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/fuse_batch_normalization/generic/impl.h"
26
27 namespace arm_compute
28 {
29 namespace cpu
30 {
31 template <typename T>
fused_batch_normalization_dwc_nchw(const ITensor * dwc_weights,const ITensor * dwc_bias,ITensor * fused_weights,ITensor * fused_bias,const ITensor * bn_mean,const ITensor * bn_var,const ITensor * bn_beta,const ITensor * bn_gamma,float epsilon,const Window & window)32 void fused_batch_normalization_dwc_nchw(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias,
33 const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
34 {
35 using ScalarType = T;
36 const int size = 16 / dwc_weights->info()->element_size();
37 using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
38
39 const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == dwc_weights);
40 const bool run_in_place_bias = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_bias);
41
42 // Set build options
43 Window win = window;
44 win.set(Window::DimX, Window::Dimension(0, 1, 1));
45
46 const int window_step_x = size;
47 const auto window_start_x = static_cast<int>(window.x().start());
48 const auto window_end_x = static_cast<int>(window.x().end());
49
50 Iterator dwc_w_in(dwc_weights, win);
51 Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win);
52
53 const auto dwc_bias_in = (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr);
54 auto dwc_bias_out = (run_in_place_bias ? dwc_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0))));
55
56 const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0)));
57 const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0)));
58 const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
59 const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;
60
61 auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
62 auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
63 auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{});
64 auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
65 auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
66 const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{});
67
68 auto mean = ScalarType(0.0);
69 auto var = ScalarType(0.0);
70 auto gamma = ScalarType(1.0);
71 auto beta = ScalarType(0.0);
72 auto dwc_bias_in_scalar = ScalarType(0.0);
73 execute_window_loop(win, [&](const Coordinates & id)
74 {
75 var = input_var[id[2]];
76 if(input_gamma != nullptr)
77 {
78 gamma = input_gamma[id[2]];
79 }
80
81 if(id[1] == 0)
82 {
83 mean = input_mean[id[2]];
84
85 // Construct vectors
86 mean_vec = wrapper::vdup_n(mean, ExactTagType{});
87 if(input_beta != nullptr)
88 {
89 beta = input_beta[id[2]];
90 beta_vec = wrapper::vdup_n(beta, ExactTagType{});
91 }
92
93 if(dwc_bias_in != nullptr)
94 {
95 dwc_bias_in_scalar = dwc_bias_in[id[2]];
96 }
97
98 auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon));
99 dwc_bias_out[id[2]] = (dwc_bias_tmp_scalar * gamma) + beta;
100 }
101
102 int x = window_start_x;
103 auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr());
104 auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr());
105 var_vec = wrapper::vdup_n(var, ExactTagType{});
106 gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
107 rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
108
109 for(; x <= (window_end_x - window_step_x); x += window_step_x)
110 {
111 auto wn = wrapper::vloadq(dwc_w_in_ptr + x);
112 wn = wrapper::vmul(wn, rvar_vec);
113 wn = wrapper::vmul(wn, gamma_vec);
114
115 // Store results
116 wrapper::vstore(dwc_w_out_ptr + x, wn);
117 }
118
119 // Compute left-over elements
120 for(; x < window_end_x; ++x)
121 {
122 *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma;
123 }
124 },
125 dwc_w_in, dwc_w_out);
126 }
127
fused_batch_normalization_dwc_nchw_f32(const ITensor * dwc_weights,const ITensor * dwc_bias,ITensor * fused_weights,ITensor * fused_bias,const ITensor * bn_mean,const ITensor * bn_var,const ITensor * bn_beta,const ITensor * bn_gamma,float epsilon,const Window & window)128 void fused_batch_normalization_dwc_nchw_f32(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias,
129 const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
130 {
131 return fused_batch_normalization_dwc_nchw<float32_t>(dwc_weights, dwc_bias, fused_weights, fused_bias,
132 bn_mean, bn_var, bn_beta, bn_gamma, epsilon, window);
133 }
134
135 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
fused_batch_normalization_dwc_nchw_f16(const ITensor * dwc_weights,const ITensor * dwc_bias,ITensor * fused_weights,ITensor * fused_bias,const ITensor * bn_mean,const ITensor * bn_var,const ITensor * bn_beta,const ITensor * bn_gamma,float epsilon,const Window & window)136 void fused_batch_normalization_dwc_nchw_f16(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias,
137 const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
138 {
139 return fused_batch_normalization_dwc_nchw<float16_t>(dwc_weights, dwc_bias, fused_weights, fused_bias,
140 bn_mean, bn_var, bn_beta, bn_gamma, epsilon, window);
141 }
142 #endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
143
144 } // namespace cpu
145 } // namespace arm_compute
146