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Diffstat (limited to 'src/gpu/cl/kernels/ClIm2ColKernel.cpp')
| -rw-r--r-- | src/gpu/cl/kernels/ClIm2ColKernel.cpp | 488 |
1 files changed, 488 insertions, 0 deletions
diff --git a/src/gpu/cl/kernels/ClIm2ColKernel.cpp b/src/gpu/cl/kernels/ClIm2ColKernel.cpp new file mode 100644 index 0000000000..ef7a52828f --- /dev/null +++ b/src/gpu/cl/kernels/ClIm2ColKernel.cpp @@ -0,0 +1,488 @@ +/* + * Copyright (c) 2017-2021, 2023 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "src/gpu/cl/kernels/ClIm2ColKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/OpenCL.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/StringUtils.h" +#include "arm_compute/core/Validate.h" + +#include "src/core/AccessWindowStatic.h" +#include "src/core/CL/CLValidate.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "support/Cast.h" +#include "support/StringSupport.h" + +#include <cmath> +#include <tuple> +#include <utility> + +namespace arm_compute +{ +using namespace misc::shape_calculator; +namespace opencl +{ +namespace kernels +{ +namespace +{ +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 *src, + const ITensorInfo *dst, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups) +{ + const unsigned int channel_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL); + + 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(is_data_type_quantized(src->data_type()) && has_bias); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(dst); + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); + ARM_COMPUTE_RETURN_ERROR_ON(num_groups == 0); + ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::NHWC && num_groups > 1); + ARM_COMPUTE_RETURN_ERROR_ON((src->dimension(channel_idx) % num_groups) != 0); + + // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions + const unsigned int width_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); + const unsigned int height_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); + const unsigned total_width = src->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); + const unsigned total_height = src->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom(); + ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height)); + + if (dst->total_size() > 0) + { + const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape( + compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, + ITensorInfo *dst, + 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, + unsigned int num_groups) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + + // Output tensor auto initialization if not yet initialized + TensorShape expected_output_shape = + compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups); + + auto_init_if_empty(*dst, src->clone()->set_tensor_shape(expected_output_shape)); + + const DataLayout data_layout = src->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 = src->dimension(width_idx); + const unsigned int input_height = src->dimension(height_idx); + + // Configure the execute window based on the selected optimal OpenCL kernel + bool window_changed = false; + Window win; + + if (data_layout == DataLayout::NHWC) + { + win = calculate_max_window(*src, Steps(num_elems_processed_per_iteration)); + } + else + { + if (is_padding_required_nchw) + { + const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), + conv_info.pad_left()); + win = calculate_max_window( + *src, Steps(num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second)); + AccessWindowStatic input_access( + src, -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 + { + // 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(*src, Steps()); + } + } + + // 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 *src, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups) +{ + const DataLayout data_layout = src->data_layout(); + const DataType data_type = src->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 = src->dimension(width_idx); + const unsigned int input_height = src->dimension(height_idx); + const unsigned int input_channel = src->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; + const UniformQuantizationInfo qinfo = src->quantization_info().uniform(); + + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); + build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(src->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(num_groups > 1, "-DNUM_GROUPS=" + support::cpp11::to_string(num_groups)); + build_opts.add_option_if_else(is_data_type_quantized(data_type), + "-DPAD_VALUE=" + support::cpp11::to_string(qinfo.offset), "-DPAD_VALUE=0"); + build_opts.add_option_if(has_bias, "-DHAS_BIAS"); + + if (data_layout == DataLayout::NHWC) + { + num_elems_processed_per_iteration = std::min(2U, input_channel); + is_padding_required_nchw = false; + + // Only the 3x3 and 9x9 cases are optimized for NHWC + if (kernel_dims == Size2D(3U, 3U)) + { + kernel_name = "im2col3x3_"; + build_opts.add_option("-DIM2COL_3X3"); + } + else if (kernel_dims == Size2D(9U, 9U)) + { + kernel_name = "im2col9x9_"; + build_opts.add_option("-DIM2COL_9X9"); + } + else + { + build_opts.add_option("-DIM2COL_GENERIC"); + } + + // Get boundary vector (the first/last vector with potentially a partial vector size) size + // If input_channel is a multiple of num_elems_processed_per_iteration, the boundary vec size is the (full) vector size + // otherwise, the boundary vec size is the (partial) remainder vector size + const unsigned int vec_size = num_elems_processed_per_iteration; + const unsigned int partial_vec_size = input_channel % vec_size; + const unsigned int boundary_vec_size = vec_size - ((vec_size - partial_vec_size) % vec_size); + build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vec_size)); + build_opts.add_option("-DBOUNDARY_VECTOR_SIZE=" + support::cpp11::to_string(boundary_vec_size)); + } + 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 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()) + { + kernel_name = "im2col1x1_stridex1_"; + num_elems_processed_per_iteration = 4; + is_padding_required_nchw = true; + } + break; + case 3: + kernel_name = "im2col3x3_"; + num_elems_processed_per_iteration = 1; + is_padding_required_nchw = true; + break; + case 5: + kernel_name = "im2col5x5_"; + num_elems_processed_per_iteration = 1; + is_padding_required_nchw = true; + break; + case 11: + // Optimized im2col11x11 if pad_x = pad_y = 0 + if (!conv_info.has_padding()) + { + kernel_name = "im2col11x11_padx0_pady0_"; + num_elems_processed_per_iteration = 1; + is_padding_required_nchw = true; + } + break; + default: + 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()) + { + kernel_name = "im2col_generic_padx0_pady0_"; + num_elems_processed_per_iteration = 1; + is_padding_required_nchw = false; + + // 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)); + } + } + } + + // 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() + : _data_layout(DataLayout::UNKNOWN), + _convolved_dims(), + _num_elems_processed_per_iteration(1), + _kernel_dims(), + _conv_info(), + _num_groups() +{ + _type = CLKernelType::ELEMENTWISE; +} + +void ClIm2ColKernel::configure(const ClCompileContext &compile_context, + ITensorInfo *src, + ITensorInfo *dst, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups)); + + auto padding_info = get_padding_info({src, dst}); + _data_layout = src->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 = src->dimension(width_idx); + const unsigned int input_height = src->dimension(height_idx); + + // 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(src, kernel_dims, conv_info, has_bias, dilation, num_groups); + + // Create kernel + _kernel = create_kernel(compile_context, im2col_config.kernel_name, im2col_config.build_options); + + _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 + _num_groups = num_groups; + + // Configure kernel window + auto win_config = validate_and_configure_window(src, dst, kernel_dims, conv_info, has_bias, dilation, + im2col_config.num_elems_processed_per_iteration, + im2col_config.is_padding_required_nchw, num_groups); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + IClKernel::configure_internal(win_config.second); + + // Set config_id for enabling LWS tuning + _config_id = im2col_config.kernel_name; + _config_id += "_"; + _config_id += lower_string(string_from_data_type(src->data_type())); + _config_id += "_"; + _config_id += support::cpp11::to_string(num_groups); + _config_id += "_"; + _config_id += support::cpp11::to_string(dst->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(dst->dimension(1)); + _config_id += "_"; + _config_id += lower_string(string_from_data_layout(_data_layout)); + + ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info)); +} + +Status ClIm2ColKernel::validate(const ITensorInfo *src, + const ITensorInfo *dst, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups)); + Im2ColConfiguration im2col_config = + configure_opencl_kernel(src, kernel_dims, conv_info, has_bias, dilation, num_groups); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), kernel_dims, + conv_info, has_bias, dilation, + im2col_config.num_elems_processed_per_iteration, + im2col_config.is_padding_required_nchw, num_groups) + .first); + return Status{}; +} + +void ClIm2ColKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(IClKernel::window(), window); + ARM_COMPUTE_ERROR_ON(tensors.empty()); + + // 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); + window_collapsed.set_dimension_step(Window::DimZ, 1); + + 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)); + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + + Window window_output; + window_output.use_tensor_dimensions(dst->info()->tensor_shape()); + + const Window first_slice_3d = window_collapsed.first_slice_window_3D(); + + Window slice = first_slice_3d; + Window slice_in = first_slice_3d; + Window slice_out = window_output.first_slice_window_2D(); + + if (_data_layout == DataLayout::NHWC) + { + 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>(dst->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 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)); + + unsigned int idx = num_arguments_per_3D_tensor() + + (_num_groups == 1 ? num_arguments_per_2D_tensor() : num_arguments_per_3D_tensor()); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3])); + _kernel.setArg<cl_uint>(idx++, + static_cast<unsigned int>(dst->info()->strides_in_bytes()[((_num_groups == 1) ? 2 : 3)])); + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, src, slice_in); + if (_num_groups == 1) + { + add_2D_tensor_argument(idx, dst, slice_out); + } + else + { + add_3D_tensor_argument(idx, dst, slice_out); + } + 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)); +} +} // namespace kernels +} // namespace opencl +} // namespace arm_compute |