| /* |
| * 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 |
| { |
| 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 |