| /* |
| * 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/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/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Size2D.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include <cmath> |
| #include <tuple> |
| |
| 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_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, 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)); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLIm2ColKernel::CLIm2ColKernel() |
| : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims() |
| { |
| } |
| |
| 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); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation)); |
| |
| _input = input; |
| _output = output; |
| _kernel_dims = kernel_dims; |
| |
| const DataType data_type = input->info()->data_type(); |
| const GPUTarget gpu_target = get_target(); |
| |
| // 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"); |
| build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| |
| int stride_x = 0; |
| int stride_y = 0; |
| |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| |
| const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4) |
| && (std::equal(input->info()->tensor_shape().cbegin() + 3, |
| input->info()->tensor_shape().cend(), |
| output->info()->tensor_shape().cbegin() + 1)) |
| && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding()); |
| |
| bool is_optimized_path = false; |
| |
| _num_elems_processed_per_iteration = 1; |
| |
| std::string kernel_name; |
| if(!run_img2col_reduced) |
| { |
| // Default kernel name |
| kernel_name = "im2col_generic_dchw"; |
| |
| _convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), |
| 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->info()->dimension(2))); |
| 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->info()->dimension(0))); |
| build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); |
| 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"); |
| |
| const bool squared_im2col = kernel_dims.width == kernel_dims.height; |
| |
| if(dilation == Size2D(1U, 1U)) |
| { |
| if(squared_im2col && !is_data_type_fixed_point(data_type)) |
| { |
| // 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()) |
| { |
| // 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; |
| kernel_name = "im2col3x3_dchw"; |
| break; |
| case 5: |
| _num_elems_processed_per_iteration = 1; |
| is_optimized_path = true; |
| kernel_name = "im2col5x5_dchw"; |
| break; |
| case 11: |
| // Optimized im2col11x11 if pad_x = pad_y = 0 |
| if(!conv_info.has_padding()) |
| { |
| _num_elems_processed_per_iteration = 1; |
| 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; |
| 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; |
| } |
| // Local work size and vector size optimized for the 11x11 AlexNet convolution on Bifrost. |
| if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72) && kernel_dims.width == 11) |
| { |
| _lws_hint = cl::NDRange(1, 1, 1); |
| 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; |
| } |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Configure kernel window |
| Window win; |
| if(is_optimized_path) |
| { |
| 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 w = kernel_dims.width * _num_elems_processed_per_iteration; |
| const int h = kernel_dims.height; |
| |
| AccessWindowRectangle input_access(input->info(), x, y, w, h); |
| |
| 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(!run_img2col_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); |
| |
| // 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)); |
| } |
| |
| 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); |
| |
| // 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 slice = window_collapsed.first_slice_window_3D(); |
| Window slice_in = window_collapsed.first_slice_window_3D(); |
| Window slice_out = window_collapsed.first_slice_window_3D(); |
| |
| // Setup slice if stride_x != 0 or stride_y != 0 |
| if(_convolved_dims.first != _input->info()->dimension(0) || _convolved_dims.second != _input->info()->dimension(1)) |
| { |
| // 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(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1)); |
| slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_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)); |
| |
| // Setup output slice |
| slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _kernel_dims.area())); |
| slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1)); |
| slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, slice_in); |
| add_2D_tensor_argument(idx, _output, slice_out); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->strides_in_bytes()[3])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[3])); |
| enqueue(queue, *this, slice, _lws_hint); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice) && window_collapsed.slide_window_slice_3D(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)); |
| } |