559 lines
32 KiB
C++
559 lines
32 KiB
C++
/*
|
|
* Copyright (c) 2017-2020 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/Types.h"
|
|
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
|
|
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
|
|
#include "arm_compute/runtime/Tensor.h"
|
|
#include "arm_compute/runtime/TensorAllocator.h"
|
|
#include "tests/NEON/Accessor.h"
|
|
#include "tests/NEON/Helper.h"
|
|
#include "tests/PaddingCalculator.h"
|
|
#include "tests/datasets/GEMMLowpFusedOffsetOutputDataset.h"
|
|
#include "tests/datasets/LargeGEMMLowpDataset.h"
|
|
#include "tests/datasets/ShapeDatasets.h"
|
|
#include "tests/datasets/SmallGEMMLowpDataset.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/GEMMLowpAssemblyFixture.h"
|
|
#include "tests/validation/fixtures/GEMMLowpFixture.h"
|
|
|
|
namespace arm_compute
|
|
{
|
|
namespace test
|
|
{
|
|
namespace validation
|
|
{
|
|
namespace
|
|
{
|
|
const auto data_matrix_multiply = framework::dataset::make("M", 12, 20) * framework::dataset::make("N", 12, 20) * framework::dataset::make("K", 16);
|
|
} // namespace
|
|
|
|
TEST_SUITE(NEON)
|
|
TEST_SUITE(GEMMLowp)
|
|
TEST_SUITE(MatrixMultiplyCore)
|
|
using NEGEMMLowpMatrixMultiplyCoreFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
|
|
|
|
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallGEMMLowpDataset(), datasets::LargeGEMMLowpDataset()),
|
|
shape_a, shape_b, shape_c, a_offset, b_offset)
|
|
{
|
|
// Create tensors
|
|
Tensor a = create_tensor<Tensor>(shape_a, DataType::QASYMM8);
|
|
Tensor b = create_tensor<Tensor>(shape_b, DataType::QASYMM8);
|
|
Tensor c = create_tensor<Tensor>(shape_c, DataType::S32);
|
|
|
|
a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
|
|
b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
|
|
|
|
ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Create and configure function
|
|
NEGEMMLowpMatrixMultiplyCore gemmlowp_mm;
|
|
gemmlowp_mm.configure(&a, &b, nullptr, &c);
|
|
|
|
// Validate padding is zero
|
|
validate(a.info()->padding(), PaddingSize());
|
|
validate(b.info()->padding(), PaddingSize());
|
|
validate(c.info()->padding(), PaddingSize());
|
|
}
|
|
|
|
// *INDENT-OFF*
|
|
// clang-format off
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
|
|
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Input not a multiple of 4
|
|
TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Mismatching data type
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
|
|
TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
|
|
TensorInfo(TensorShape(16U, 32U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)),
|
|
}),
|
|
framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
|
|
TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
|
|
TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
|
|
TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
|
|
TensorInfo(TensorShape(64U, 16U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
|
|
})),
|
|
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(8U, 11U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(64U, 32U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("Expected", { true, false, false, false, true })),
|
|
a_info, b_info, output_info, expected)
|
|
{
|
|
// Lock tensors
|
|
Status status = NEGEMMLowpMatrixMultiplyCore::validate(&a_info.clone()->set_is_resizable(false),
|
|
&b_info.clone()->set_is_resizable(false),
|
|
nullptr,
|
|
&output_info.clone()->set_is_resizable(false));
|
|
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpDataset())
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpDataset())
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture = GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
|
|
TEST_SUITE(FusedOffsetOutput)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL, combine(datasets::SmallGEMMLowpFusedOffsetOutputUint8Dataset(),
|
|
framework::dataset::make("DataType", { DataType::QASYMM8 })))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeGEMMLowpFusedOffsetOutputUint8Dataset(),
|
|
framework::dataset::make("DataType", { DataType::QASYMM8 })))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // FusedOffsetOutput
|
|
TEST_SUITE_END() // MatrixMultiplyCore
|
|
|
|
TEST_SUITE(OutputStage)
|
|
|
|
TEST_SUITE(QuantizeDownInt32Scale)
|
|
|
|
TEST_SUITE(QASYMM8)
|
|
|
|
const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
|
|
3)
|
|
* framework::dataset::make("min", 0) * framework::dataset::make("max", 255) * framework::dataset::make("addBias", { false, true });
|
|
|
|
const auto quantize_down_int32_to_uint8_scale_relu_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1,
|
|
2)
|
|
* framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true });
|
|
|
|
using NEGEMMLowpQuantizeDownInt32ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpOutputStage>;
|
|
|
|
// *INDENT-OFF*
|
|
// clang-format off
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
|
|
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
|
|
}),
|
|
framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(20U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8),
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("Min",{ 0,
|
|
13,
|
|
})),
|
|
framework::dataset::make("Max",{ 205,
|
|
180,
|
|
})),
|
|
framework::dataset::make("Expected", { true, false })),
|
|
a_info, b_info, output_info, min, max, expected)
|
|
{
|
|
|
|
GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo();
|
|
output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
|
|
output_stage.gemmlowp_min_bound = min;
|
|
output_stage.gemmlowp_max_bound = max;
|
|
output_stage.output_data_type = DataType::QASYMM8;
|
|
|
|
// Lock tensors
|
|
Status status = NEGEMMLowpOutputStage::validate(&a_info.clone()->set_is_resizable(false),
|
|
&b_info.clone()->set_is_resizable(false),
|
|
&output_info.clone()->set_is_resizable(false),
|
|
output_stage);
|
|
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
TEST_CASE(NoPaddingAdded, framework::DatasetMode::PRECOMMIT)
|
|
{
|
|
Tensor input1 = create_tensor<Tensor>(TensorShape(21U, 13U), DataType::S32);
|
|
Tensor input2 = create_tensor<Tensor>(TensorShape(21U, 1U), DataType::S32);
|
|
Tensor output = create_tensor<Tensor>(TensorShape(21U, 13U), DataType::QASYMM8);
|
|
|
|
GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo();
|
|
output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
|
|
output_stage.gemmlowp_min_bound = 0;
|
|
output_stage.gemmlowp_max_bound = 205;
|
|
output_stage.output_data_type = DataType::QASYMM8;
|
|
|
|
NEGEMMLowpOutputStage f;
|
|
f.configure(&input1, &input2, &output, output_stage);
|
|
|
|
// Validate padding is zero
|
|
validate(input1.info()->padding(), PaddingSize());
|
|
validate(input2.info()->padding(), PaddingSize());
|
|
validate(output.info()->padding(), PaddingSize());
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
TEST_SUITE(BoundedReLu)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
TEST_SUITE_END() // BoundedReLu
|
|
|
|
TEST_SUITE_END() // QASYMM8
|
|
|
|
TEST_SUITE(QASYMM8_SIGNED)
|
|
|
|
const auto quantize_down_int32_to_int8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
|
|
3)
|
|
* framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true });
|
|
|
|
const auto quantize_down_int32_to_int8_scale_relu_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1,
|
|
2)
|
|
* framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", -100, -98) * framework::dataset::make("max", 71, 74) * framework::dataset::make("addBias", { false, true });
|
|
|
|
using NEGEMMLowpQuantizeDownInt32ScaleFixture = GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpOutputStage>;
|
|
|
|
// *INDENT-OFF*
|
|
// clang-format off
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
|
|
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16
|
|
TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
|
|
}),
|
|
framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(20U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
|
|
TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("Min",{ -10,
|
|
-200,
|
|
-113,
|
|
})),
|
|
framework::dataset::make("Max",{ 105,
|
|
300,
|
|
-18,
|
|
})),
|
|
framework::dataset::make("Expected", { true, false, false })),
|
|
a_info, b_info, output_info, min, max, expected)
|
|
{
|
|
GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo();
|
|
output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
|
|
output_stage.gemmlowp_min_bound = min;
|
|
output_stage.gemmlowp_max_bound = max;
|
|
output_stage.output_data_type = DataType::QASYMM8_SIGNED;
|
|
|
|
// Lock tensors
|
|
Status status = NEGEMMLowpOutputStage::validate(&a_info.clone()->set_is_resizable(false),
|
|
&b_info.clone()->set_is_resizable(false),
|
|
&output_info.clone()->set_is_resizable(false),
|
|
output_stage);
|
|
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int8_scale_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
TEST_SUITE(BoundedReLu)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int8_scale_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
TEST_SUITE_END() // BoundedReLu
|
|
|
|
TEST_SUITE_END() // QASYMM8_SIGNED
|
|
|
|
TEST_SUITE_END() // QuantizeDownInt32Scale
|
|
|
|
TEST_SUITE(QuantizeDownInt32ToUint8ScaleByFixedPoint)
|
|
|
|
const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
|
|
2)
|
|
* framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", 0) * framework::dataset::make("max", 255) * framework::dataset::make("addBias", { false, true });
|
|
|
|
const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
|
|
2)
|
|
* framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true });
|
|
|
|
using NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture =
|
|
GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint>;
|
|
|
|
using NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture =
|
|
GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint>;
|
|
|
|
// *INDENT-OFF*
|
|
// clang-format off
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
|
|
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
|
|
}),
|
|
framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(20U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8),
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("Min",{ 0,
|
|
13,
|
|
})),
|
|
framework::dataset::make("Max",{ 205,
|
|
180,
|
|
})),
|
|
framework::dataset::make("Expected", { true, false })),
|
|
a_info, b_info, output_info, min, max, expected)
|
|
{
|
|
// Lock tensors
|
|
Status status = NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false),
|
|
&b_info.clone()->set_is_resizable(false),
|
|
&output_info.clone()->set_is_resizable(false),
|
|
min,
|
|
max);
|
|
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_uint8_scale_by_fixedpoint_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(),
|
|
quantize_down_int32_to_uint8_scale_by_fixedpoint_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
TEST_SUITE(BoundedReLu)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(),
|
|
quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // BoundedReLu
|
|
|
|
TEST_SUITE_END() // QuantizeDownInt32ToUint8ScaleByFixedPoint
|
|
|
|
TEST_SUITE(QuantizeDownInt32ToInt8ScaleByFixedPoint)
|
|
|
|
const auto quantize_down_int32_to_int8_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
|
|
2)
|
|
* framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", -128) * framework::dataset::make("max", 128) * framework::dataset::make("addBias", { false, true });
|
|
|
|
const auto quantize_down_int32_to_int8_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
|
|
2)
|
|
* framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true });
|
|
|
|
using NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointFixture =
|
|
GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint>;
|
|
|
|
// *INDENT-OFF*
|
|
// clang-format off
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
|
|
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::F32), // Invalid input data type
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
|
|
TensorInfo(TensorShape(21U, 13U), 1, DataType::S32),
|
|
}),
|
|
framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(20U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
|
|
})),
|
|
framework::dataset::make("Min",{ -110,
|
|
-113,
|
|
-113,
|
|
})),
|
|
framework::dataset::make("Max",{ 87,
|
|
97,
|
|
97,
|
|
})),
|
|
framework::dataset::make("Expected", { false, false, true })),
|
|
a_info, b_info, output_info, min, max, expected)
|
|
{
|
|
// Lock tensors
|
|
Status status = NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false),
|
|
&b_info.clone()->set_is_resizable(false),
|
|
&output_info.clone()->set_is_resizable(false),
|
|
min,
|
|
max);
|
|
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_int8_scale_by_fixedpoint_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
|
|
TEST_SUITE(BoundedReLu)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_int8_scale_by_fixedpoint_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // BoundedReLu
|
|
TEST_SUITE_END() // QuantizeDownInt32ToInt8ScaleByFixedPoint
|
|
|
|
TEST_SUITE(QuantizeDownInt32ToInt16ScaleByFixedPoint)
|
|
|
|
const auto quantize_down_int32_to_int16_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
|
|
2)
|
|
* framework::dataset::make("min", -32768) * framework::dataset::make("max", 32767) * framework::dataset::make("addBias", { false, true });
|
|
|
|
const auto quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
|
|
2)
|
|
* framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true });
|
|
const auto quantize_down_int32_to_int16_scale_by_fixedpoint_multgreat1_cases = framework::dataset::make("result_fixedpoint_multiplier", 1073741823,
|
|
1073741825)
|
|
* framework::dataset::make("result_shift", -3,
|
|
-2)
|
|
* framework::dataset::make("min", -32768) * framework::dataset::make("max", 32767) * framework::dataset::make("addBias", { false, true });
|
|
|
|
const auto quantize_down_int32_to_int16_scale_by_fixedpoint_multgreat1_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600,
|
|
254601602)
|
|
* framework::dataset::make("result_shift", -3,
|
|
-1)
|
|
* framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true });
|
|
|
|
using NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture =
|
|
GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint>;
|
|
|
|
// *INDENT-OFF*
|
|
// clang-format off
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
|
|
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
|
|
}),
|
|
framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
|
|
TensorInfo(TensorShape(20U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16),
|
|
TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
|
|
})),
|
|
framework::dataset::make("Min",{ -205,
|
|
-180,
|
|
})),
|
|
framework::dataset::make("Max",{ 205,
|
|
180,
|
|
})),
|
|
framework::dataset::make("Expected", { true, false })),
|
|
a_info, b_info, output_info, min, max, expected)
|
|
{
|
|
// Lock tensors
|
|
Status status = NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false),
|
|
&b_info.clone()->set_is_resizable(false),
|
|
&output_info.clone()->set_is_resizable(false),
|
|
min,
|
|
max);
|
|
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
TEST_SUITE(NoRelu)
|
|
TEST_SUITE(MultSmallerEq1)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_int16_scale_by_fixedpoint_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // MultSmallerEq1
|
|
TEST_SUITE(MultGreater1)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_int16_scale_by_fixedpoint_multgreat1_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // MultGreater1
|
|
TEST_SUITE_END() // NoRelu
|
|
TEST_SUITE(BoundedReLu)
|
|
TEST_SUITE(MultSmallerEq1)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // MultSmallerEq1
|
|
TEST_SUITE(MultGreater1)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
|
|
quantize_down_int32_to_int16_scale_by_fixedpoint_multgreat1_relu_cases))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference);
|
|
}
|
|
TEST_SUITE_END() // MultGreater1
|
|
TEST_SUITE_END() // BoundedReLu
|
|
TEST_SUITE_END() // QuantizeDownInt32ToInt16ScaleByFixedPoint
|
|
TEST_SUITE_END() // OutputStage
|
|
TEST_SUITE_END() // GEMMLowp
|
|
TEST_SUITE_END() // NEON
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|