COMPMID-1277 - Optimizing CLIm2ColKernel for NHWC.
This patch includes:
- Im2Col optimizations for NHWC using a new data layout
- Refactoring of CLIm2ColKernel adding validation method and auto-init
- Removed im2col_reduced from CLIm2ColKernel and created a new kernel CLFlattenLayerKernel
Change-Id: I1620640b6796baa268324b33ae92cdd8de53e27c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141241
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index a09129b..39654e2 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -31,7 +31,6 @@
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
@@ -40,12 +39,22 @@
#include <cmath>
#include <tuple>
+#include <utility>
using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, bool has_bias, const Size2D &dilation)
+struct Im2ColConfiguration
+{
+ std::string kernel_name{};
+ std::set<std::string> build_options{};
+ unsigned int num_elems_processed_per_iteration{};
+ bool is_padding_required_nchw{};
+};
+
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
@@ -54,263 +63,255 @@
ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
- // Checks performed when output is configured
- if(output->total_size() != 0)
+ if(output->total_size() > 0)
{
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, true));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
return Status{};
}
-inline bool run_im2col_reduced(ITensorInfo *input, ITensorInfo *output, const PadStrideInfo &conv_info)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
+ unsigned int num_elems_processed_per_iteration, bool is_padding_required_nchw)
{
- int stride_x = 0;
- int stride_y = 0;
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- std::tie(stride_x, stride_y) = conv_info.stride();
+ // Output tensor auto initialization if not yet initialized
+ TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, true);
- return (output->dimension(0) == (input->dimension(0) * input->dimension(1) * input->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
- && (std::equal(input->tensor_shape().cbegin() + 3,
- input->tensor_shape().cend(),
- output->tensor_shape().cbegin() + 1))
- && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
-}
+ auto_init_if_empty(*output, input->clone()->set_tensor_shape(expected_output_shape));
-} // namespace
+ const DataLayout data_layout = input->data_layout();
+ const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int input_width = input->dimension(width_idx);
+ const unsigned int input_height = input->dimension(height_idx);
-CLIm2ColKernel::CLIm2ColKernel()
- : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
-{
-}
+ // Configure the execute window based on the selected optimal OpenCL kernel
+ bool window_changed = false;
+ Window win;
-std::string
-CLIm2ColKernel::configure_window(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims,
- const Size2D &dilation, const PadStrideInfo &conv_info, CLBuildOptions &build_opts)
-{
- std::string kernel_name;
- bool is_optimized_path = false;
- const bool reduced = run_im2col_reduced(input->info(), output->info(), conv_info);
- const DataType data_type = input->info()->data_type();
- const bool squared_im2col = kernel_dims.width == kernel_dims.height;
- const DataLayout data_layout = input->info()->data_layout();
-
- const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
- const unsigned int input_width = input->info()->dimension(width_idx);
- const unsigned int input_height = input->info()->dimension(height_idx);
- const unsigned int input_channel = input->info()->dimension(channel_idx);
-
- if(!reduced)
+ if(data_layout == DataLayout::NHWC)
{
- // Default kernel name
- if(data_layout == DataLayout::NCHW)
+ win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+
+ const int xin_start = 0;
+ const int xin_end = input->dimension(0) < num_elems_processed_per_iteration ? ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration) : input->dimension(0);
+ const int yin_start = 0;
+ const int yin_end = input->dimension(1);
+
+ const int xout_start = 0;
+ const int xout_end = input->dimension(0) < num_elems_processed_per_iteration ? ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration) : output->dimension(0);
+ const int yout_start = 0;
+ const int yout_end = output->dimension(1);
+
+ AccessWindowStatic input_access(input, xin_start, yin_start, xin_end, yin_end);
+ AccessWindowStatic output_access(output, xout_start, yout_start, xout_end, yout_end);
+ window_changed = window_changed || update_window_and_padding(win, input_access, output_access);
+ }
+ else
+ {
+ if(is_padding_required_nchw)
{
- kernel_name = "im2col_generic_dchw";
+ const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
+ win = calculate_max_window(*input,
+ Steps(num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second));
+ AccessWindowStatic input_access(input,
+ -border.left,
+ -border.top,
+ ceil_to_multiple(input_width + border.right, kernel_dims.width * num_elems_processed_per_iteration),
+ input_height + border.bottom);
+ window_changed = window_changed || update_window_and_padding(win, input_access);
}
else
{
- kernel_name = "im2col_generic_nhwc";
+ // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so
+ // update_window_and_padding() can be skipped
+ win = calculate_max_window(*input, Steps());
+ }
+ }
+
+ output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+ // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
+ win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+
+Im2ColConfiguration configure_opencl_kernel(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
+{
+ const DataLayout data_layout = input->data_layout();
+ const DataType data_type = input->data_type();
+ const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+ const unsigned int input_width = input->dimension(width_idx);
+ const unsigned int input_height = input->dimension(height_idx);
+ const unsigned int input_channel = input->dimension(channel_idx);
+
+ const std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
+
+ // Im2Col configuration
+ std::string kernel_name = "im2col_generic_";
+ CLBuildOptions build_opts;
+ unsigned int num_elems_processed_per_iteration = 1;
+ bool is_padding_required_nchw = false;
+
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
+ build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->element_size()));
+ build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
+ build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
+ build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(convolved_dims.first));
+ build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(convolved_dims.second));
+ build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
+ build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
+ build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
+ build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
+ build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
+ build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
+ build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
+ build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
+ build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(input_channel));
+ build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
+ build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
+ build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->quantization_info().offset), "-DPAD_VALUE=0");
+ build_opts.add_option_if(has_bias, "-DHAS_BIAS");
+
+ if(data_layout == DataLayout::NHWC)
+ {
+ num_elems_processed_per_iteration = 2;
+ is_padding_required_nchw = false;
+
+ // Only the 3x3 case is optimized for NHWC
+ if(kernel_dims == Size2D(3U, 3U))
+ {
+ kernel_name = "im2col3x3_";
}
- _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
-
- build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
- build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
- build_opts.add_option("-DKERNEL_DEPTH=" + support::cpp11::to_string(input_channel));
- build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(_convolved_dims.first));
- build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(_convolved_dims.second));
- build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
- build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
- build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
- build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
- build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
- build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
- build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
- build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
- build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
- build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
- build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0");
-
+ build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
+ build_opts.add_option("-DLAST_ACCESSED=" + support::cpp11::to_string(std::max(static_cast<int>(input_channel - num_elems_processed_per_iteration), 0)));
+ }
+ else
+ {
if(dilation == Size2D(1U, 1U))
{
+ const bool squared_im2col = kernel_dims.width == kernel_dims.height;
if(squared_im2col)
{
- // Check if we can run an optimized im2col
+ // Check if we can run an optimized im2col for NCHW
switch(kernel_dims.width)
{
case 1:
// Optimized im2col1x1 if stride_x = 1 and conv_info.has_padding() = false
- if(conv_info.stride().first == 1 && !conv_info.has_padding() && data_layout == DataLayout::NCHW)
+ if(conv_info.stride().first == 1 && !conv_info.has_padding())
{
- // Set hint for LWS
- _lws_hint = cl::NDRange(1, 1, 8);
- _num_elems_processed_per_iteration = 4;
- is_optimized_path = true;
- kernel_name = "im2col1x1_stridex1_dchw";
+ kernel_name = "im2col1x1_stridex1_";
+ num_elems_processed_per_iteration = 4;
+ is_padding_required_nchw = true;
}
break;
case 3:
- _lws_hint = cl::NDRange(1, 1, 8);
- _num_elems_processed_per_iteration = 1;
- is_optimized_path = true;
- switch(data_layout)
- {
- case DataLayout::NCHW:
- kernel_name = "im2col3x3_dchw";
- break;
- case DataLayout::NHWC:
- kernel_name = "im2col3x3_nhwc";
- break;
- default:
- ARM_COMPUTE_ERROR("Not supported.");
- break;
- }
+ kernel_name = "im2col3x3_";
+ num_elems_processed_per_iteration = 1;
+ is_padding_required_nchw = true;
break;
case 5:
- _num_elems_processed_per_iteration = 1;
- switch(data_layout)
- {
- case DataLayout::NCHW:
- is_optimized_path = true;
- kernel_name = "im2col5x5_dchw";
- break;
- default:
- // using generic_nhwc
- is_optimized_path = false;
- break;
- }
+ kernel_name = "im2col5x5_";
+ num_elems_processed_per_iteration = 1;
+ is_padding_required_nchw = true;
break;
case 11:
- _num_elems_processed_per_iteration = 1;
// Optimized im2col11x11 if pad_x = pad_y = 0
- if(!conv_info.has_padding() && data_layout == DataLayout::NCHW)
+ if(!conv_info.has_padding())
{
- is_optimized_path = true;
- kernel_name = "im2col11x11_padx0_pady0_dchw";
+ kernel_name = "im2col11x11_padx0_pady0_";
+ num_elems_processed_per_iteration = 1;
+ is_padding_required_nchw = true;
}
break;
default:
- is_optimized_path = false;
+ kernel_name = "im2col_generic_";
+ num_elems_processed_per_iteration = 1;
+ is_padding_required_nchw = false;
break;
}
}
else if(kernel_dims.width > 1 && !conv_info.has_padding())
{
- _num_elems_processed_per_iteration = 1;
- is_optimized_path = false;
+ kernel_name = "im2col_generic_padx0_pady0_";
+ num_elems_processed_per_iteration = 1;
+ is_padding_required_nchw = false;
- if(data_layout == DataLayout::NCHW)
- {
- kernel_name = "im2col_generic_padx0_pady0_dchw";
-
- // Optimized im2col is performed using one or more vector operations with the specified vector size
- // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
- // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
- // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
- // Using the vector size of 8, however, may be faster.
- size_t vector_size = 4;
- // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
- // is used instead.)
- if(kernel_dims.width < vector_size)
- {
- vector_size = kernel_dims.width;
- }
- // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
- const GPUTarget gpu_target = get_target();
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G76) && kernel_dims.width == 11)
- {
- vector_size = 8;
- }
- const size_t width_mod_vector_size = kernel_dims.width % vector_size;
- build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
- build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
- }
+ // Optimized im2col is performed using one or more vector operations with the specified vector size
+ // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
+ // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
+ // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
+ // Using the vector size of 8, however, may be faster.
+ // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
+ // is used instead.)
+ const size_t vector_size = std::min(static_cast<size_t>(4), kernel_dims.width);
+ const size_t width_mod_vector_size = kernel_dims.width % vector_size;
+ build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
+ build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
}
}
- _run_func = &CLIm2ColKernel::run_generic;
- }
- else
- {
- _num_elems_processed_per_iteration = 1;
- kernel_name = "im2col_reduced_dchw";
- _run_func = &CLIm2ColKernel::run_reduced;
- }
- // Configure kernel window
- Window win;
- if(is_optimized_path)
- {
- if(data_layout == DataLayout::NHWC)
- {
- win = calculate_max_window(*input->info(),
- Steps(_num_elems_processed_per_iteration),
- false,
- BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
- const int x = -conv_info.pad_left();
- const int y = -conv_info.pad_top();
- const int h = kernel_dims.width * _num_elems_processed_per_iteration;
- const int w = 1;
- AccessWindowRectangle input_access(input->info(), x, y, w, h);
- update_window_and_padding(win, input_access);
- }
- else
- {
- const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
- win = calculate_max_window(*input->info(),
- Steps(_num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second));
- AccessWindowStatic input_access(input->info(),
- -border.left,
- -border.top,
- ceil_to_multiple(input_width + border.right, kernel_dims.width * _num_elems_processed_per_iteration),
- input_height + border.bottom);
- update_window_and_padding(win, input_access);
- }
- }
- else
- {
- // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so
- // update_window_and_padding() can be skipped
- win = calculate_max_window(*input->info(), Steps());
}
- output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
- if(!reduced)
- {
- // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
- win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
- }
- ICLKernel::configure(win);
- return kernel_name;
+ // Append the data layout to the kernel_name
+ kernel_name += lower_string(string_from_data_layout(data_layout));
+
+ Im2ColConfiguration im2col_config;
+ im2col_config.kernel_name = kernel_name;
+ im2col_config.build_options = build_opts.options();
+ im2col_config.num_elems_processed_per_iteration = num_elems_processed_per_iteration;
+ im2col_config.is_padding_required_nchw = is_padding_required_nchw;
+
+ return im2col_config;
+}
+} // namespace
+
+CLIm2ColKernel::CLIm2ColKernel()
+ : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _kernel_dims(), _conv_info()
+{
}
void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation));
- _input = input;
- _output = output;
- _kernel_dims = kernel_dims;
- _conv_info = conv_info;
+ const DataLayout data_layout = input->info()->data_layout();
+ const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int input_width = input->info()->dimension(width_idx);
+ const unsigned int input_height = input->info()->dimension(height_idx);
- const DataType data_type = input->info()->data_type();
+ // Select and configure the optimal OpenCL kernel to run.
+ // This function returns the OpenCL kernel's name, the arguments to pass at compile time, the number of elements processed per iteration
+ // and the padding requirement flag
+ Im2ColConfiguration im2col_config = configure_opencl_kernel(input->info(), kernel_dims, conv_info, has_bias, dilation);
// Create kernel
- CLBuildOptions build_opts;
- build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
- build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
- build_opts.add_option_if(has_bias, "-DHAS_BIAS");
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(im2col_config.kernel_name, im2col_config.build_options));
- _num_elems_processed_per_iteration = 1;
+ _input = input;
+ _output = output;
+ _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
+ _num_elems_processed_per_iteration = im2col_config.num_elems_processed_per_iteration;
+ _kernel_dims = kernel_dims; // Only needed by the Tuner
+ _conv_info = conv_info; // Only needed by the Tuner
- const std::string kernel_name = configure_window(input, output, kernel_dims, dilation, conv_info, build_opts);
- // Create kernel
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
+ im2col_config.is_padding_required_nchw);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICLKernel::configure(win_config.second);
// Set config_id for enabling LWS tuning
- _config_id = kernel_name;
+ _config_id = im2col_config.kernel_name;
_config_id += "_";
_config_id += lower_string(string_from_data_type(input->info()->data_type()));
_config_id += "_";
@@ -323,31 +324,24 @@
Status CLIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
{
- ARM_COMPUTE_UNUSED(kernel_dims);
- ARM_COMPUTE_UNUSED(conv_info);
- ARM_COMPUTE_UNUSED(has_bias);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, has_bias, dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation));
+ Im2ColConfiguration im2col_config = configure_opencl_kernel(input, kernel_dims, conv_info, has_bias, dilation);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
+ im2col_config.is_padding_required_nchw)
+ .first);
return Status{};
}
void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
{
- ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
- (this->*_run_func)(window, queue);
-}
-
-void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
-{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
- const DataLayout data_layout = _input->info()->data_layout();
- const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const DataLayout data_layout = _input->info()->data_layout();
// Get initial windows
+ // Collapse in order to have (SRC_DEPTH * BATCH_SIZE) on the 3rd dimension
Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
- // Change the Z dimension's step back to 1
window_collapsed.set_dimension_step(Window::DimZ, 1);
Window window_output;
@@ -359,36 +353,32 @@
Window slice_in = first_slice_3d;
Window slice_out = window_output.first_slice_window_2D();
- const bool out_dim_not_same_input_dim = _convolved_dims.first != _input->info()->dimension(width_idx) || _convolved_dims.second != _input->info()->dimension(height_idx);
-
- // Setup slice if convolved dims are not the same as input dims
- if(out_dim_not_same_input_dim)
+ if(data_layout == DataLayout::NHWC)
{
- // If the stride_x or stride_y are not 1, the output tensor of matrix multiply (Convolved tensor) will not
- // have the same shape of the im2col input tensor
- // In this case we need to re-compute the window using the shape of the tensor after matrix multiply (convolved_dims)
- slice.set(width_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
- if(data_layout == DataLayout::NHWC)
- {
- // if layout is NHWC, we need to multiply convolved_dims.height by the number of batches as for this
- // format we collapsed HEIGHT and all subsequent dimensions (batches) together. This is necessary to ensure
- // global_id(2) values are in the correct range.
- const Window tmp_win = window.collapse_if_possible(ICLKernel::window(), 3);
- const int num_batches = tmp_win[3].end();
- slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second) * num_batches, 1));
- }
- else
- {
- slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
- }
+ const Window tmp_win = window.collapse_if_possible(ICLKernel::window(), 3);
+ const int num_batches = tmp_win[3].end();
+
+ slice.set(1, Window::Dimension(0, static_cast<int>(_output->info()->tensor_shape()[1]), 1));
+ slice.set(2, Window::Dimension(0, static_cast<int>(num_batches), 1));
+ }
+ else
+ {
+ slice.set(0, Window::Dimension(0, static_cast<int>(ceil_to_multiple(_convolved_dims.first, _num_elems_processed_per_iteration)), _num_elems_processed_per_iteration));
+ slice.set(1, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+ // Note: In case of NCHW the 3rd dimension is already set collapsing the input window
}
// Setup input slice
- // The first three dimensions of the input are increased by the inner loops
+ // The dimensions of the input are increased within the OpenCL kernel
slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+ // Setup output slice
+ // The dimensions of the output are increased within the OpenCL kernel
+ slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+ slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+
do
{
unsigned int idx = 0;
@@ -399,30 +389,4 @@
enqueue(queue, *this, slice, _lws_hint);
}
while(window_collapsed.slide_window_slice_3D(slice) && window_output.slide_window_slice_2D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in));
-}
-
-void CLIm2ColKernel::run_reduced(const Window &window, cl::CommandQueue &queue)
-{
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
-
- Window out_window;
- out_window.use_tensor_dimensions(_output->info()->tensor_shape());
-
- Window out_slice = out_window.first_slice_window_1D();
- Window in_slice = window.first_slice_window_3D();
-
- // Run kernel
- do
- {
- // Set arguments
- unsigned int idx = 0;
- add_3D_tensor_argument(idx, _input, in_slice);
- add_1D_tensor_argument(idx, _output, out_slice);
-
- _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(0));
- _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(1));
- enqueue(queue, *this, in_slice, _lws_hint);
- }
- while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
-}
+}
\ No newline at end of file