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Diffstat (limited to 'src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp')
| -rw-r--r-- | src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp new file mode 100644 index 0000000000..003f1f8330 --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp @@ -0,0 +1,219 @@ +/* + * Copyright (c) 2017 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/CLDepthwiseConvolutionLayer3x3Kernel.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/ICLKernel.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +using namespace arm_compute; + +namespace +{ +/** Calculates expected output shape dimension + * + * @param[in] Input shape + * + * @return Expected output shape + */ +TensorShape get_output_shape(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info) +{ + unsigned int output_width = 0; + unsigned int output_height = 0; + + std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info); + + TensorShape output_shape = input_shape; + output_shape.set(0, output_width); + output_shape.set(1, output_height); + + return output_shape; +} +} // namespace + +CLDepthwiseConvolutionLayer3x3Kernel::CLDepthwiseConvolutionLayer3x3Kernel() + : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0) +{ +} + +BorderSize CLDepthwiseConvolutionLayer3x3Kernel::border_size() const +{ + return _border_size; +} + +void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3); + + if(biases != nullptr) + { + if(is_data_type_quantized_asymmetric(weights->info()->data_type())) + { + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); + } + ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2)); + ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); + } + + // Get convolved dimensions + TensorShape output_shape = get_output_shape(input->info()->tensor_shape(), weights->info()->tensor_shape(), conv_info); + + // 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(), + input->info()->quantization_info()); + + ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); + + _input = input; + _output = output; + _weights = weights; + _biases = biases; + _conv_stride_x = conv_info.stride().first; + _conv_stride_y = conv_info.stride().second; + _conv_pad_left = conv_info.pad_left(); + _conv_pad_top = conv_info.pad_top(); + _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left); + + // Set build options + ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3); + CLBuildOptions build_opts; + build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); + build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); + + // Create kernel + std::string kernel_name = is_data_type_quantized_asymmetric(_input->info()->data_type()) ? "depthwise_convolution_3x3_quantized" : "depthwise_convolution_3x3"; + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + // Set static arguments + if(is_data_type_quantized_asymmetric(_input->info()->data_type())) + { + float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale; + int output_multiplier = 0; + int output_shift = 0; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0); + + _kernel.setArg(idx++, -_input->info()->quantization_info().offset); + _kernel.setArg(idx++, -_weights->info()->quantization_info().offset); + _kernel.setArg(idx++, _output->info()->quantization_info().offset); + _kernel.setArg(idx++, output_multiplier); + _kernel.setArg(idx++, output_shift); + } + + // Configure the local work size for Bifrost with a value obtained + // via exhaustive autotuning for the MobileNets tensor shapes. + const GPUTarget gpu_target = get_arch_from_target(get_target()); + if(gpu_target == GPUTarget::BIFROST) + { + const size_t width = input->info()->dimension(0); + if(width >= 56) // 56 or 112 + { + _lws_hint = cl::NDRange(8, 5, 2); + } + else if(width >= 14) // 14 or 28 + { + _lws_hint = cl::NDRange(1, 5, 2); + } + else // 7 + { + _lws_hint = cl::NDRange(1, 1, 2); + } + } + + // Configure kernel window + const unsigned int num_elems_processed_per_iteration = 2; + const unsigned int num_elems_written_per_iteration = 2; + const unsigned int num_elems_read_per_iteration = 3 + _conv_stride_x; + const unsigned int num_rows_read_per_iteration = 3; + + Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); + + AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration, _conv_stride_x, _conv_stride_y); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); + AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1)); + + update_window_and_padding(win, input_access, weights_access, output_access); + + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + + ICLKernel::configure(win); +} + +void CLDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + // Create input window and adjust + Window win_in = window; + win_in.adjust(Window::DimX, -_conv_pad_left, true); + win_in.adjust(Window::DimY, -_conv_pad_top, true); + win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x); + win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y); + + Window slice_in = win_in.first_slice_window_3D(); + Window slice_out = window.first_slice_window_3D(); + Window slice_weights = window.first_slice_window_3D(); + slice_weights.set_dimension_step(Window::DimX, 0); + slice_weights.set_dimension_step(Window::DimY, 0); + + // Set biases + if(_biases != nullptr) + { + unsigned int idx = 3 * num_arguments_per_3D_tensor(); + Window slice_biases; + slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); + add_1D_tensor_argument(idx, _biases, slice_biases); + } + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice_in); + add_3D_tensor_argument(idx, _output, slice_out); + add_3D_tensor_argument(idx, _weights, slice_weights); + + enqueue(queue, *this, slice_out, _lws_hint); + } + while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in)); +} |