path: root/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h

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#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