COMPMID-2792: Add support for QASYMM8_SIGNED in CLGEMMLowpMatrixMultiplyReshapedKernel
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I005e604253394f31173f37ec0296caf76b5e697c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/227008
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2798
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index 1154d6c..d13ada9 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -656,7 +656,7 @@
public:
template <typename...>
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
- bool interleave_rhs)
+ bool interleave_rhs, DataType data_type)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -676,24 +676,39 @@
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
- _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info);
- _reference = compute_reference(lhs_shape, rhs_shape);
+ _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type);
+ _reference = compute_reference(lhs_shape, rhs_shape, data_type);
}
protected:
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);
+ switch(tensor.data_type())
+ {
+ case DataType::QASYMM8:
+ {
+ // 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);
+ }
+ break;
+ case DataType::QASYMM8_SIGNED:
+ {
+ std::uniform_int_distribution<> distribution(-127, 126);
+ library->fill(tensor, distribution, i);
+ }
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
}
- TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info)
+ TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type)
{
// Create tensors
- TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
- TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
+ TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
+ TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType lhs_reshaped;
TensorType rhs_reshaped;
TensorType dst;
@@ -740,21 +755,41 @@
return dst;
}
- SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape)
+ SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
dst_shape[1] = lhs_shape[1];
- // Create reference
- SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
- SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };
+ switch(data_type)
+ {
+ case DataType::QASYMM8:
+ {
+ // Create reference
+ SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
- // Fill reference
- fill(lhs, 0);
- fill(rhs, 1);
+ // Fill reference
+ fill(lhs, 0);
+ fill(rhs, 1);
- return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
+ return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
+ }
+ case DataType::QASYMM8_SIGNED:
+ {
+ // Create reference
+ SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
+
+ // Fill reference
+ fill(lhs, 0);
+ fill(rhs, 1);
+
+ return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
+ }
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
}
TensorType _target{};
@@ -767,7 +802,7 @@
public:
template <typename...>
void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
- bool interleave_lhs, bool interleave_rhs)
+ bool interleave_lhs, bool interleave_rhs, DataType data_type)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -790,24 +825,40 @@
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
- _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h);
- _reference = compute_reference(lhs_shape, rhs_shape, m_h);
+ _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h, data_type);
+ _reference = compute_reference(lhs_shape, rhs_shape, m_h, data_type);
}
protected:
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);
+ switch(tensor.data_type())
+ {
+ case DataType::QASYMM8:
+ {
+ // 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);
+ }
+ break;
+ case DataType::QASYMM8_SIGNED:
+ {
+ std::uniform_int_distribution<> distribution(-127, 126);
+ library->fill(tensor, distribution, i);
+ }
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
}
- TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h)
+ TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h,
+ DataType data_type)
{
// Create tensors
- TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
- TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
+ TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
+ TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType lhs_reshaped;
TensorType rhs_reshaped;
TensorType dst;
@@ -854,7 +905,7 @@
return dst;
}
- SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h)
+ SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h, DataType data_type)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
@@ -862,15 +913,35 @@
dst_shape.set(2, m_h);
dst_shape.set(3, lhs_shape[2]);
- // Create reference
- SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
- SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };
+ switch(data_type)
+ {
+ case DataType::QASYMM8:
+ {
+ // Create reference
+ SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
- // Fill reference
- fill(lhs, 0);
- fill(rhs, 1);
+ // Fill reference
+ fill(lhs, 0);
+ fill(rhs, 1);
- return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
+ return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
+ }
+ case DataType::QASYMM8_SIGNED:
+ {
+ // Create reference
+ SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
+
+ // Fill reference
+ fill(lhs, 0);
+ fill(rhs, 1);
+
+ return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
+ }
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
}
TensorType _target{};