blob: bed7b26f74b2581b1da22b008e999f1e157c1868 [file] [log] [blame]
//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "GpuFsaConvolution2dValidate.hpp"
#include <armnn/Types.hpp>
#include <armnn/utility/IgnoreUnused.hpp>
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <arm_compute/core/ITensorInfo.h>
#include <arm_compute/core/TensorInfo.h>
#include <arm_compute/core/TensorShape.h>
#include <arm_compute/core/CL/CLKernelLibrary.h>
#include <arm_compute/core/CL/CLCompileContext.h>
#include <arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h>
#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h>
#include <vector>
#include <iostream>
namespace armnn
{
using namespace armcomputetensorutils;
inline arm_compute::Status ValidateAndCreateOp(const TensorInfo& input,
const Convolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
const bool createOp = false)
{
// Create a new workload sketch, for validation purposes
auto compileCtx = arm_compute::CLKernelLibrary::get().get_compile_context();
auto gpuCtx = GpuWorkloadContext(&compileCtx);
GpuWorkloadSketch sketch{ &gpuCtx };
// Build and create tensor infos using the sketch
const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);
aclWeightsInfo.set_are_values_constant(weights.IsConstant());
auto inputInfo = gpuCtx.create_tensor_info(aclInputInfo);
auto weightInfo = gpuCtx.create_tensor_info(aclWeightsInfo);
// Only create the bias tensor info if enabled, otherwise pass nullptr to validate_op
arm_compute::TensorInfo aclBiasInfo;
arm_compute::TensorInfo biasSketchInfo;
arm_compute::TensorInfo* biasSketchInfoPtr = nullptr;
if (descriptor.m_BiasEnabled)
{
if(!biases.has_value())
{
throw InvalidArgumentException("GpuFsaConvolution2dValidate: No biases set when biases are enabled");
}
aclBiasInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
aclBiasInfo.set_are_values_constant(biases.value().IsConstant());
biasSketchInfo = gpuCtx.create_tensor_info(aclBiasInfo);
biasSketchInfoPtr = &biasSketchInfo;
}
// Set Conv2d attributes using descriptor
const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(descriptor.m_DilationX,
descriptor.m_DilationY);
const arm_compute::Padding2D aclPadInfo = BuildArmComputePaddingInfo(descriptor);
const arm_compute::Size2D aclStrideInfo = BuildArmComputeSize2D(descriptor.m_StrideX, descriptor.m_StrideY);
Conv2dAttributes conv2DAttributes{};
conv2DAttributes.dilation(aclDilationInfo);
conv2DAttributes.pad(aclPadInfo);
conv2DAttributes.stride(aclStrideInfo);
// Validate operator, check status and update reasonIfUnsupported
arm_compute::Status aclStatus = GpuConv2d::validate_op(sketch,
&inputInfo,
&weightInfo,
biasSketchInfoPtr,
conv2DAttributes);
if (createOp)
{
const bool supported = (aclStatus.error_code() == arm_compute::ErrorCode::OK);
if (!supported)
{
throw BackendCapabilityException("\"GpuFsa\" backend failed during operation validation when attempting "
"to fuse a GpuConv2d operator into the existing workload sketch.");
}
arm_compute::ITensorInfo* convOutInfo = GpuConv2d::create_op(sketch,
&inputInfo,
&weightInfo,
biasSketchInfoPtr,
conv2DAttributes);
// Temporary fix until fusing attempt is make for GpuFsa backend and Output layer workload is created.
auto outputInfo = gpuCtx.create_tensor_info();
GpuOutput::create_op(sketch, convOutInfo, &outputInfo);
}
return aclStatus;
}
arm_compute::Status GpuFsaConvolution2dValidate(const TensorInfo& input,
const Convolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases)
{
return ValidateAndCreateOp(input, descriptor, weights, biases);
}
void GpuFsaConvolution2dCreateOp(const TensorInfo& input,
const Convolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases)
{
ValidateAndCreateOp(input, descriptor, weights, biases, true);
}
} // namespace armnn