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/operators/ClGemmConv2d.cpp b/src/gpu/cl/operators/ClGemmConv2d.cpp
index 5620471..55d815a 100644
--- a/src/gpu/cl/operators/ClGemmConv2d.cpp
+++ b/src/gpu/cl/operators/ClGemmConv2d.cpp
@@ -28,10 +28,12 @@
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include "src/common/utils/Log.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/MemoryHelpers.h"
#include "src/gpu/cl/kernels/ClActivationKernel.h"
@@ -41,8 +43,6 @@
#include "src/gpu/cl/operators/ClGemm.h"
#include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h"
#include "src/gpu/cl/utils/ClAuxTensorHandler.h"
-
-#include "src/common/utils/Log.h"
#include "support/Cast.h"
namespace arm_compute
@@ -53,18 +53,38 @@
namespace opencl
{
ClGemmConv2d::ClGemmConv2d()
- : _weights_reshape_kernel(nullptr), _im2col_kernel(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _col2im_kernel(nullptr), _activation_kernel(nullptr), _im2col_output(), _weights_reshaped(),
- _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _append_bias(false), _is_prepared(false), _aux_mem(AuxTensorIdx::Count)
+ : _weights_reshape_kernel(nullptr),
+ _im2col_kernel(nullptr),
+ _mm_gemm(nullptr),
+ _mm_gemmlowp(nullptr),
+ _col2im_kernel(nullptr),
+ _activation_kernel(nullptr),
+ _im2col_output(),
+ _weights_reshaped(),
+ _gemm_output(),
+ _skip_im2col(false),
+ _skip_col2im(false),
+ _is_quantized(false),
+ _fuse_activation(true),
+ _append_bias(false),
+ _is_prepared(false),
+ _aux_mem(AuxTensorIdx::Count)
{
}
ClGemmConv2d::~ClGemmConv2d() = default;
-void ClGemmConv2d::configure_mm(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
+void ClGemmConv2d::configure_mm(const ClCompileContext &compile_context,
+ const ITensorInfo *src,
+ ITensorInfo *weights,
+ ITensorInfo *biases,
+ ITensorInfo *dst,
const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
- int gemm_3d_depth, const ActivationLayerInfo &act_info)
+ int gemm_3d_depth,
+ const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights);
- ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info));
+ ARM_COMPUTE_ERROR_THROW_ON(
+ validate_mm(src, weights, biases, dst, gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info));
const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
false, // is_b_reshaped
@@ -77,18 +97,20 @@
false, // fp_mixed_precision
true, // broadcast_bias
act_info // activation_info
- );
+ );
- TensorInfo tmp_src{ *src };
- if(_is_quantized)
+ TensorInfo tmp_src{*src};
+ if (_is_quantized)
{
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
// Extract and negate input and weights offset
const QuantizationInfo input_quantization_info = src->quantization_info();
const QuantizationInfo weights_quantization_info = weights->quantization_info();
- tmp_src.set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
- weights->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
+ tmp_src.set_quantization_info(
+ QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
+ weights->set_quantization_info(
+ QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
_mm_gemmlowp = std::make_unique<ClGemmLowpMatrixMultiplyCore>();
_mm_gemmlowp->configure(compile_context, &tmp_src, weights, biases, dst, gemm_info);
@@ -97,7 +119,7 @@
weights->set_quantization_info(weights_quantization_info);
auto mm_mem_req = _mm_gemmlowp->workspace();
- for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont)
+ for (unsigned int cont = 0; cont < mm_mem_req.size(); ++cont)
{
_aux_mem[cont] = mm_mem_req[cont];
}
@@ -108,15 +130,21 @@
_mm_gemm = std::make_unique<ClGemm>();
_mm_gemm->configure(compile_context, &tmp_src, weights, biases, dst, 1.0f, 1.0f, gemm_info);
auto mm_mem_req = _mm_gemm->workspace();
- for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont)
+ for (unsigned int cont = 0; cont < mm_mem_req.size(); ++cont)
{
_aux_mem[cont] = mm_mem_req[cont];
}
}
}
-Status ClGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
- const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info)
+Status ClGemmConv2d::validate_mm(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *dst,
+ const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
+ int gemm_3d_depth,
+ bool skip_im2col,
+ const ActivationLayerInfo &act_info)
{
const bool is_quantized = is_data_type_quantized_asymmetric(src->data_type());
@@ -131,9 +159,9 @@
false, // fp_mixed_precision
true, // broadcast_bias
act_info // activation_info
- );
+ );
- if(is_quantized)
+ if (is_quantized)
{
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
// Extract and negate input and weights offset
@@ -142,8 +170,10 @@
std::unique_ptr<ITensorInfo> src_qa = src->clone();
std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
- src_qa->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
- weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
+ src_qa->set_quantization_info(
+ QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
+ weights_qa->set_quantization_info(
+ QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
// Perform validation step on GEMMLowp
return ClGemmLowpMatrixMultiplyCore::validate(src_qa.get(), weights_qa.get(), biases, dst, gemm_info);
@@ -155,14 +185,17 @@
}
}
-void ClGemmConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
- const Conv2dInfo &conv2d_info, const WeightsInfo &weights_info)
+void ClGemmConv2d::configure(const CLCompileContext &compile_context,
+ ITensorInfo *src,
+ ITensorInfo *weights,
+ ITensorInfo *biases,
+ ITensorInfo *dst,
+ const Conv2dInfo &conv2d_info,
+ const WeightsInfo &weights_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
- ARM_COMPUTE_ERROR_THROW_ON(ClGemmConv2d::validate(src, weights, biases, dst,
- conv2d_info,
- weights_info));
+ ARM_COMPUTE_ERROR_THROW_ON(ClGemmConv2d::validate(src, weights, biases, dst, conv2d_info, weights_info));
ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv2d_info, weights_info);
const DataType data_type = src->data_type();
@@ -180,7 +213,8 @@
_is_prepared = weights_info.retain_internal_weights();
_is_quantized = is_data_type_quantized_asymmetric(src->data_type());
- _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1 && conv2d_info.conv_info.stride().second == 1);
+ _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 &&
+ conv2d_info.conv_info.stride().first == 1 && conv2d_info.conv_info.stride().second == 1);
_skip_col2im = data_layout == DataLayout::NHWC;
// Only for quantize there are few cases where we cannot fuse the activation function in GEMM
@@ -197,12 +231,8 @@
// Get convolved dimensions
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
- src->dimension(idx_height),
- kernel_width,
- kernel_height,
- conv2d_info.conv_info,
- conv2d_info.dilation);
+ std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), src->dimension(idx_height), kernel_width,
+ kernel_height, conv2d_info.conv_info, conv2d_info.dilation);
unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups;
@@ -210,28 +240,31 @@
_append_bias = false;
_weights_reshape_kernel = std::make_unique<kernels::ClWeightsReshapeKernel>();
- if(conv2d_info.num_groups != 1 && biases != nullptr)
+ if (conv2d_info.num_groups != 1 && biases != nullptr)
{
// num_groups != 1 can only be for NCHW
// Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor
biases_to_use = nullptr;
_append_bias = true;
- _weights_reshape_kernel->configure(compile_context, weights, biases, &_weights_reshaped, conv2d_info.num_groups);
+ _weights_reshape_kernel->configure(compile_context, weights, biases, &_weights_reshaped,
+ conv2d_info.num_groups);
}
else
{
- _weights_reshape_kernel->configure(compile_context, weights, nullptr, &_weights_reshaped, conv2d_info.num_groups);
+ _weights_reshape_kernel->configure(compile_context, weights, nullptr, &_weights_reshaped,
+ conv2d_info.num_groups);
}
// Create tensor to store im2col reshaped inputs
- if(!_skip_im2col)
+ if (!_skip_im2col)
{
// Configure and tune im2col. im2col output shape is auto-initialized
_im2col_kernel = std::make_unique<opencl::kernels::ClIm2ColKernel>();
// Set the GPU target for im2col
_im2col_kernel->set_target(CLScheduler::get().target());
- _im2col_kernel->configure(compile_context, src, &_im2col_output, Size2D(kernel_width, kernel_height), conv2d_info.conv_info, _append_bias, conv2d_info.dilation, conv2d_info.num_groups);
+ _im2col_kernel->configure(compile_context, src, &_im2col_output, Size2D(kernel_width, kernel_height),
+ conv2d_info.conv_info, _append_bias, conv2d_info.dilation, conv2d_info.num_groups);
// Set quantization info
_im2col_output.set_quantization_info(src->quantization_info());
@@ -242,7 +275,7 @@
}
// Create GEMM output tensor
- if(!_skip_col2im)
+ if (!_skip_col2im)
{
TensorShape shape_gemm;
@@ -263,7 +296,7 @@
gemmlowp_output_stage.gemmlowp_offset = 0;
// Configure output stage for quantized case
- if(_is_quantized)
+ if (_is_quantized)
{
const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info;
const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type());
@@ -286,16 +319,16 @@
auto min_activation = min_val.get<int32_t>();
auto max_activation = max_val.get<int32_t>();
- const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
- ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
- ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
- };
+ const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = {
+ ActivationLayerInfo::ActivationFunction::RELU, ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU};
- if(conv2d_info.act_info.enabled())
+ if (conv2d_info.act_info.enabled())
{
- if(supported_acts.count(conv2d_info.act_info.activation()) != 0)
+ if (supported_acts.count(conv2d_info.act_info.activation()) != 0)
{
- std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info);
+ std::tie(min_activation, max_activation) =
+ get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info);
}
else
{
@@ -313,48 +346,60 @@
// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
- configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info);
+ configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use,
+ gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info);
- if(!_skip_col2im)
+ if (!_skip_col2im)
{
// Set the GPU target for col2im
_col2im_kernel = std::make_unique<opencl::kernels::ClCol2ImKernel>();
_col2im_kernel->set_target(CLScheduler::get().target());
// Configure and tune Col2Im
- _col2im_kernel->configure(compile_context, gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups);
+ _col2im_kernel->configure(compile_context, gemm_output_to_use, dst, Size2D(conv_w, conv_h),
+ conv2d_info.num_groups);
CLScheduler::get().tune_kernel_static(*_col2im_kernel.get());
}
ARM_COMPUTE_ERROR_ON_MSG((dst->dimension(idx_width) != conv_w) || (dst->dimension(idx_height) != conv_h),
"Output shape does not match the expected one");
- if(!_fuse_activation)
+ if (!_fuse_activation)
{
_activation_kernel = std::make_unique<opencl::kernels::ClActivationKernel>();
_activation_kernel->configure(compile_context, dst, nullptr, conv2d_info.act_info);
}
- _aux_mem[Im2ColOutput] = MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size());
- _aux_mem[WeightsReshaped] = MemoryInfo(offset_int_vec(WeightsReshaped), MemoryLifetime::Persistent, _weights_reshaped.total_size());
- _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size());
+ _aux_mem[Im2ColOutput] =
+ MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size());
+ _aux_mem[WeightsReshaped] =
+ MemoryInfo(offset_int_vec(WeightsReshaped), MemoryLifetime::Persistent, _weights_reshaped.total_size());
+ _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size());
}
-Status ClGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &conv2d_info,
+Status ClGemmConv2d::validate(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *dst,
+ const Conv2dInfo &conv2d_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
- 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_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+ DataType::F16, DataType::F32);
const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type());
- if(!is_quantized_per_channel)
+ if (!is_quantized_per_channel)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
}
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8");
- ARM_COMPUTE_RETURN_ERROR_ON(((src->dimension(2) / weights->dimension(2)) != conv2d_info.num_groups) && (src->data_layout() == DataLayout::NCHW));
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW),
+ "Grouping (num_groups != 1) with NHWC data layout is not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_type() == DataType::QASYMM8),
+ "Grouping (num_groups != 1) is not supported with QASYMM8");
+ ARM_COMPUTE_RETURN_ERROR_ON(((src->dimension(2) / weights->dimension(2)) != conv2d_info.num_groups) &&
+ (src->data_layout() == DataLayout::NCHW));
const DataLayout data_layout = src->data_layout();
const DataType data_type = src->data_type();
@@ -374,18 +419,19 @@
const ITensorInfo *gemm_output_to_use = dst;
const ITensorInfo *weights_to_use = weights;
const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
- const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1
- && conv2d_info.conv_info.stride().second == 1);
- const bool skip_col2im = data_layout == DataLayout::NHWC;
- bool fuse_activation = true;
+ const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 &&
+ conv2d_info.conv_info.stride().first == 1 && conv2d_info.conv_info.stride().second == 1);
+ const bool skip_col2im = data_layout == DataLayout::NHWC;
+ bool fuse_activation = true;
- ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * conv2d_info.num_groups) != src->dimension(idx_channel));
+ ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * conv2d_info.num_groups) !=
+ src->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
// Validate biases
- if(biases != nullptr)
+ if (biases != nullptr)
{
- if(is_quantized)
+ if (is_quantized)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
}
@@ -397,7 +443,7 @@
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
- if(conv2d_info.act_info.enabled())
+ if (conv2d_info.act_info.enabled())
{
ARM_COMPUTE_ERROR_ON(conv2d_info.act_info.b() > conv2d_info.act_info.a());
}
@@ -406,48 +452,50 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
- src->dimension(idx_height),
- kernel_width,
- kernel_height,
- conv2d_info.conv_info,
- conv2d_info.dilation);
+ std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), src->dimension(idx_height), kernel_width,
+ kernel_height, conv2d_info.conv_info, conv2d_info.dilation);
unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups;
const ITensorInfo *biases_to_use = biases;
bool append_bias = false;
- if(conv2d_info.num_groups != 1 && biases != nullptr)
+ if (conv2d_info.num_groups != 1 && biases != nullptr)
{
// num_groups != 1 can only be for NCHW
// Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor
- biases_to_use = nullptr;
- append_bias = true;
- weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, conv2d_info.num_groups), 1, data_type);
+ biases_to_use = nullptr;
+ append_bias = true;
+ weights_reshaped_info =
+ TensorInfo(compute_weights_reshaped_shape(*weights, true, conv2d_info.num_groups), 1, data_type);
}
else
{
- weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, conv2d_info.num_groups), 1, data_type);
+ weights_reshaped_info =
+ TensorInfo(compute_weights_reshaped_shape(*weights, false, conv2d_info.num_groups), 1, data_type);
}
weights_to_use = &weights_reshaped_info;
- if(!skip_im2col)
+ if (!skip_im2col)
{
const Size2D kernel_dims(kernel_width, kernel_height);
// Output tensor auto initialization if not yet initialized
- TensorShape expected_output_shape = compute_im2col_conv_shape(src, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups == 1, conv2d_info.num_groups);
+ TensorShape expected_output_shape =
+ compute_im2col_conv_shape(src, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation,
+ conv2d_info.num_groups == 1, conv2d_info.num_groups);
auto_init_if_empty(im2col_reshaped_info, src->clone()->set_tensor_shape(expected_output_shape));
- ARM_COMPUTE_RETURN_ON_ERROR(opencl::kernels::ClIm2ColKernel::validate(src, &im2col_reshaped_info, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ opencl::kernels::ClIm2ColKernel::validate(src, &im2col_reshaped_info, kernel_dims, conv2d_info.conv_info,
+ append_bias, conv2d_info.dilation, conv2d_info.num_groups));
gemm_input_to_use = &im2col_reshaped_info;
}
// Create GEMM output tensor
- if(!skip_col2im)
+ if (!skip_col2im)
{
TensorShape shape_gemm;
@@ -465,7 +513,7 @@
gemmlowp_output_stage.gemmlowp_offset = 0;
gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;
- if(is_quantized)
+ if (is_quantized)
{
const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();
@@ -483,16 +531,16 @@
int min_activation = 0;
int max_activation = 0;
- const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
- ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
- ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
- };
+ const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = {
+ ActivationLayerInfo::ActivationFunction::RELU, ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU};
- if(conv2d_info.act_info.enabled())
+ if (conv2d_info.act_info.enabled())
{
- if(supported_acts.count(conv2d_info.act_info.activation()) != 0)
+ if (supported_acts.count(conv2d_info.act_info.activation()) != 0)
{
- std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info);
+ std::tie(min_activation, max_activation) =
+ get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info);
}
else
{
@@ -509,16 +557,18 @@
// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use,
+ gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info));
// Validate Col2Im
- if(!skip_col2im)
+ if (!skip_col2im)
{
- ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ kernels::ClCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups));
}
// Validate Activation Layer
- if(!fuse_activation)
+ if (!fuse_activation)
{
ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, nullptr, conv2d_info.act_info));
}
@@ -541,30 +591,26 @@
CLAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors, false);
// Run im2col
- if(!_skip_im2col)
+ if (!_skip_im2col)
{
- ITensorPack pack =
- {
- { TensorType::ACL_SRC, src },
- { TensorType::ACL_DST, im2col_output.get() }
- };
+ ITensorPack pack = {{TensorType::ACL_SRC, src}, {TensorType::ACL_DST, im2col_output.get()}};
CLScheduler::get().enqueue_op(*_im2col_kernel, pack, false);
gemm_input_to_use = im2col_output.get();
}
- if(!_skip_col2im)
+ if (!_skip_col2im)
{
gemm_output_to_use = gemm_output.get();
}
ITensorPack pack_mm = tensors;
pack_mm.add_const_tensor(TensorType::ACL_SRC_0, gemm_input_to_use);
pack_mm.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get());
- if(!_append_bias)
+ if (!_append_bias)
{
pack_mm.add_const_tensor(TensorType::ACL_SRC_2, biases);
}
pack_mm.add_tensor(TensorType::ACL_DST, gemm_output_to_use);
// Runs ClGemm or ClGemmLowpMatrixMultiplyCore functions
- if(_is_quantized)
+ if (_is_quantized)
{
// Run gemmlowp
_mm_gemmlowp->run(pack_mm);
@@ -576,43 +622,32 @@
}
// Reshape output matrix
- if(!_skip_col2im)
+ if (!_skip_col2im)
{
- ITensorPack pack =
- {
- { TensorType::ACL_SRC, gemm_output_to_use },
- { TensorType::ACL_DST, dst }
- };
+ ITensorPack pack = {{TensorType::ACL_SRC, gemm_output_to_use}, {TensorType::ACL_DST, dst}};
CLScheduler::get().enqueue_op(*_col2im_kernel.get(), pack, false);
}
//Run Activation Layer if we cannot fuse in GEMM
- if(!_fuse_activation)
+ if (!_fuse_activation)
{
- ITensorPack pack =
- {
- { TensorType::ACL_SRC, dst },
- { TensorType::ACL_DST, dst }
- };
+ ITensorPack pack = {{TensorType::ACL_SRC, dst}, {TensorType::ACL_DST, dst}};
CLScheduler::get().enqueue_op(*_activation_kernel.get(), pack, false);
}
}
void ClGemmConv2d::prepare(ITensorPack &tensors)
{
- if(!_is_prepared)
+ if (!_is_prepared)
{
// Run weights reshaping and mark original weights tensor as unused
- ICLTensor *weights_reshaped_p = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(WeightsReshaped)));
+ ICLTensor *weights_reshaped_p =
+ utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(WeightsReshaped)));
CLAuxTensorHandler weights_reshaped(_weights_reshaped, *weights_reshaped_p);
auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1);
- ITensorPack pack =
- {
- { TensorType::ACL_SRC, weights },
- { TensorType::ACL_DST, weights_reshaped.get() }
- };
+ ITensorPack pack = {{TensorType::ACL_SRC, weights}, {TensorType::ACL_DST, weights_reshaped.get()}};
- if(_append_bias)
+ if (_append_bias)
{
const auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2);
pack.add_const_tensor(TensorType::ACL_BIAS, biases);