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//
// Copyright © 2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "GpuFsaActivation.hpp"
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuTanh.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuSigmoid.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h>
using namespace arm_compute::experimental::dynamic_fusion;
using namespace armnn::armcomputetensorutils;
namespace armnn
{
arm_compute::Status GpuFsaActivationValidate(const TensorInfo& input,
const ActivationDescriptor& descriptor)
{
// Create a new workload sketch, for validation purposes
auto compileCtx = arm_compute::CLKernelLibrary::get().get_compile_context();
auto workloadContext = GpuWorkloadContext(&compileCtx);
GpuWorkloadSketch sketch{ &workloadContext };
arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, input.GetNumDimensions());
aclInputInfo.set_are_values_constant(input.IsConstant());
arm_compute::ITensorInfo* inputInfo = workloadContext.create_tensor_info(aclInputInfo);
switch (descriptor.m_Function)
{
case ActivationFunction::TanH:
{
if ( descriptor.m_A != 1 || descriptor.m_B != 1)
{
return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
"Activation function TanH only works with a=1 and b=1");
}
return GpuTanh::validate_op(sketch, inputInfo);
}
case ActivationFunction::Sigmoid:
{
return GpuSigmoid::validate_op(sketch, inputInfo);
}
default:
return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
std::string("Activation function currently not supported in GpuFsa: ")
+ GetActivationFunctionAsCString(descriptor.m_Function));
}
}
void GpuFsaActivationCreateOp(GpuFsaPreCompiledBlob* blob,
const TensorInfo& input,
const ActivationDescriptor& descriptor)
{
GpuWorkloadSketch* sketch = blob->sketch.get();
GpuWorkloadContext* workloadContext = blob->workloadContext.get();
std::vector<arm_compute::ITensorInfo*> inputTensorInfos = {};
std::vector<arm_compute::ITensorInfo*> outputTensorInfos = {};
arm_compute::TensorInfo aclInput0Info = BuildArmComputeTensorInfo(input, input.GetNumDimensions());
aclInput0Info.set_are_values_constant(input.IsConstant());
inputTensorInfos.emplace_back(workloadContext->create_tensor_info(aclInput0Info));
// Validate operator, check status and update reasonIfUnsupported
arm_compute::Status aclStatus{};
switch (descriptor.m_Function)
{
case ActivationFunction::TanH:
{
aclStatus = GpuTanh::validate_op(*sketch, inputTensorInfos[0]);
break;
}
case ActivationFunction::Sigmoid:
{
aclStatus = GpuSigmoid::validate_op(*sketch, inputTensorInfos[0]);
break;
}
default:
throw InvalidArgumentException(std::string("Activation function currently not supported in GpuFsa: ")
+ GetActivationFunctionAsCString(descriptor.m_Function));
}
const bool supported = aclStatus.error_code() == arm_compute::ErrorCode::OK;
if (!supported)
{
throw BackendCapabilityException("\"GpuFsa\" backend failed during Activation layer validation");
}
arm_compute::ITensorInfo* activationOutputInfo{};
switch (descriptor.m_Function)
{
case ActivationFunction::TanH:
{
activationOutputInfo = GpuTanh::create_op(*sketch, inputTensorInfos[0]);
break;
}
case ActivationFunction::Sigmoid:
{
activationOutputInfo = GpuSigmoid::create_op(*sketch, inputTensorInfos[0]);
break;
}
default:
throw InvalidArgumentException(std::string("Activation function currently not supported in GpuFsa: ")
+ GetActivationFunctionAsCString(descriptor.m_Function));
}
// Temporary fix until fusing attempt is make for GpuFsa backend and Output layer workload is created.
outputTensorInfos.emplace_back(workloadContext->create_tensor_info());
GpuOutput::create_op(*sketch, activationOutputInfo, outputTensorInfos[0]);
// Store the TensorInfos within the blob as unique_ptrs to be used later
blob->inputTensorInfos = std::make_unique<std::vector<arm_compute::ITensorInfo*>>(inputTensorInfos);
blob->outputTensorInfos = std::make_unique<std::vector<arm_compute::ITensorInfo*>>(outputTensorInfos);
}
} // namespace armnn
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