/* * Copyright (c) 2017-2018 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 "arm_compute/core/CL/kernels/CLIm2ColKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLValidate.h" #include "arm_compute/core/CL/ICLTensor.h" #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" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/ToolchainSupport.h" #include #include using namespace arm_compute; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, 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); ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); 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) { 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) { int stride_x = 0; int stride_y = 0; std::tie(stride_x, stride_y) = conv_info.stride(); 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()); } } // namespace CLIm2ColKernel::CLIm2ColKernel() : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims() { } 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) { // Default kernel name if(data_layout == DataLayout::NCHW) { kernel_name = "im2col_generic_dchw"; } else { kernel_name = "im2col_generic_nhwc"; } _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"); if(dilation == Size2D(1U, 1U)) { if(squared_im2col) { // Check if we can run an optimized im2col 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) { // 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"; } 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; } 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; } 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) { is_optimized_path = true; kernel_name = "im2col11x11_padx0_pady0_dchw"; } break; default: is_optimized_path = false; break; } } else if(kernel_dims.width > 1 && !conv_info.has_padding()) { _num_elems_processed_per_iteration = 1; is_optimized_path = 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::TNOX) && 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)); } } } _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), 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; } 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_ON_MSG(output->info()->data_layout() != DataLayout::NCHW, "Special case Im2Col output layout is NCHW"); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation)); _input = input; _output = output; _kernel_dims = kernel_dims; _conv_info = conv_info; const DataType data_type = input->info()->data_type(); // 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"); _num_elems_processed_per_iteration = 1; const std::string kernel_name = configure_window(input, output, kernel_dims, dilation, conv_info, build_opts); // Create kernel _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; _config_id += lower_string(string_from_data_type(input->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); _config_id += "_"; _config_id += lower_string(string_from_data_layout(input->info()->data_layout())); } 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)); 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); // Get initial windows 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; window_output.use_tensor_dimensions(_output->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(); 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 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(_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(_convolved_dims.second) * num_batches, 1)); } else { slice.set(height_idx, Window::Dimension(0, static_cast(_convolved_dims.second), 1)); } } // Setup input slice // The first three dimensions of the input are increased by the inner loops 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)); do { unsigned int idx = 0; add_3D_tensor_argument(idx, _input, slice_in); add_2D_tensor_argument(idx, _output, slice_out); _kernel.setArg(idx++, static_cast(_input->info()->strides_in_bytes()[3])); _kernel.setArg(idx++, static_cast(_output->info()->strides_in_bytes()[2])); 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(idx++, _input->info()->dimension(0)); _kernel.setArg(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)); }