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/compute_kernel_writer/prototype/include/ckw/KernelWriter.h b/compute_kernel_writer/prototype/include/ckw/KernelWriter.h
index fdb5fed..f9e0066 100644
--- a/compute_kernel_writer/prototype/include/ckw/KernelWriter.h
+++ b/compute_kernel_writer/prototype/include/ckw/KernelWriter.h
@@ -94,7 +94,9 @@
*
* @return The @ref TensorOperand object.
*/
- TensorOperand &declare_tensor_argument(const std::string &name, const TensorInfo &info, TensorStorageType storage_type = TensorStorageType::BufferUint8Ptr);
+ TensorOperand &declare_tensor_argument(const std::string &name,
+ const TensorInfo &info,
+ TensorStorageType storage_type = TensorStorageType::BufferUint8Ptr);
/** Declare a compile-time constant scalar argument.
*
@@ -134,7 +136,10 @@
* @param[in] sampler The tensor sampling information.
* @param[in] dilation_y Dilation in the Y dimension.
*/
- void op_load(TileOperand &tile, const TensorOperand &tensor, const TensorTileSampler &sampler, const TileOperand &dilation_y = TileOperand("dil_y", 1));
+ void op_load(TileOperand &tile,
+ const TensorOperand &tensor,
+ const TensorTileSampler &sampler,
+ const TileOperand &dilation_y = TileOperand("dil_y", 1));
/** Load the data from the tensor memory to the tile using the indirect buffer approach and respective of the sampling information.
*
@@ -221,7 +226,10 @@
* @param[in] first The first argument tile.
* @param[in] second The second argument tile.
*/
- void op_binary_elementwise_function(const TileOperand &dst, BinaryFunction func, const TileOperand &first, const TileOperand &second);
+ void op_binary_elementwise_function(const TileOperand &dst,
+ BinaryFunction func,
+ const TileOperand &first,
+ const TileOperand &second);
/** Write function applied to scalar value: `<dst> = <func>(<first>, <second>, <third>);`.
*
@@ -231,7 +239,11 @@
* @param[in] second The second argument tile.
* @param[in] third The third argument tile.
*/
- void op_ternary_elementwise_function(const TileOperand &dst, TernaryFunction func, const TileOperand &first, const TileOperand &second, const TileOperand &third);
+ void op_ternary_elementwise_function(const TileOperand &dst,
+ TernaryFunction func,
+ const TileOperand &first,
+ const TileOperand &second,
+ const TileOperand &third);
/** Write if-statement: `if(<lhs> <op> <rhs>) { <body> }`.
*
@@ -267,7 +279,13 @@
* @param[in, out] update_value The value which is updated at every iteration.
* @param[in] body The body of the for-loop.
*/
- void op_for_loop(const TileOperand &var_name, BinaryOp cond_op, const TileOperand &cond_value_name, const TileOperand &update_var_name, AssignmentOp update_op, const TileOperand &update_value_name, const std::function<void()> &body);
+ void op_for_loop(const TileOperand &var_name,
+ BinaryOp cond_op,
+ const TileOperand &cond_value_name,
+ const TileOperand &update_var_name,
+ AssignmentOp update_op,
+ const TileOperand &update_value_name,
+ const std::function<void()> &body);
/** Write the return statement: `return;`
*/
@@ -311,8 +329,8 @@
::std::unique_ptr<prototype::GpuKernelWriterAttribute> _impl_attr;
::std::unique_ptr<prototype::IGpuKernelWriter> _impl;
- int32_t _id_space{ 0 };
- int32_t _max_id_space{ 0 };
+ int32_t _id_space{0};
+ int32_t _max_id_space{0};
};
} // namespace ckw