1 /*
2 * Copyright (c) 2019-2022 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 #include "src/cpu/kernels/instancenorm/generic/neon/impl.h"
25 #include "src/core/NEON/wrapper/wrapper.h"
26
27 namespace arm_compute
28 {
29 class ITensor;
30 class Window;
31 namespace cpu
32 {
33 template <typename InputType, typename AccType>
vector_float_sum(AccType & result,AccType & result_square,const InputType & inputs)34 void vector_float_sum(AccType &result, AccType &result_square, const InputType &inputs)
35 {
36 result = wrapper::vadd(result, inputs);
37 result_square = wrapper::vadd(result_square, wrapper::vmul(inputs, inputs));
38 }
39
40 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
41 template <>
vector_float_sum(float32x4_t & result,float32x4_t & result_square,const float16x8_t & inputs)42 inline void vector_float_sum(float32x4_t &result, float32x4_t &result_square, const float16x8_t &inputs)
43 {
44 vector_float_sum(result, result_square, wrapper::vcvt<float>(wrapper::vgetlow(inputs)));
45 vector_float_sum(result, result_square, wrapper::vcvt<float>(wrapper::vgethigh(inputs)));
46 }
47 template <>
vector_float_norm(const float16x8_t & inputs,const float32x4_t & vec_mean,const float32x4_t & vec_multip,const float32x4_t & vec_beta)48 inline float16x8_t vector_float_norm(const float16x8_t &inputs, const float32x4_t &vec_mean, const float32x4_t &vec_multip, const float32x4_t &vec_beta)
49 {
50 const auto input_low = wrapper::vcvt<float>(wrapper::vgetlow(inputs));
51 const auto input_high = wrapper::vcvt<float>(wrapper::vgethigh(inputs));
52 const auto result_low = wrapper::vcvt<float16_t>(vector_float_norm(input_low, vec_mean, vec_multip, vec_beta));
53 const auto result_high = wrapper::vcvt<float16_t>(vector_float_norm(input_high, vec_mean, vec_multip, vec_beta));
54 float16x8_t result = wrapper::vcombine(result_low, result_high);
55
56 return result;
57 }
58 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
59
60 template <typename InputType, typename AccType>
vector_float_norm(const InputType & inputs,const AccType & vec_mean,const AccType & vec_multip,const AccType & vec_beta)61 InputType vector_float_norm(const InputType &inputs, const AccType &vec_mean, const AccType &vec_multip, const AccType &vec_beta)
62 {
63 return wrapper::vadd(wrapper::vmul(wrapper::vsub(inputs, vec_mean), vec_multip), vec_beta);
64 }
65
66 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
67
68 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
69 template <typename T, typename AccType>
instance_normalization_nchw(ITensor * input,ITensor * output,float gamma,float beta,float epsilon,const Window & window)70 void instance_normalization_nchw(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window)
71 {
72 /** SIMD vector tag type. */
73 using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
74
75 // Clear X/Y dimensions on execution window as we handle the planes manually
76 Window win = window;
77 win.set(Window::DimX, Window::Dimension(0, 1, 1));
78 win.set(Window::DimY, Window::Dimension(0, 1, 1));
79
80 constexpr int window_step_x = 16 / sizeof(T);
81 const unsigned int elements_plane = input->info()->dimension(0) * output->info()->dimension(1);
82
83 Iterator input_it(input, win);
84 execute_window_loop(win, [&](const Coordinates & id)
85 {
86 Window win_plane = window;
87 win_plane.set(Window::DimX, Window::Dimension(0, 1, 1));
88 win_plane.set(Window::DimZ, Window::Dimension(id[2], id[2] + 1, 1));
89 win_plane.set(3, Window::Dimension(id[3], id[3] + 1, 1));
90
91 Iterator input_plane_it(input, win_plane);
92 Iterator output_plane_it(output, win_plane);
93
94 auto sum_h_w = static_cast<AccType>(0.f);
95 auto sum_squares_h_w = static_cast<AccType>(0.f);
96
97 execute_window_loop(win_plane, [&](const Coordinates &)
98 {
99 const auto input_ptr = reinterpret_cast<const T *>(input_plane_it.ptr());
100
101 auto vec_sum_h_w = wrapper::vdup_n(static_cast<AccType>(0.f), ExactTagType{});
102 auto vec_sum_squares_h_w = wrapper::vdup_n(static_cast<AccType>(0.f), ExactTagType{});
103
104 // Compute S elements per iteration
105 int x = window.x().start();
106 for(; x <= (window.x().end() - window_step_x); x += window_step_x)
107 {
108 auto vec_input_val = wrapper::vloadq(input_ptr + x);
109 vector_float_sum(vec_sum_h_w, vec_sum_squares_h_w, vec_input_val);
110 }
111
112 auto vec2_sum_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_h_w), wrapper::vgetlow(vec_sum_h_w));
113 auto vec2_sum_squares_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_squares_h_w), wrapper::vgetlow(vec_sum_squares_h_w));
114
115 vec2_sum_h_w = wrapper::vpadd(vec2_sum_h_w, vec2_sum_h_w);
116 vec2_sum_squares_h_w = wrapper::vpadd(vec2_sum_squares_h_w, vec2_sum_squares_h_w);
117
118 sum_h_w += wrapper::vgetlane(vec2_sum_h_w, 0);
119 sum_squares_h_w += wrapper::vgetlane(vec2_sum_squares_h_w, 0);
120
121 // Compute left-over elements
122 for(; x < window.x().end(); ++x)
123 {
124 const auto value = static_cast<AccType>(*(input_ptr + x));
125 sum_h_w += value;
126 sum_squares_h_w += value * value;
127 }
128 },
129 input_plane_it, output_plane_it);
130
131 const auto mean_h_w = sum_h_w / elements_plane;
132 const auto var_h_w = sum_squares_h_w / elements_plane - mean_h_w * mean_h_w;
133
134 const auto multip_h_w = gamma / std::sqrt(var_h_w + epsilon);
135 const auto vec_mean_h_w = wrapper::vdup_n(static_cast<AccType>(mean_h_w), ExactTagType{});
136 const auto vec_multip_h_w = wrapper::vdup_n(static_cast<AccType>(multip_h_w), ExactTagType{});
137 const auto vec_beta = wrapper::vdup_n(static_cast<AccType>(beta), ExactTagType{});
138
139 execute_window_loop(win_plane, [&](const Coordinates &)
140 {
141 auto input_ptr = reinterpret_cast<T *>(input_plane_it.ptr());
142 auto output_ptr = reinterpret_cast<T *>(output_plane_it.ptr());
143
144 // Compute S elements per iteration
145 int x = window.x().start();
146 //auto vec_val = wrapper::vdup_n(static_cast<T>(0.0f), ExactTagType{});
147 for(; x <= (window.x().end() - window_step_x); x += window_step_x)
148 {
149 const auto vec_val = wrapper::vloadq(input_ptr + x);
150 const auto normalized_vec = vector_float_norm(vec_val, vec_mean_h_w, vec_multip_h_w, vec_beta);
151 wrapper::vstore(output_ptr + x, normalized_vec);
152 }
153
154 // Compute left-over elements
155 for(; x < window.x().end(); ++x)
156 {
157 const auto val = static_cast<AccType>(*(input_ptr + x));
158 *(output_ptr + x) = static_cast<T>((val - mean_h_w) * multip_h_w + beta);
159 }
160 },
161 input_plane_it, output_plane_it);
162 },
163 input_it);
164 }
165
166 template void instance_normalization_nchw<float>(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window);
167 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
168 template void instance_normalization_nchw<float16_t, float>(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window);
169 template void instance_normalization_nchw<float16_t>(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window);
170 #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
171 } // namespace cpu
172 } // namespace arm_compute
173