COMPMID-675 - Reworked NEGEMMLowp interface/function
The new interface makes NEGEMMLowp able to work with ASYMM8 data types.
Implemented 2 new functions:
- NEGEMMLowpMatrixMultiplyCore
- NEGEMMLowpOutputStage
These functions should make the integration in android NN doable
For more information about GEMMLowp:
https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
Change-Id: Ie2c775f45234f68ca53dba644b3a912b997fd890
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95504
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index 4924f98..4407eff 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -23,12 +23,15 @@
*/
#include "arm_compute/core/NEON/kernels/NEGEMMInterleaveBlockedKernel.h"
#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/NEON/functions/NEGEMMLowp.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/LargeGEMMLowpDataset.h"
+#include "tests/datasets/ShapeDatasets.h"
#include "tests/datasets/SmallGEMMLowpDataset.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
@@ -45,16 +48,13 @@
{
namespace
{
-const auto data_int_blk = framework::dataset::make("M", 8, 12) * framework::dataset::make("N", 8, 12) * framework::dataset::make("by", 8, 13) * framework::dataset::make("block", 4, 9);
-const auto data_int_blk_tr = framework::dataset::make("M", 8, 17) * framework::dataset::make("N", 8, 14) * framework::dataset::make("by", 12) * framework::dataset::make("block", 4);
-const auto data_matrix_multiply = framework::dataset::make("M", 12, 20) * framework::dataset::make("N", 12, 20) * framework::dataset::make("K", 16);
+const auto data_int_blk = framework::dataset::make("M", 8, 12) * framework::dataset::make("N", 8, 12) * framework::dataset::make("by", 8, 13) * framework::dataset::make("block", 4, 9);
+const auto data_int_blk_tr = framework::dataset::make("M", 8, 17) * framework::dataset::make("N", 8, 14) * framework::dataset::make("by", 12) * framework::dataset::make("block", 4);
} // namespace
TEST_SUITE(NEON)
TEST_SUITE(GEMMLowp)
-TEST_SUITE(S8)
-
TEST_SUITE(INTERLEAVE_BLOCKED)
using NEInterleaveBlocked = NESynthetizeFunction<NEGEMMInterleaveBlockedKernel>;
@@ -77,50 +77,95 @@
TEST_SUITE_END()
-using NEGEMMLowpOffsetFixture = GEMMLowpOffsetValidationFixture<Tensor, Accessor, NEGEMMLowp>;
+TEST_SUITE(MatrixMultiplyCore)
+using NEGEMMLowpMatrixMultiplyCoreFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallGEMMLowpDataset(), datasets::LargeGEMMLowpDataset()), framework::dataset::make("DataType",
- DataType::S8)),
- shape_a, shape_b, shape_c, a_offset, b_offset, c_offset, c_mult_int, out_shift, data_type)
+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, data_type);
- Tensor b = create_tensor<Tensor>(shape_b, data_type);
- Tensor c = create_tensor<Tensor>(shape_c, data_type);
+ 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
- NEGEMMLowp gemmlowp;
- gemmlowp.configure(&a, &b, &c, a_offset, b_offset, c_offset, c_mult_int, out_shift);
+ NEGEMMLowpMatrixMultiplyCore gemmlowp_mm;
+ gemmlowp_mm.configure(&a, &b, &c);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpOffsetFixture, framework::DatasetMode::ALL, combine(datasets::SmallGEMMLowpDataset(), framework::dataset::make("DataType", DataType::S8)))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpDataset())
{
// Validate output
validate(Accessor(_target), _reference);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpOffsetFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeGEMMLowpDataset(), framework::dataset::make("DataType", DataType::S8)))
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpDataset())
{
// Validate output
validate(Accessor(_target), _reference);
}
-TEST_SUITE_END() // U8
-TEST_SUITE(S32)
-using NEGEMMLowpMatrixMultiplyFixture = GEMMLowpMatrixMultiplyValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
-FIXTURE_DATA_TEST_CASE(MatrixMultiply, NEGEMMLowpMatrixMultiplyFixture, framework::DatasetMode::PRECOMMIT, data_matrix_multiply)
+TEST_SUITE_END() // MatrixMultiplyCore
+
+TEST_SUITE(OutputStage)
+
+TEST_SUITE(QuantizeDownInt32ToUint8Scale)
+
+using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8Scale>;
+
+const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -4, 4) * framework::dataset::make("result_mult_int", 1, 3) * framework::dataset::make("result_shift", 2,
+ 4);
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), quantize_down_int32_to_uint8_scale_cases),
+ shape, result_offset, result_mult_int, result_shift)
+{
+ // Create tensors
+ Tensor in = create_tensor<Tensor>(shape, DataType::S32);
+ Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8);
+
+ ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Create and configure function
+ NEGEMMLowpQuantizeDownInt32ToUint8Scale output_stage;
+ output_stage.configure(&in, &out, result_offset, result_mult_int, result_shift);
+
+ // Validate valid region
+ const ValidRegion valid_region = shape_to_valid_region(shape);
+ validate(in.info()->valid_region(), valid_region);
+ validate(out.info()->valid_region(), valid_region);
+
+ // Validate padding
+ const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+ validate(in.info()->padding(), padding);
+ validate(out.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases))
{
// Validate output
validate(Accessor(_target), _reference);
}
-TEST_SUITE_END()
-TEST_SUITE_END()
-TEST_SUITE_END()
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale
+
+TEST_SUITE_END() // OutputStage
+
+TEST_SUITE_END() // GEMMLowp
+TEST_SUITE_END() // NEON
} // namespace validation
} // namespace test
} // namespace arm_compute