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/*
* Copyright (c) 2019-2020 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<CLGEMMReshapeLHSMatrixKernel>;
// Create function for CLGEMMReshapeRHSMatrixKernel
using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
// Create function for CLGEMMMatrixMultiplyKernel
using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction<CLGEMMMatrixMultiplyKernel>;
// Fixture for GEMMMatrixMultiplyInterleavedTransposedValidationFixture
template <typename T>
using CLGEMMMatrixMultiplyReshapedFixture =
GEMMMatrixMultiplyInterleavedTransposedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
// Fixture for GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture
template <typename T>
using CLGEMMMatrixMultiplyReshaped3DFixture =
GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
namespace
{
// *INDENT-OFF*
// clang-format off
RelativeTolerance<float> rel_tolerance_f32(0.001f);
constexpr float abs_tolerance_f32(0.0001f);
RelativeTolerance<half> rel_tolerance_f16(half(0.2));
constexpr float tolerance_num_f16 = 0.02f;
/** Alpha values to test */
const auto alpha_values = framework::dataset::make("alpha", {1.0f, -0.75f} );
/** Beta values to test */
const auto beta_values = framework::dataset::make("beta", {-0.35f, 0.0f} );
/** M values to test */
const auto m_values = framework::dataset::make("M", {37, 1});
/** N values to test */
const auto n_values = framework::dataset::make("N", 51);
/** K values to test */
const auto k_values = framework::dataset::make("K", 23);
/** M_W values to test */
const auto m_w_values = framework::dataset::make("M_W", 5);
/** M_H values to test */
const auto m_h_values = framework::dataset::make("M_H", 7);
/** 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 */
const auto v0_values = framework::dataset::make("V0", 2);
/** H0 values to test */
const auto h0_values = framework::dataset::make("H0", 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});
} // namespace
TEST_SUITE(CL)
TEST_SUITE(GEMMMatrixMultiplyInterleavedTransposed)
TEST_CASE(Negative, framework::DatasetMode::ALL)
{
// The following tests are already integrated in the GEMMMatrixMultiply validation because
// in common with this validation
// - Unsupported QASYMM8 data type
// - Unsupported SIZE_T data type
// - Mixed precision with F32
// - Max number of dimensions LHS matrix
// - Max number of dimensions RHS matrix
// Invalid LHS dimensions
{
// The correct shape should be: lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
const auto lhs = TensorInfo(TensorShape(256U, 2U, 1U, 1U), 1, DataType::F32);
const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
constexpr float alpha = 1.3f;
constexpr float beta = 0.7f;
const bool is_interleaved_transposed = true;
const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
const GPUTarget gpu_target = GPUTarget::MIDGARD;
const bool fp_mixed_precision = false;
const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
// Invalid RHS dimensions
{
const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
// The correct shape should be rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
const auto rhs = TensorInfo(TensorShape(104U, 4U, 1U, 1U), 1, DataType::F32);
const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
constexpr float alpha = 1.3f;
constexpr float beta = 0.7f;
const bool is_interleaved_transposed = true;
const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
const GPUTarget gpu_target = GPUTarget::MIDGARD;
const bool fp_mixed_precision = false;
const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
// Broadcast bias
{
const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
// The correct shape should be bias = TensorInfo(TensorShape(24U, 1U, 1U, 1U), 1, DataType::F32);
const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
constexpr float alpha = 1.3f;
constexpr float beta = 0.7f;
const bool is_interleaved_transposed = true;
const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, true);
const GPUTarget gpu_target = GPUTarget::MIDGARD;
const bool fp_mixed_precision = false;
const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
// Invalid dimensions for the bias
{
const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
// The correct shape should be bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
const auto bias = TensorInfo(TensorShape(25U, 16U, 1U, 1U), 1, DataType::F32);
const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
constexpr float alpha = 1.3f;
constexpr float beta = 0.7f;
const bool is_interleaved_transposed = true;
const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
const GPUTarget gpu_target = GPUTarget::MIDGARD;
const bool fp_mixed_precision = false;
const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
// Invalid dimensions for the output
{
const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32);
const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32);
const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
// The correct shape should be out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32);
const auto out = TensorInfo(TensorShape(24U, 13U, 1U, 1U), 1, DataType::F32);
constexpr float alpha = 1.3f;
constexpr float beta = 0.7f;
const bool is_interleaved_transposed = true;
const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
const GPUTarget gpu_target = GPUTarget::MIDGARD;
const bool fp_mixed_precision = false;
const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
}
TEST_SUITE(Float)
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<float>, framework::DatasetMode::ALL,
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
m_values,
n_values),
k_values),
b_values),
alpha_values),
beta_values),
v0_values),
h0_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, CLGEMMMatrixMultiplyReshaped3DFixture<float>, framework::DatasetMode::ALL,
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
m_w_values,
m_h_values),
n_values),
k_values),
b_values),
alpha_values),
beta_values),
v0_values),
h0_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, CLGEMMMatrixMultiplyReshapedFixture<half>, framework::DatasetMode::ALL,
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
m_values,
n_values),
k_values),
b_values),
alpha_values),
beta_values),
v0_values),
h0_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, CLGEMMMatrixMultiplyReshaped3DFixture<half>, framework::DatasetMode::ALL,
combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
m_w_values,
m_h_values),
n_values),
k_values),
b_values),
alpha_values),
beta_values),
v0_values),
h0_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() // GEMMMatrixMulipltyInterleavedTransposed
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute
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