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/ClPool2dKernel.cpp b/src/gpu/cl/kernels/ClPool2dKernel.cpp
index a1afc58..41ab4d6 100644
--- a/src/gpu/cl/kernels/ClPool2dKernel.cpp
+++ b/src/gpu/cl/kernels/ClPool2dKernel.cpp
@@ -28,6 +28,7 @@
#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/core/CL/CLValidate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
@@ -43,37 +44,47 @@
namespace
{
-Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+Status validate_arguments(const ITensorInfo *src,
+ const ITensorInfo *dst,
+ const PoolingLayerInfo &pool_info,
+ const ITensorInfo *indices)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2),
- "Unsupported combination of parameters!");
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+ DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+ (is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2),
+ "Unsupported combination of parameters!");
- const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
- const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- const bool is_global_pooling = pool_info.is_global_pooling;
- unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
- unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
- int output_width = 0;
- int output_height = 0;
+ const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const bool is_global_pooling = pool_info.is_global_pooling;
+ unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
+ unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
+ int output_width = 0;
+ int output_height = 0;
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_region_entirely_outside_input(pool_info),
+ "Pooling region that is entirely outside input tensor is unsupported");
- std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height],
- pool_size_x, pool_size_y, pool_info.pad_stride_info);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), "Calculated output dimension size is invalid");
+ std::tie(output_width, output_height) =
+ scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height], pool_size_x,
+ pool_size_y, pool_info.pad_stride_info);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1),
+ "Calculated output dimension size is invalid");
// Check indices
- if(indices)
+ if (indices)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX,
+ "Pooling indices only supported for MAX pooling method");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)),
+ "Pooling indices only supported for pool size 2x2");
- if(indices->total_size() != 0)
+ if (indices->total_size() != 0)
{
TensorInfo idx_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, DataType::U32));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
@@ -81,7 +92,7 @@
}
// Checks performed when dst is configured
- if(dst->total_size() != 0)
+ if (dst->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
@@ -98,42 +109,47 @@
_type = CLKernelType::POOL;
}
-void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
+void ClPool2dKernel::configure(const ClCompileContext &compile_context,
+ ITensorInfo *src,
+ ITensorInfo *dst,
+ const PoolingLayerInfo &pool_info,
+ ITensorInfo *indices)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices));
- auto padding_info = get_padding_info({ src, dst, indices });
+ auto padding_info = get_padding_info({src, dst, indices});
// Auto init if empty
TensorShape out_shape = compute_pool_shape(*src, pool_info);
auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));
- if(indices)
+ if (indices)
{
auto_init_if_empty(*indices, src->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
}
// Set instance variables
- _pool_info = pool_info;
- _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
- _num_elems_processed_per_iteration = (_data_layout == DataLayout::NCHW) ? 1 : ((dst->data_type() == DataType::F32) ? 2 : 4);
+ _pool_info = pool_info;
+ _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+ _num_elems_processed_per_iteration =
+ (_data_layout == DataLayout::NCHW) ? 1 : ((dst->data_type() == DataType::F32) ? 2 : 4);
_num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(0));
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- const PoolingType pool_type = pool_info.pool_type;
- const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
- const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
- const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
- const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
- const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
- const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
- const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
- const bool exclude_padding = pool_info.exclude_padding;
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ const PoolingType pool_type = pool_info.pool_type;
+ const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+ const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+ const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
+ const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
+ const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
+ const bool exclude_padding = pool_info.exclude_padding;
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
- const int pool_pad_top = pad_stride_info.pad_top();
- const int pool_pad_left = pad_stride_info.pad_left();
- const DataType data_type = src->data_type();
+ const int pool_pad_top = pad_stride_info.pad_top();
+ const int pool_pad_left = pad_stride_info.pad_left();
+ const DataType data_type = src->data_type();
// Set build options
CLBuildOptions build_opts;
@@ -148,20 +164,23 @@
build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y));
build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
- build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
- build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
+ build_opts.add_option("-DMAX_WIDTH=" +
+ support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
+ build_opts.add_option("-DMAX_HEIGHT=" +
+ support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
// Tensor paddings are used to calculate the indicies for MAX pooling
- if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
+ if (pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices &&
+ is_data_type_float(data_type))
{
build_opts.add_option("-DSRC_BATCH=" + support::cpp11::to_string(src->tensor_shape().total_size_lower(3)));
}
- if(is_data_type_quantized_asymmetric(data_type))
+ if (is_data_type_quantized_asymmetric(data_type))
{
build_opts.add_option("-DQUANTIZED");
- if(src->quantization_info() != dst->quantization_info())
+ if (src->quantization_info() != dst->quantization_info())
{
const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();
@@ -174,9 +193,9 @@
}
// Set the initial value for the pooling operation accordingly with the data type
- if(pool_type == PoolingType::MAX)
+ if (pool_type == PoolingType::MAX)
{
- if(is_data_type_quantized(data_type))
+ if (is_data_type_quantized(data_type))
{
PixelValue type_min{};
std::tie(type_min, std::ignore) = get_min_max(data_type);
@@ -184,7 +203,9 @@
}
else
{
- std::string initial_value = pool_info.use_inf_as_limit ? "(-INFINITY)" : float_to_string_with_full_precision(std::numeric_limits<float>::lowest());
+ std::string initial_value = pool_info.use_inf_as_limit
+ ? "(-INFINITY)"
+ : float_to_string_with_full_precision(std::numeric_limits<float>::lowest());
build_opts.add_option("-DINITIAL_VALUE=" + initial_value);
}
}
@@ -195,22 +216,25 @@
}
// Create kernel
- switch(_data_layout)
+ switch (_data_layout)
{
case DataLayout::NCHW:
{
const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision;
const auto use_wider_accumulator = use_fp_mixed_precision && (pool_type != PoolingType::MAX);
- const auto acc_data_type = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : (is_data_type_quantized(data_type) ? DataType::S32 : data_type));
+ const auto acc_data_type = get_cl_type_from_data_type(
+ use_wider_accumulator ? DataType::F32
+ : (is_data_type_quantized(data_type) ? DataType::S32 : data_type));
build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");
- if(pool_type != PoolingType::MAX)
+ if (pool_type != PoolingType::MAX)
{
build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
}
- if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
+ if (pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices &&
+ is_data_type_float(data_type))
{
// For max pooling with pool2x2, store indicies which will be used in max unpooling
std::string kernel_name = "pooling_layer_2_nchw_indices";
@@ -226,18 +250,19 @@
case DataLayout::NHWC:
{
// Floating point mixed precision is support on F16 only
- const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;
+ const auto use_fp_mixed_precision =
+ (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;
// Wider accumulation is required to avoid accuracy loss
// Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation)
// Cast 2: Quantized (int8/uint8 src data and int32 accumulation )
DataType acc_data_type = data_type;
- if(use_fp_mixed_precision)
+ if (use_fp_mixed_precision)
{
acc_data_type = DataType::F32;
}
- else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX)
+ else if (is_data_type_quantized(data_type) && pool_type != PoolingType::MAX)
{
acc_data_type = DataType::S32;
}
@@ -250,8 +275,9 @@
build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel)));
build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size)));
- build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration));
- if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
+ build_opts.add_option("-DVEC_SIZE_LEFTOVER=" +
+ support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration));
+ if (pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
{
build_opts.add_option_if(indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");
@@ -260,7 +286,9 @@
}
else
{
- std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
+ std::string kernel_name = is_data_type_quantized_asymmetric(data_type)
+ ? "pooling_layer_MxN_quantized_nhwc"
+ : "pooling_layer_MxN_nhwc";
_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
}
break;
@@ -290,7 +318,10 @@
ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
}
-Status ClPool2dKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+Status ClPool2dKernel::validate(const ITensorInfo *src,
+ const ITensorInfo *dst,
+ const PoolingLayerInfo &pool_info,
+ const ITensorInfo *indices)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices));
return Status{};
@@ -301,18 +332,19 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- unsigned int pool_stride_x = 0;
- unsigned int pool_stride_y = 0;
+ unsigned int pool_stride_x = 0;
+ unsigned int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();
- const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
- auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
- auto indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1));
+ const auto src =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
+ auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
+ auto indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1));
// Collapse window
Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
- switch(_data_layout)
+ switch (_data_layout)
{
case DataLayout::NCHW:
{
@@ -323,13 +355,12 @@
unsigned int idx = 0;
add_3D_tensor_argument(idx, src, slice);
add_3D_tensor_argument(idx, dst, slice);
- if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
+ if (indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
{
add_3D_tensor_argument(idx, indices, slice);
}
enqueue(queue, *this, slice, lws_hint());
- }
- while(window_collapsed.slide_window_slice_3D(slice));
+ } while (window_collapsed.slide_window_slice_3D(slice));
break;
}
case DataLayout::NHWC:
@@ -338,7 +369,8 @@
Window slice = window_collapsed.first_slice_window_4D();
Window in_slice = window_collapsed.first_slice_window_4D();
- in_slice.set(Window::DimX, Window::Dimension(0, src->info()->dimension(0), _num_elems_processed_per_iteration));
+ in_slice.set(Window::DimX,
+ Window::Dimension(0, src->info()->dimension(0), _num_elems_processed_per_iteration));
in_slice.set(Window::DimY, Window::Dimension(0, src->info()->dimension(1), pool_stride_x));
in_slice.set(Window::DimZ, Window::Dimension(0, src->info()->dimension(2), pool_stride_y));
in_slice.set(3, Window::Dimension(0, batch_size, 1));
@@ -348,13 +380,13 @@
unsigned int idx = 0;
add_4D_tensor_argument(idx, src, in_slice);
add_4D_tensor_argument(idx, dst, slice);
- if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
+ if (indices && is_data_type_float(src->info()->data_type()) &&
+ (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
{
add_4D_tensor_argument(idx, indices, slice);
}
enqueue(queue, *this, slice, lws_hint());
- }
- while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice));
+ } while (window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice));
break;
}
default: