1 /*
2 * Copyright (c) 2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #ifndef SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
25 #define SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
26
27 #include "arm_compute/core/Types.h"
28 #include "arm_compute/core/utils/misc/Traits.h"
29 #include "arm_compute/runtime/FunctionDescriptors.h"
30 #include "src/core/NEON/wrapper/wrapper.h"
31 #include "src/core/helpers/WindowHelpers.h"
32 #include "src/cpu/kernels/conv3d/neon/quantized.h"
33
34 namespace arm_compute
35 {
36 namespace cpu
37 {
38 template <typename T>
directconv3d_float_neon_ndhwc(const ITensor * src0,const ITensor * src1,const ITensor * src2,ITensor * dst,const Conv3dInfo & conv_info,const Window & window)39 void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window)
40 {
41 const ITensor *src = src0;
42 const ITensor *weights = src1;
43 const ITensor *biases = src2;
44
45 using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
46 using vector_type = typename vtype::type;
47 using tag_type = typename vtype::tag_type;
48 constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
49
50 // Scalar quantities (N D H W Cin)
51 const int element_size = src->info()->element_size();
52 const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
53 const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
54 const int input_stride_d = src->info()->strides_in_bytes()[3] / element_size;
55 const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size;
56 const int input_dim_w = src->info()->dimension(1);
57 const int input_dim_h = src->info()->dimension(2);
58 const int input_dim_d = src->info()->dimension(3);
59
60 // Kernel info (D H W Cin Cout)
61 const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size;
62 const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size;
63 const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size;
64 const int kernel_dim_w = weights->info()->dimension(2);
65 const int kernel_dim_h = weights->info()->dimension(3);
66 const int kernel_dim_d = weights->info()->dimension(4);
67
68 // Convolution padding and stride
69 const int conv_pad_top = conv_info.padding.top;
70 const int conv_pad_left = conv_info.padding.left;
71 const int conv_pad_front = conv_info.padding.front;
72 const int conv_stride_w = conv_info.stride.width;
73 const int conv_stride_h = conv_info.stride.height;
74 const int conv_stride_d = conv_info.stride.depth;
75
76 // Setup input window for the output iterator
77 Window window_out = window;
78 window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
79
80 // Setup input window for the weights iterator
81 Window window_w = calculate_max_window(*weights->info(), Steps());
82 window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
83 window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
84 window_w.set(Window::DimW, Window::Dimension(0, 1, 1));
85 window_w.set(4, Window::Dimension(0, 1, 1));
86
87 Iterator out(dst, window_out);
88 Iterator wei(weights, window_w);
89
90 const T *biases_ptr = nullptr;
91 if(biases != nullptr)
92 {
93 biases_ptr = reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
94 }
95 execute_window_loop(window_out, [&](const Coordinates & id)
96 {
97 // We are computing the theoretical input starting points
98 const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
99 const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
100 const int in_d_start_t = static_cast<int>(id[3]) * conv_stride_d - conv_pad_front;
101 const int in_w_end_t = in_w_start_t + kernel_dim_w;
102 const int in_h_end_t = in_h_start_t + kernel_dim_h;
103 const int in_d_end_t = in_d_start_t + kernel_dim_d;
104
105 // We are computing the valid initial and ending input points by checking the borders
106 const int in_w_start = std::max(in_w_start_t, 0);
107 const int in_h_start = std::max(in_h_start_t, 0);
108 const int in_d_start = std::max(in_d_start_t, 0);
109 const int in_w_end = std::min(in_w_end_t, input_dim_w);
110 const int in_h_end = std::min(in_h_end_t, input_dim_h);
111 const int in_d_end = std::min(in_d_end_t, input_dim_d);
112
113 // We use the input points to select the valid weight points to use
114 const int wei_w_start = in_w_start - in_w_start_t;
115 const int wei_h_start = in_h_start - in_h_start_t;
116 const int wei_d_start = in_d_start - in_d_start_t;
117 const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end);
118 const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
119 const int wei_d_end = kernel_dim_d - (in_d_end_t - in_d_end);
120
121 const int index_c_out_end = weights->info()->dimension(0);
122 const int index_c_in_end = weights->info()->dimension(1);
123 const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[4] * input_stride_n;
124
125 execute_window_loop(window_w, [&](const Coordinates & id_w)
126 {
127 /*
128 * This is the loop in the weights, and it goes along OFM (output feature map)
129 */
130 const auto weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
131 T out_temp = static_cast<T>(0);
132 T *out_ptr = reinterpret_cast<T *>(out.ptr());
133 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)
134 {
135 const auto in_ptr_d = in_ptr_start + index_in_d * input_stride_d;
136 const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d;
137 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)
138 {
139 const T *const in_ptr_row = in_ptr_d + index_in_h * input_stride_h;
140 const T *const weights_ptr_row = weights_ptr_d + index_wei_h * kernel_stride_h;
141 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)
142 {
143 const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w;
144 const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w;
145 int index_c_in = 0;
146 vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
147 vector_type w_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
148 for(; index_c_in <= index_c_in_end - num_elems_read_per_iteration;
149 index_c_in += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
150 {
151 const auto src_vec = wrapper::vloadq(in_ptr_mover);
152 //Load Cin weights
153 for(int k = 0; k < num_elems_read_per_iteration; ++k, weights_ptr_mover += index_c_out_end)
154 {
155 w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k);
156 }
157 out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
158 }
159 out_temp += vreduce(out_temp_vec);
160 for(; index_c_in < index_c_in_end; ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end)
161 {
162 const auto src_val = *(in_ptr_mover);
163 const auto w_val = *(weights_ptr_mover);
164 out_temp += src_val * w_val;
165 }
166 }
167 }
168 }
169 *(reinterpret_cast<T *>(out_ptr + id_w[0])) = (biases_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp;
170 },
171 wei);
172 },
173 out);
174 }
175
176 } // namespace cpu
177 } // namespace arm_compute
178 #endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H