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/*
* Copyright (c) 2022-2024 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 ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H
#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/DepthwiseConv2dAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuDepthwiseConv2d.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
#include "tests/CL/CLAccessor.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/validation/reference/DepthwiseConvolutionLayer.h"
#include "tests/validation/Validation.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 DynamicFusionGpuDepthwiseConv2dValidationGenericFixture : 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
void setup(TensorShape input_shape,
Size2D kernel_size,
const PadStrideInfo &pad_stride,
const Size2D &dilation,
const unsigned int depth_multiplier,
const DataType data_type,
const DataLayout data_layout)
{
ARM_COMPUTE_ERROR_ON(data_layout !=
DataLayout::NHWC); // Dynamic fusion depthwise conv2d only supports NHWC layout
DepthwiseConv2dAttributes dwc_conv2d_attr;
const Padding2D padding_2d(pad_stride.pad_left(), pad_stride.pad_right(), pad_stride.pad_top(),
pad_stride.pad_bottom());
dwc_conv2d_attr.pad(padding_2d)
.stride(Size2D(pad_stride.stride().first, pad_stride.stride().second))
.dilation(dilation)
.depth_multiplier(depth_multiplier)
.dimension_rounding_type(pad_stride.round());
// Calculate Output and Weight Shapes
TensorShape weights_shape = TensorShape(kernel_size.width, kernel_size.height);
const TensorInfo in_info(input_shape, 1, data_type);
const TensorInfo we_info(weights_shape, 1, data_type);
const ConvolutionInfo info{pad_stride, depth_multiplier, ActivationLayerInfo(), dilation};
const TensorShape output_shape =
misc::shape_calculator::compute_depthwise_convolution_shape(in_info, we_info, info);
weights_shape.set(2, output_shape.z());
const TensorShape bias_shape = TensorShape(weights_shape[2]);
_data_type = data_type;
_data_layout = data_layout;
_target = compute_target(input_shape, weights_shape, bias_shape, dwc_conv2d_attr);
_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, dwc_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,
const DepthwiseConv2dAttributes dwc_conv2d_attr)
{
ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC);
// Our test shapes are assumed in NCHW data layout, thus the permutation
permute(input_shape, PermutationVector(2U, 0U, 1U));
permute(weights_shape, PermutationVector(2U, 0U, 1U));
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
auto context = GpuWorkloadContext{&cl_compile_ctx};
GpuWorkloadSketch sketch{&context};
// Create sketch tensors
ITensorInfo *input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
ITensorInfo *weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
ITensorInfo *bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
ITensorInfo *dst_info = context.create_tensor_info();
ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, weight_info, bias_info, dwc_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())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
AuxMemoryInfo aux_mem_req = std::get<2>(data);
tensor->allocator()->init(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,
DepthwiseConv2dAttributes dwc_conv2d_attr)
{
// Create reference
SimpleTensor<T> src{input_shape, _data_type, 1};
SimpleTensor<T> weight{weights_shape, _data_type, 1};
SimpleTensor<TBias> bias{bias_shape, _data_type, 1};
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(dwc_conv2d_attr.stride().x(), dwc_conv2d_attr.stride().y(),
dwc_conv2d_attr.pad().left, dwc_conv2d_attr.pad().right,
dwc_conv2d_attr.pad().top, dwc_conv2d_attr.pad().bottom,
DimensionRoundingType{});
auto dst_nchw =
reference::depthwise_convolution(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride,
dwc_conv2d_attr.depth_multiplier(), dwc_conv2d_attr.dilation());
return dst_nchw;
}
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _data_type{};
DataLayout _data_layout{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuDepthwiseConv2dValidationFixture
: public DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(TensorShape input_shape,
Size2D kernel_size,
const PadStrideInfo &info,
const Size2D &dilation,
const unsigned int depth_multiplier,
DataType data_type,
DataLayout data_layout)
{
DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
input_shape, kernel_size, info, dilation, depth_multiplier, data_type, data_layout);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H
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