COMPMID-2966 Add support for QASYMM8_SIGNED in NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
Signed-off-by: Luca Foschiani <luca.foschiani@arm.com>
Change-Id: Ia8692f8fda16fa3b73f343e4b5b1b55e14403225
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2750
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index be9ce96..e3dc738 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -301,8 +301,16 @@
TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);
// Create and configure function
- FunctionType output_stage;
- output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max);
+ FunctionType output_stage;
+ GEMMLowpOutputStageInfo output_stage_info = GEMMLowpOutputStageInfo();
+ output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
+ output_stage_info.gemmlowp_offset = result_offset;
+ output_stage_info.gemmlowp_multiplier = result_mult_int;
+ output_stage_info.gemmlowp_shift = result_shift;
+ output_stage_info.gemmlowp_min_bound = min;
+ output_stage_info.gemmlowp_max_bound = max;
+ output_stage_info.output_data_type = DataType::QASYMM8;
+ output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info);
ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -367,6 +375,108 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType>
+class GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
+ {
+ _target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
+ _reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_int_distribution<> distribution(-6000, 6000);
+ library->fill(tensor, distribution, i);
+ }
+
+ TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
+ {
+ TensorShape shape_bias(shape[0]);
+
+ // Create tensors
+ TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
+ TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
+ TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8_SIGNED, 1);
+
+ // Create and configure function
+ FunctionType output_stage;
+ GEMMLowpOutputStageInfo output_stage_info = GEMMLowpOutputStageInfo();
+ output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
+ output_stage_info.gemmlowp_offset = result_offset;
+ output_stage_info.gemmlowp_multiplier = result_mult_int;
+ output_stage_info.gemmlowp_shift = result_shift;
+ output_stage_info.gemmlowp_min_bound = min;
+ output_stage_info.gemmlowp_max_bound = max;
+ output_stage_info.output_data_type = DataType::QASYMM8_SIGNED;
+ output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info);
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ c.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensor
+ fill(AccessorType(a), 0);
+
+ if(add_bias)
+ {
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate bias tensor
+ b.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensor
+ fill(AccessorType(b), 1);
+ }
+
+ // Compute GEMM function
+ output_stage.run();
+ return c;
+ }
+
+ SimpleTensor<int8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
+ {
+ // Create reference
+ TensorShape shape_bias(shape[0]);
+
+ SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
+ SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
+
+ // Fill reference
+ fill(a, 0);
+
+ const std::vector<int32_t> result_mult_int_vec = { result_mult_int };
+ const std::vector<int32_t> result_shift_vec = { result_shift };
+
+ if(add_bias)
+ {
+ // Fill bias
+ fill(b, 1);
+
+ return reference::gemmlowp_quantize_down_scale<int32_t, int8_t>(a, b, result_offset, result_mult_int_vec, result_shift_vec, min, max);
+ }
+ else
+ {
+ return reference::gemmlowp_quantize_down_scale<int32_t, int8_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max);
+ }
+ }
+
+ TensorType _target{};
+ SimpleTensor<int8_t> _reference{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType>
class GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture : public framework::Fixture
{
public: