Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
index 41ba5d5..a1fa9fa 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
@@ -28,9 +28,9 @@
#include "arm_compute/core/ITensorPack.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/utils/ActivationFunctionUtils.h"
-#include "arm_compute/core/utils/StringUtils.h"
#include "arm_compute/core/utils/helpers/AdjustVecSize.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/StringUtils.h"
#include "src/common/utils/Log.h"
#include "src/core/CL/CLUtils.h"
@@ -38,7 +38,6 @@
#include "src/core/helpers/WindowHelpers.h"
#include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h"
#include "src/gpu/cl/kernels/helpers/MatMulKernelHelpers.h"
-
#include "support/Cast.h"
#include "support/StringSupport.h"
@@ -61,20 +60,23 @@
// Validate M0
ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");
- if(adj_lhs)
+ if (adj_lhs)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(((m0 & (m0 - 1)) && (m0 != 3)) || (m0 > 16), "Only 1,2,3,4,8,16 are supported for M0 for Lhs transposed");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((m0 & (m0 - 1)) && (m0 != 3)) || (m0 > 16),
+ "Only 1,2,3,4,8,16 are supported for M0 for Lhs transposed");
}
// Validate N0
ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 < 1, "Only positive integers are supported for N0");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) || (n0 > 16), "Only 1,2,3,4,8,16 are supported for N0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) || (n0 > 16),
+ "Only 1,2,3,4,8,16 are supported for N0");
// Validate K0
ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 < 1, "Only positive integers are supported for K0");
- if(!adj_lhs || adj_rhs)
+ if (!adj_lhs || adj_rhs)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(((k0 & (k0 - 1)) && (k0 != 3)) || (k0 > 16), "Only 1,2,3,4,8,16 are supported for K0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((k0 & (k0 - 1)) && (k0 != 3)) || (k0 > 16),
+ "Only 1,2,3,4,8,16 are supported for K0");
}
return Status{};
@@ -83,30 +85,37 @@
Status validate_export_to_cl_image(const ITensorInfo *rhs, const MatMulKernelInfo &matmul_kernel_info)
{
ARM_COMPUTE_RETURN_ERROR_ON(matmul_kernel_info.export_rhs_to_cl_image && rhs->lock_paddings());
- if(matmul_kernel_info.export_rhs_to_cl_image)
+ if (matmul_kernel_info.export_rhs_to_cl_image)
{
- if(matmul_kernel_info.adj_rhs)
+ if (matmul_kernel_info.adj_rhs)
{
const int k0 = matmul_kernel_info.k0;
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 != 4 && k0 != 8 && k0 != 16, "K0 can only be: 4, 8, and 16 for Rhs transposed");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 != 4 && k0 != 8 && k0 != 16,
+ "K0 can only be: 4, 8, and 16 for Rhs transposed");
}
else
{
const int n0 = matmul_kernel_info.n0;
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 != 4 && n0 != 8 && n0 != 16, "N0 can only be: 4, 8, and 16 for Rhs non-transposed");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 != 4 && n0 != 8 && n0 != 16,
+ "N0 can only be: 4, 8, and 16 for Rhs non-transposed");
}
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(!export_to_cl_image(rhs), "Export to CLImage is not supported for this device/configuration");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(!export_to_cl_image(rhs),
+ "Export to CLImage is not supported for this device/configuration");
}
return Status{};
}
-}
+} // namespace
ClMatMulNativeKernel::ClMatMulNativeKernel()
{
_type = CLKernelType::GEMM;
}
-Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs,
+ const ITensorInfo *rhs,
+ const ITensorInfo *bias,
+ const ITensorInfo *dst,
+ const MatMulKernelInfo &matmul_kernel_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_UNUSED(act_info);
@@ -114,28 +123,36 @@
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ validate_matmul_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_export_to_cl_image(rhs, matmul_kernel_info));
- const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
+ const TensorShape expected_output_shape =
+ misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
- if(dst->total_size() != 0)
+ if (dst->total_size() != 0)
{
const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
- if(bias != nullptr)
+ if (bias != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bias, lhs);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0],
+ "First dimension of bias and output tensors must match.");
}
return Status{};
}
-void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context,
+ ITensorInfo *lhs,
+ ITensorInfo *rhs,
+ ITensorInfo *bias,
+ ITensorInfo *dst,
+ const MatMulKernelInfo &matmul_kernel_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
@@ -143,7 +160,8 @@
ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
// dst tensor auto initialization if not yet initialized
- auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
+ auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(
+ lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
const int m = dst->dimension(1);
const int n = dst->dimension(0);
@@ -187,7 +205,7 @@
// A macro guard to compile ONLY the kernel of interest
build_opts.add_option("-D" + upper_string(kernel_name));
- if(_export_rhs_to_cl_image)
+ if (_export_rhs_to_cl_image)
{
gemm::update_padding_for_cl_image(rhs);
}
@@ -222,10 +240,13 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
- const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
- const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
- ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+ const ICLTensor *lhs =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+ const ICLTensor *rhs =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+ const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(
+ tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
+ ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
@@ -235,7 +256,7 @@
add_3d_tensor_nhw_argument(idx, lhs);
cl::Image2D rhs_cl_image;
- if(_export_rhs_to_cl_image)
+ if (_export_rhs_to_cl_image)
{
const size_t image_w = rhs->info()->dimension(0) / 4;
const size_t image_h = rhs->info()->tensor_shape().total_size() / rhs->info()->dimension(0);
@@ -243,12 +264,13 @@
const size_t image_row_pitch = rhs->info()->strides_in_bytes()[1];
// Export cl_buffer to cl_image
- rhs_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), rhs->cl_buffer(), shape2d, rhs->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
+ rhs_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), rhs->cl_buffer(), shape2d,
+ rhs->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
_kernel.setArg(idx++, rhs_cl_image);
}
add_3d_tensor_nhw_argument(idx, rhs);
- if(bias != nullptr)
+ if (bias != nullptr)
{
add_3d_tensor_nhw_argument(idx, bias);
}