// // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "armnn_delegate.hpp" #include #include #include namespace tflite { /** * This file defines two symbols that need to be exported to use the TFLite external delegate provider. This is a plugin * that can be used for fast integration of delegates into benchmark tests and other tools. It allows loading of * a dynamic delegate library at runtime. * * The external delegate also has Tensorflow Lite Python bindings. Therefore the dynamic external delegate * can be directly used with Tensorflow Lite Python APIs. * * See tensorflow/lite/delegates/external for details or visit the tensorflow guide * [here](https://www.tensorflow.org/lite/performance/implementing_delegate#option_2_leverage_external_delegate) */ extern "C" { std::vector gpu_options {"gpu-tuning-level", "gpu-tuning-file", "gpu-kernel-profiling-enabled"}; /** * Create an ArmNN delegate plugin * * Available options: * * Option key: "backends" \n * Possible values: ["EthosNPU"/"GpuAcc"/"CpuAcc"/"CpuRef"] \n * Descriptions: A comma separated list without whitespaces of * backends which should be used for execution. Falls * back to next backend in list if previous doesn't * provide support for operation. e.g. "GpuAcc,CpuAcc" * * Option key: "logging-severity" \n * Possible values: ["trace"/"debug"/"info"/"warning"/"error"/"fatal"] \n * Description: Sets the logging severity level for ArmNN. Logging * is turned off if this option is not provided. * * Option key: "gpu-tuning-level" \n * Possible values: ["0"/"1"/"2"/"3"] \n * Description: 0=UseOnly(default), 1=RapidTuning, 2=NormalTuning, * 3=ExhaustiveTuning. Requires option gpu-tuning-file. * 1,2 and 3 will create a tuning-file, 0 will apply the * tunings from an existing file * * Option key: "gpu-tuning-file" \n * Possible values: [filenameString] \n * Description: File name for the tuning file. * * Option key: "gpu-kernel-profiling-enabled" \n * Possible values: ["true"/"false"] \n * Description: Enables GPU kernel profiling * * Option key: "reduce-fp32-to-fp16" \n * Possible values: ["true"/"false"] \n * Description: Reduce Fp32 data to Fp16 for faster processing * * Option key: "reduce-fp32-to-bf16" \n * Possible values: ["true"/"false"] \n * Description: Reduce Fp32 data to Bf16 for faster processing * * Option key: "debug-data" \n * Possible values: ["true"/"false"] \n * Description: Add debug data for easier troubleshooting * * Option key: "memory-import" \n * Possible values: ["true"/"false"] \n * Description: Enable memory import * * * @param[in] option_keys Delegate option names * @param[in] options_values Delegate option values * @param[in] num_options Number of delegate options * @param[in,out] report_error Error callback function * * @return An ArmNN delegate if it succeeds else NULL */ TfLiteDelegate* tflite_plugin_create_delegate(char** options_keys, char** options_values, size_t num_options, void (*report_error)(const char*)) { // Returning null indicates an error during delegate creation so we initialize with that TfLiteDelegate* delegate = nullptr; try { // (Initializes with CpuRef backend) armnnDelegate::DelegateOptions options = armnnDelegate::TfLiteArmnnDelegateOptionsDefault(); armnn::OptimizerOptions optimizerOptions; for (size_t i = 0; i < num_options; ++i) { // Process backends if (std::string(options_keys[i]) == std::string("backends")) { // The backend option is a comma separated string of backendIDs that needs to be split std::vector backends; char* pch; pch = strtok(options_values[i],","); while (pch != NULL) { backends.push_back(pch); pch = strtok (NULL, ","); } options.SetBackends(backends); } // Process logging level else if (std::string(options_keys[i]) == std::string("logging-severity")) { options.SetLoggingSeverity(options_values[i]); } // Process GPU backend options else if (std::string(options_keys[i]) == std::string("gpu-tuning-level")) { armnn::BackendOptions option("GpuAcc", {{"TuningLevel", atoi(options_values[i])}}); options.AddBackendOption(option); } else if (std::string(options_keys[i]) == std::string("gpu-mlgo-tuning-file")) { armnn::BackendOptions option("GpuAcc", {{"MLGOTuningFilePath", std::string(options_values[i])}}); options.AddBackendOption(option); } else if (std::string(options_keys[i]) == std::string("gpu-tuning-file")) { armnn::BackendOptions option("GpuAcc", {{"TuningFile", std::string(options_values[i])}}); options.AddBackendOption(option); } else if (std::string(options_keys[i]) == std::string("gpu-kernel-profiling-enabled")) { armnn::BackendOptions option("GpuAcc", {{"KernelProfilingEnabled", (*options_values[i] != '0')}}); options.AddBackendOption(option); } // Process reduce-fp32-to-fp16 option else if (std::string(options_keys[i]) == std::string("reduce-fp32-to-fp16")) { optimizerOptions.m_ReduceFp32ToFp16 = *options_values[i] != '0'; } // Process reduce-fp32-to-bf16 option else if (std::string(options_keys[i]) == std::string("reduce-fp32-to-bf16")) { optimizerOptions.m_ReduceFp32ToBf16 = *options_values[i] != '0'; } // Process debug-data else if (std::string(options_keys[i]) == std::string("debug-data")) { optimizerOptions.m_Debug = *options_values[i] != '0'; } // Process memory-import else if (std::string(options_keys[i]) == std::string("memory-import")) { optimizerOptions.m_ImportEnabled = *options_values[i] != '0'; } else { throw armnn::Exception("Unknown option for the ArmNN Delegate given: " + std::string(options_keys[i])); } } options.SetOptimizerOptions(optimizerOptions); delegate = TfLiteArmnnDelegateCreate(options); } catch (const std::exception& ex) { if(report_error) { report_error(ex.what()); } } return delegate; } /** Destroy a given delegate plugin * * @param[in] delegate Delegate to destruct */ void tflite_plugin_destroy_delegate(TfLiteDelegate* delegate) { armnnDelegate::TfLiteArmnnDelegateDelete(delegate); } } // extern "C" } // namespace tflite