328 lines
18 KiB
C++
328 lines
18 KiB
C++
/*
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* Copyright (c) 2019-2020 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/KernelDescriptors.h"
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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/CLGEMMMatrixMultiplyKernel.h"
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#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.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/GEMMFixture.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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using namespace arm_compute::misc::shape_calculator;
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// Create function for CLGEMMReshapeLHSMatrixKernel
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using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>;
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// Create function for CLGEMMReshapeRHSMatrixKernel
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using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
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// Create function for CLGEMMMatrixMultiplyKernel
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using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction<CLGEMMMatrixMultiplyKernel>;
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// Fixture for GEMMMatrixMultiplyInterleavedTransposedValidationFixture
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template <typename T>
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using CLGEMMMatrixMultiplyReshapedFixture =
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GEMMMatrixMultiplyInterleavedTransposedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
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// Fixture for GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture
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template <typename T>
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using CLGEMMMatrixMultiplyReshaped3DFixture =
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GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
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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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RelativeTolerance<float> rel_tolerance_f32(0.001f);
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constexpr float abs_tolerance_f32(0.0001f);
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RelativeTolerance<half> rel_tolerance_f16(half(0.2));
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constexpr float tolerance_num_f16 = 0.02f;
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/** Alpha values to test */
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const auto alpha_values = framework::dataset::make("alpha", {1.0f, -0.75f} );
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/** Beta values to test */
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const auto beta_values = framework::dataset::make("beta", {-0.35f, 0.0f} );
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/** M values to test */
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const auto m_values = framework::dataset::make("M", {37, 1});
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/** N values to test */
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const auto n_values = framework::dataset::make("N", 51);
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/** K values to test */
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const auto k_values = framework::dataset::make("K", 23);
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/** M_W values to test */
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const auto m_w_values = framework::dataset::make("M_W", 5);
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/** M_H values to test */
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const auto m_h_values = framework::dataset::make("M_H", 7);
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/** Batch size values to test */
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const auto b_values = framework::dataset::make("batch_size", 1, 3);
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/** Activation values to test */
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const auto act_values = framework::dataset::make("Activation",
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{
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ActivationLayerInfo(),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f),
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});
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/** V0 values to test */
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const auto v0_values = framework::dataset::make("V0", 2);
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/** H0 values to test */
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const auto h0_values = framework::dataset::make("H0", 4);
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/** Broadcast bias from vector to matrix */
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const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} );
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/** GPU architectures values to test */
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const auto gpu_arch_values = framework::dataset::make("GPUArch",
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{
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GPUTarget::MIDGARD,
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GPUTarget::BIFROST
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});
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/** Data types values to test in the configuration */
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const auto data_type_values = framework::dataset::make("DataType",
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{
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DataType::F32,
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DataType::F16
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});
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/** M values to test */
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const auto fp16_mixed_precision_values = framework::dataset::make("fp16_mixed_precision", {true, false});
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(GEMMMatrixMultiplyInterleavedTransposed)
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TEST_CASE(Negative, framework::DatasetMode::ALL)
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{
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// The following tests are already integrated in the GEMMMatrixMultiply validation because
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// in common with this validation
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// - Unsupported QASYMM8 data type
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// - Unsupported SIZE_T data type
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// - Mixed precision with F32
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// - Max number of dimensions LHS matrix
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// - Max number of dimensions RHS matrix
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// Invalid LHS dimensions
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{
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// The correct shape should be: lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
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const auto lhs = TensorInfo(TensorShape(256U, 2U, 1U, 1U), 1, DataType::F32);
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const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
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const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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constexpr float alpha = 1.3f;
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constexpr float beta = 0.7f;
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const bool is_interleaved_transposed = true;
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const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
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const GPUTarget gpu_target = GPUTarget::MIDGARD;
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const bool fp_mixed_precision = false;
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const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
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ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
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}
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// Invalid RHS dimensions
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{
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const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
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// The correct shape should be rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
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const auto rhs = TensorInfo(TensorShape(104U, 4U, 1U, 1U), 1, DataType::F32);
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const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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constexpr float alpha = 1.3f;
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constexpr float beta = 0.7f;
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const bool is_interleaved_transposed = true;
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const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
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const GPUTarget gpu_target = GPUTarget::MIDGARD;
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const bool fp_mixed_precision = false;
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const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
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ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
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}
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// Broadcast bias
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{
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const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
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const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
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// The correct shape should be bias = TensorInfo(TensorShape(24U, 1U, 1U, 1U), 1, DataType::F32);
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const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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constexpr float alpha = 1.3f;
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constexpr float beta = 0.7f;
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const bool is_interleaved_transposed = true;
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const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, true);
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const GPUTarget gpu_target = GPUTarget::MIDGARD;
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const bool fp_mixed_precision = false;
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const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
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ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
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}
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// Invalid dimensions for the bias
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{
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const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
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const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
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// The correct shape should be bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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const auto bias = TensorInfo(TensorShape(25U, 16U, 1U, 1U), 1, DataType::F32);
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const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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constexpr float alpha = 1.3f;
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constexpr float beta = 0.7f;
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const bool is_interleaved_transposed = true;
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const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
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const GPUTarget gpu_target = GPUTarget::MIDGARD;
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const bool fp_mixed_precision = false;
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const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
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ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
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}
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// Invalid dimensions for the output
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{
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const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
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const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
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const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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// The correct shape should be out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
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const auto out = TensorInfo(TensorShape(24U, 13U, 1U, 1U), 1, DataType::F32);
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constexpr float alpha = 1.3f;
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constexpr float beta = 0.7f;
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const bool is_interleaved_transposed = true;
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const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
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const GPUTarget gpu_target = GPUTarget::MIDGARD;
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const bool fp_mixed_precision = false;
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const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
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ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
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}
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}
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TEST_SUITE(Float)
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<float>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
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m_values,
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n_values),
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k_values),
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b_values),
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alpha_values),
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beta_values),
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v0_values),
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h0_values),
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broadcast_bias_values),
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framework::dataset::make("fp16_mixed_precision", false)),
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act_values),
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framework::dataset::make("DataType", DataType::F32)),
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gpu_arch_values))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
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m_w_values,
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m_h_values),
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n_values),
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k_values),
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b_values),
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alpha_values),
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beta_values),
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v0_values),
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h0_values),
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broadcast_bias_values),
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framework::dataset::make("fp16_mixed_precision", false)),
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act_values),
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framework::dataset::make("DataType", DataType::F32)),
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gpu_arch_values))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
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}
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TEST_SUITE_END() // FP32
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<half>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
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m_values,
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n_values),
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k_values),
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b_values),
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alpha_values),
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beta_values),
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v0_values),
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h0_values),
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broadcast_bias_values),
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fp16_mixed_precision_values),
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act_values),
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framework::dataset::make("DataType", DataType::F16)),
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gpu_arch_values))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
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m_w_values,
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m_h_values),
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n_values),
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k_values),
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b_values),
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alpha_values),
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beta_values),
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v0_values),
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h0_values),
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broadcast_bias_values),
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fp16_mixed_precision_values),
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act_values),
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framework::dataset::make("DataType", DataType::F16)),
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gpu_arch_values))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
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}
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TEST_SUITE_END() // FP16
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TEST_SUITE_END() // Float
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TEST_SUITE_END() // GEMMMatrixMulipltyInterleavedTransposed
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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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