Apply clang-format on repository
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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/cpu/operators/CpuDepthwiseConv2d.cpp b/src/cpu/operators/CpuDepthwiseConv2d.cpp
index 884fe5c..54075f2 100644
--- a/src/cpu/operators/CpuDepthwiseConv2d.cpp
+++ b/src/cpu/operators/CpuDepthwiseConv2d.cpp
@@ -24,10 +24,11 @@
#include "src/cpu/operators/CpuDepthwiseConv2d.h"
#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/InfoHelpers.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
+
#include "src/common/utils/Log.h"
#include "src/cpu/kernels/CpuDepthwiseConv2dNativeKernel.h"
@@ -37,11 +38,16 @@
{
namespace
{
-Status validate_arguments_optimized(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ConvolutionInfo &info)
+Status validate_arguments_optimized(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *dst,
+ const ConvolutionInfo &info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
- if(!is_data_type_quantized_per_channel(weights->data_type()))
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+ DataType::F16, DataType::F32);
+ if (!is_data_type_quantized_per_channel(weights->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
}
@@ -49,14 +55,17 @@
ARM_COMPUTE_RETURN_ERROR_ON(info.dilation.x() < 1 || info.dilation.y() < 1);
const size_t idx_w = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
const size_t idx_h = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
- ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (info.dilation.x() - 1) > src->dimension(idx_w) + info.pad_stride_info.pad_left() +
- info.pad_stride_info.pad_right());
- ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (info.dilation.y() - 1) > src->dimension(idx_h) + info.pad_stride_info.pad_top() +
- info.pad_stride_info.pad_bottom());
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (info.dilation.x() - 1) >
+ src->dimension(idx_w) + info.pad_stride_info.pad_left() +
+ info.pad_stride_info.pad_right());
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (info.dilation.y() - 1) >
+ src->dimension(idx_h) + info.pad_stride_info.pad_top() +
+ info.pad_stride_info.pad_bottom());
- if(biases != nullptr)
+ if (biases != nullptr)
{
- const unsigned int channel_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL);
+ const unsigned int channel_idx =
+ get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx));
}
@@ -64,7 +73,7 @@
ARM_COMPUTE_RETURN_ON_ERROR(CpuDepthwiseConv2dAssemblyDispatch::validate(src, weights, biases, dst, info));
// Validate Activation Layer
- if(info.act_info.enabled() && !CpuDepthwiseConv2dAssemblyDispatch::is_activation_supported(info.act_info))
+ if (info.act_info.enabled() && !CpuDepthwiseConv2dAssemblyDispatch::is_activation_supported(info.act_info))
{
ARM_COMPUTE_RETURN_ON_ERROR(CpuActivation::validate(dst, nullptr, info.act_info));
}
@@ -80,8 +89,8 @@
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
// Perform validation step
- ARM_COMPUTE_ERROR_THROW_ON(CpuDepthwiseConv2dOptimizedInternal::validate(src, weights, (biases == nullptr) ? nullptr : biases,
- dst, info));
+ ARM_COMPUTE_ERROR_THROW_ON(
+ CpuDepthwiseConv2dOptimizedInternal::validate(src, weights, (biases == nullptr) ? nullptr : biases, dst, info));
_is_quantized = is_data_type_quantized_asymmetric(src->data_type());
_has_bias = biases != nullptr;
@@ -91,10 +100,11 @@
_are_weights_const = weights->are_values_constant();
// Configure pipeline
- _is_activationlayer_enabled = info.act_info.enabled() && !CpuDepthwiseConv2dAssemblyDispatch::is_activation_supported(info.act_info);
+ _is_activationlayer_enabled =
+ info.act_info.enabled() && !CpuDepthwiseConv2dAssemblyDispatch::is_activation_supported(info.act_info);
_dwc_optimized_func = std::make_unique<CpuDepthwiseConv2dAssemblyDispatch>();
- if(_is_nchw)
+ if (_is_nchw)
{
_permute_input = std::make_unique<cpu::CpuPermute>();
_permute_weights = std::make_unique<cpu::CpuPermute>();
@@ -128,7 +138,7 @@
}
// Configure activation
- if(_is_activationlayer_enabled)
+ if (_is_activationlayer_enabled)
{
_activationlayer_function = std::make_unique<cpu::CpuActivation>();
_activationlayer_function->configure(dst, nullptr, info.act_info);
@@ -155,7 +165,7 @@
auto packed_weights = tensors.get_tensor(TensorType::ACL_INT_4);
// Permute input
- if(_permute)
+ if (_permute)
{
ITensorPack pack;
auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0);
@@ -166,7 +176,7 @@
}
// Run assembly function
- if(_is_nchw)
+ if (_is_nchw)
{
auto src_perm = tensors.get_tensor(TensorType::ACL_INT_0);
auto weights_perm = tensors.get_tensor(TensorType::ACL_INT_1);
@@ -198,7 +208,7 @@
}
// Permute output
- if(_is_nchw)
+ if (_is_nchw)
{
ITensorPack pack;
auto dst_perm = tensors.get_tensor(TensorType::ACL_INT_2);
@@ -208,7 +218,7 @@
}
// Run activation
- if(_is_activationlayer_enabled)
+ if (_is_activationlayer_enabled)
{
ITensorPack pack;
pack.add_tensor(TensorType::ACL_SRC, dst);
@@ -221,7 +231,7 @@
{
// if weights are not constant then we need to repack so that weights
// can be updated in-place
- if(!_are_weights_const)
+ if (!_are_weights_const)
{
auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1);
auto bias = tensors.get_const_tensor(TensorType::ACL_SRC_2);
@@ -238,14 +248,14 @@
return;
}
- if(!_is_prepared)
+ if (!_is_prepared)
{
auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1);
auto bias = tensors.get_const_tensor(TensorType::ACL_SRC_2);
auto packed_weights = tensors.get_tensor(TensorType::ACL_INT_4);
// Permute weights
- if(_permute)
+ if (_permute)
{
auto permuted_weights = tensors.get_tensor(TensorType::ACL_INT_1);
@@ -279,11 +289,15 @@
}
}
-void CpuDepthwiseConv2d::CpuDepthwiseConv2dGeneric::configure(ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ConvolutionInfo &info)
+void CpuDepthwiseConv2d::CpuDepthwiseConv2dGeneric::configure(ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ ITensorInfo *dst,
+ const ConvolutionInfo &info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
- ARM_COMPUTE_ERROR_THROW_ON(CpuDepthwiseConv2d::validate(src, weights, (biases == nullptr) ? nullptr : biases,
- dst, info));
+ ARM_COMPUTE_ERROR_THROW_ON(
+ CpuDepthwiseConv2d::validate(src, weights, (biases == nullptr) ? nullptr : biases, dst, info));
_is_nchw = src->data_layout() == DataLayout::NCHW;
_is_prepared = !_is_nchw;
@@ -294,9 +308,10 @@
auto input_perm = std::make_unique<TensorInfo>();
auto weights_perm = std::make_unique<TensorInfo>();
- auto output_perm = std::make_unique<TensorInfo>(dst->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(TensorShape()));
+ auto output_perm = std::make_unique<TensorInfo>(
+ dst->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(TensorShape()));
- if(_is_nchw)
+ if (_is_nchw)
{
_permute_input = std::make_unique<cpu::CpuPermute>();
_permute_weights = std::make_unique<cpu::CpuPermute>();
@@ -315,7 +330,7 @@
_depthwise_conv_kernel = std::make_unique<cpu::kernels::CpuDepthwiseConv2dNativeKernel>();
_depthwise_conv_kernel->configure(input_to_use, weights_to_use, biases, output_to_use, info);
- if(_is_nchw)
+ if (_is_nchw)
{
_permute_output = std::make_unique<cpu::CpuPermute>();
_permute_output->configure(output_perm.get(), dst, PermutationVector(1U, 2U, 0U));
@@ -324,43 +339,61 @@
//Configure Activation Layer
_is_activationlayer_enabled = info.act_info.enabled();
- if(_is_activationlayer_enabled)
+ if (_is_activationlayer_enabled)
{
_activationlayer_function = std::make_unique<cpu::CpuActivation>();
_activationlayer_function->configure(dst, nullptr, info.act_info);
}
}
-Status CpuDepthwiseConv2d::CpuDepthwiseConv2dGeneric::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
+Status CpuDepthwiseConv2d::CpuDepthwiseConv2dGeneric::validate(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *dst,
const ConvolutionInfo &info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
- if(src->data_layout() == DataLayout::NCHW)
+ if (src->data_layout() == DataLayout::NCHW)
{
TensorShape permuted_input_shape = src->tensor_shape();
TensorShape permuted_weights_shape = weights->tensor_shape();
- TensorShape permuted_output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*src, *weights, info);
+ TensorShape permuted_output_shape =
+ misc::shape_calculator::compute_depthwise_convolution_shape(*src, *weights, info);
permute(permuted_input_shape, PermutationVector(2U, 0U, 1U));
permute(permuted_weights_shape, PermutationVector(2U, 0U, 1U));
permute(permuted_output_shape, PermutationVector(2U, 0U, 1U));
- const TensorInfo permuted_input = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC));
- const TensorInfo permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC));
- const TensorInfo permuted_output = TensorInfo(dst->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW));
+ const TensorInfo permuted_input = TensorInfo(src->clone()
+ ->set_is_resizable(true)
+ .reset_padding()
+ .set_tensor_shape(permuted_input_shape)
+ .set_data_layout(DataLayout::NHWC));
+ const TensorInfo permuted_weights = TensorInfo(weights->clone()
+ ->set_is_resizable(true)
+ .reset_padding()
+ .set_tensor_shape(permuted_weights_shape)
+ .set_data_layout(DataLayout::NHWC));
+ const TensorInfo permuted_output = TensorInfo(dst->clone()
+ ->set_is_resizable(true)
+ .reset_padding()
+ .set_tensor_shape(permuted_output_shape)
+ .set_data_layout(DataLayout::NCHW));
ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(src, &permuted_input, PermutationVector(2U, 0U, 1U)));
ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U)));
ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(&permuted_output, dst, PermutationVector(1U, 2U, 0U)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuDepthwiseConv2dNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, info));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuDepthwiseConv2dNativeKernel::validate(
+ &permuted_input, &permuted_weights, biases, &permuted_output, info));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuDepthwiseConv2dNativeKernel::validate(src, weights, biases, dst, info));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ cpu::kernels::CpuDepthwiseConv2dNativeKernel::validate(src, weights, biases, dst, info));
}
// Validate Activation Layer
- if(info.act_info.enabled() && !CpuDepthwiseConv2dAssemblyDispatch::is_activation_supported(info.act_info))
+ if (info.act_info.enabled() && !CpuDepthwiseConv2dAssemblyDispatch::is_activation_supported(info.act_info))
{
ARM_COMPUTE_RETURN_ON_ERROR(CpuActivation::validate(dst, nullptr, info.act_info));
}
@@ -375,7 +408,7 @@
auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2);
auto dst = tensors.get_tensor(TensorType::ACL_DST_0);
- if(_is_nchw)
+ if (_is_nchw)
{
prepare(tensors);
auto src_perm = tensors.get_tensor(TensorType::ACL_INT_0);
@@ -392,7 +425,8 @@
pack_depth.add_const_tensor(TensorType::ACL_SRC_1, weights_perm);
pack_depth.add_tensor(TensorType::ACL_SRC_2, biases);
pack_depth.add_tensor(TensorType::ACL_DST, dst_perm);
- NEScheduler::get().schedule_op(_depthwise_conv_kernel.get(), Window::DimY, _depthwise_conv_kernel->window(), pack_depth);
+ NEScheduler::get().schedule_op(_depthwise_conv_kernel.get(), Window::DimY, _depthwise_conv_kernel->window(),
+ pack_depth);
}
else
{
@@ -401,10 +435,11 @@
pack_depth.add_tensor(TensorType::ACL_SRC_1, weights);
pack_depth.add_tensor(TensorType::ACL_SRC_2, biases);
pack_depth.add_tensor(TensorType::ACL_DST, dst);
- NEScheduler::get().schedule_op(_depthwise_conv_kernel.get(), Window::DimY, _depthwise_conv_kernel->window(), pack_depth);
+ NEScheduler::get().schedule_op(_depthwise_conv_kernel.get(), Window::DimY, _depthwise_conv_kernel->window(),
+ pack_depth);
}
- if(_is_nchw)
+ if (_is_nchw)
{
ITensorPack pack;
auto dst_perm = tensors.get_tensor(TensorType::ACL_INT_2);
@@ -413,7 +448,7 @@
_permute_output->run(pack);
}
- if(_is_activationlayer_enabled)
+ if (_is_activationlayer_enabled)
{
ITensorPack pack;
pack.add_tensor(TensorType::ACL_SRC, dst);
@@ -424,7 +459,7 @@
void CpuDepthwiseConv2d::CpuDepthwiseConv2dGeneric::prepare(ITensorPack &tensors)
{
- if(!_is_prepared)
+ if (!_is_prepared)
{
auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1);
auto weights_perm = tensors.get_tensor(TensorType::ACL_INT_1);
@@ -441,12 +476,17 @@
}
}
-void CpuDepthwiseConv2d::configure(ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ConvolutionInfo &info)
+void CpuDepthwiseConv2d::configure(ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ ITensorInfo *dst,
+ const ConvolutionInfo &info)
{
ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, info);
- _depth_conv_func = get_depthwiseconvolution_function(src, weights, (biases != nullptr) ? biases : nullptr, dst, info);
- switch(_depth_conv_func)
+ _depth_conv_func =
+ get_depthwiseconvolution_function(src, weights, (biases != nullptr) ? biases : nullptr, dst, info);
+ switch (_depth_conv_func)
{
case DepthwiseConvolutionFunction::OPTIMIZED:
_func_optimized.configure(src, weights, biases, dst, info);
@@ -459,10 +499,14 @@
}
}
-Status CpuDepthwiseConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ConvolutionInfo &info)
+Status CpuDepthwiseConv2d::validate(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *dst,
+ const ConvolutionInfo &info)
{
DepthwiseConvolutionFunction depth_conv_func = get_depthwiseconvolution_function(src, weights, biases, dst, info);
- switch(depth_conv_func)
+ switch (depth_conv_func)
{
case DepthwiseConvolutionFunction::OPTIMIZED:
return CpuDepthwiseConv2dOptimizedInternal::validate(src, weights, biases, dst, info);
@@ -475,10 +519,13 @@
}
}
-DepthwiseConvolutionFunction CpuDepthwiseConv2d::get_depthwiseconvolution_function(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
+DepthwiseConvolutionFunction CpuDepthwiseConv2d::get_depthwiseconvolution_function(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *dst,
const ConvolutionInfo &info)
{
- if(bool(CpuDepthwiseConv2dOptimizedInternal::validate(src, weights, biases, dst, info)))
+ if (bool(CpuDepthwiseConv2dOptimizedInternal::validate(src, weights, biases, dst, info)))
{
return DepthwiseConvolutionFunction::OPTIMIZED;
}
@@ -490,7 +537,7 @@
void CpuDepthwiseConv2d::run(ITensorPack &tensors)
{
- switch(_depth_conv_func)
+ switch (_depth_conv_func)
{
case DepthwiseConvolutionFunction::OPTIMIZED:
_func_optimized.run(tensors);
@@ -505,7 +552,7 @@
void CpuDepthwiseConv2d::prepare(ITensorPack &tensors)
{
- switch(_depth_conv_func)
+ switch (_depth_conv_func)
{
case DepthwiseConvolutionFunction::OPTIMIZED:
_func_optimized.prepare(tensors);