src/cpu/kernels/directconv2d/nhwc/neon/impl.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2022 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/cpu/kernels/directconv2d/nhwc/neon/impl.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/IAccessWindow.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"

 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h"
 #include "src/core/NEON/wrapper/wrapper.h"

 #include <algorithm>

 using namespace arm_compute::detail;

 namespace arm_compute
 {
 namespace cpu
 {
 namespace kernels
 {
 namespace
 {
 bool have_zero_x_internal_padding(ITensorInfo *src, const ITensorInfo *weights)
 {
     return (src->padding().left == 0 && weights->padding().left == 0 && src->padding().right == 0 &&
             weights->padding().right == 0);
 }
 } // namespace

 template <typename T>
 void convolve_nhwc(
     const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
 {
     // Declare useful types
     using vtype       = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
     using vector_type = typename vtype::type;
     using tag_type    = typename vtype::tag_type;

     // Scalar quantities
     const int element_size   = src->info()->element_size();
     const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
     const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
     const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
     const int input_dim_w    = src->info()->dimension(1);
     const int input_dim_h    = src->info()->dimension(2);

     const int output_stride_c = dst->info()->strides_in_bytes().x();

     const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size;
     const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size;
     const int          kernel_dim_w    = weights->info()->dimension(1);
     const int          kernel_dim_h    = weights->info()->dimension(2);

     const int conv_pad_top  = conv_info.pad_top();
     const int conv_pad_left = conv_info.pad_left();
     const int conv_stride_w = std::get<0>(conv_info.stride());
     const int conv_stride_h = std::get<1>(conv_info.stride());

     // Setup input window for the output iterator
     Window window_out = window;
     window_out.set(Window::DimX, Window::Dimension(0, 1, 1));

     // Setup input window for the weights iterator
     Window window_w = calculate_max_window(*weights->info(), Steps());
     window_w.set(Window::DimX, Window::Dimension(0, 1, 1));
     window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
     window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));

     Iterator out(dst, window_out);
     Iterator wei(weights, window_w);

     constexpr int num_elems_read_per_iteration = 16 / sizeof(T);

     // nhwc optimized
     if (have_zero_x_internal_padding(src->info(), weights->info()))
     {
         // This function assumes that input and weights have not padding in channel

         /*
         * This implementation parallelize the full WC plane of input and weights by
         * treating them as series of elements. So for example, a 3x3 weights and
         * floating point vector operations of 4 elements per time, the first 3
         * channel elements of the first row would be taken and additionally the first
         * element of the second row. The 9 elements in each single WC weight plane
         * would require 2 4-element vector operations and a last single element operation.
         *
         * This works since when we create the input vector to multiply with the weights,
         * the exact required elements are loaded in the same order. Therefore the
         * multiplication works on the correct input/weight elements.
         */
         execute_window_loop(
             window_out,
             [&](const Coordinates &id)
             {
                 /*
             * In here we create theoretical indexes which then we validate for both
             * inputs and weights.
             * As a reminder, this loop take each output point in NHW, C is treated
             * in the weights loop.
             */
                 // We are computing the theoretical starting input starting points
                 const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
                 const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
                 const int in_w_end_t   = in_w_start_t + kernel_dim_w;
                 const int in_h_end_t   = in_h_start_t + kernel_dim_h;

                 // We are computing the valid initial and ending input points by checking the borders
                 const int in_w_start = std::max(in_w_start_t, 0);
                 const int in_h_start = std::max(in_h_start_t, 0);
                 const int in_w_end   = std::min(in_w_end_t, input_dim_w);
                 const int in_h_end   = std::min(in_h_end_t, input_dim_h);

                 // We use the input points to select the valid weight points to use
                 const int index_wc_start = (in_w_start - in_w_start_t) * kernel_stride_w;
                 const int index_h_start  = in_h_start - in_h_start_t;
                 const int index_wc_end   = (kernel_dim_w - (in_w_end_t - in_w_end)) * kernel_stride_w;
                 const int index_h_end    = kernel_dim_h - (in_h_end_t - in_h_end);

                 execute_window_loop(
                     window_w,
                     [&](const Coordinates &id_w)
                     {
                         /*
                 * This is the loop in the weights, and it goes along N (the batches)
                 * As a reminder, the batches of the weights are translated into the
                 * channels of the output
                 */
                         const T *in_ptr_row =
                             reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) +
                             id[3] * input_stride_n + in_w_start * input_stride_w + in_h_start * input_stride_h;
                         const T *weights_ptr_row =
                             reinterpret_cast<const T *>(wei.ptr()) + index_h_start * kernel_stride_h;
                         uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;

                         T out_temp = static_cast<T>(0);
                         for (int index_h = index_h_start; index_h < index_h_end;
                              ++index_h, in_ptr_row += input_stride_h, weights_ptr_row += kernel_stride_h)
                         {
                             const T    *in_ptr_mover = in_ptr_row;
                             int         index_wc     = index_wc_start;
                             vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
                             for (; index_wc <= index_wc_end - num_elems_read_per_iteration;
                                  index_wc += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
                             {
                                 const auto src_vec = wrapper::vloadq(in_ptr_mover);
                                 const auto w_vec   = wrapper::vloadq(weights_ptr_row + index_wc);
                                 out_temp_vec       = wrapper::vmla(out_temp_vec, w_vec, src_vec);
                             }
                             out_temp += vreduce(out_temp_vec);
                             for (; index_wc < index_wc_end; ++index_wc, ++in_ptr_mover)
                             {
                                 const auto src_val = *(in_ptr_mover);
                                 const auto w_val   = *(weights_ptr_row + index_wc);
                                 out_temp += src_val * w_val;
                             }
                         }
                         *(reinterpret_cast<T *>(out_ptr)) = out_temp;
                     },
                     wei);
             },
             out);
     }
     else // nhwc non optimized
     {
         execute_window_loop(
             window_out,
             [&](const Coordinates &id)
             {
                 // We are computing the theoretical starting input starting points
                 const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
                 const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
                 const int in_w_end_t   = in_w_start_t + kernel_dim_w;
                 const int in_h_end_t   = in_h_start_t + kernel_dim_h;

                 // We are computing the valid initial and ending input points by checking the borders
                 const int in_w_start = std::max(in_w_start_t, 0);
                 const int in_h_start = std::max(in_h_start_t, 0);
                 const int in_w_end   = std::min(in_w_end_t, input_dim_w);
                 const int in_h_end   = std::min(in_h_end_t, input_dim_h);

                 // We use the input points to select the valid weight points to use
                 const int wei_w_start = in_w_start - in_w_start_t;
                 const int wei_h_start = in_h_start - in_h_start_t;
                 const int wei_w_end   = kernel_dim_w - (in_w_end_t - in_w_end);
                 const int wei_h_end   = kernel_dim_h - (in_h_end_t - in_h_end);

                 const int      index_c_end = weights->info()->dimension(0);
                 const T *const in_ptr_start =
                     reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) +
                     id[3] * input_stride_n;

                 execute_window_loop(
                     window_w,
                     [&](const Coordinates &id_w)
                     {
                         const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
                         uint8_t       *out_ptr           = out.ptr() + id_w[3] * output_stride_c;

                         T out_temp = static_cast<T>(0);
                         for (int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end;
                              ++index_wei_h, ++index_in_h)
                         {
                             const T *const in_ptr_row      = in_ptr_start + index_in_h * input_stride_h;
                             const T *const weights_ptr_row = weights_ptr_start + index_wei_h * kernel_stride_h;
                             for (int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end;
                                  ++index_wei_w, ++index_in_w)
                             {
                                 const T    *in_ptr_mover      = in_ptr_row + index_in_w * input_stride_w;
                                 const T    *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w;
                                 int         index_c           = 0;
                                 vector_type out_temp_vec      = wrapper::vdup_n(static_cast<T>(0), tag_type());
                                 for (; index_c <= index_c_end - num_elems_read_per_iteration;
                                      index_c += num_elems_read_per_iteration,
                                      in_ptr_mover += num_elems_read_per_iteration,
                                      weights_ptr_mover += num_elems_read_per_iteration)
                                 {
                                     const auto src_vec = wrapper::vloadq(in_ptr_mover);
                                     const auto w_vec   = wrapper::vloadq(weights_ptr_mover);
                                     out_temp_vec       = wrapper::vmla(out_temp_vec, w_vec, src_vec);
                                 }
                                 out_temp += vreduce(out_temp_vec);
                                 for (; index_c < index_c_end; ++index_c, ++in_ptr_mover, ++weights_ptr_mover)
                                 {
                                     const auto src_val = *(in_ptr_mover);
                                     const auto w_val   = *(weights_ptr_mover);
                                     out_temp += src_val * w_val;
                                 }
                             }
                         }
                         *(reinterpret_cast<T *>(out_ptr)) = out_temp;
                     },
                     wei);
             },
             out);
     }
 }

 template void convolve_nhwc<float>(
     const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);

 } // namespace kernels
 } // namespace cpu
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2022 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/cpu/kernels/directconv2d/nhwc/neon/impl.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/IAccessWindow.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"

	#include "src/core/helpers/WindowHelpers.h"
	#include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h"
	#include "src/core/NEON/wrapper/wrapper.h"

	#include <algorithm>

	using namespace arm_compute::detail;

	namespace arm_compute
	{
	namespace cpu
	{
	namespace kernels
	{
	namespace
	{
	bool have_zero_x_internal_padding(ITensorInfo src, const ITensorInfo weights)
	{
	return (src->padding().left == 0 && weights->padding().left == 0 && src->padding().right == 0 &&
	weights->padding().right == 0);
	}
	} // namespace

	template <typename T>
	void convolve_nhwc(
	const Window &window, const ITensor src, const ITensor weights, ITensor *dst, const PadStrideInfo &conv_info)
	{
	// Declare useful types
	using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
	using vector_type = typename vtype::type;
	using tag_type = typename vtype::tag_type;

	// Scalar quantities
	const int element_size = src->info()->element_size();
	const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
	const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
	const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
	const int input_dim_w = src->info()->dimension(1);
	const int input_dim_h = src->info()->dimension(2);

	const int output_stride_c = dst->info()->strides_in_bytes().x();

	const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size;
	const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size;
	const int kernel_dim_w = weights->info()->dimension(1);
	const int kernel_dim_h = weights->info()->dimension(2);

	const int conv_pad_top = conv_info.pad_top();
	const int conv_pad_left = conv_info.pad_left();
	const int conv_stride_w = std::get<0>(conv_info.stride());
	const int conv_stride_h = std::get<1>(conv_info.stride());

	// Setup input window for the output iterator
	Window window_out = window;
	window_out.set(Window::DimX, Window::Dimension(0, 1, 1));

	// Setup input window for the weights iterator
	Window window_w = calculate_max_window(*weights->info(), Steps());
	window_w.set(Window::DimX, Window::Dimension(0, 1, 1));
	window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
	window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));

	Iterator out(dst, window_out);
	Iterator wei(weights, window_w);

	constexpr int num_elems_read_per_iteration = 16 / sizeof(T);

	// nhwc optimized
	if (have_zero_x_internal_padding(src->info(), weights->info()))
	{
	// This function assumes that input and weights have not padding in channel

	/*
	* This implementation parallelize the full WC plane of input and weights by
	* treating them as series of elements. So for example, a 3x3 weights and
	* floating point vector operations of 4 elements per time, the first 3
	* channel elements of the first row would be taken and additionally the first
	* element of the second row. The 9 elements in each single WC weight plane
	* would require 2 4-element vector operations and a last single element operation.
	*
	* This works since when we create the input vector to multiply with the weights,
	* the exact required elements are loaded in the same order. Therefore the
	* multiplication works on the correct input/weight elements.
	*/
	execute_window_loop(
	window_out,
	[&](const Coordinates &id)
	{
	/*
	* In here we create theoretical indexes which then we validate for both
	* inputs and weights.
	* As a reminder, this loop take each output point in NHW, C is treated
	* in the weights loop.
	*/
	// We are computing the theoretical starting input starting points
	const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
	const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
	const int in_w_end_t = in_w_start_t + kernel_dim_w;
	const int in_h_end_t = in_h_start_t + kernel_dim_h;

	// We are computing the valid initial and ending input points by checking the borders
	const int in_w_start = std::max(in_w_start_t, 0);
	const int in_h_start = std::max(in_h_start_t, 0);
	const int in_w_end = std::min(in_w_end_t, input_dim_w);
	const int in_h_end = std::min(in_h_end_t, input_dim_h);

	// We use the input points to select the valid weight points to use
	const int index_wc_start = (in_w_start - in_w_start_t) * kernel_stride_w;
	const int index_h_start = in_h_start - in_h_start_t;
	const int index_wc_end = (kernel_dim_w - (in_w_end_t - in_w_end)) * kernel_stride_w;
	const int index_h_end = kernel_dim_h - (in_h_end_t - in_h_end);

	execute_window_loop(
	window_w,
	[&](const Coordinates &id_w)
	{
	/*
	* This is the loop in the weights, and it goes along N (the batches)
	* As a reminder, the batches of the weights are translated into the
	* channels of the output
	*/
	const T *in_ptr_row =
	reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) +
	id[3] * input_stride_n + in_w_start * input_stride_w + in_h_start * input_stride_h;
	const T *weights_ptr_row =
	reinterpret_cast<const T >(wei.ptr()) + index_h_start kernel_stride_h;
	uint8_t out_ptr = out.ptr() + id_w[3] output_stride_c;

	T out_temp = static_cast<T>(0);
	for (int index_h = index_h_start; index_h < index_h_end;
	++index_h, in_ptr_row += input_stride_h, weights_ptr_row += kernel_stride_h)
	{
	const T *in_ptr_mover = in_ptr_row;
	int index_wc = index_wc_start;
	vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
	for (; index_wc <= index_wc_end - num_elems_read_per_iteration;
	index_wc += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
	{
	const auto src_vec = wrapper::vloadq(in_ptr_mover);
	const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wc);
	out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
	}
	out_temp += vreduce(out_temp_vec);
	for (; index_wc < index_wc_end; ++index_wc, ++in_ptr_mover)
	{
	const auto src_val = *(in_ptr_mover);
	const auto w_val = *(weights_ptr_row + index_wc);
	out_temp += src_val * w_val;
	}
	}
	(reinterpret_cast<T >(out_ptr)) = out_temp;
	},
	wei);
	},
	out);
	}
	else // nhwc non optimized
	{
	execute_window_loop(
	window_out,
	[&](const Coordinates &id)
	{
	// We are computing the theoretical starting input starting points
	const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
	const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
	const int in_w_end_t = in_w_start_t + kernel_dim_w;
	const int in_h_end_t = in_h_start_t + kernel_dim_h;

	// We are computing the valid initial and ending input points by checking the borders
	const int in_w_start = std::max(in_w_start_t, 0);
	const int in_h_start = std::max(in_h_start_t, 0);
	const int in_w_end = std::min(in_w_end_t, input_dim_w);
	const int in_h_end = std::min(in_h_end_t, input_dim_h);

	// We use the input points to select the valid weight points to use
	const int wei_w_start = in_w_start - in_w_start_t;
	const int wei_h_start = in_h_start - in_h_start_t;
	const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end);
	const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);

	const int index_c_end = weights->info()->dimension(0);
	const T *const in_ptr_start =
	reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) +
	id[3] * input_stride_n;

	execute_window_loop(
	window_w,
	[&](const Coordinates &id_w)
	{
	const T const weights_ptr_start = reinterpret_cast<const T >(wei.ptr());
	uint8_t out_ptr = out.ptr() + id_w[3] output_stride_c;

	T out_temp = static_cast<T>(0);
	for (int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end;
	++index_wei_h, ++index_in_h)
	{
	const T const in_ptr_row = in_ptr_start + index_in_h input_stride_h;
	const T const weights_ptr_row = weights_ptr_start + index_wei_h kernel_stride_h;
	for (int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end;
	++index_wei_w, ++index_in_w)
	{
	const T in_ptr_mover = in_ptr_row + index_in_w input_stride_w;
	const T weights_ptr_mover = weights_ptr_row + index_wei_w kernel_stride_w;
	int index_c = 0;
	vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
	for (; index_c <= index_c_end - num_elems_read_per_iteration;
	index_c += num_elems_read_per_iteration,
	in_ptr_mover += num_elems_read_per_iteration,
	weights_ptr_mover += num_elems_read_per_iteration)
	{
	const auto src_vec = wrapper::vloadq(in_ptr_mover);
	const auto w_vec = wrapper::vloadq(weights_ptr_mover);
	out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
	}
	out_temp += vreduce(out_temp_vec);
	for (; index_c < index_c_end; ++index_c, ++in_ptr_mover, ++weights_ptr_mover)
	{
	const auto src_val = *(in_ptr_mover);
	const auto w_val = *(weights_ptr_mover);
	out_temp += src_val * w_val;
	}
	}
	}
	(reinterpret_cast<T >(out_ptr)) = out_temp;
	},
	wei);
	},
	out);
	}
	}

	template void convolve_nhwc<float>(
	const Window &window, const ITensor src, const ITensor weights, ITensor *dst, const PadStrideInfo &conv_info);

	} // namespace kernels
	} // namespace cpu
	} // namespace arm_compute