src/cpu/kernels/conv3d/neon/list.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 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.
  */
 #ifndef SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
 #define SRC_CORE_NEON_KERNELS_CONV3D_LIST_H

 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/Traits.h"
 #include "arm_compute/runtime/FunctionDescriptors.h"
 #include "src/core/NEON/wrapper/wrapper.h"
 #include "src/core/helpers/WindowHelpers.h"

 namespace arm_compute
 {
 namespace cpu
 {
 template <typename T>
 void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window)
 {
     const ITensor *src     = src0;
     const ITensor *weights = src1;
     const ITensor *biases  = src2;

     using vtype                                = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
     using vector_type                          = typename vtype::type;
     using tag_type                             = typename vtype::tag_type;
     constexpr int num_elems_read_per_iteration = 16 / sizeof(T);

     // Scalar quantities (N D H W Cin)
     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_d = src->info()->strides_in_bytes()[3] / element_size;
     const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size;
     const int input_dim_w    = src->info()->dimension(1);
     const int input_dim_h    = src->info()->dimension(2);
     const int input_dim_d    = src->info()->dimension(3);

     // Kernel info (D H W Cin Cout)
     const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size;
     const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size;
     const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size;
     const int          kernel_dim_w    = weights->info()->dimension(2);
     const int          kernel_dim_h    = weights->info()->dimension(3);
     const int          kernel_dim_d    = weights->info()->dimension(4);

     // Convolution padding and stride
     const int conv_pad_top   = conv_info.padding.top;
     const int conv_pad_left  = conv_info.padding.left;
     const int conv_pad_front = conv_info.padding.front;
     const int conv_stride_w  = conv_info.stride.width;
     const int conv_stride_h  = conv_info.stride.height;
     const int conv_stride_d  = conv_info.stride.depth;

     // 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::DimY, Window::Dimension(0, 1, 1));
     window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
     window_w.set(Window::DimW, Window::Dimension(0, 1, 1));
     window_w.set(4, Window::Dimension(0, 1, 1));

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

     const T *biases_ptr = nullptr;
     if(biases != nullptr)
     {
         biases_ptr = reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
     }
     execute_window_loop(window_out, [&](const Coordinates & id)
     {
         // We are computing the theoretical 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_d_start_t = static_cast<int>(id[3]) * conv_stride_d - conv_pad_front;
         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;
         const int in_d_end_t   = in_d_start_t + kernel_dim_d;

         // 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_d_start = std::max(in_d_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);
         const int in_d_end   = std::min(in_d_end_t, input_dim_d);

         // 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_d_start = in_d_start - in_d_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 wei_d_end   = kernel_dim_d - (in_d_end_t - in_d_end);

         const int      index_c_out_end = weights->info()->dimension(0);
         const int      index_c_in_end  = weights->info()->dimension(1);
         const T *const in_ptr_start    = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[4] * input_stride_n;

         execute_window_loop(window_w, [&](const Coordinates & id_w)
         {
             /*
             * This is the loop in the weights, and it goes along OFM (output feature map)
             */
             const auto weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
             T          out_temp          = static_cast<T>(0);
             T         *out_ptr           = reinterpret_cast<T *>(out.ptr());
             for(int index_wei_d = wei_d_start, index_in_d = in_d_start; index_wei_d < wei_d_end; ++index_wei_d, ++index_in_d)
             {
                 const auto in_ptr_d      = in_ptr_start + index_in_d * input_stride_d;
                 const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d;
                 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_d + index_in_h * input_stride_h;
                     const T *const weights_ptr_row = weights_ptr_d + 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_in        = 0;
                         vector_type out_temp_vec      = wrapper::vdup_n(static_cast<T>(0), tag_type());
                         vector_type w_vec             = wrapper::vdup_n(static_cast<T>(0), tag_type());
                         for(; index_c_in <= index_c_in_end - num_elems_read_per_iteration;
                             index_c_in += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
                         {
                             const auto src_vec = wrapper::vloadq(in_ptr_mover);
                             //Load Cin weights
                             for(unsigned int k = 0; k < num_elems_read_per_iteration; ++k, weights_ptr_mover += index_c_out_end)
                             {
                                 w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k);
                             }
                             out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
                         }
                         out_temp += vreduce(out_temp_vec);
                         for(; index_c_in < index_c_in_end; ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end)
                         {
                             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 + id_w[0])) = (biases_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp;
         },
         wei);
     },
     out);
 }
 } // namespace cpu
 } // namespace arm_compute
 #endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
	/*
	* Copyright (c) 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.
	*/
	#ifndef SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
	#define SRC_CORE_NEON_KERNELS_CONV3D_LIST_H

	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/Traits.h"
	#include "arm_compute/runtime/FunctionDescriptors.h"
	#include "src/core/NEON/wrapper/wrapper.h"
	#include "src/core/helpers/WindowHelpers.h"

	namespace arm_compute
	{
	namespace cpu
	{
	template <typename T>
	void directconv3d_float_neon_ndhwc(const ITensor src0, const ITensor src1, const ITensor src2, ITensor dst, const Conv3dInfo &conv_info, const Window &window)
	{
	const ITensor *src = src0;
	const ITensor *weights = src1;
	const ITensor *biases = src2;

	using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
	using vector_type = typename vtype::type;
	using tag_type = typename vtype::tag_type;
	constexpr int num_elems_read_per_iteration = 16 / sizeof(T);

	// Scalar quantities (N D H W Cin)
	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_d = src->info()->strides_in_bytes()[3] / element_size;
	const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size;
	const int input_dim_w = src->info()->dimension(1);
	const int input_dim_h = src->info()->dimension(2);
	const int input_dim_d = src->info()->dimension(3);

	// Kernel info (D H W Cin Cout)
	const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size;
	const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size;
	const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size;
	const int kernel_dim_w = weights->info()->dimension(2);
	const int kernel_dim_h = weights->info()->dimension(3);
	const int kernel_dim_d = weights->info()->dimension(4);

	// Convolution padding and stride
	const int conv_pad_top = conv_info.padding.top;
	const int conv_pad_left = conv_info.padding.left;
	const int conv_pad_front = conv_info.padding.front;
	const int conv_stride_w = conv_info.stride.width;
	const int conv_stride_h = conv_info.stride.height;
	const int conv_stride_d = conv_info.stride.depth;

	// 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::DimY, Window::Dimension(0, 1, 1));
	window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
	window_w.set(Window::DimW, Window::Dimension(0, 1, 1));
	window_w.set(4, Window::Dimension(0, 1, 1));

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

	const T *biases_ptr = nullptr;
	if(biases != nullptr)
	{
	biases_ptr = reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
	}
	execute_window_loop(window_out, [&](const Coordinates & id)
	{
	// We are computing the theoretical 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_d_start_t = static_cast<int>(id[3]) * conv_stride_d - conv_pad_front;
	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;
	const int in_d_end_t = in_d_start_t + kernel_dim_d;

	// 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_d_start = std::max(in_d_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);
	const int in_d_end = std::min(in_d_end_t, input_dim_d);

	// 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_d_start = in_d_start - in_d_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 wei_d_end = kernel_dim_d - (in_d_end_t - in_d_end);

	const int index_c_out_end = weights->info()->dimension(0);
	const int index_c_in_end = weights->info()->dimension(1);
	const T const in_ptr_start = reinterpret_cast<const T >(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[4] * input_stride_n;

	execute_window_loop(window_w, [&](const Coordinates & id_w)
	{
	/*
	* This is the loop in the weights, and it goes along OFM (output feature map)
	*/
	const auto weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
	T out_temp = static_cast<T>(0);
	T out_ptr = reinterpret_cast<T >(out.ptr());
	for(int index_wei_d = wei_d_start, index_in_d = in_d_start; index_wei_d < wei_d_end; ++index_wei_d, ++index_in_d)
	{
	const auto in_ptr_d = in_ptr_start + index_in_d * input_stride_d;
	const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d;
	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_d + index_in_h input_stride_h;
	const T const weights_ptr_row = weights_ptr_d + 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_in = 0;
	vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
	vector_type w_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
	for(; index_c_in <= index_c_in_end - num_elems_read_per_iteration;
	index_c_in += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
	{
	const auto src_vec = wrapper::vloadq(in_ptr_mover);
	//Load Cin weights
	for(unsigned int k = 0; k < num_elems_read_per_iteration; ++k, weights_ptr_mover += index_c_out_end)
	{
	w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k);
	}
	out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
	}
	out_temp += vreduce(out_temp_vec);
	for(; index_c_in < index_c_in_end; ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end)
	{
	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 + id_w[0])) = (biases_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp;
	},
	wei);
	},
	out);
	}
	} // namespace cpu
	} // namespace arm_compute
	#endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H