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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/ClMatMulLowpNativeMMULKernel.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
{
// TODO: enable
// constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */
}
template <typename T>
using CLMatMulLowpNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeMMULKernel>;
template <typename T>
using CLMatMulLowpKernelWithBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulLowpNativeMMULKernel>;
TEST_SUITE(CL)
TEST_SUITE(MatMulLowpNativeMMULKernel)
TEST_SUITE(Validate)
TEST_CASE(SupportedKernelConfigurations, 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
// TODO: Test Cases
};
// 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::QASYMM8_SIGNED);
const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::QASYMM8_SIGNED);
for(auto &pair : supported_block_sizes)
{
TensorInfo output_info;
Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &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, TensorShape, bool>;
const std::vector<ShapeConfigurationTuple> shape_configurations =
{
{ TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true },
{ TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true },
{ TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
{ TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension
{ TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
{ TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
{ TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting
{ TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension
{ TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false }, // invalid broadcast of bias
{ TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false }, // 2d bias is invalid
};
for(auto &tuple : shape_configurations)
{
const bool expected = std::get<3>(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);
TensorShape bia_shape = std::get<2>(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::QASYMM8_SIGNED);
const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::QASYMM8_SIGNED);
const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::S32);
TensorInfo output_info;
MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
}
}
TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
{
using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>;
const std::vector<DataTypeConfigurationTuple> data_type_configurations =
{
{ DataType::F32, DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types
{ DataType::F16, DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types
{ DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
{ DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, true },
{ DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, true },
{ DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported
{ DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported
{ DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported
{ DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported
{ DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8, false }, // no mixed data types
{ DataType::S64, DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
{ DataType::S32, DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
{ DataType::S16, DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
{ DataType::S8, DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
{ DataType::U64, DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
{ DataType::U32, DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
{ DataType::U16, DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
{ DataType::U8, DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
{ DataType::QASYMM8, DataType::QASYMM8, DataType::F32, DataType::QASYMM8, false } // Only S32 bias is supported
};
// It's enough to test a single shape and block size configuration while checking data types
const TensorShape shape = TensorShape(48U, 48U);
const TensorShape bia_shape = TensorShape(48U);
const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
for(auto &tuple : data_type_configurations)
{
const bool expected = std::get<4>(tuple);
const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple));
TensorInfo output_info(shape, 1, std::get<3>(tuple));
Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
TEST_SUITE_END() // Validate
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8_SIGNED)
// TODO: tests
TEST_SUITE_END() // QASYMM8_SIGNED
TEST_SUITE(QASYMM8)
// TODO: tests
TEST_SUITE_END() // QASYMM8
TEST_SUITE_END() // Quantized
TEST_SUITE_END() // MatMulLowpNativeMMULKernel
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute
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