From fd472f05dc73005a89a5e6275940ab5c9a609485 Mon Sep 17 00:00:00 2001
From: Viet-Hoa Do <viet-hoa.do@arm.com>
Date: Wed, 15 Mar 2023 14:05:06 +0000
Subject: 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>
---
 tests/validation/NEON/ElementwiseRsqrtLayer.cpp | 33 ++++++++++++++++++++++++-
 1 file changed, 32 insertions(+), 1 deletion(-)

(limited to 'tests/validation/NEON/ElementwiseRsqrtLayer.cpp')

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
-- 
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