From d1f54767fc9d6398a5eea38e639dd0ce3df8e5d8 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Fri, 19 Jul 2019 09:54:47 +0100 Subject: COMPMID-1979: Fuse Activation Function in CLGEMM - part 3 Fused beta*bias in in the old cl gemm kernels Fused activation function in the old cl gemm kernels Change-Id: I695fb9189e6d4792010abd256784624982d17d79 Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/1587 Reviewed-by: Giuseppe Rossini Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- tests/CL/Helper.h | 13 + tests/validation/CL/GEMMMatrixMultiply.cpp | 344 ++++++++++++++ .../CL/GEMMMatrixMultiplyInterleavedTransposed.cpp | 404 +++++++++++++++++ tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp | 2 +- tests/validation/fixtures/GEMMFixture.h | 500 +++++++++++++++++++++ 5 files changed, 1262 insertions(+), 1 deletion(-) create mode 100644 tests/validation/CL/GEMMMatrixMultiply.cpp create mode 100644 tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp (limited to 'tests') diff --git a/tests/CL/Helper.h b/tests/CL/Helper.h index ab2f8ccb22..0a4566be8d 100644 --- a/tests/CL/Helper.h +++ b/tests/CL/Helper.h @@ -53,6 +53,19 @@ public: k->configure(std::forward(args)...); _kernel = std::move(k); } + /** Configure the kernel setting the GPU target as well + * + * @param[in] gpu_target GPUTarget to set + * @param[in] args Configuration arguments. + */ + template + void configure(GPUTarget gpu_target, Args &&... args) + { + auto k = arm_compute::support::cpp14::make_unique(); + k->set_target(gpu_target); + k->configure(std::forward(args)...); + _kernel = std::move(k); + } /** Validate input arguments * * @param[in] args Configuration arguments. diff --git a/tests/validation/CL/GEMMMatrixMultiply.cpp b/tests/validation/CL/GEMMMatrixMultiply.cpp new file mode 100644 index 0000000000..21fd7125ec --- /dev/null +++ b/tests/validation/CL/GEMMMatrixMultiply.cpp @@ -0,0 +1,344 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" +#include "arm_compute/core/KernelDescriptors.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "tests/CL/CLAccessor.h" +#include "tests/CL/Helper.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/ShapeDatasets.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/GEMMFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::misc::shape_calculator; + +// Create function for CLGEMMMatrixMultiplyKernel +using CLGEMMMatrixMultiplyNative = CLSynthetizeFunction; + +// Fixture for GEMMMatrixMultiplyValidationFixture +template +using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyValidationFixture; + +// Fixture for GEMMMatrixMultiply3DValidationFixture +template +using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiply3DValidationFixture; + +namespace +{ +// *INDENT-OFF* +// clang-format off +RelativeTolerance rel_tolerance_f32(0.001f); +constexpr float abs_tolerance_f32(0.0001f); + +RelativeTolerance rel_tolerance_f16(half(0.2)); +constexpr float tolerance_num_f16 = 0.02f; + +/** Alpha values to test - Precommit */ +const auto alpha_values = framework::dataset::make("alpha", {0.0f, 1.0f, -0.75f} ); + +/** Beta values to test - Precommit */ +const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} ); + +/** M values to test - Precommit */ +const auto m_values_precommit = framework::dataset::make("M", {37, 1}); + +/** N values to test - Precommit */ +const auto n_values_precommit = framework::dataset::make("N", 51); + +/** K values to test - Precommit */ +const auto k_values_precommit = framework::dataset::make("K", 23); + +/** M values to test - Nightly */ +const auto m_values_nightly = framework::dataset::make("M", {421, 1}); + +/** N values to test - Nightly */ +const auto n_values_nightly = framework::dataset::make("N", {323, 1103}); + +/** K values to test - Nightly */ +const auto k_values_nightly = framework::dataset::make("K", 207); + +/** M_W values to test - Precommit */ +const auto m_w_values_precommit = framework::dataset::make("M_W", 5); + +/** M_H values to test - Precommit */ +const auto m_h_values_precommit = framework::dataset::make("M_H", 7); + +/** M_W values to test - Nightly */ +const auto m_w_values_nightly = framework::dataset::make("M_W", 13); + +/** M_H values to test - Nightly */ +const auto m_h_values_nightly = framework::dataset::make("M_H", 27); + +/** Batch size values to test */ +const auto b_values = framework::dataset::make("batch_size", 1, 3); + +/** Activation values to test */ +const auto act_values = framework::dataset::make("Activation", +{ + ActivationLayerInfo(), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), +}); + +/** Broadcast bias from vector to matrix */ +const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} ); + +/** GPU architectures values to test */ +const auto gpu_arch_values = framework::dataset::make("GPUArch", +{ + GPUTarget::MIDGARD, + GPUTarget::BIFROST +}); + +/** Data types values to test in the configuration */ +const auto data_type_values = framework::dataset::make("DataType", +{ + DataType::F32, + DataType::F16 +}); + +/** M values to test */ +const auto fp16_mixed_precision_values = framework::dataset::make("fp16_mixed_precision", {true, false}); + +/** Configuration test */ +void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch_value) +{ + GEMMReshapeInfo reshape_info(m_value, n_value, k_value, 1, 1, 0, false, broadcast_bias); + + const TensorShape lhs_shape(k_value, m_value, b_value); + const TensorShape rhs_shape(n_value, k_value, b_value); + + const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type), + TensorInfo(rhs_shape, 1, data_type), + reshape_info); + + const TensorShape bias_shape(n_value, + broadcast_bias? 1 : m_value, + broadcast_bias? 1 : b_value); + + // Create tensors + CLTensor lhs = create_tensor(lhs_shape, data_type); + CLTensor rhs = create_tensor(rhs_shape, data_type); + CLTensor bias = create_tensor(bias_shape, data_type); + CLTensor dst = create_tensor(dst_shape, data_type); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLGEMMMatrixMultiplyNative gemm; + gemm.configure(gpu_arch_value, &lhs, &rhs, &bias, &dst, 1.0f, 2.0f, false, reshape_info, fp16_mixed_precision, act_info); +} +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(GEMMMatrixMultiply) +TEST_SUITE(Float) +TEST_SUITE(FP32) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine( + m_values_precommit, + n_values_precommit), + k_values_precommit), + framework::dataset::make("batch_size", 1)), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + data_type_values), + gpu_arch_values), +m_value, n_value, k_value, b_value, broadcast_bias, fp16_mixed_precision_value, act_value, data_type_value, gpu_arch_value) +{ + validate_configuration(m_value, n_value, k_value, b_value, broadcast_bias, fp16_mixed_precision_value, act_value, data_type_value, gpu_arch_value); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_precommit, + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_nightly, + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_precommit, + m_h_values_precommit), + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_nightly, + m_h_values_nightly), + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +TEST_SUITE_END() // FP32 + +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_precommit, + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_nightly, + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_precommit, + m_h_values_precommit), + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_nightly, + m_h_values_nightly), + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // Float +TEST_SUITE_END() // GEMMMatrixMuliplty +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute \ No newline at end of file diff --git a/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp b/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp new file mode 100644 index 0000000000..cae94b2e15 --- /dev/null +++ b/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp @@ -0,0 +1,404 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" +#include "arm_compute/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" +#include "arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" +#include "arm_compute/core/KernelDescriptors.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "tests/CL/CLAccessor.h" +#include "tests/CL/Helper.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/ShapeDatasets.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/GEMMFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::misc::shape_calculator; + +// Create function for CLGEMMReshapeLHSMatrixKernel +using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction; + +// Create function for CLGEMMReshapeRHSMatrixKernel +using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction; + +// Create function for CLGEMMMatrixMultiplyKernel +using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction; + +// Fixture for GEMMMatrixMultiplyInterleavedTransposedValidationFixture +template +using CLGEMMMatrixMultiplyReshapedFixture = + GEMMMatrixMultiplyInterleavedTransposedValidationFixture; + +// Fixture for GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture +template +using CLGEMMMatrixMultiplyReshaped3DFixture = + GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture; + +namespace +{ +// *INDENT-OFF* +// clang-format off +RelativeTolerance rel_tolerance_f32(0.001f); +constexpr float abs_tolerance_f32(0.0001f); + +RelativeTolerance rel_tolerance_f16(half(0.2)); +constexpr float tolerance_num_f16 = 0.02f; + +/** Alpha values to test - Precommit */ +const auto alpha_values = framework::dataset::make("alpha", {0.0f, 1.0f, -0.75f} ); + +/** Beta values to test - Precommit */ +const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} ); + +/** M values to test - Precommit */ +const auto m_values_precommit = framework::dataset::make("M", 37); + +/** N values to test - Precommit */ +const auto n_values_precommit = framework::dataset::make("N", 51); + +/** K values to test - Precommit */ +const auto k_values_precommit = framework::dataset::make("K", 23); + +/** M values to test - Nightly */ +const auto m_values_nightly = framework::dataset::make("M", {421, 1}); + +/** N values to test - Nightly */ +const auto n_values_nightly = framework::dataset::make("N", 323); + +/** K values to test - Nightly */ +const auto k_values_nightly = framework::dataset::make("K", 207); + +/** M_W values to test - Precommit */ +const auto m_w_values_precommit = framework::dataset::make("M_W", 5); + +/** M_H values to test - Precommit */ +const auto m_h_values_precommit = framework::dataset::make("M_H", 7); + +/** M_W values to test - Nightly */ +const auto m_w_values_nightly = framework::dataset::make("M_W", 13); + +/** M_H values to test - Nightly */ +const auto m_h_values_nightly = framework::dataset::make("M_H", 27); + +/** Batch size values to test */ +const auto b_values = framework::dataset::make("batch_size", 1, 3); + +/** Activation values to test */ +const auto act_values = framework::dataset::make("Activation", +{ + ActivationLayerInfo(), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), +}); + +/** V0 values to test - Precommit */ +const auto v0_values_precommit = framework::dataset::make("V0", 2); + +/** H0 values to test - Precommit */ +const auto h0_values_precommit = framework::dataset::make("H0", 4); + +/** V0 values to test - Nightly */ +const auto v0_values_nightly = framework::dataset::make("V0", {2, 4}); + +/** H0 values to test - Nightly */ +const auto h0_values_nightly = framework::dataset::make("H0", { 2, 4 }); + +/** Broadcast bias from vector to matrix */ +const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} ); + +/** GPU architectures values to test */ +const auto gpu_arch_values = framework::dataset::make("GPUArch", +{ + GPUTarget::MIDGARD, + GPUTarget::BIFROST +}); + +/** Data types values to test in the configuration */ +const auto data_type_values = framework::dataset::make("DataType", +{ + DataType::F32, + DataType::F16 +}); + +/** M values to test */ +const auto fp16_mixed_precision_values = framework::dataset::make("fp16_mixed_precision", {true, false}); + +/** Configuration test */ +void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int v0_value, unsigned int h0_value, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch_value) +{ + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = 4; + lhs_info.k0 = 4; + lhs_info.v0 = v0_value; + lhs_info.interleave = true; + lhs_info.transpose = true; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = data_type == DataType::F32? 4 : 8; + rhs_info.k0 = 1; + rhs_info.h0 = h0_value; + rhs_info.interleave = false; + rhs_info.transpose = false; + + GEMMReshapeInfo reshape_info(m_value, n_value, k_value, rhs_info.h0, lhs_info.v0, 0, false, broadcast_bias); + + const TensorShape lhs_shape(k_value, m_value, b_value); + const TensorShape lhs_shape_reshaped = compute_lhs_reshaped_shape(TensorInfo(lhs_shape, 1, data_type), + lhs_info, + false); + + const TensorShape rhs_shape(n_value, k_value, b_value); + const TensorShape rhs_shape_reshaped = compute_rhs_reshaped_shape(TensorInfo(rhs_shape, 1, data_type), + rhs_info); + + const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape_reshaped, 1, data_type), + TensorInfo(rhs_shape_reshaped, 1, data_type), + reshape_info); + + const TensorShape bias_shape(n_value, + broadcast_bias? 1 : m_value, + broadcast_bias? 1 : b_value); + + // Create tensors + CLTensor lhs_reshaped = create_tensor(lhs_shape_reshaped, data_type); + CLTensor rhs_reshaped = create_tensor(rhs_shape_reshaped, data_type); + CLTensor bias = create_tensor(bias_shape, data_type); + CLTensor dst = create_tensor(dst_shape, data_type); + + ARM_COMPUTE_EXPECT(lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLGEMMMatrixMultiplyReshaped gemm; + gemm.configure(gpu_arch_value, &lhs_reshaped, &rhs_reshaped, &bias, &dst, 1.0f, 2.0f, true, reshape_info, fp16_mixed_precision, act_info); +} +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(GEMMMatrixMultiplyInterleavedTransposed) +TEST_SUITE(Float) +TEST_SUITE(FP32) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_precommit, + n_values_precommit), + k_values_precommit), + framework::dataset::make("batch_size", 1)), + v0_values_precommit), + h0_values_precommit), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + data_type_values), + gpu_arch_values), +m_value, n_value, k_value, b_value, v0_value, h0_value, broadcast_bias, fp16_mixed_precision_value, act_value, data_type_value, gpu_arch_value) +{ + validate_configuration(m_value, n_value, k_value, b_value, v0_value, h0_value, broadcast_bias, fp16_mixed_precision_value, act_value, data_type_value, gpu_arch_value); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_precommit, + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + v0_values_precommit), + h0_values_precommit), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_nightly, + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + v0_values_nightly), + h0_values_nightly), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_precommit, + m_h_values_precommit), + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + v0_values_precommit), + h0_values_precommit), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_nightly, + m_h_values_nightly), + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + v0_values_nightly), + h0_values_nightly), + broadcast_bias_values), + framework::dataset::make("fp16_mixed_precision", false)), + act_values), + framework::dataset::make("DataType", DataType::F32)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} + +TEST_SUITE_END() // FP32 + +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_precommit, + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + v0_values_precommit), + h0_values_precommit), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_values_nightly, + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + v0_values_nightly), + h0_values_nightly), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_precommit, + m_h_values_precommit), + n_values_precommit), + k_values_precommit), + b_values), + alpha_values), + beta_values), + v0_values_precommit), + h0_values_precommit), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + m_w_values_nightly, + m_h_values_nightly), + n_values_nightly), + k_values_nightly), + b_values), + alpha_values), + beta_values), + v0_values_nightly), + h0_values_nightly), + broadcast_bias_values), + fp16_mixed_precision_values), + act_values), + framework::dataset::make("DataType", DataType::F16)), + gpu_arch_values)) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); +} + +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // Float +TEST_SUITE_END() // GEMMMatrixMulipltyInterleavedTransposed +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute \ No newline at end of file diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp index 99af2965d2..25221451ed 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp @@ -418,7 +418,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, } TEST_SUITE_END() // FP16 TEST_SUITE_END() // Float -TEST_SUITE_END() // GEMMMatrixMulipltyReshaped +TEST_SUITE_END() // GEMMMatrixMultiplyReshaped TEST_SUITE_END() // CL } // namespace validation } // namespace test diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h index ac8ab2a949..b36bb99246 100644 --- a/tests/validation/fixtures/GEMMFixture.h +++ b/tests/validation/fixtures/GEMMFixture.h @@ -153,6 +153,506 @@ protected: SimpleTensor _reference{}; }; +template +class GEMMMatrixMultiplyValidationFixture : public framework::Fixture +{ +public: + template + void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, + DataType data_type, GPUTarget gpu_arch) + { + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + const TensorShape bias_shape(n, + broadcast_bias ? 1 : m, + broadcast_bias ? 1 : batch_size); + + _target = compute_target(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, fp16_mixed_precision, act_info, gpu_arch); + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + + // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) + std::uniform_real_distribution<> distribution_inf(std::numeric_limits::infinity(), std::numeric_limits::infinity()); + library->fill_borders_with_garbage(tensor, distribution_inf, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias, + bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); + TensorType dst; + + const unsigned int m = lhs_shape[1]; + const unsigned int n = rhs_shape[0]; + const unsigned int k = lhs_shape[0]; + GEMMReshapeInfo reshape_info(m, n, k, 1, 1, 0, false, broadcast_bias); + + // The output tensor will be auto-initialized within the function + + // Create and configure function + GEMMFunctionType gemm; + gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); + + // Compute GEMM + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias, + const ActivationLayerInfo &act_info) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor lhs{ lhs_shape, data_type, 1 }; + SimpleTensor rhs{ rhs_shape, data_type, 1 }; + SimpleTensor bias{ dst_shape, data_type, 1 }; + + const int n = rhs_shape[0]; + const int m = lhs_shape[1]; + const int batch_size = lhs_shape[2]; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + fill(bias, 2); + + if(broadcast_bias) + { + // In case of broadcast, we need simply copy the first into the following "M" ones + for(int i = 1; i < m * batch_size; i++) + { + memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); + } + } + + return reference::activation_layer(reference::gemm(lhs, rhs, bias, alpha, beta), act_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class GEMMMatrixMultiply3DValidationFixture : public framework::Fixture +{ +public: + template + void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision, + const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch) + { + // In case of GEMM3D, m is the product between m_w and m_h + const unsigned int m = m_w * m_h; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + const TensorShape bias_shape(n, 1, 1); + + _target = compute_target(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, fp16_mixed_precision, act_info, gpu_arch); + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h, + bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); + TensorType dst; + + const unsigned int m = lhs_shape[1]; + const unsigned int n = rhs_shape[0]; + const unsigned int k = lhs_shape[0]; + GEMMReshapeInfo reshape_info(m, n, k, 1, 1, m_h, false, true); + + // The output tensor will be auto-initialized within the function + + // Create and configure function + GEMMFunctionType gemm; + gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); + + // Compute GEMM + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h, + const ActivationLayerInfo &act_info) + { + TensorShape dst_shape = lhs_shape; + dst_shape.set(0, rhs_shape[0]); + dst_shape.set(1, lhs_shape[1] / m_h); + dst_shape.set(2, m_h); + dst_shape.set(3, lhs_shape[2]); + + // Create reference + SimpleTensor lhs{ lhs_shape, data_type, 1 }; + SimpleTensor rhs{ rhs_shape, data_type, 1 }; + SimpleTensor bias{ dst_shape, data_type, 1 }; + + const int n = rhs_shape[0]; + const int m = lhs_shape[1]; + const int batch_size = lhs_shape[2]; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + fill(bias, 2); + + // In case of broadcast, we need simply copy the first into the following "M" ones + for(int i = 1; i < m * batch_size; i++) + { + memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); + } + + return reference::activation_layer(reference::gemm(lhs, rhs, bias, alpha, beta), act_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class GEMMMatrixMultiplyInterleavedTransposedValidationFixture : public framework::Fixture +{ +public: + template + void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, unsigned int v0, unsigned int h0, bool broadcast_bias, bool fp16_mixed_precision, + const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = 4; + lhs_info.k0 = 4; + lhs_info.v0 = v0; + lhs_info.interleave = true; + lhs_info.transpose = true; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = 16 / sizeof(T); + rhs_info.k0 = 1; + rhs_info.h0 = h0; + rhs_info.interleave = false; + rhs_info.transpose = false; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + const TensorShape bias_shape(n, + broadcast_bias ? 1 : m, + broadcast_bias ? 1 : batch_size); + + _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, fp16_mixed_precision, act_info, gpu_arch); + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + + // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) + std::uniform_real_distribution<> distribution_inf(std::numeric_limits::infinity(), std::numeric_limits::infinity()); + library->fill_borders_with_garbage(tensor, distribution_inf, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + DataType data_type, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); + TensorType lhs_reshaped; + TensorType rhs_reshaped; + TensorType dst; + + const unsigned int m = lhs_shape[1]; + const unsigned int n = rhs_shape[0]; + const unsigned int k = lhs_shape[0]; + GEMMReshapeInfo reshape_info(m, n, k, rhs_info.h0, lhs_info.v0, 0, false, broadcast_bias); + + // The output tensor will be auto-initialized within the function + + // Create and configure function + ReshapeLHSFunctionType reshape_lhs; + ReshapeRHSFunctionType reshape_rhs; + GEMMFunctionType gemm; + reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info); + reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); + gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + lhs_reshaped.allocator()->allocate(); + rhs_reshaped.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); + + // Compute GEMM + reshape_lhs.run(); + reshape_rhs.run(); + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias, + const ActivationLayerInfo &act_info) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor lhs{ lhs_shape, data_type, 1 }; + SimpleTensor rhs{ rhs_shape, data_type, 1 }; + SimpleTensor bias{ dst_shape, data_type, 1 }; + + const int n = rhs_shape[0]; + const int m = lhs_shape[1]; + const int batch_size = lhs_shape[2]; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + fill(bias, 2); + + if(broadcast_bias) + { + // In case of broadcast, we need simply copy the first into the following "M" ones + for(int i = 1; i < m * batch_size; i++) + { + memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); + } + } + + return reference::activation_layer(reference::gemm(lhs, rhs, bias, alpha, beta), act_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture : public framework::Fixture +{ +public: + template + void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, unsigned int v0, unsigned int h0, bool broadcast_bias, + bool fp16_mixed_precision, const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = 4; + lhs_info.k0 = 4; + lhs_info.v0 = v0; + lhs_info.interleave = true; + lhs_info.transpose = true; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = 16 / sizeof(T); + rhs_info.k0 = 1; + rhs_info.h0 = h0; + rhs_info.interleave = false; + rhs_info.transpose = false; + + // In case of GEMM3D, m is the product between m_w and m_h + const unsigned int m = m_w * m_h; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + const TensorShape bias_shape(n, 1, 1); + + _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, fp16_mixed_precision, act_info, gpu_arch); + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + DataType data_type, float alpha, float beta, unsigned int m_h, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); + TensorType lhs_reshaped; + TensorType rhs_reshaped; + TensorType dst; + + const unsigned int m = lhs_shape[1]; + const unsigned int n = rhs_shape[0]; + const unsigned int k = lhs_shape[0]; + GEMMReshapeInfo reshape_info(m, n, k, rhs_info.h0, lhs_info.v0, m_h, false, true); + + // The output tensor will be auto-initialized within the function + + // Create and configure function + ReshapeLHSFunctionType reshape_lhs; + ReshapeRHSFunctionType reshape_rhs; + GEMMFunctionType gemm; + reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info); + reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); + gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + lhs_reshaped.allocator()->allocate(); + rhs_reshaped.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); + + // Compute GEMM + reshape_lhs.run(); + reshape_rhs.run(); + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h, + const ActivationLayerInfo &act_info) + { + TensorShape dst_shape = lhs_shape; + dst_shape.set(0, rhs_shape[0]); + dst_shape.set(1, lhs_shape[1] / m_h); + dst_shape.set(2, m_h); + dst_shape.set(3, lhs_shape[2]); + + // Create reference + SimpleTensor lhs{ lhs_shape, data_type, 1 }; + SimpleTensor rhs{ rhs_shape, data_type, 1 }; + SimpleTensor bias{ dst_shape, data_type, 1 }; + + const int n = rhs_shape[0]; + const int m = lhs_shape[1]; + const int batch_size = lhs_shape[2]; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + fill(bias, 2); + + // In case of broadcast, we need simply copy the first into the following "M" ones + for(int i = 1; i < m * batch_size; i++) + { + memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); + } + + return reference::activation_layer(reference::gemm(lhs, rhs, bias, alpha, beta), act_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + template class GEMMMatrixMultiplyReshapedValidationFixture : public framework::Fixture { -- cgit v1.2.1