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| author | Viet-Hoa Do <viet-hoa.do@arm.com> | 2023-03-15 14:05:06 +0000 |
|---|---|---|
| committer | Viet-Hoa Do <viet-hoa.do@arm.com> | 2023-03-29 14:03:30 +0000 |
| commit | fd472f05dc73005a89a5e6275940ab5c9a609485 (patch) | |
| tree | 4a00f42f64f4bea72c489961aaa376665d324c60 /tests/validation/NEON/ElementwiseRsqrtLayer.cpp | |
| parent | 5a7d1571a2de24eefc6f1d8d22deeef9f47521ee (diff) | |
| download | ComputeLibrary-fd472f05dc73005a89a5e6275940ab5c9a609485.tar.gz | |
Add quantized support for unary elementwise in CPU
* Add quantized unary elementwise in CPU using LUT.
* Widen the input data range of the test suite.
- Fix CPU exponential function overflow/underflow range.
- Fix saturation issue of CL round operator.
Resolves: COMPMID-5763
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: I41445de2b4a33ec6b01e0ab701516c240c852d0b
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9367
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/NEON/ElementwiseRsqrtLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/ElementwiseRsqrtLayer.cpp | 33 |
1 files changed, 32 insertions, 1 deletions
diff --git a/tests/validation/NEON/ElementwiseRsqrtLayer.cpp b/tests/validation/NEON/ElementwiseRsqrtLayer.cpp index 2d52183b15..1d291ac6dc 100644 --- a/tests/validation/NEON/ElementwiseRsqrtLayer.cpp +++ b/tests/validation/NEON/ElementwiseRsqrtLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2021 Arm Limited. + * Copyright (c) 2018-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -46,6 +46,8 @@ RelativeTolerance<float> tolerance_fp32(0.000001f); #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC RelativeTolerance<float> tolerance_fp16(0.01f); #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(0); +constexpr AbsoluteTolerance<int8_t> tolerance_qasymm8_signed(0); } // namespace TEST_SUITE(NEON) TEST_SUITE(RsqrtLayer) @@ -72,6 +74,9 @@ TEST_SUITE_END() // DynamicShape template <typename T> using NERsqrtLayerFixture = RsqrtValidationFixture<Tensor, Accessor, NERsqrtLayer, T>; +template <typename T> +using NERsqrtLayerQuantizedFixture = RsqrtQuantizedValidationFixture<Tensor, Accessor, NERsqrtLayer, T>; + TEST_SUITE(Float) #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(FP16) @@ -102,6 +107,32 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NERsqrtLayerFixture<float>, framework::DatasetM TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8) +FIXTURE_DATA_TEST_CASE(RunSmall, NERsqrtLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine( + datasets::SmallShapes(), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("InputQInfo", { QuantizationInfo(20, 0) })), + framework::dataset::make("OutputQInfo", { QuantizationInfo(0.5, 10) }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +TEST_SUITE_END() // QASYMM8 + +TEST_SUITE(QASYMM8_SIGNED) +FIXTURE_DATA_TEST_CASE(RunSmall, NERsqrtLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine( + datasets::SmallShapes(), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("InputQInfo", { QuantizationInfo(25, -128) })), + framework::dataset::make("OutputQInfo", { QuantizationInfo(0.1, -7) }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8_signed); +} +TEST_SUITE_END() // QASYMM8_SIGNED + +TEST_SUITE_END() // Quantized TEST_SUITE_END() // RsqrtLayer TEST_SUITE_END() // Neon } // namespace validation |