1 /*
2 * Copyright (c) 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 namespace arm_compute
41 {
42 namespace cpu
43 {
44 namespace kernels
45 {
46 template <typename T>
47 void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
48
49 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
neon_fp16_nchw_directconv2d(const Window & window,const ITensor * src,const ITensor * weights,ITensor * dst,const PadStrideInfo & conv_info)50 void neon_fp16_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
51 {
52 convolve_nchw<float16_t>(window, src, weights, dst, conv_info);
53 }
54 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
55
neon_fp32_nchw_directconv2d(const Window & window,const ITensor * src,const ITensor * weights,ITensor * dst,const PadStrideInfo & conv_info)56 void neon_fp32_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
57 {
58 convolve_nchw<float>(window, src, weights, dst, conv_info);
59 }
60
61 template <typename T>
convolve_nchw(const Window & window,const ITensor * src,const ITensor * weights,ITensor * dst,const PadStrideInfo & conv_info)62 void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
63 {
64 ARM_COMPUTE_UNUSED(conv_info);
65
66 // Declare useful types
67 using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
68 using vector_type = typename vtype::type;
69 using tag_type = typename vtype::tag_type;
70
71 // Scalar quantities
72 const int element_size = src->info()->element_size();
73 const int input_stride_w = src->info()->strides_in_bytes()[0] / element_size;
74 const int input_stride_h = src->info()->strides_in_bytes()[1] / element_size;
75 const int input_stride_c = src->info()->strides_in_bytes()[2] / element_size;
76 const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
77
78 const int input_dim_w = src->info()->dimension(0);
79 const int input_dim_h = src->info()->dimension(1);
80
81 const int output_stride_c = dst->info()->strides_in_bytes()[2];
82
83 const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().x() / element_size;
84 const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().y() / element_size;
85 const unsigned int kernel_stride_c = weights->info()->strides_in_bytes().z() / element_size;
86
87 const int kernel_dim_w = weights->info()->dimension(0);
88 const int kernel_dim_h = weights->info()->dimension(1);
89
90 const int conv_pad_top = conv_info.pad_top();
91 const int conv_pad_left = conv_info.pad_left();
92 const int conv_stride_w = std::get<0>(conv_info.stride());
93 const int conv_stride_h = std::get<1>(conv_info.stride());
94
95 // Setup input window for the output iterator
96 Window window_out = window;
97 window_out.set(Window::DimZ, Window::Dimension(0, 1, 1));
98
99 // Setup input window for the weights iterator
100 Window window_w = calculate_max_window(*weights->info(), Steps());
101 window_w.set(Window::DimX, Window::Dimension(0, 1, 1));
102 window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
103 window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
104
105 Iterator out(dst, window_out);
106 Iterator wei(weights, window_w);
107
108 constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
109
110 execute_window_loop(window_out, [&](const Coordinates & id)
111 {
112 // We are computing the theoretical starting input starting points
113 const int in_w_start_t = static_cast<int>(id.x()) * conv_stride_w - conv_pad_left;
114 const int in_h_start_t = static_cast<int>(id.y()) * conv_stride_h - conv_pad_top;
115 const int in_w_end_t = in_w_start_t + kernel_dim_w;
116 const int in_h_end_t = in_h_start_t + kernel_dim_h;
117
118 // We are computing the valid initial and ending input points by checking the borders
119 const int in_w_start = std::max(in_w_start_t, 0);
120 const int in_h_start = std::max(in_h_start_t, 0);
121 const int in_w_end = std::min(in_w_end_t, input_dim_w);
122 const int in_h_end = std::min(in_h_end_t, input_dim_h);
123
124 // We use the input points to select the valid weight points to use
125 const int wei_w_start = in_w_start - in_w_start_t;
126 const int wei_h_start = in_h_start - in_h_start_t;
127 const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
128
129 const int index_c_end = weights->info()->dimension(2);
130 const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n;
131 execute_window_loop(window_w, [&](const Coordinates & id_w)
132 {
133 const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
134 uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;
135 T out_temp = static_cast<T>(0);
136
137 for(int index_wei_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c)
138 {
139 const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c;
140 const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c;
141 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)
142 {
143 const T *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h;
144 const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h;
145 int index_w = in_w_start;
146 int index_wei_w = wei_w_start;
147 vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
148 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)
149 {
150 const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w);
151 const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w);
152 out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
153 }
154 out_temp += vreduce(out_temp_vec);
155 for(; index_w < in_w_end; ++index_w, ++index_wei_w)
156 {
157 const auto src_val = *(in_ptr_row + index_w * input_stride_w);
158 const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w);
159 out_temp += src_val * w_val;
160 }
161 }
162 }
163 *(reinterpret_cast<T *>(out_ptr)) = out_temp;
164
165 },
166 wei);
167 },
168 out);
169 }
170
171 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
172 template void convolve_nchw<float16_t>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
173 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
174
175 template void convolve_nchw<float>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
176
177 } // namespace kernels
178 } // namespace cpu
179 } // namespace arm_compute
180