COMPMID-675 - Reworked NEGEMMLowp interface/function
The new interface makes NEGEMMLowp able to work with ASYMM8 data types.
Implemented 2 new functions:
- NEGEMMLowpMatrixMultiplyCore
- NEGEMMLowpOutputStage
These functions should make the integration in android NN doable
For more information about GEMMLowp:
https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
Change-Id: Ie2c775f45234f68ca53dba644b3a912b997fd890
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95504
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index fba4400..f9b0dbd 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -43,109 +43,36 @@
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType>
-class GEMMLowpOffsetValidationFixture : public framework::Fixture
+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, int32_t c_offset, int32_t c_mult_int, int32_t out_shift, DataType data_type)
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
{
- _target = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset, c_offset, c_mult_int, out_shift, data_type);
- _reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset, c_offset, c_mult_int, out_shift, data_type);
+ _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);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
- ARM_COMPUTE_ERROR_ON(tensor.data_type() != DataType::S8);
- std::uniform_int_distribution<> distribution(0, 3);
+ // 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);
}
TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c,
- int32_t a_offset, int32_t b_offset, int32_t c_offset, int32_t c_mult_int, int32_t out_shift, DataType data_type)
+ int32_t a_offset, int32_t b_offset)
{
// Create tensors
- TensorType a = create_tensor<TensorType>(shape_a, data_type, 1);
- TensorType b = create_tensor<TensorType>(shape_b, data_type, 1);
- TensorType c = create_tensor<TensorType>(shape_c, data_type, 1);
-
- // Create and configure function
- FunctionType gemmlowp;
- gemmlowp.configure(&a, &b, &c, a_offset, b_offset, c_offset, c_mult_int, out_shift);
-
- 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);
- fill(AccessorType(c), 2);
-
- // Compute GEMM function
- gemmlowp.run();
- return c;
- }
-
- SimpleTensor<int8_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c,
- int32_t a_offset, int32_t b_offset, int32_t c_offset, int32_t c_mult_int, int32_t out_shift, DataType data_type)
- {
- // Create reference
- SimpleTensor<int8_t> a{ shape_a, data_type, 1 };
- SimpleTensor<int8_t> b{ shape_b, data_type, 1 };
- SimpleTensor<int8_t> c{ shape_c, data_type, 1 };
-
- // Fill reference
- fill(a, 0);
- fill(b, 1);
- fill(c, 2);
-
- return reference::gemmlowp<int8_t>(a, b, c, a_offset, b_offset, c_offset, c_mult_int, out_shift);
- }
-
- TensorType _target{};
- SimpleTensor<int8_t> _reference{};
-};
-
-template <typename TensorType, typename AccessorType, typename FunctionType>
-class GEMMLowpMatrixMultiplyValidationFixture : public framework::Fixture
-{
-public:
- template <typename...>
- void setup(size_t m, size_t n, size_t k)
- {
- const TensorShape shape_a(k, m);
- const TensorShape shape_b(n, k);
- const TensorShape shape_c(n, m);
- _target = compute_target(shape_a, shape_b, shape_c);
- _reference = compute_reference(shape_a, shape_b, shape_c);
- }
-
-protected:
- template <typename U>
- void fill(U &&tensor, int i, int lo, int hi)
- {
- std::uniform_int_distribution<> distribution(lo, hi);
- library->fill(tensor, distribution, i);
- }
-
- TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c)
- {
- // Create tensors
- TensorType a = create_tensor<TensorType>(shape_a, DataType::S8, 1);
- TensorType b = create_tensor<TensorType>(shape_b, DataType::S8, 1);
+ 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
FunctionType gemmlowp;
gemmlowp.configure(&a, &b, &c);
@@ -164,34 +91,93 @@
ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
- fill(AccessorType(a), 0, -128, 127);
- fill(AccessorType(b), 1, -128, 127);
- fill(AccessorType(c), 2, 0, 0);
+ 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)
+ 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)
{
// Create reference
- SimpleTensor<int8_t> a{ shape_a, DataType::S8, 1 };
- SimpleTensor<int8_t> b{ shape_b, DataType::S8, 1 };
- SimpleTensor<int32_t> c{ shape_c, DataType::S32, 1 };
+ SimpleTensor<uint8_t> a{ shape_a, DataType::QASYMM8, 1 };
+ SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
// Fill reference
- fill(a, 0, -128, 127);
- fill(b, 1, -128, 127);
- fill(c, 2, 0, 0);
+ fill(a, 0);
+ fill(b, 1);
- return reference::gemmlowp(a, b, c);
+ return reference::gemmlowp_matrix_multiply_core<uint8_t>(a, b, a_offset, b_offset);
}
TensorType _target{};
SimpleTensor<int32_t> _reference{};
};
+template <typename TensorType, typename AccessorType, typename FunctionType>
+class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift)
+ {
+ _target = compute_target(shape, result_offset, result_mult_int, result_shift);
+ _reference = compute_reference(shape, result_offset, result_mult_int, result_shift);
+ }
+
+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)
+ {
+ // Create tensors
+ TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
+ TensorType b = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);
+
+ // Create and configure function
+ FunctionType output_stage;
+ output_stage.configure(&a, &b, result_offset, result_mult_int, result_shift);
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ b.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(a), 0);
+
+ // Compute GEMM function
+ output_stage.run();
+ return b;
+ }
+
+ SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift)
+ {
+ // Create reference
+ SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
+
+ // Fill reference
+ fill(a, 0);
+
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift);
+ }
+
+ TensorType _target{};
+ SimpleTensor<uint8_t> _reference{};
+};
} // namespace validation
} // namespace test
} // namespace arm_compute