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576 lines
29 KiB
576 lines
29 KiB
/*
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* Copyright (c) 2019-2022 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/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.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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using namespace arm_compute::opencl::kernels;
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// Create function for ClGemmMatrixMultiplyNativeKernel
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using CLGEMMMatrixMultiplyNative = CLSynthetizeOperator<ClGemmMatrixMultiplyNativeKernel>;
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// Fixture for CLGEMMMatrixMultiplyNative
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template <typename T>
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using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
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// Fixture for CLGEMMMatrixMultiplyNative with post ops
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template <typename T>
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using CLGEMMMatrixMultiplyNativeWithPostOpsFixture =
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GEMMMatrixMultiplyNativeWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
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// Fixture for CLGEMMMatrixMultiplyNative3D
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template <typename T>
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using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
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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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/** Alpha values to test - Precommit */
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const auto a_values = framework::dataset::make("alpha", {1.0f, -0.75f} );
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/** Beta values to test - Precommit */
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const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} );
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/** M values to test */
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const auto m_values = framework::dataset::make("M", 37);
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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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/** 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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/** 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(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ELU),
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});
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/** M0 values to test - Precommit */
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const auto m0_values_precommit = framework::dataset::make("M0", { 4, 6 });
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/** N0 values to test - Precommit */
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const auto n0_values_precommit = framework::dataset::make("N0", { 4 });
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/** K0 values to test - Precommit */
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const auto k0_values_precommit = framework::dataset::make("K0", { 4 });
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/** H0 values to test - Precommit */
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const auto h0_values_precommit = framework::dataset::make("H0", 1, 3);
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/** M0 values to test - Nightly */
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const auto m0_values_nightly = framework::dataset::make("M0", 1, 8);
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/** N0 values to test - Nightly */
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const auto n0_values_nightly = framework::dataset::make("N0", { 2, 3, 4, 8 });
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/** K0 values to test - Nightly */
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const auto k0_values_nightly = framework::dataset::make("K0", { 2, 3, 4, 8 });
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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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/** Boundary handling cases for testing partial/non-partial (full) block dimensions, resulting from different combinations
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* of M, M0, N and N0 values.
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* M0 and N0 are kept constant, while the different test cases need to vary M and N.
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*
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* Eg. M = 64 and N = 33 result in a block dimension that has no partial blocks (all full blocks) in Y dimension and
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* parital blocks in X dimension.
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*/
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const auto boundary_handling_cases = combine(combine(combine(combine(combine(combine(combine(combine(combine(
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// Large k to force potential out-of-bound reads on input0
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framework::dataset::make("K", 315),
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// Batch size == 1 to force potential out-of-bound reads on input0
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framework::dataset::make("batch_size", 1)),
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framework::dataset::make("M0", 4)),
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framework::dataset::make("N0", 4)),
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framework::dataset::make("K0", 4)),
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// Only need to test F32 as F16 shares identical boundary handling logics
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framework::dataset::make("DataType", DataType::F32)),
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framework::dataset::make("alpha", -0.75f )),
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framework::dataset::make("beta", -0.35f )),
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broadcast_bias_values),
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framework::dataset::make("Activation", ActivationLayerInfo()));
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/** Post Ops */
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using PostOpArgBroadcast = CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>::PostOpArgBroadcast;
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experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
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{
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experimental::PostOpList<PostOpArgBroadcast> post_ops{};
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
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std::make_tuple(true, true, false), // If broadcast in dims 0, 1 and 2
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0,
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ConvertPolicy::SATURATE);
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
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return post_ops;
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}
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experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
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{
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experimental::PostOpList<PostOpArgBroadcast> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
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std::make_tuple(false, true, true), // If broadcast in dims 0, 1 and 2
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1,
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ConvertPolicy::SATURATE);
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
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return post_ops;
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}
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experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
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{
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experimental::PostOpList<PostOpArgBroadcast> post_ops{};
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// post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
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std::make_tuple(false, false, false), // If broadcast in dims 0, 1 and 2
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1,
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ConvertPolicy::SATURATE);
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return post_ops;
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}
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// To test that the output of the main op is the first parameter in prelu post op
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experimental::PostOpList<PostOpArgBroadcast> post_ops_4()
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{
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experimental::PostOpList<PostOpArgBroadcast> post_ops{};
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
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post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
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std::make_tuple(false, false, true), // If true, broadcast in corresponding dim: 0, 1 or 2
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0,
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ConvertPolicy::SATURATE);
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
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return post_ops;
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}
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// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param
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experimental::PostOpList<PostOpArgBroadcast> post_ops_5()
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{
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experimental::PostOpList<PostOpArgBroadcast> post_ops{};
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
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post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
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std::make_tuple(false, false, false), // If true, broadcast in corresponding dim: 0, 1 or 2
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1,
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ConvertPolicy::SATURATE);
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post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
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return post_ops;
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}
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/** Different Post Op Lists */
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const auto post_op_lists = framework::dataset::make("post_op_lists", {
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post_ops_1(),
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post_ops_2(),
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post_ops_3(),
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post_ops_4(),
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post_ops_5()
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} );
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bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
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{
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const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
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const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
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// Create TensorInfo for post op arguments
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TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
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TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
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TensorInfo input2_info(TensorShape(n), 1, data_type);
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TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
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GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
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false /**< reinterpret the input as 3D */,
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true /**< Flag used to broadcast the bias addition */,
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false /**< wider accumm */,
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false /**< has pad y */,
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ActivationLayerInfo::ActivationFunction::IDENTITY,
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1 /**< Multiplication factor for the width of the 1xW transposed block */,
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1 /**< Multiplication factor for the height of the 4x4 interleaved block */,
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lhs_info,
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rhs_info,
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0 /**< Offset to be added to each element of the matrix A */,
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0 /**< Offset to be added to each element of the matrix B */,
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post_ops);
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return bool(ClGemmMatrixMultiplyNativeKernel::validate(&input0_info.clone()->set_is_resizable(true),
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&input1_info.clone()->set_is_resizable(true),
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&input2_info.clone()->set_is_resizable(true),
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&output_info.clone()->set_is_resizable(true),1.f,1.f,
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lhs_info,
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rhs_info,
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gemm_info));
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}
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/** Configuration test */
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void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info)
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{
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const unsigned int M = m_value;
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const unsigned int N = n_value;
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const unsigned int K = k_value;
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GEMMLHSMatrixInfo lhs_info;
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lhs_info.m0 = m0_value;
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lhs_info.k0 = k0_value;
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GEMMRHSMatrixInfo rhs_info;
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rhs_info.n0 = n0_value;
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rhs_info.k0 = k0_value;
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GEMMKernelInfo kernel_info;
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kernel_info.m = M;
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kernel_info.n = N;
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kernel_info.k = K;
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kernel_info.broadcast_bias = broadcast_bias;
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kernel_info.activation_info = act_info;
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const TensorShape lhs_shape(K, M, b_value);
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const TensorShape rhs_shape(N, K, b_value);
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const TensorShape bias_shape(N,
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broadcast_bias? 1 : M,
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broadcast_bias? 1 : b_value);
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const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type),
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TensorInfo(rhs_shape, 1, data_type),
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kernel_info);
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// Create tensors
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CLTensor lhs = create_tensor<CLTensor>(lhs_shape, data_type);
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CLTensor rhs = create_tensor<CLTensor>(rhs_shape, data_type);
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CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type);
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CLTensor dst = create_tensor<CLTensor>(dst_shape, data_type);
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ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Create and configure function
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CLGEMMMatrixMultiplyNative gemm;
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gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), 1.0f, 1.0f, lhs_info, rhs_info, kernel_info);
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}
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(GEMMMatrixMultiplyNative)
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TEST_SUITE(ValidateFusedPostOpsConfigs)
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TEST_SUITE(Invalid)
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TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
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{
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const auto data_type = DataType::F32;
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const unsigned int m = 17;
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const unsigned int n = 1;
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const unsigned int k = 13;
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const unsigned int batch = 2;
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TensorShape post_op_arg0_shape(n, m, batch);
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TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
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auto post_op_arg1_info = post_op_arg_info.clone();
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// Unsupported sequence of post ops
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experimental::PostOpList<ITensorInfo*> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
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&post_op_arg_info,
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1,
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ConvertPolicy::SATURATE);
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
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post_op_arg1_info.get(),
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0,
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ConvertPolicy::SATURATE);
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
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}
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TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
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{
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// Invalid broadcast: post op tensors "widen" the output tensor
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const auto data_type = DataType::F32;
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const unsigned int m = 1;
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const unsigned int n = 18;
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const unsigned int k = 13;
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const unsigned int batch = 2;
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TensorShape post_op_arg_shape(n, m + 1, batch); // output's Y dimension (m) is "widened", which is not allowed
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TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
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experimental::PostOpList<ITensorInfo*> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
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}
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TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
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{
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// Invalid broadcast: post op tensors broadcast in the first dimension (X) only
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const auto data_type = DataType::F32;
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const unsigned int m = 22;
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const unsigned int n = 16;
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const unsigned int k = 15;
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const unsigned int batch = 3;
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TensorShape post_op_arg_shape(1, m, batch);
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TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
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experimental::PostOpList<ITensorInfo*> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
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}
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TEST_SUITE_END() // Invalid
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TEST_SUITE(Valid)
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TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
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{
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const auto data_type = DataType::F32;
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const unsigned int m = 22;
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const unsigned int n = 16;
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const unsigned int k = 15;
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const unsigned int batch = 3;
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experimental::PostOpList<ITensorInfo*> post_ops{};
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
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}
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TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL)
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{
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const auto data_type = DataType::F32;
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const unsigned int m = 22;
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const unsigned int n = 16;
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const unsigned int k = 15;
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const unsigned int batch = 3;
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TensorShape post_op_arg_shape(n, 1, batch);
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TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
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experimental::PostOpList<ITensorInfo*> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
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}
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TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL)
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{
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const auto data_type = DataType::F32;
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const unsigned int m = 22;
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const unsigned int n = 16;
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const unsigned int k = 15;
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const unsigned int batch = 3;
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TensorShape post_op_arg_shape(1, 1, batch);
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TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
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experimental::PostOpList<ITensorInfo*> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
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}
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TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
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{
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const auto data_type = DataType::F32;
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const unsigned int m = 22;
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const unsigned int n = 16;
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const unsigned int k = 15;
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const unsigned int batch = 3;
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TensorShape post_op_arg_shape(1, 1, 1);
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TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
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experimental::PostOpList<ITensorInfo*> post_ops{};
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post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
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|
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ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
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}
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TEST_SUITE_END() // Valid
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TEST_SUITE_END() // ValidateFusedPostOps
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|
TEST_SUITE(Float)
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|
TEST_SUITE(FP32)
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|
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, 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),
|
|
framework::dataset::make("batch_size", 1)),
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|
m0_values_precommit),
|
|
n0_values_precommit),
|
|
k0_values_precommit),
|
|
broadcast_bias_values),
|
|
act_values),
|
|
m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, act_value)
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|
{
|
|
validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, DataType::F32, act_value);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingPartialInXPartialInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(
|
|
framework::dataset::make("M", 3),
|
|
framework::dataset::make("N", 1)),
|
|
boundary_handling_cases))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingPartialInXFullInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(
|
|
framework::dataset::make("M", 64),
|
|
framework::dataset::make("N", 51)),
|
|
boundary_handling_cases))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingFullInXFullInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(
|
|
framework::dataset::make("M", 64),
|
|
framework::dataset::make("N", 32)),
|
|
boundary_handling_cases))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingFullInXPartialInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(
|
|
framework::dataset::make("M", 37),
|
|
framework::dataset::make("N", 32)),
|
|
boundary_handling_cases))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
|
|
m_values,
|
|
n_values),
|
|
k_values),
|
|
b_values),
|
|
m0_values_precommit),
|
|
n0_values_precommit),
|
|
k0_values_precommit),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
a_values),
|
|
beta_values),
|
|
broadcast_bias_values),
|
|
act_values))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::DISABLED,
|
|
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
|
|
m_values,
|
|
n_values),
|
|
k_values),
|
|
b_values),
|
|
m0_values_nightly),
|
|
n0_values_nightly),
|
|
k0_values_nightly),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
a_values),
|
|
beta_values),
|
|
broadcast_bias_values),
|
|
act_values))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
|
|
m_w_values,
|
|
m_h_values),
|
|
n_values),
|
|
k_values),
|
|
b_values),
|
|
m0_values_precommit),
|
|
n0_values_precommit),
|
|
k0_values_precommit),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
a_values),
|
|
beta_values),
|
|
act_values))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture<float>, framework::DatasetMode::DISABLED,
|
|
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
|
|
m_w_values,
|
|
m_h_values),
|
|
n_values),
|
|
k_values),
|
|
b_values),
|
|
m0_values_nightly),
|
|
n0_values_nightly),
|
|
k0_values_nightly),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
a_values),
|
|
beta_values),
|
|
act_values))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
TEST_SUITE(FusedPostOps)
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>, framework::DatasetMode::ALL,
|
|
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
|
|
m_values,
|
|
n_values),
|
|
k_values),
|
|
b_values),
|
|
framework::dataset::make("M0", { 4 })),
|
|
n0_values_precommit),
|
|
k0_values_precommit),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
framework::dataset::make("alpha", {1.0f} )),
|
|
framework::dataset::make("beta", {1.0f} )),
|
|
framework::dataset::make("broadcast_bias", { false, true } )),
|
|
framework::dataset::make("Activation", { ActivationLayerInfo() })),
|
|
post_op_lists)
|
|
)
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
|
|
}
|
|
|
|
TEST_SUITE_END() // FusedPostOps
|
|
|
|
TEST_SUITE_END() // FP32
|
|
TEST_SUITE_END() // Float
|
|
TEST_SUITE_END() // GEMMMatrixMulipltyNative
|
|
TEST_SUITE_END() // CL
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|