COMPMID-2763 [CL] add support for QASYMM8_SIGNED to SoftmaxLayer

Change-Id: I4556bde3aa51eb874a4e674dbbd575fa4491c088
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2375
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

7 files changed, 44 insertions, 18 deletions

diff --git a/tests/validation/CL/SoftmaxLayer.cpp b/tests/validation/CL/SoftmaxLayer.cpp
index ae7adec9f2..5ee929f6b9 100644
--- a/tests/validation/CL/SoftmaxLayer.cpp
+++ b/tests/validation/CL/SoftmaxLayer.cpp
@@ -49,6 +49,15 @@ RelativeTolerance<float> tolerance_f32(0.001f);
 
 /** Tolerance for quantized operations */
 constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);
+constexpr AbsoluteTolerance<int8_t>  tolerance_qasymm8_signed(1);
+
+/*
+ The following tolerance number is used as a workaround for the mismatches
+ caused by float computation in reference (and NEON) kernel
+ and integer computations in OpenCL kernel.
+ COMPMID-2958 is created to investigate this.
+*/
+constexpr float tolerance_number_qasymm8_signed = 0.05f;
 
 /** CNN data types */
 const auto CNNDataTypes = framework::dataset::make("DataType",
@@ -110,6 +119,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(
                                                        TensorInfo(TensorShape(32U, 13U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(32U, 13U), 1, DataType::QASYMM8,
                                                                   QuantizationInfo(1.f/256, 12)),
+                                                       TensorInfo(TensorShape(32U, 13U), 1, DataType::QASYMM8_SIGNED,
+                                                                  QuantizationInfo(1.f/256, 12))
                                                       }),
                framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U), 1, DataType::F16),
                                                        TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
@@ -120,8 +131,10 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(
                                                        TensorInfo(TensorShape(32U, 13U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(32U, 13U), 1, DataType::QASYMM8,
                                                                   QuantizationInfo(1.f/256, 0)),
+                                                       TensorInfo(TensorShape(32U, 13U), 1, DataType::QASYMM8_SIGNED,
+                                                                  QuantizationInfo(1.f/256, -128)),
                                                      })),
-               framework::dataset::make("Expected", { false, false, false, false, false, true, true })),
+               framework::dataset::make("Expected", { false, false, false, false, false, true, true, true })),
                input_info, output_info, expected)
 {
     ARM_COMPUTE_EXPECT(bool(CLSoftmaxLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
@@ -221,11 +234,24 @@ FIXTURE_DATA_TEST_CASE(Run4D, CLSoftmaxLayerQuantizedFixture<uint8_t>, framework
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
 
-TEST_SUITE_END()
-TEST_SUITE_END()
+TEST_SUITE_END() // QASYMM8
 
-TEST_SUITE_END()
-TEST_SUITE_END()
+TEST_SUITE(QASYMM8_SIGNED)
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLSoftmaxLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SoftmaxLayerSmallShapes(),
+                                                                                                                      framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
+                                                                                                                      combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }),
+                                                                                                                              framework::dataset::make("Beta", { 1.0f, 2.f }))),
+                                                                                                              framework::dataset::make("Axis", { 1, 2 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_qasymm8_signed, tolerance_number_qasymm8_signed);
+}
+
+TEST_SUITE_END() // QASYMM8_SIGNED
+TEST_SUITE_END() // Quantized
+TEST_SUITE_END() // SoftmaxLayer
+TEST_SUITE_END() // CL
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h
index 100f4f05c1..942b2396bf 100644
--- a/tests/validation/Helpers.h
+++ b/tests/validation/Helpers.h
@@ -177,7 +177,7 @@ void fill_lookuptable(T &&table)
     }
 }
 
-/** Convert a quantized simple tensor into float using tensor quantization information.
+/** Convert an asymmetric quantized simple tensor into float using tensor quantization information.
  *
  * @param[in] src Quantized tensor.
  *
diff --git a/tests/validation/fixtures/SoftmaxLayerFixture.h b/tests/validation/fixtures/SoftmaxLayerFixture.h
index f747ab3574..82daf34f13 100644
--- a/tests/validation/fixtures/SoftmaxLayerFixture.h
+++ b/tests/validation/fixtures/SoftmaxLayerFixture.h
@@ -65,7 +65,7 @@ protected:
             std::uniform_real_distribution<> distribution(-1000.f, 1000.f);
             library->fill(tensor, distribution, 0);
         }
-        else // data type is quantized_asymmetric
+        else // data type is quantized_asymmetric (signed or unsigned)
         {
             std::uniform_int_distribution<> distribution(0, 100);
             library->fill(tensor, distribution, 0);
@@ -77,7 +77,7 @@ protected:
     {
         // Create tensors
         TensorType src = create_tensor<TensorType>(shape, data_type, 1, quantization_info);
-        TensorType dst = create_tensor<TensorType>(shape, data_type, 1, QuantizationInfo(1.f / 256, 0));
+        TensorType dst = create_tensor<TensorType>(shape, data_type, 1, get_softmax_output_quantization_info(data_type, IS_LOG));
 
         // Create and configure function
         FunctionType smx_layer;
diff --git a/tests/validation/reference/LogSoftmaxLayer.cpp b/tests/validation/reference/LogSoftmaxLayer.cpp
index 3f21d85dd0..e4403956ab 100644
--- a/tests/validation/reference/LogSoftmaxLayer.cpp
+++ b/tests/validation/reference/LogSoftmaxLayer.cpp
@@ -40,21 +40,21 @@ SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &src, float beta, size_t
     return softmax_layer_generic<T>(src, beta, axis, true);
 }
 
-template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int>::type>
 SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
 {
-    // Note: Output quantization info should always have scale = 1/256 and offset = 0
-    const QuantizationInfo output_quantization_info = QuantizationInfo(1.f / 256, 0);
+    const QuantizationInfo output_quantization_info = arm_compute::get_softmax_output_quantization_info(src.data_type(), true);
 
     SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
     SimpleTensor<float> dst_tmp = log_softmax_layer<float>(src_tmp, beta, axis);
-    SimpleTensor<T>     dst     = convert_to_asymmetric<uint8_t>(dst_tmp, output_quantization_info);
+    SimpleTensor<T>     dst     = convert_to_asymmetric<T>(dst_tmp, output_quantization_info);
     return dst;
 }
 
 template SimpleTensor<float> log_softmax_layer(const SimpleTensor<float> &src, float beta, size_t axis);
 template SimpleTensor<half> log_softmax_layer(const SimpleTensor<half> &src, float beta, size_t axis);
 template SimpleTensor<uint8_t> log_softmax_layer(const SimpleTensor<uint8_t> &src, float beta, size_t axis);
+template SimpleTensor<int8_t> log_softmax_layer(const SimpleTensor<int8_t> &src, float beta, size_t axis);
 } // namespace reference
 } // namespace validation
 } // namespace test
diff --git a/tests/validation/reference/LogSoftmaxLayer.h b/tests/validation/reference/LogSoftmaxLayer.h
index 065315ff2c..c2e3f5974e 100644
--- a/tests/validation/reference/LogSoftmaxLayer.h
+++ b/tests/validation/reference/LogSoftmaxLayer.h
@@ -38,7 +38,7 @@ namespace reference
 template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
 SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis = 1);
 
-template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type = 0>
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int>::type = 0>
 SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis = 1);
 } // namespace reference
 } // namespace validation
diff --git a/tests/validation/reference/SoftmaxLayer.cpp b/tests/validation/reference/SoftmaxLayer.cpp
index ef2468df59..0e470260a9 100644
--- a/tests/validation/reference/SoftmaxLayer.cpp
+++ b/tests/validation/reference/SoftmaxLayer.cpp
@@ -107,21 +107,21 @@ SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axi
     return softmax_layer_generic<T>(src, beta, axis, false);
 }
 
-template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int>::type>
 SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
 {
-    // Note: Output quantization info should always have scale = 1/256 and offset = 0
-    const QuantizationInfo output_quantization_info = QuantizationInfo(1.f / 256, 0);
+    const QuantizationInfo output_quantization_info = arm_compute::get_softmax_output_quantization_info(src.data_type(), false);
 
     SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
     SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta, axis);
-    SimpleTensor<T>     dst     = convert_to_asymmetric<uint8_t>(dst_tmp, output_quantization_info);
+    SimpleTensor<T>     dst     = convert_to_asymmetric<T>(dst_tmp, output_quantization_info);
     return dst;
 }
 
 template SimpleTensor<float> softmax_layer(const SimpleTensor<float> &src, float beta, size_t axis);
 template SimpleTensor<half> softmax_layer(const SimpleTensor<half> &src, float beta, size_t axis);
 template SimpleTensor<uint8_t> softmax_layer(const SimpleTensor<uint8_t> &src, float beta, size_t axis);
+template SimpleTensor<int8_t> softmax_layer(const SimpleTensor<int8_t> &src, float beta, size_t axis);
 } // namespace reference
 } // namespace validation
 } // namespace test
diff --git a/tests/validation/reference/SoftmaxLayer.h b/tests/validation/reference/SoftmaxLayer.h
index 2708c807f2..2be575c2af 100644
--- a/tests/validation/reference/SoftmaxLayer.h
+++ b/tests/validation/reference/SoftmaxLayer.h
@@ -41,7 +41,7 @@ SimpleTensor<T> softmax_layer_generic(const SimpleTensor<T> &src, float beta, si
 template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
 SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis = 1);
 
-template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type = 0>
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int>::type = 0>
 SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis = 1);
 } // namespace reference
 } // namespace validation