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/core/NEON/kernels/assembly/depthwise_common.hpp b/src/core/NEON/kernels/assembly/depthwise_common.hpp
index a5db793..5ff848e 100644
--- a/src/core/NEON/kernels/assembly/depthwise_common.hpp
+++ b/src/core/NEON/kernels/assembly/depthwise_common.hpp
@@ -49,11 +49,7 @@
bool is_default = false;
uint64_t cycle_estimate = 0;
- KernelDescription(
- DepthwiseMethod method,
- std::string name,
- bool is_default,
- uint64_t cycle_estimate)
+ KernelDescription(DepthwiseMethod method, std::string name, bool is_default, uint64_t cycle_estimate)
: method(method), name(name), is_default(is_default), cycle_estimate(cycle_estimate)
{
}
@@ -78,58 +74,51 @@
// pointer the bias vector (which may be nullptr in the case of no bias) and
// a pointer to the array of weights (stored in HWIO order).
virtual void pack_parameters(
- void *buffer,
- const void *biases,
- const void *weights,
- size_t ld_weight_col = 0,
- size_t ld_weight_row = 0) = 0;
+ void *buffer, const void *biases, const void *weights, size_t ld_weight_col = 0, size_t ld_weight_row = 0) = 0;
// Determine the amount of working space required
virtual size_t get_working_size(unsigned int n_threads) const = 0;
// Execute the convolution over the specified area of memory.
- virtual void execute(
- const void *input, // Pointer to input tensor
- const void *parameters, // Packed parameters buffer
- void *output,
- void *working_space,
- unsigned int thread_id,
- unsigned int n_threads) const = 0;
+ virtual void execute(const void *input, // Pointer to input tensor
+ const void *parameters, // Packed parameters buffer
+ void *output,
+ void *working_space,
+ unsigned int thread_id,
+ unsigned int n_threads) const = 0;
- virtual void execute(
- const void *input,
- size_t ld_input_col,
- size_t ld_input_row,
- size_t ld_input_batch,
- const void *parameters,
- void *output,
- size_t ld_output_col,
- size_t ld_output_row,
- size_t ld_output_batch,
- void *working_space,
- unsigned int thread_id,
- unsigned int n_threads) const = 0;
+ virtual void execute(const void *input,
+ size_t ld_input_col,
+ size_t ld_input_row,
+ size_t ld_input_batch,
+ const void *parameters,
+ void *output,
+ size_t ld_output_col,
+ size_t ld_output_row,
+ size_t ld_output_batch,
+ void *working_space,
+ unsigned int thread_id,
+ unsigned int n_threads) const = 0;
- virtual void execute(
- unsigned int batches,
- unsigned int input_height,
- unsigned int input_width,
- unsigned int channels,
- const PaddingValues &,
- const void *input,
- size_t ld_input_col,
- size_t ld_input_row,
- size_t ld_input_batch,
- const void *parameters,
- unsigned int output_height,
- unsigned int output_width,
- void *output,
- size_t ld_output_col,
- size_t ld_output_row,
- size_t ld_output_batch,
- void *working_space,
- unsigned int thread_id,
- unsigned int n_threads) const = 0;
+ virtual void execute(unsigned int batches,
+ unsigned int input_height,
+ unsigned int input_width,
+ unsigned int channels,
+ const PaddingValues &,
+ const void *input,
+ size_t ld_input_col,
+ size_t ld_input_row,
+ size_t ld_input_batch,
+ const void *parameters,
+ unsigned int output_height,
+ unsigned int output_width,
+ void *output,
+ size_t ld_output_col,
+ size_t ld_output_row,
+ size_t ld_output_batch,
+ void *working_space,
+ unsigned int thread_id,
+ unsigned int n_threads) const = 0;
};
// To handle a dilation factor of D execute the kernel once for each d in
@@ -145,12 +134,13 @@
// - Number of valid input pixels corresponding to `d`
// - Offset of the first pixel corresponding to `d`
// - Amount of padding in the view for `d`
-std::tuple<size_t, size_t, size_t, size_t, size_t>
-get_reduced_view_for_dilation(
- size_t out_size, size_t in_size,
- size_t d, size_t dilation_factor,
- size_t kernel_size, size_t stride,
- size_t pad_before);
+std::tuple<size_t, size_t, size_t, size_t, size_t> get_reduced_view_for_dilation(size_t out_size,
+ size_t in_size,
+ size_t d,
+ size_t dilation_factor,
+ size_t kernel_size,
+ size_t stride,
+ size_t pad_before);
} // namespace depthwise
} // namespace arm_conv