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-rw-r--r--tests/validation/CL/MatMulKernel.cpp391
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diff --git a/tests/validation/CL/MatMulKernel.cpp b/tests/validation/CL/MatMulKernel.cpp
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+/*
+ * Copyright (c) 2023 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/runtime/CL/CLTensor.h"
+#include "src/gpu/cl/kernels/ClNativeMatMulKernel.h"
+#include "tests/datasets/LargeMatMulDataset.h"
+#include "tests/datasets/SmallMatMulDataset.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/MatMulKernelFixture.h"
+#include "tests/validation/reference/Permute.h"
+
+#include <tuple>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+constexpr float abs_tolerance_f32(
+ 0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for floating point data types in case using relative tolerance fails because of small values */
+constexpr float abs_tolerance_f16(
+ 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */
+RelativeTolerance<half_float::half> tolerance_f16(half(0.01)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+} // namespace
+
+/** M0 values to test --precommit*/
+const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });
+
+/** N0 values to test --precommit*/
+const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 });
+
+/** K0 values to test --precommit*/
+const auto k0_values_precommit = framework::dataset::make("K0", { 2, 3 });
+
+/** M0 values to test --nightly*/
+const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 });
+const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 });
+
+/** N0 values to test --nightly*/
+const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 });
+const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 });
+
+/** K0 values to test --nightly*/
+const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 });
+const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 });
+const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 });
+
+template <typename T>
+using CLMatMulKernelFixture = MatMulKernelValidationFixture<T>;
+
+TEST_SUITE(CL)
+TEST_SUITE(MatMulKernel)
+TEST_SUITE(Validate)
+
+TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
+{
+ using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>;
+
+ const std::vector<MatMulConfigurationPair> supported_block_sizes =
+ {
+ // MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false)
+ // Lhs not-transposed, Rhs-not-transposed
+ { MatMulKernelInfo(false, false, 0, 1, 1), false }, // M0 should be > 0
+ { MatMulKernelInfo(false, false, 3, 5, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 6, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 3, 17), false }, // K0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 3, 7), false }, // K0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 9, 1, 2), true },
+ { MatMulKernelInfo(false, false, 3, 16, 3), true },
+ { MatMulKernelInfo(false, false, 7, 3, 4), true },
+
+ // Lhs not-transposed, Rhs transposed
+ { MatMulKernelInfo(false, true, 0, 1, 1), false }, // M0 should be > 0
+ { MatMulKernelInfo(false, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, true, 3, 3, 12), false }, // K0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, true, 3, 3, 6), false }, // K0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, true, 5, 1, 2), true },
+ { MatMulKernelInfo(false, true, 3, 3, 3), true },
+ { MatMulKernelInfo(false, true, 2, 4, 8), true },
+
+ // // Lhs transposed, Rhs-not-transposed
+ { MatMulKernelInfo(true, false, 1, 1, 0), false }, // K0 should be > 0
+ { MatMulKernelInfo(true, false, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, false, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, false, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, false, 5, 3, 6), false }, // M0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, false, 4, 1, 22), true },
+ { MatMulKernelInfo(true, false, 3, 3, 3), true },
+ { MatMulKernelInfo(true, false, 2, 4, 8), true },
+
+ // // Lhs transposed, Rhs-transposed
+ { MatMulKernelInfo(true, true, 2, 1, 5), false }, // K0 should in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, true, 1, 8, 7), false }, // K0 should in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, true, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, true, 5, 3, 6), false }, // M0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(true, true, 4, 8, 16), true },
+ { MatMulKernelInfo(true, true, 3, 3, 4), true },
+ { MatMulKernelInfo(true, true, 16, 4, 8), true },
+ };
+
+ // Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal
+ // so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here,
+ // not the shapes themselves.
+ const TensorInfo lhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32);
+ const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32);
+
+ for(auto &pair : supported_block_sizes)
+ {
+ TensorInfo output_info;
+ Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+
+ ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
+ }
+}
+
+TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
+{
+ // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+ const std::vector<ShapeConfigurationTuple> shape_configurations =
+ {
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
+ { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
+ { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
+ { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+ };
+
+ for(auto &tuple : shape_configurations)
+ {
+ const bool expected = std::get<2>(tuple);
+
+ for(bool adj_lhs :
+ {
+ false, true
+ })
+ {
+ for(bool adj_rhs :
+ {
+ false, true
+ })
+ {
+ TensorShape lhs_shape = std::get<0>(tuple);
+ TensorShape rhs_shape = std::get<1>(tuple);
+
+ if(adj_lhs)
+ {
+ permute(lhs_shape, PermutationVector(1U, 0U));
+ }
+
+ if(adj_rhs)
+ {
+ permute(rhs_shape, PermutationVector(1U, 0U));
+ }
+
+ const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
+ const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
+ TensorInfo output_info;
+
+ MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
+
+ Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+ }
+ }
+ }
+}
+
+TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
+{
+ // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
+ using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+ const std::vector<DataTypeConfigurationTuple> data_type_configurations =
+ {
+ { DataType::F32, DataType::F32, DataType::F32, true },
+ { DataType::F16, DataType::F16, DataType::F16, true },
+ { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision
+ { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
+ { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types
+ { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types
+ { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
+ { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types
+ { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types
+ { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized types
+ { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
+ { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
+ { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
+ { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
+ { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
+ { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
+ { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
+ { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
+ };
+
+ const TensorShape shape = TensorShape(10U, 10U);
+ const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
+ for(auto &tuple : data_type_configurations)
+ {
+ const bool expected = std::get<3>(tuple);
+
+ const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
+ const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
+ TensorInfo output_info(shape, 1, std::get<2>(tuple));
+
+ Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+ }
+}
+
+TEST_SUITE_END() // Validate
+
+TEST_SUITE(Float)
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false, true })),
+ framework::dataset::make("pretransose_B", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false, true })),
+ framework::dataset::make("pretransose_B", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false })),
+ framework::dataset::make("pretransose_B", { false })),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_nt_rhs_nt),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false })),
+ framework::dataset::make("pretransose_B", { true })),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { true })),
+ framework::dataset::make("pretransose_B", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_t_rhs_nt),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { true })),
+ framework::dataset::make("pretransose_B", { true })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+// Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0
+// It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels
+FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false, true })),
+ framework::dataset::make("pretransose_B", { false, true })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 2 })),
+ framework::dataset::make("K0", { 2 })),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false, true })),
+ framework::dataset::make("pretransose_B", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false })),
+ framework::dataset::make("pretransose_B", { false })),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_nt_rhs_nt),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { false })),
+ framework::dataset::make("pretransose_B", { true })),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { true })),
+ framework::dataset::make("pretransose_B", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_t_rhs_nt),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("pretransose_A", { true })),
+ framework::dataset::make("pretransose_B", { true })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // Float
+TEST_SUITE_END() // MatMulKernel
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
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