COMPMID-1694: Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore
Change-Id: Ic1a681e4cc03e1eba3bf8485d9cdb17b3e926047
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/561
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index 836f8ed..90a4b5c 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -42,88 +42,166 @@
{
namespace validation
{
+namespace
+{
+template <typename U>
+void fill(U &&tensor, int i)
+{
+ // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
+ std::uniform_int_distribution<> distribution(1, 254);
+ library->fill(tensor, distribution, i);
+}
+
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false>
+TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo())
+{
+ // Create tensors
+ TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
+ TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
+ TensorType output = create_tensor<TensorType>(shape_output, output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : DataType::QASYMM8, 1);
+
+ a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
+ b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
+
+ TensorType bias;
+ if(is_fused)
+ {
+ TensorShape bias_shape(shape_b[0]);
+ bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1);
+ }
+
+ // Create and configure function
+ // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
+ FunctionType gemmlowp;
+ // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
+ gemmlowp.configure(&a, &b, is_fused ? &bias : nullptr, &output, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_output[2] : 0), reinterpret_input_as_3d, false, output_stage));
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ b.allocator()->allocate();
+ output.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(a), 0);
+ fill(AccessorType(b), 1);
+
+ if(is_fused)
+ {
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ bias.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ fill(AccessorType(bias), 2);
+ }
+
+ // Compute GEMM function
+ gemmlowp.run();
+ return output;
+}
+
+template <bool reinterpret_input_as_3d>
+SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
+{
+ TensorShape shape_a_to_use = shape_a;
+ if(reinterpret_input_as_3d)
+ {
+ // Collapse the second and third dimension if the input is 3D
+ shape_a_to_use.collapse(2U, 1U);
+ }
+
+ // Create reference
+ SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
+ SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
+
+ // Fill reference
+ fill(a, 0);
+ fill(b, 1);
+
+ return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_output, a_offset, b_offset);
+}
+}
+
template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset)
{
- _target = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset);
- _reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset);
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset);
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset);
}
protected:
- template <typename U>
- void fill(U &&tensor, int i)
+ TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
{
- // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
- std::uniform_int_distribution<> distribution(1, 254);
- library->fill(tensor, distribution, i);
+ return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t>(shape_a, shape_b, shape_output, a_offset, b_offset);
}
- TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
+ SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
{
- // Create tensors
- TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
- TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
- TensorType c = create_tensor<TensorType>(shape_c, DataType::S32, 1);
-
- a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
- b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
-
- // Create and configure function
- // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
- FunctionType gemmlowp;
- // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
- gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 0), reinterpret_input_as_3d));
-
- 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);
-
- // Allocate tensors
- a.allocator()->allocate();
- b.allocator()->allocate();
- c.allocator()->allocate();
-
- 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);
-
- // Fill tensors
- fill(AccessorType(a), 0);
- fill(AccessorType(b), 1);
-
- // Compute GEMM function
- gemmlowp.run();
- return c;
- }
-
- SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
- {
- TensorShape shape_a_to_use = shape_a;
- if(reinterpret_input_as_3d)
- {
- // Collapse the second and third dimension if the input is 3D
- shape_a_to_use.collapse(2U, 1U);
- }
-
- // Create reference
- SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
- SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
-
- // Fill reference
- fill(a, 0);
- fill(b, 1);
-
- return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_c, a_offset, b_offset);
+ return compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset);
}
TensorType _target{};
SimpleTensor<int32_t> _reference{};
};
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
+class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+ {
+ ARM_COMPUTE_EXPECT(output_stage.type != GEMMLowpOutputStageType::NONE, framework::LogLevel::ERRORS);
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage);
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage);
+ }
+
+protected:
+ TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+ {
+ return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true>(shape_a, shape_b, shape_output, a_offset, b_offset,
+ output_stage);
+ }
+
+ SimpleTensor<qasymm8_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage)
+ {
+ SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset);
+
+ TensorShape bias_shape(shape_b[0]);
+ SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 };
+ fill(bias, 2);
+
+ switch(output_stage.type)
+ {
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(output, bias,
+ output_stage.gemmlowp_offset, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ break;
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(output, bias,
+ output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not Supported!");
+ }
+ }
+
+ TensorType _target{};
+ SimpleTensor<qasymm8_t> _reference{};
+};
+
template <typename TensorType, typename AccessorType, typename FunctionType>
class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture
{
@@ -536,4 +614,4 @@
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
\ No newline at end of file
+#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */