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//
// Copyright © 2022-2023 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);
delegateOptions.SetInternalProfilingParams(m_EnableProfiling, armnn::ProfilingDetailsMethod::DetailsWithEvents);
// GPU Backend options first.
{
armnn::BackendOptions gpuOption("GpuAcc", {{"TuningLevel", m_TuningLevel}});
delegateOptions.AddBackendOption(gpuOption);
}
{
armnn::BackendOptions gpuOption("GpuAcc", {{"TuningFile", m_TuningPath.c_str()}});
delegateOptions.AddBackendOption(gpuOption);
}
{
armnn::BackendOptions gpuOption("GpuAcc", {{"KernelProfilingEnabled", m_EnableProfiling}});
delegateOptions.AddBackendOption(gpuOption);
}
// Optimizer options next.
armnn::OptimizerOptionsOpaque optimizerOptions;
optimizerOptions.SetReduceFp32ToFp16(m_EnableFp16TurboMode);
optimizerOptions.SetDebugEnabled(m_PrintIntermediate);
optimizerOptions.SetDebugToFileEnabled(m_PrintIntermediateOutputsToFile);
optimizerOptions.SetProfilingEnabled(m_EnableProfiling);
optimizerOptions.SetShapeInferenceMethod(armnn::ShapeInferenceMethod::ValidateOnly);
if (m_InferOutputShape)
{
optimizerOptions.SetShapeInferenceMethod(armnn::ShapeInferenceMethod::InferAndValidate);
armnn::BackendOptions networkOption("ShapeInferenceMethod",
{
{"InferAndValidate", true}
});
optimizerOptions.AddModelOption(networkOption);
}
{
armnn::BackendOptions option("GpuAcc", {{"FastMathEnabled", m_EnableFastMath}});
optimizerOptions.AddModelOption(option);
}
{
armnn::BackendOptions option("GpuAcc", {{"CachedNetworkFilePath", m_CachedNetworkFilePath}});
optimizerOptions.AddModelOption(option);
}
{
armnn::BackendOptions option("GpuAcc", {{"MLGOTuningFilePath", m_MLGOTuningFilePath}});
optimizerOptions.AddModelOption(option);
}
armnn::BackendOptions cpuAcc("CpuAcc",
{
{ "FastMathEnabled", m_EnableFastMath },
{ "NumberOfThreads", m_NumberOfThreads }
});
optimizerOptions.AddModelOption(cpuAcc);
if (m_AllowExpandedDims)
{
armnn::BackendOptions networkOption("AllowExpandedDims",
{
{"AllowExpandedDims", true}
});
optimizerOptions.AddModelOption(networkOption);
}
delegateOptions.SetOptimizerOptions(optimizerOptions);
return delegateOptions;
}
#endif
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