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/*
* Copyright (c) 2022-2023 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 ACL_SRC_CPU_KERNELS_DIRECTCONV2D_NCHW_IMPL_H
#define ACL_SRC_CPU_KERNELS_DIRECTCONV2D_NCHW_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>
namespace arm_compute
{
namespace cpu
{
namespace kernels
{
template <typename T>
void convolve_nchw(
const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
{
ARM_COMPUTE_UNUSED(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()[0] / element_size;
const int input_stride_h = src->info()->strides_in_bytes()[1] / element_size;
const int input_stride_c = src->info()->strides_in_bytes()[2] / element_size;
const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
const int input_dim_w = src->info()->dimension(0);
const int input_dim_h = src->info()->dimension(1);
const int output_stride_c = dst->info()->strides_in_bytes()[2];
const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().x() / element_size;
const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().y() / element_size;
const unsigned int kernel_stride_c = weights->info()->strides_in_bytes().z() / element_size;
const int kernel_dim_w = weights->info()->dimension(0);
const int kernel_dim_h = weights->info()->dimension(1);
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::DimZ, 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);
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.x()) * conv_stride_w - conv_pad_left;
const int in_h_start_t = static_cast<int>(id.y()) * 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_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
const int index_c_end = weights->info()->dimension(2);
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_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c)
{
const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c;
const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c;
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 *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h;
const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h;
int index_w = in_w_start;
int index_wei_w = wei_w_start;
vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
for (; index_w <= ((in_w_end - num_elems_read_per_iteration));
index_w += num_elems_read_per_iteration, index_wei_w += num_elems_read_per_iteration)
{
const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w);
const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w);
out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
}
out_temp += vreduce(out_temp_vec);
for (; index_w < in_w_end; ++index_w, ++index_wei_w)
{
const auto src_val = *(in_ptr_row + index_w * input_stride_w);
const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w);
out_temp += src_val * w_val;
}
}
}
*(reinterpret_cast<T *>(out_ptr)) = out_temp;
},
wei);
},
out);
}
} // namespace kernels
} // namespace cpu
} // namespace arm_compute
#endif // ACL_SRC_CPU_KERNELS_DIRECTCONV2D_NCHW_IMPL_H
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