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/*
* 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/helpers/WindowHelpers.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include "src/cpu/kernels/conv3d/neon/quantized.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 (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
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