1 /*
2 * Copyright (c) 2018-2022 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
25 #include "src/cpu/kernels/directconv2d/nhwc/neon/impl.h"
26
27 #include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h"
28 #include "src/core/NEON/wrapper/wrapper.h"
29
30 #include "arm_compute/core/Error.h"
31 #include "arm_compute/core/Helpers.h"
32 #include "arm_compute/core/IAccessWindow.h"
33 #include "arm_compute/core/ITensor.h"
34 #include "arm_compute/core/Types.h"
35 #include "arm_compute/core/Utils.h"
36 #include "src/core/helpers/WindowHelpers.h"
37
38 #include <algorithm>
39
40 using namespace arm_compute::detail;
41
42 namespace arm_compute
43 {
44 namespace cpu
45 {
46 namespace kernels
47 {
48 namespace
49 {
have_zero_x_internal_padding(ITensorInfo * src,const ITensorInfo * weights)50 bool have_zero_x_internal_padding(ITensorInfo *src, const ITensorInfo *weights)
51 {
52 return (src->padding().left == 0 && weights->padding().left == 0 && src->padding().right == 0 && weights->padding().right == 0);
53 }
54 }
55
56 template <typename T>
convolve_nhwc(const Window & window,const ITensor * src,const ITensor * weights,ITensor * dst,const PadStrideInfo & conv_info)57 void convolve_nhwc(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
58 {
59 // Declare useful types
60 using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
61 using vector_type = typename vtype::type;
62 using tag_type = typename vtype::tag_type;
63
64 // Scalar quantities
65 const int element_size = src->info()->element_size();
66 const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
67 const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
68 const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
69 const int input_dim_w = src->info()->dimension(1);
70 const int input_dim_h = src->info()->dimension(2);
71
72 const int output_stride_c = dst->info()->strides_in_bytes().x();
73
74 const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size;
75 const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size;
76 const int kernel_dim_w = weights->info()->dimension(1);
77 const int kernel_dim_h = weights->info()->dimension(2);
78
79 const int conv_pad_top = conv_info.pad_top();
80 const int conv_pad_left = conv_info.pad_left();
81 const int conv_stride_w = std::get<0>(conv_info.stride());
82 const int conv_stride_h = std::get<1>(conv_info.stride());
83
84 // Setup input window for the output iterator
85 Window window_out = window;
86 window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
87
88 // Setup input window for the weights iterator
89 Window window_w = calculate_max_window(*weights->info(), Steps());
90 window_w.set(Window::DimX, Window::Dimension(0, 1, 1));
91 window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
92 window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
93
94 Iterator out(dst, window_out);
95 Iterator wei(weights, window_w);
96
97 constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
98
99 // nhwc optimized
100 if(have_zero_x_internal_padding(src->info(), weights->info()))
101 {
102 // This function assumes that input and weights have not padding in channel
103
104 /*
105 * This implementation parallelize the full WC plane of input and weights by
106 * treating them as series of elements. So for example, a 3x3 weights and
107 * floating point vector operations of 4 elements per time, the first 3
108 * channel elements of the first row would be taken and additionally the first
109 * element of the second row. The 9 elements in each single WC weight plane
110 * would require 2 4-element vector operations and a last single element operation.
111 *
112 * This works since when we create the input vector to multiply with the weights,
113 * the exact required elements are loaded in the same order. Therefore the
114 * multiplication works on the correct input/weight elements.
115 */
116 execute_window_loop(
117 window_out, [&](const Coordinates & id)
118 {
119 /*
120 * In here we create theoretical indexes which then we validate for both
121 * inputs and weights.
122 * As a reminder, this loop take each output point in NHW, C is treated
123 * in the weights loop.
124 */
125 // We are computing the theoretical starting input starting points
126 const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
127 const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
128 const int in_w_end_t = in_w_start_t + kernel_dim_w;
129 const int in_h_end_t = in_h_start_t + kernel_dim_h;
130
131 // We are computing the valid initial and ending input points by checking the borders
132 const int in_w_start = std::max(in_w_start_t, 0);
133 const int in_h_start = std::max(in_h_start_t, 0);
134 const int in_w_end = std::min(in_w_end_t, input_dim_w);
135 const int in_h_end = std::min(in_h_end_t, input_dim_h);
136
137 // We use the input points to select the valid weight points to use
138 const int index_wc_start = (in_w_start - in_w_start_t) * kernel_stride_w;
139 const int index_h_start = in_h_start - in_h_start_t;
140 const int index_wc_end = (kernel_dim_w - (in_w_end_t - in_w_end)) * kernel_stride_w;
141 const int index_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
142
143 execute_window_loop(
144 window_w, [&](const Coordinates & id_w)
145 {
146 /*
147 * This is the loop in the weights, and it goes along N (the batches)
148 * As a reminder, the batches of the weights are translated into the
149 * channels of the output
150 */
151 const T *in_ptr_row = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes())
152 + id[3] * input_stride_n + in_w_start * input_stride_w + in_h_start * input_stride_h;
153 const T *weights_ptr_row = reinterpret_cast<const T *>(wei.ptr()) + index_h_start * kernel_stride_h;
154 uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;
155
156 T out_temp = static_cast<T>(0);
157 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)
158 {
159 const T *in_ptr_mover = in_ptr_row;
160 int index_wc = index_wc_start;
161 vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
162 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)
163 {
164 const auto src_vec = wrapper::vloadq(in_ptr_mover);
165 const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wc);
166 out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
167 }
168 out_temp += vreduce(out_temp_vec);
169 for(; index_wc < index_wc_end; ++index_wc, ++in_ptr_mover)
170 {
171 const auto src_val = *(in_ptr_mover);
172 const auto w_val = *(weights_ptr_row + index_wc);
173 out_temp += src_val * w_val;
174 }
175 }
176 *(reinterpret_cast<T *>(out_ptr)) = out_temp;
177 },
178 wei);
179 },
180 out);
181 }
182 else // nhwc non optimized
183 {
184 execute_window_loop(
185 window_out, [&](const Coordinates & id)
186 {
187 // We are computing the theoretical starting input starting points
188 const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
189 const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
190 const int in_w_end_t = in_w_start_t + kernel_dim_w;
191 const int in_h_end_t = in_h_start_t + kernel_dim_h;
192
193 // We are computing the valid initial and ending input points by checking the borders
194 const int in_w_start = std::max(in_w_start_t, 0);
195 const int in_h_start = std::max(in_h_start_t, 0);
196 const int in_w_end = std::min(in_w_end_t, input_dim_w);
197 const int in_h_end = std::min(in_h_end_t, input_dim_h);
198
199 // We use the input points to select the valid weight points to use
200 const int wei_w_start = in_w_start - in_w_start_t;
201 const int wei_h_start = in_h_start - in_h_start_t;
202 const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end);
203 const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
204
205 const int index_c_end = weights->info()->dimension(0);
206 const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n;
207
208 execute_window_loop(
209 window_w, [&](const Coordinates & id_w)
210 {
211 const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
212 uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;
213
214 T out_temp = static_cast<T>(0);
215 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)
216 {
217 const T *const in_ptr_row = in_ptr_start + index_in_h * input_stride_h;
218 const T *const weights_ptr_row = weights_ptr_start + index_wei_h * kernel_stride_h;
219 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)
220 {
221 const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w;
222 const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w;
223 int index_c = 0;
224 vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
225 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)
226 {
227 const auto src_vec = wrapper::vloadq(in_ptr_mover);
228 const auto w_vec = wrapper::vloadq(weights_ptr_mover);
229 out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
230 }
231 out_temp += vreduce(out_temp_vec);
232 for(; index_c < index_c_end; ++index_c, ++in_ptr_mover, ++weights_ptr_mover)
233 {
234 const auto src_val = *(in_ptr_mover);
235 const auto w_val = *(weights_ptr_mover);
236 out_temp += src_val * w_val;
237 }
238 }
239 }
240 *(reinterpret_cast<T *>(out_ptr)) = out_temp;
241 },
242 wei);
243 },
244 out);
245 }
246 }
247
248 template void convolve_nhwc<float>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
249
250 } // namespace kernels
251 } // namespace cpu
252 } // namespace arm_compute
253