From 900289936c458eff95499e0a0eaba989a27aaa4d Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Wed, 30 Jun 2021 18:29:18 +0100 Subject: Port NEIm2ColKernel Resolves: COMPMID-4510 Change-Id: Ia3e588f599449d975dabad4afafb2974dd44d0ad Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5899 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- src/core/NEON/kernels/NEIm2ColKernel.cpp | 460 ------------------------------- 1 file changed, 460 deletions(-) delete mode 100644 src/core/NEON/kernels/NEIm2ColKernel.cpp (limited to 'src/core/NEON/kernels/NEIm2ColKernel.cpp') diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp deleted file mode 100644 index a28a77a4fb..0000000000 --- a/src/core/NEON/kernels/NEIm2ColKernel.cpp +++ /dev/null @@ -1,460 +0,0 @@ -/* - * Copyright (c) 2017-2021 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/core/NEON/kernels/NEIm2ColKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.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 "src/core/CPP/Validate.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include "arm_compute/core/utils/misc/ShapeCalculator.h" - -#include -#include -#include -#include -#include - -using namespace arm_compute; -using namespace misc::shape_calculator; - -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups) -{ - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(input->data_type()) && has_bias); - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Number of groups greater than one are not supported on Neon"); - - // 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(input->data_layout(), DataLayoutDimension::WIDTH); - const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - const unsigned total_width = input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); - const unsigned total_height = input->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(output->total_size() > 0) - { - TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false))); - - const DataLayout data_layout = input->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); - - std::pair convolved_dims = scaled_dimensions(input->dimension(width_idx), input->dimension(height_idx), - kernel_dims.width, kernel_dims.height, - conv_info, dilation); - - Window win = calculate_max_window(*input, Steps()); - win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1)); - win.set(height_idx, Window::Dimension(0, convolved_dims.second, 1)); - win.set(channel_idx, Window::Dimension(0, 1, 1)); - - // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped - - return std::make_pair(Status{}, win); -} - -template -inline void linearize_volume_nchw(const uint8_t *const in_ptr, - T *out_ptr, - bool has_bias, - int top_left_x, - int top_left_y, - int kernel_width, - int kernel_height, - int kernel_depth, - int input_w, - int input_h, - int input_stride_x, - int input_stride_y, - int input_stride_z, - int pad_value, - int dilation_x, - int dilation_y) -{ - const int kernel_size2 = kernel_width * kernel_height; - const int x_e = top_left_x + kernel_width * dilation_x; - const int y_e = top_left_y + kernel_height * dilation_y; - - // Linearize volume - int d = 0; - // This for loop linearize a volume with 3 slices. This allows: - // 1) to reduce the iterations of the outer for loop "d" - // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs - for(; d <= (kernel_depth - 3); d += 3) - { - for(int y = top_left_y; y < y_e; y += dilation_y) - { - if((y < 0 || y >= input_h) && has_pads) - { - // All the values will be the offset (will be zeros when not quantized) - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) - { - *(out_ptr + 0 * kernel_size2) = pad_value; - *(out_ptr + 1 * kernel_size2) = pad_value; - *(out_ptr + 2 * kernel_size2) = pad_value; - } - } - else - { - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) - { - if((x < 0 || x >= input_w) && has_pads) - { - *(out_ptr + 0 * kernel_size2) = pad_value; - *(out_ptr + 1 * kernel_size2) = pad_value; - *(out_ptr + 2 * kernel_size2) = pad_value; - } - else - { - *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); - *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); - *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); - } - } - } - } - out_ptr += 2 * kernel_size2; - } - - // Left over - for(; d < kernel_depth; d++) - { - for(int y = top_left_y; y < y_e; y += dilation_y) - { - if((y < 0 || y >= input_h) && has_pads) - { - // All the values will be the offset (will be zeros when not quantized) - memset(static_cast(out_ptr), pad_value, kernel_width * sizeof(T)); - out_ptr += kernel_width; - } - else - { - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) - { - if((x < 0 || x >= input_w) && has_pads) - { - *out_ptr = pad_value; - } - else - { - *out_ptr = *(reinterpret_cast(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); - } - } - } - } - } - - // Append 1 if the convolution layer has biases - if(has_bias) - { - *out_ptr = static_cast(1); - } -} - -template -inline void linearize_volume_nhwc(const uint8_t *const in_ptr, - T *out_ptr, - bool has_bias, - int start_x, - int start_y, - int kernel_width, - int kernel_height, - int input_w, - int input_h, - int input_c, - int input_stride_y, - int input_stride_z, - int pad_value, - int dilation_x, - int dilation_y) -{ - const int end_x = start_x + kernel_width * dilation_x; - const int end_y = start_y + kernel_height * dilation_y; - const int pad_quant = kernel_width * input_c; - const int element_size = static_cast(sizeof(T)); - if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == input_c * element_size)) - { - for(int y = start_y; y < end_y; y += dilation_y) - { - //optimized for no dilation and no boundary pixels - memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); - out_ptr += input_c * kernel_width; - } - } - else - { - for(int y = start_y; y < end_y; y += dilation_y) - { - if(y < 0 || y >= input_h) - { - memset(static_cast(out_ptr), pad_value, pad_quant * element_size); - out_ptr += pad_quant; - } - else if(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) - { - for(int x = start_x; x < end_x; x += dilation_x) - { - if(x < 0 || x >= input_w) - { - memset(static_cast(out_ptr), pad_value, input_c * element_size); - out_ptr += input_c; - } - else - { - memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * element_size); - out_ptr += input_c; - } - } - } - else - { - //optimized for no dilation and no boundary pixels - memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); - out_ptr += input_c * kernel_width; - } - } - } - // Append 1 if the convolution layer has biases - if(has_bias) - { - *out_ptr = static_cast(1); - } -} -} // namespace - -template -void NEIm2ColKernel::run_im2col(const Window &window) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - 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 int input_w = _input->info()->dimension(width_idx); - const int input_h = _input->info()->dimension(height_idx); - const int input_c = _input->info()->dimension(channel_idx); - const int input_stride_x = _input->info()->strides_in_bytes().x(); - const int input_stride_y = _input->info()->strides_in_bytes().y(); - const int input_stride_z = _input->info()->strides_in_bytes().z(); - const int pad_left = _conv_info.pad_left(); - const int pad_top = _conv_info.pad_top(); - const int stride_x = _conv_info.stride().first; - const int stride_y = _conv_info.stride().second; - const int pad_value = is_data_type_quantized(_input->info()->data_type()) ? _input->info()->quantization_info().uniform().offset : 0; - - Window window_in_out(window); - // The first three dimensions of the input and output are increased by the inner loops - window_in_out.set(Window::DimX, Window::Dimension(0, 0, 0)); - window_in_out.set(Window::DimY, Window::Dimension(0, 0, 0)); - window_in_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - // Create iterators - Iterator in(_input, window_in_out); - Iterator out(_output, window_in_out); - - execute_window_loop(window, [&](const Coordinates & id) - { - const int start_w = id[width_idx] * stride_x - pad_left; - const int start_h = id[height_idx] * stride_y - pad_top; - - // Get pointers - const uint8_t *const input_ptr = in.ptr(); - auto output_ptr = reinterpret_cast(out.ptr() + (id[width_idx] + id[height_idx] * _convolved_dims.first) * _output->info()->strides_in_bytes().y()); - - // Linearize volume - if(is_nchw) - { - linearize_volume_nchw(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_c, - input_w, - input_h, - input_stride_x, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y()); - } - else - { - linearize_volume_nhwc(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_w, - input_h, - input_c, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y()); - } - }, - in, out); -} - -NEIm2ColKernel::NEIm2ColKernel() - : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1U, 1U), _data_layout(DataLayout::UNKNOWN) -{ -} - -void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups)); - ARM_COMPUTE_UNUSED(num_groups); - - _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); - - _input = input; - _output = output; - _conv_info = conv_info; - _kernel_width = kernel_dims.width; - _kernel_height = kernel_dims.height; - _dilation = dilation; - _convolved_dims = scaled_dimensions(input->info()->dimension(width_idx), input->info()->dimension(height_idx), - _kernel_width, _kernel_height, - _conv_info, _dilation); - _has_bias = has_bias; - - if(_data_layout == DataLayout::NCHW) - { - switch(_input->info()->data_type()) - { - case DataType::F32: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) - case DataType::BFLOAT16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::QASYMM8_SIGNED: - case DataType::QASYMM8: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - else - { - switch(_input->info()->data_type()) - { - case DataType::F32: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) - case DataType::BFLOAT16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::QASYMM8: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; - case DataType::QASYMM8_SIGNED: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - - // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, 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(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation).first); - return Status{}; -} - -void NEIm2ColKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - (this->*_func)(window); -} -- cgit v1.2.1