COMPMID-3326: Update heuristic for GEMMReshaped and GEMMReshapedOnlyRHS
- Update the heuristic for Arm Mali-G76 (F32) in order to use the OpenCL image2d object
on GEMM
- Create utility function to validate the support for image2d
Change-Id: I0913ac5f27fd07992b0ac188af753a2abeb034ca
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3559
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfigurationBifrost.cpp b/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfigurationBifrost.cpp
index f662089..581c2d2 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfigurationBifrost.cpp
+++ b/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfigurationBifrost.cpp
@@ -27,6 +27,9 @@
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/gemm/CLGEMMHelpers.h"
#include "arm_compute/core/GPUTarget.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <map>
#include <utility>
@@ -35,6 +38,8 @@
{
namespace cl_gemm
{
+using namespace arm_compute::misc::shape_calculator;
+
CLGEMMReshapedOnlyRHSKernelConfigurationBifrost::CLGEMMReshapedOnlyRHSKernelConfigurationBifrost(GPUTarget gpu)
: ICLGEMMKernelConfiguration(gpu)
{
@@ -139,14 +144,47 @@
ARM_COMPUTE_UNUSED(k);
ARM_COMPUTE_UNUSED(b);
+ GEMMLHSMatrixInfo lhs_info_buf;
+ GEMMRHSMatrixInfo rhs_info_buf;
+ GEMMLHSMatrixInfo lhs_info_img;
+ GEMMRHSMatrixInfo rhs_info_img;
+
+ // Get lhs_info/rhs_info in case of OpenCL buffer
if(m == 1)
{
const unsigned int h0 = std::max(n / 2, 1U);
- return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, false, true, false, true);
+ std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, false, true, false, true);
}
else
{
- return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, true, false, true);
+ std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, true, false, true);
+ }
+
+ // Get lhs_info/rhs_info in case of OpenCL image
+ if(m == 1)
+ {
+ std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 4, 1, 4, false, true, false, false, true);
+ }
+ else
+ {
+ const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(16)), static_cast<int>(1));
+ std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, h0, false, true, false, false, true);
+ }
+
+ const TensorInfo tensor_rhs_info(TensorShape(n, k, b), 1, DataType::F32);
+ const TensorShape shape = compute_rhs_reshaped_shape(tensor_rhs_info, rhs_info_img);
+ const TensorInfo tensor_reshaped_info(shape, 1, DataType::F32);
+
+ // In case of vector by matrix with few work-items, we use the OpenCL buffer rather than the OpenCL image2d
+ const bool use_cl_image2d = (m == 1 && n <= 4096) ? false : true;
+
+ if(bool(validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info_img)) && use_cl_image2d)
+ {
+ return std::make_pair(lhs_info_img, rhs_info_img);
+ }
+ else
+ {
+ return std::make_pair(lhs_info_buf, rhs_info_buf);
}
}