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/CL/kernels/CLQLSTMLayerNormalizationKernel.cpp b/src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.cpp
index bd573e5..731fcb8 100644
--- a/src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.cpp
+++ b/src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.cpp
@@ -22,10 +22,12 @@
* SOFTWARE.
*/
#include "src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h"
+
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/core/utils/StringUtils.h"
+
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "support/StringSupport.h"
@@ -49,14 +51,19 @@
const uint32_t temp_num_elems_processed_per_iteration = max_cl_vector_width / input->element_size();
/* If width is less then step, then make step same as width to avoid global size being step instead of actual width. */
/* Or we should fix in arm_compute::enqueue() or arm_compute::calculate_max_window(). */
- const uint32_t num_elems_processed_per_iteration = (input->dimension(0) < temp_num_elems_processed_per_iteration) ? input->dimension(0) : temp_num_elems_processed_per_iteration;
+ const uint32_t num_elems_processed_per_iteration = (input->dimension(0) < temp_num_elems_processed_per_iteration)
+ ? input->dimension(0)
+ : temp_num_elems_processed_per_iteration;
// This kernel doesn't need padding
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
return std::make_pair(Status{}, win);
}
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias)
+Status validate_arguments(const ITensorInfo *input,
+ const ITensorInfo *output,
+ const ITensorInfo *weight,
+ const ITensorInfo *bias)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weight, bias, output);
@@ -72,7 +79,7 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(weight, bias);
// Checks performed when output is configured
- if(output->total_size() != 0)
+ if (output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
@@ -87,10 +94,14 @@
_type = CLKernelType::ELEMENTWISE;
}
-void CLQLSTMLayerNormalizationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias)
+void CLQLSTMLayerNormalizationKernel::configure(const CLCompileContext &compile_context,
+ const ICLTensor *input,
+ ICLTensor *output,
+ const ICLTensor *weight,
+ const ICLTensor *bias)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, bias, output);
- auto padding_info = get_padding_info({ input, weight, bias, output });
+ auto padding_info = get_padding_info({input, weight, bias, output});
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), weight->info(), bias->info()));
@@ -104,7 +115,8 @@
int32_t output_multiplier{};
int32_t output_shift{};
const UniformQuantizationInfo quan_info = _weight->info()->quantization_info().uniform();
- const Status status = quantization::calculate_quantized_multiplier(quan_info.scale, &output_multiplier, &output_shift);
+ const Status status =
+ quantization::calculate_quantized_multiplier(quan_info.scale, &output_multiplier, &output_shift);
output_shift *= -1;
// Set build options
@@ -114,8 +126,12 @@
build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
- build_opts.add_option("-DMIN_BOUND=" + support::cpp11::to_string(std::get<0>(quantization::get_min_max_values_from_quantized_data_type(input->info()->data_type()))));
- build_opts.add_option("-DMAX_BOUND=" + support::cpp11::to_string(std::get<1>(quantization::get_min_max_values_from_quantized_data_type(input->info()->data_type()))));
+ build_opts.add_option("-DMIN_BOUND=" +
+ support::cpp11::to_string(std::get<0>(
+ quantization::get_min_max_values_from_quantized_data_type(input->info()->data_type()))));
+ build_opts.add_option("-DMAX_BOUND=" +
+ support::cpp11::to_string(std::get<1>(
+ quantization::get_min_max_values_from_quantized_data_type(input->info()->data_type()))));
// Create kernel
_kernel = create_kernel(compile_context, "qlstm_layer_normalization", build_opts.options());
@@ -135,12 +151,18 @@
ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
}
-void CLQLSTMLayerNormalizationKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias)
+void CLQLSTMLayerNormalizationKernel::configure(const ICLTensor *input,
+ ICLTensor *output,
+ const ICLTensor *weight,
+ const ICLTensor *bias)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, weight, bias);
}
-Status CLQLSTMLayerNormalizationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias)
+Status CLQLSTMLayerNormalizationKernel::validate(const ITensorInfo *input,
+ const ITensorInfo *output,
+ const ITensorInfo *weight,
+ const ITensorInfo *bias)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, weight, bias));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
@@ -171,7 +193,6 @@
add_2D_tensor_argument(idx, _output, slice);
enqueue(queue, *this, slice, lws_hint());
- }
- while(window.slide_window_slice_2D(slice));
+ } while (window.slide_window_slice_2D(slice));
}
} // namespace arm_compute