265 lines
14 KiB
C++
265 lines
14 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/utils/misc/ShapeCalculator.h"
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#include "arm_compute/runtime/CL/CLTensor.h"
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#include "arm_compute/runtime/CL/CLTensorAllocator.h"
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#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/CL/Helper.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/ShapeDatasets.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/GEMMReshapeRHSMatrixFixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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// *INDENT-OFF*
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// clang-format off
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/** Batch size values to test */
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const auto b_values = framework::dataset::make("batchsize", 1, 3);
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/** N0 values to test */
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const auto n0_values_nt_s32 = framework::dataset::make("N0", { 1, 2, 3 });
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const auto n0_values_nt_s16 = framework::dataset::make("N0", { 4, 8 });
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const auto n0_values_nt_s8 = framework::dataset::make("N0", { 16 });
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const auto n0_values_t_s32 = framework::dataset::make("N0", { 4, 8 });
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const auto n0_values_t_s16 = framework::dataset::make("N0", { 16 });
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const auto n0_values_t_s8 = framework::dataset::make("N0", { 2, 3 });
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/** K0 values to test */
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const auto k0_values_nt_s32 = framework::dataset::make("K0", { 1, 2 });
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const auto k0_values_nt_s16 = framework::dataset::make("K0", { 16 });
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const auto k0_values_nt_s8 = framework::dataset::make("K0", { 3,4 });
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const auto k0_values_t_s32 = framework::dataset::make("K0", { 2, 3 });
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const auto k0_values_t_s16 = framework::dataset::make("K0", { 4, 8 });
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const auto k0_values_t_s8 = framework::dataset::make("K0", { 16 });
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/** H0 values to test */
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const auto h0_values = framework::dataset::make("H0", 1, 4);
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/** Interleave values to test */
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const auto i_values = framework::dataset::make("interleave", { true, false });
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} // namespace
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using namespace arm_compute::misc::shape_calculator;
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// Initialize the output tensor with zero and fill the border with zero
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using CLGEMMReshapeRHSMatrix = CLSynthetizeFunctionInitOutputWithZeroAndWithZeroConstantBorder<CLGEMMReshapeRHSMatrixKernel, 16>;
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template <typename T>
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using CLGEMMReshapeRHSMatrixFixture = GEMMReshapeRHSMatrixValidationFixture<CLTensor, CLAccessor, CLGEMMReshapeRHSMatrix, T>;
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TEST_SUITE(CL)
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TEST_SUITE(GEMMReshapeRHSMatrix)
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // Mismatching data types
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // Wrong n0 value
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // Wrong k0 value
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // Wrong h0 value
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // n0 > 16
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // k0 > 16
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // k0 == 1 && transpose
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}),
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framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(64U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F16),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 2U, 2U), 1, DataType::F32),
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})),
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framework::dataset::make("N0",{ 4, 0, 4, 4, 4, 17, 4, 4 })),
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framework::dataset::make("K0",{ 4, 4, 0, 4, 4, 4, 17, 1 })),
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framework::dataset::make("H0",{ 4, 4, 4, 0, 4, 4, 4, 4 })),
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framework::dataset::make("Expected", { false, false, false, false, false, false, false})),
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input_info, output_info, n0, k0, h0, expected)
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{
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GEMMRHSMatrixInfo rhs_info;
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rhs_info.n0 = n0;
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rhs_info.k0 = k0;
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rhs_info.h0 = h0;
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rhs_info.transpose = true;
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rhs_info.interleave = true;
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bool has_error = bool(CLGEMMReshapeRHSMatrixKernel::validate(&input_info.clone()->set_is_resizable(false), (output_info.total_size() == 0) ? nullptr : &output_info.clone()->set_is_resizable(false), rhs_info));
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ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
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}
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DATA_TEST_CASE(ValidatePadding, framework::DatasetMode::ALL, combine(combine(combine(combine(
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framework::dataset::make("InputShape", { TensorShape(32U, 16U, 1U),
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TensorShape(32U, 16U, 2U)
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}),
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framework::dataset::make("N0",{ 4 })),
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framework::dataset::make("K0",{ 4, 8, 16 })),
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framework::dataset::make("H0",{ 1, 2, 4 })),
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framework::dataset::make("DataType",{ DataType::F32, DataType::F16 })),
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input_shape, n0, k0, h0, data_type)
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{
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CLTensor input;
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CLTensor output;
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input.info()->init(input_shape, 1, data_type);
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unsigned int padding = 0;
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GEMMRHSMatrixInfo rhs_info;
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rhs_info.n0 = n0;
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rhs_info.k0 = k0;
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rhs_info.h0 = h0;
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rhs_info.transpose = true;
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rhs_info.interleave = true;
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rhs_info.export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()) && (get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) != 0);
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if(rhs_info.export_to_cl_image)
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{
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TensorShape output_shape = compute_rhs_reshaped_shape(*input.info(), rhs_info);
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constexpr unsigned int num_floats_per_pixel = 4;
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const unsigned int pixel_aligment = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device());
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const unsigned int row_pitch_alignment = pixel_aligment * num_floats_per_pixel;
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const unsigned int round_up_width = ((output_shape[0] + row_pitch_alignment - 1) / row_pitch_alignment) * row_pitch_alignment;
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padding = round_up_width - output_shape[0];
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}
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CLGEMMReshapeRHSMatrixKernel kernel;
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kernel.configure(&input, &output, rhs_info);
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ARM_COMPUTE_EXPECT((output.info()->padding().right == padding), framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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// Run S32 tests only for transpose = false
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FIXTURE_DATA_TEST_CASE(S32_NT, CLGEMMReshapeRHSMatrixFixture<int>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(datasets::SmallGEMMReshape2DShapes(),
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b_values),
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framework::dataset::make("DataType", DataType::S32)),
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n0_values_nt_s32),
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k0_values_nt_s32),
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h0_values),
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i_values),
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framework::dataset::make("transpose", false)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference);
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}
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// Run S32 tests only for transpose = true
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FIXTURE_DATA_TEST_CASE(S32_T, CLGEMMReshapeRHSMatrixFixture<int>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(datasets::SmallGEMMReshape2DShapes(),
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b_values),
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framework::dataset::make("DataType", DataType::S32)),
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n0_values_t_s32),
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k0_values_t_s32),
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h0_values),
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i_values),
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framework::dataset::make("transpose", true)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference);
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}
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// Run S16 tests only for transpose = false
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FIXTURE_DATA_TEST_CASE(S16_NT, CLGEMMReshapeRHSMatrixFixture<short>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(datasets::SmallGEMMReshape2DShapes(),
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b_values),
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framework::dataset::make("DataType", DataType::S16)),
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n0_values_nt_s16),
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k0_values_nt_s16),
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h0_values),
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i_values),
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framework::dataset::make("transpose", false)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference);
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}
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// Run S16 tests only for transpose = true
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FIXTURE_DATA_TEST_CASE(S16_T, CLGEMMReshapeRHSMatrixFixture<short>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(datasets::SmallGEMMReshape2DShapes(),
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b_values),
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framework::dataset::make("DataType", DataType::S16)),
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n0_values_t_s16),
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k0_values_t_s16),
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h0_values),
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i_values),
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framework::dataset::make("transpose", true)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference);
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}
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// Run S8 tests only for transpose = false
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FIXTURE_DATA_TEST_CASE(S8_NT, CLGEMMReshapeRHSMatrixFixture<char>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(datasets::SmallGEMMReshape2DShapes(),
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b_values),
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framework::dataset::make("DataType", DataType::S8)),
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n0_values_nt_s8),
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k0_values_nt_s8),
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h0_values),
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i_values),
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framework::dataset::make("transpose", false)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference);
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}
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// Run S8 tests only for transpose = true
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FIXTURE_DATA_TEST_CASE(S8_T, CLGEMMReshapeRHSMatrixFixture<char>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(datasets::SmallGEMMReshape2DShapes(),
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b_values),
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framework::dataset::make("DataType", DataType::S8)),
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n0_values_t_s8),
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k0_values_t_s8),
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h0_values),
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i_values),
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framework::dataset::make("transpose", true)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference);
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}
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TEST_SUITE_END() // GEMMReshapeRHSMatrix
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TEST_SUITE_END() // CL
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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