| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ExecuteNetworkParams.hpp" |
| |
| #include "NetworkExecutionUtils/NetworkExecutionUtils.hpp" |
| #include <armnn/Logging.hpp> |
| |
| #include <fmt/format.h> |
| #include <armnnUtils/Filesystem.hpp> |
| |
| void CheckClTuningParameter(const int& tuningLevel, |
| const std::string& tuningPath, |
| const std::vector<armnn::BackendId> computeDevices) |
| { |
| if (!tuningPath.empty()) |
| { |
| if (tuningLevel == 0) |
| { |
| ARMNN_LOG(info) << "Using cl tuning file: " << tuningPath << "\n"; |
| if (!ValidatePath(tuningPath, true)) |
| { |
| throw armnn::InvalidArgumentException("The tuning path is not valid"); |
| } |
| } |
| else if ((1 <= tuningLevel) && (tuningLevel <= 3)) |
| { |
| ARMNN_LOG(info) << "Starting execution to generate a cl tuning file: " << tuningPath << "\n" |
| << "Tuning level in use: " << tuningLevel << "\n"; |
| } |
| else if ((0 < tuningLevel) || (tuningLevel > 3)) |
| { |
| throw armnn::InvalidArgumentException(fmt::format("The tuning level {} is not valid.", |
| tuningLevel)); |
| } |
| |
| // Ensure that a GpuAcc is enabled. Otherwise no tuning data are used or genereted |
| // Only warn if it's not enabled |
| auto it = std::find(computeDevices.begin(), computeDevices.end(), "GpuAcc"); |
| if (it == computeDevices.end()) |
| { |
| ARMNN_LOG(warning) << "To use Cl Tuning the compute device GpuAcc needs to be active."; |
| } |
| } |
| } |
| |
| void ExecuteNetworkParams::ValidateParams() |
| { |
| if (m_DynamicBackendsPath == "") |
| { |
| // Check compute devices are valid unless they are dynamically loaded at runtime |
| std::string invalidBackends; |
| if (!CheckRequestedBackendsAreValid(m_ComputeDevices, armnn::Optional<std::string&>(invalidBackends))) |
| { |
| ARMNN_LOG(fatal) << "The list of preferred devices contains invalid backend IDs: " |
| << invalidBackends; |
| } |
| } |
| CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices); |
| |
| if (m_EnableBf16TurboMode && !m_EnableFastMath) |
| { |
| throw armnn::InvalidArgumentException("To use BF16 please use --enable-fast-math. "); |
| } |
| |
| // Check input tensor shapes |
| if ((m_InputTensorShapes.size() != 0) && |
| (m_InputTensorShapes.size() != m_InputNames.size())) |
| { |
| throw armnn::InvalidArgumentException("input-name and input-tensor-shape must have " |
| "the same amount of elements. "); |
| } |
| |
| if (m_InputTensorDataFilePaths.size() != 0) |
| { |
| if (!ValidatePaths(m_InputTensorDataFilePaths, true)) |
| { |
| throw armnn::InvalidArgumentException("One or more input data file paths are not valid."); |
| } |
| |
| if (m_InputTensorDataFilePaths.size() < m_InputNames.size()) |
| { |
| throw armnn::InvalidArgumentException( |
| fmt::format("According to the number of input names the user provided the network has {} " |
| "inputs. But only {} input-tensor-data file paths were provided. Each input of the " |
| "model is expected to be stored in it's own file.", |
| m_InputNames.size(), |
| m_InputTensorDataFilePaths.size())); |
| } |
| } |
| |
| // Check that threshold time is not less than zero |
| if (m_ThresholdTime < 0) |
| { |
| throw armnn::InvalidArgumentException("Threshold time supplied as a command line argument is less than zero."); |
| } |
| |
| // Warn if ExecuteNetwork will generate dummy input data |
| if (m_GenerateTensorData) |
| { |
| ARMNN_LOG(warning) << "No input files provided, input tensors will be filled with 0s."; |
| } |
| |
| if (m_AllowExpandedDims && m_InferOutputShape) |
| { |
| throw armnn::InvalidArgumentException("infer-output-shape and allow-expanded-dims cannot be used together."); |
| } |
| } |
| |
| #if defined(ARMNN_TFLITE_DELEGATE) |
| /** |
| * A utility method that populates a DelegateOptions object from this ExecuteNetworkParams. |
| * |
| * @return a populated armnnDelegate::DelegateOptions object. |
| */ |
| armnnDelegate::DelegateOptions ExecuteNetworkParams::ToDelegateOptions() const |
| { |
| armnnDelegate::DelegateOptions delegateOptions(m_ComputeDevices); |
| delegateOptions.SetDynamicBackendsPath(m_DynamicBackendsPath); |
| delegateOptions.SetGpuProfilingState(m_EnableProfiling); |
| |
| armnn::OptimizerOptions options; |
| options.m_ReduceFp32ToFp16 = m_EnableFp16TurboMode; |
| options.m_Debug = m_PrintIntermediate; |
| options.m_DebugToFile = m_PrintIntermediateOutputsToFile; |
| options.m_ProfilingEnabled = m_EnableProfiling; |
| delegateOptions.SetInternalProfilingParams(m_EnableProfiling, armnn::ProfilingDetailsMethod::DetailsWithEvents); |
| options.m_shapeInferenceMethod = armnn::ShapeInferenceMethod::ValidateOnly; |
| if (m_InferOutputShape) |
| { |
| options.m_shapeInferenceMethod = armnn::ShapeInferenceMethod::InferAndValidate; |
| } |
| |
| armnn::BackendOptions gpuAcc("GpuAcc", |
| { |
| { "FastMathEnabled", m_EnableFastMath }, |
| { "SaveCachedNetwork", m_SaveCachedNetwork }, |
| { "CachedNetworkFilePath", m_CachedNetworkFilePath }, |
| { "TuningLevel", m_TuningLevel}, |
| { "TuningFile", m_TuningPath.c_str()}, |
| { "KernelProfilingEnabled", m_EnableProfiling}, |
| { "MLGOTuningFilePath", m_MLGOTuningFilePath} |
| }); |
| |
| armnn::BackendOptions cpuAcc("CpuAcc", |
| { |
| { "FastMathEnabled", m_EnableFastMath }, |
| { "NumberOfThreads", m_NumberOfThreads } |
| }); |
| options.m_ModelOptions.push_back(gpuAcc); |
| options.m_ModelOptions.push_back(cpuAcc); |
| |
| if (m_InferOutputShape) |
| { |
| armnn::BackendOptions networkOption("ShapeInferenceMethod", |
| { |
| {"InferAndValidate", true} |
| }); |
| options.m_ModelOptions.push_back(networkOption); |
| } |
| if (m_AllowExpandedDims) |
| { |
| armnn::BackendOptions networkOption("AllowExpandedDims", |
| { |
| {"AllowExpandedDims", true} |
| }); |
| options.m_ModelOptions.push_back(networkOption); |
| } |
| delegateOptions.SetOptimizerOptions(options); |
| |
| return delegateOptions; |
| } |
| |
| #endif |