COMPMID-477 - Optimizing CLDirectConvolution 3x3 on OpenCL and added the auto configuration
Change-Id: I3c8384dcbc9d7786943134bb658dafb35356d90d
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83253
Reviewed-by: Steven Niu <steven.niu@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index 1f481de..5f14d16 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -32,6 +32,7 @@
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "support/ToolchainSupport.h"
@@ -49,20 +50,17 @@
void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
{
- const unsigned int kernel_size = weights->info()->dimension(0);
- ARM_COMPUTE_ERROR_ON_MSG(kernel_size != 1 && kernel_size != 3,
- "Kernel sizes other than 1x1 or 3x3 are not supported");
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != weights->info()->dimension(1),
+ "Only kernel sizes 1x1 and 3x3 are supported");
+ ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3,
+ "Only kernel sizes 1x1 and 3x3 are supported");
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
- ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())),
- "Pad > 0 not supported for 1x1 weights");
- ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1),
- "Pad > 1 not supported for 3x3 weights");
- ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported.");
- ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!");
+ ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution.");
+ ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 3) && std::get<0>(conv_info.stride()) > 2, "Strides larger than 2 not supported for 3x3 convolution.");
if(biases != nullptr)
{
@@ -71,10 +69,29 @@
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
+ const unsigned int kernel_size = weights->info()->dimension(0);
+
+ // Get convolved dimensions
+ unsigned int output_width = 0;
+ unsigned int output_height = 0;
+ std::tie(output_width, output_height) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_size, kernel_size, conv_info);
+
+ TensorShape output_shape = input->info()->tensor_shape();
+ output_shape.set(0, output_width);
+ output_shape.set(1, output_height);
+ output_shape.set(2, weights->info()->dimension(3));
+
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
_conv_stride_x = std::get<0>(conv_info.stride());
_conv_stride_y = std::get<1>(conv_info.stride());
- _conv_pad_x = std::get<0>(conv_info.pad());
- _conv_pad_y = std::get<1>(conv_info.pad());
+ _conv_pad_x = std::min(std::get<0>(conv_info.pad()), kernel_size / 2);
+ _conv_pad_y = std::min(std::get<1>(conv_info.pad()), kernel_size / 2);
_input = input;
_weights = weights;
@@ -86,9 +103,9 @@
std::set<std::string> options;
kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
- options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
- options.insert("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
-
+ options.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+ options.emplace("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
+ options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));
options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
if(_biases != nullptr)
@@ -98,33 +115,27 @@
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), options));
- unsigned int idx = (_biases == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());
- _kernel.setArg<cl_uint>(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w
- _kernel.setArg<cl_uint>(idx++, _weights->info()->dimension(2)); // filter depth
-
- // Using this local workgroup size gives better performance over others that have been tried.
- _lws_hint = cl::NDRange(4, 1, 8);
-
// Configure kernel window
Window win = calculate_max_window(*output->info());
- unsigned int num_elems_read_per_iteration = 16 * _conv_stride_x;
- unsigned int num_elems_written_per_iteration = 8;
+ bool is_kernel3x3_stride2 = ((kernel_size == 3) && (_conv_stride_x == 2));
+
+ const unsigned int num_elems_read_per_iteration_x = 8 + 2 * (kernel_size / 2) + (is_kernel3x3_stride2 ? 7 : 0);
+ const unsigned int num_elems_read_per_iteration_y = kernel_size;
+ const unsigned int num_elems_written_per_iteration_x = 8;
+ const unsigned int num_elems_written_per_iteration_y = 1;
// Calculate right and bottom border
- const int input_width = input->info()->dimension(0);
- const int input_height = input->info()->dimension(1);
- const int upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * _conv_stride_x + kernel_size), num_elems_read_per_iteration) - _conv_pad_x - input_width;
- const int upper_bound_h = ((output->info()->dimension(1) - 1) * _conv_stride_y - _conv_pad_y + kernel_size) - input_height;
- const int padding_right = std::max(upper_bound_w, static_cast<int>(kernel_size));
- const int padding_bottom = std::max(upper_bound_h, static_cast<int>(kernel_size));
+ const int input_width = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x;
+ const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y;
// Create window and update padding
- win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration));
- AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);
+ win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
- AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+ AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y);
+ AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
+ AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+
update_window_and_padding(win, input_access, weights_access, output_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
@@ -158,6 +169,8 @@
add_1D_tensor_argument(idx1, _biases, slice_biases);
}
+ _kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3]));
+
do
{
unsigned int idx = 0;