model_config.h" | ||
| #include "src/servables/tensorrt/loader.h" | ||
| #include "src/servables/tensorrt/plan_utils.h" | ||
| #include "tensorflow/core/lib/io/path.h" | ||
| #include "tensorflow/core/platform/env.h" | ||
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| namespace nvidia { namespace inferenceserver { | ||
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| tensorflow::Status | ||
| AutoFillPlan::Create( | ||
| const std::string& model_name, const std::string& model_path, | ||
| std::unique_ptr<AutoFillPlan>* autofill) | ||
| { | ||
| std::set<std::string> version_dirs; | ||
| TF_RETURN_IF_ERROR(GetSubdirs(model_path, &version_dirs)); | ||
|
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||
| // There must be at least one version directory that we can inspect | ||
| // to attempt to determine the platform. For now we only handle the | ||
| // case where there is one version directory. | ||
| if (version_dirs.size() != 1) { | ||
| return tensorflow::errors::Internal( | ||
| "unable to autofill for '", model_name, "' due to multiple versions"); | ||
| } | ||
|
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| const auto version_path = | ||
| tensorflow::io::JoinPath(model_path, *(version_dirs.begin())); | ||
|
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| // There must be a single plan file within the version directory... | ||
| std::set<std::string> plan_files; | ||
| TF_RETURN_IF_ERROR(GetFiles(version_path, &plan_files)); | ||
| if (plan_files.size() != 1) { | ||
| return tensorflow::errors::Internal( | ||
| "unable to autofill for '", model_name, "', unable to find plan file"); | ||
| } | ||
|
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| const std::string plan_file = *(plan_files.begin()); | ||
| const auto plan_path = tensorflow::io::JoinPath(version_path, plan_file); | ||
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| tensorflow::string plan_data_str; | ||
| TF_RETURN_IF_ERROR(tensorflow::ReadFileToString( | ||
| tensorflow::Env::Default(), plan_path, &plan_data_str)); | ||
| std::vector<char> plan_data(plan_data_str.begin(), plan_data_str.end()); | ||
|
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| nvinfer1::IRuntime* runtime = nullptr; | ||
| nvinfer1::ICudaEngine* engine = nullptr; | ||
| if (!LoadPlan(plan_data, &runtime, &engine).ok()) { | ||
| if (engine != nullptr) { | ||
| engine->destroy(); | ||
| } | ||
| if (runtime != nullptr) { | ||
| runtime->destroy(); | ||
| } | ||
| return tensorflow::errors::Internal( | ||
| "unable to autofill for '", model_name, | ||
| "', unable to create TensorRT runtime and engine"); | ||
| } | ||
|
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| const int32_t max_batch_size = engine->getMaxBatchSize(); | ||
|
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| // Inputs and outputs. | ||
| ModelConfig config; | ||
| for (int i = 0; i < engine->getNbBindings(); ++i) { | ||
| if (engine->bindingIsInput(i)) { | ||
| ModelInput* config_input = config.add_input(); | ||
| config_input->set_name(engine->getBindingName(i)); | ||
| config_input->set_data_type( | ||
| ConvertDatatype(engine->getBindingDataType(i))); | ||
| nvinfer1::Dims dims = engine->getBindingDimensions(i); | ||
| for (int didx = 0; didx < dims.nbDims; ++didx) { | ||
| config_input->mutable_dims()->Add(dims.d[didx]); | ||
| } | ||
| } else { | ||
| ModelOutput* config_output = config.add_output(); | ||
| config_output->set_name(engine->getBindingName(i)); | ||
| config_output->set_data_type( | ||
| ConvertDatatype(engine->getBindingDataType(i))); | ||
| nvinfer1::Dims dims = engine->getBindingDimensions(i); | ||
| for (int didx = 0; didx < dims.nbDims; ++didx) { | ||
| config_output->mutable_dims()->Add(dims.d[didx]); | ||
| } | ||
| } | ||
| } | ||
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| engine->destroy(); | ||
| runtime->destroy(); | ||
|
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| autofill->reset( | ||
| new AutoFillPlan(model_name, plan_file, max_batch_size, config)); | ||
| return tensorflow::Status::OK(); | ||
| } | ||
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| tensorflow::Status | ||
| AutoFillPlan::Fix(ModelConfig* config) | ||
| { | ||
| config->set_platform(kTensorRTPlanPlatform); | ||
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| // Set name if not already set. | ||
| if (config->name().empty()) { | ||
| config->set_name(model_name_); | ||
| } | ||
|
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| if (config->default_model_filename().empty()) { | ||
| config->set_default_model_filename(plan_filename_); | ||
| } | ||
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| if (config->max_batch_size() == 0) { | ||
| config->set_max_batch_size(max_batch_size_); | ||
| } | ||
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| // Inputs | ||
| if (config->input().size() == 0) { | ||
| config->mutable_input()->CopyFrom(config_.input()); | ||
| } | ||
|
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| // Outputs | ||
| if (config->output().size() == 0) { | ||
| config->mutable_output()->CopyFrom(config_.output()); | ||
| } | ||
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| return tensorflow::Status::OK(); | ||
| } | ||
|
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| }} // namespace nvidia::inferenceserver |
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