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/*
* Copyright (c) 2022-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE
#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/OperatorAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
#include "tests/CL/CLAccessor.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/validation/Validation.h"
#include "tests/validation/reference/ConvolutionLayer.h"
#include "tests/validation/reference/Permute.h"
using namespace arm_compute::experimental::dynamic_fusion;
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuConv2dValidationGenericFixture : public framework::Fixture
{
public:
using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value
|| std::is_same<typename std::decay<T>::type, int8_t>::value,
int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info, const Size2D &dilation, DataType data_type,
DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info)
{
ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout
const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, dilation);
_data_type = data_type;
_data_layout = data_layout;
_is_quantized = is_data_type_quantized_asymmetric(data_type);
_quantization_info = quantization_info;
_weight_quantization_info = weight_quantization_info;
_bias_data_type = _is_quantized ? DataType::S32 : data_type;
_target = compute_target(input_shape, weights_shape, bias_shape, conv2d_attr);
_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
switch(tensor.data_type())
{
case DataType::F16:
{
arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
library->fill(tensor, distribution, i);
break;
}
case DataType::F32:
{
std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
break;
}
default:
library->fill_tensor_uniform(tensor, i);
}
}
// Given input is in nchw format
TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, Conv2dAttributes conv2d_attr)
{
ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC);
permute(input_shape, PermutationVector(2U, 0U, 1U));
permute(weights_shape, PermutationVector(2U, 0U, 1U));
CLScheduler::get().default_reinit();
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx };
GpuWorkloadSketch sketch{ &gpu_ctx };
// Create sketch tensors
TensorInfo input_info = sketch.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
TensorInfo weight_info = sketch.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
TensorInfo bias_info = sketch.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
TensorInfo dst_info = sketch.create_tensor_info();
ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
GpuOutput::create_op(sketch, ans_info, &dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
for(auto &data : runtime.get_auxiliary_tensors())
{
auto tensor = data.first;
const auto aux_mem_req = data.second;
tensor->allocator()->init(*data.first->info(), aux_mem_req.alignment);
tensor->allocator()->allocate(); // Use ACL allocated memory
}
// Construct user tensors
TensorType t_input{};
TensorType t_weight{};
TensorType t_bias{};
TensorType t_dst{};
// Initialize user tensors
t_input.allocator()->init(input_info);
t_weight.allocator()->init(weight_info);
t_bias.allocator()->init(bias_info);
t_dst.allocator()->init(dst_info);
// Allocate and fill user tensors
t_input.allocator()->allocate();
t_weight.allocator()->allocate();
t_bias.allocator()->allocate();
t_dst.allocator()->allocate();
fill(AccessorType(t_input), 0);
fill(AccessorType(t_weight), 1);
fill(AccessorType(t_bias), 2);
// Run runtime
runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
return t_dst;
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape,
const TensorShape &output_shape, Conv2dAttributes conv2d_attr)
{
// Create reference
SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info };
SimpleTensor<T> weight{ weights_shape, _data_type, 1, _weight_quantization_info };
SimpleTensor<TBias> bias{ bias_shape, _data_type, 1, _quantization_info };
fill(src, 0);
fill(weight, 1);
fill(bias, 2);
auto src_nchw = src;
auto weights_nchw = weight;
auto bias_nchw = bias;
auto output_shape_nchw = output_shape;
PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
DimensionRoundingType{});
auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
return dst_nchw;
}
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _data_type{};
DataType _bias_data_type{};
DataLayout _data_layout{};
QuantizationInfo _quantization_info{};
QuantizationInfo _weight_quantization_info{};
bool _is_quantized = false;
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuConv2dValidationFixture : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape output_shape, TensorShape bias_shape,
const PadStrideInfo &info, const Size2D &dialation, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
{
DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, output_shape, bias_shape, info, dialation,
data_type, data_layout, quantization_info, quantization_info);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE */
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