From 7068f9900d136312318ff430aef588b14e0c87ad Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Thu, 26 Oct 2017 15:23:08 +0100 Subject: COMPMID-631: Merge branches/gles_compute branch Last commit: commit b25c5f68042b0c81bf611d59a1bb8535e1c42497 Author: Xinghang Zhou Date: Wed Oct 25 18:48:10 2017 +0800 Synced validation's tolerances of GCSoftmax from cl side Change-Id: Ibe72054205c1c8721845d679a31af7ed0a7c5cf6 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/93283 Reviewed-by: Anthony Barbier Tested-by: Kaizen --- .../GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp | 254 +++++++++++++++++++++ 1 file changed, 254 insertions(+) create mode 100644 src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp (limited to 'src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp') diff --git a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp new file mode 100644 index 0000000000..c877da3783 --- /dev/null +++ b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp @@ -0,0 +1,254 @@ +/* + * 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/GLES_COMPUTE/kernels/GCPoolingLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" +#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h" +#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h" +#include "arm_compute/core/GLES_COMPUTE/OpenGLES.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include +#include +#include + +using namespace arm_compute; + +GCPoolingLayerKernel::GCPoolingLayerKernel() + : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1) +{ +} + +BorderSize GCPoolingLayerKernel::border_size() const +{ + return _border_size; +} + +void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const PoolingLayerInfo &pool_info) +{ + int pool_pad_x = 0; + int pool_pad_y = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + unsigned int pooled_w = 0; + unsigned int pooled_h = 0; + const PoolingType pool_type = pool_info.pool_type(); + const int pool_size = pool_info.pool_size(); + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); + std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); + std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); + + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size); + ARM_COMPUTE_ERROR_ON(pool_size > 7 && is_data_type_fixed_point(input->info()->data_type())); + + // Check output dimensions + std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), + input->info()->dimension(1), + pool_size, + pool_size, + pool_info.pad_stride_info()); + + // Output auto initialization if not yet initialized + { + TensorShape output_shape{ input->info()->tensor_shape() }; + output_shape.set(0, pooled_w); + output_shape.set(1, pooled_h); + + auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); + } + + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h)); + + const int input_width = input->info()->dimension(0); + const int input_height = input->info()->dimension(1); + + // Set instance variables + _input = input; + _output = output; + _pool_info = pool_info; + _border_size = BorderSize(pool_pad_y, pool_pad_x); + + // Set build options + std::set build_opts; + build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1)); + build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1)); + build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1)); + if(input->info()->data_type() == DataType::F32) + { + build_opts.insert("#define DATA_TYPE_FP32"); + } + else + { + build_opts.insert("#define DATA_TYPE_FP16"); + } + build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type))); + build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x))); + build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + pool_pad_x))); + build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + pool_pad_y))); + build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y))); + build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x))); + build_opts.emplace(("#define PAD_Y " + support::cpp11::to_string(pool_pad_y))); + + // Create kernel + if((pool_size == 2) || (pool_size == 3) || (pool_size == 7)) + { + // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where + // each thread computes 4 output elements + const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type()); + + int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size; + + if(input->info()->data_type() == DataType::F32) + { + if(is_pool3x3_stride_le3) + { + // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3 + _num_elems_processed_per_iteration = 4; + num_elements_read_per_iteration = pool_size * (pool_stride_x + 1); + } + } + else + { + num_elements_read_per_iteration = pool_size; + if(is_pool3x3_stride_le3) + { + _num_elems_processed_per_iteration = 4; + } + else + { + _num_elems_processed_per_iteration = 2; + } + } + + const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; + + _border_size.right = std::max(upper_bound_w, pool_pad_x); + _border_size.bottom = std::max(upper_bound_h, pool_pad_y); + + std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size); + if(is_pool3x3_stride_le3) + { + build_opts.insert("#define POOLING_LAYER_3_OPTIMIZED"); + _kernel = static_cast(GCKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts)); + } + else + { + build_opts.insert("#define POOLING_LAYER_" + support::cpp11::to_string(pool_size)); + _kernel = static_cast(GCKernelLibrary::get().create_kernel(kernel_name, build_opts)); + } + } + else // Run general case + { + if(input->info()->data_type() == DataType::F32) + { + _num_elems_processed_per_iteration = 1; + } + else + { + _num_elems_processed_per_iteration = 2; + } + const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; + + _border_size.right = std::max(upper_bound_w, pool_pad_x); + _border_size.bottom = std::max(upper_bound_h, pool_pad_y); + + build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size))); + + build_opts.insert("#define POOLING_LAYER_N"); + _kernel = static_cast(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts)); + } + + Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration)); + + if(input->info()->data_type() == DataType::F32) + { + AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); + AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration); + update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + } + else + { + // Calculate output right and bottom border + const int output_width = output->info()->dimension(0); + const int output_height = output->info()->dimension(1); + const int output_padding_right = ceil_to_multiple(output_width, _num_elems_processed_per_iteration) - output_width; + const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height; + const int input_padding_right = ceil_to_multiple(input_width + 2 * _border_size.right, _num_elems_processed_per_iteration) - (input_width + 2 * _border_size.right); + const int input_padding_bottom = ceil_to_multiple(input_height + 2 * _border_size.bottom, 1) - (input_height + 2 * _border_size.bottom); + + // Configure kernel window + AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right + input_padding_right, input_height + _border_size.bottom + input_padding_bottom); + AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom); + update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + } + + _kernel.clear_params(); + _kernel.set_shader_params_binding_point(0); + + IGCKernel::configure(win); +} + +void GCPoolingLayerKernel::run(const Window &window) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0; + std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); + + _kernel.use(); + + Window window_collapsed = window.collapse_if_possible(IGCKernel::window(), Window::DimZ); + Window slice = window_collapsed.first_slice_window_3D(); + + do + { + // Upsample input by pool size + Window in_slice(slice); + in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration)); + in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y)); + + // Set inputs + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, 1, in_slice); + add_3D_tensor_argument(idx, _output, 2, slice); + + _kernel.update_shader_params(); + enqueue(*this, slice); + } + while(window_collapsed.slide_window_slice_3D(slice)); +} -- cgit v1.2.1