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Marat Dukhanb39689d2020-01-24 13:32:20 -08001// Auto-generated file. Do not edit!
2// Template: src/f32-raddstoreexpminusmax/sse2-p5.c.in
3// Generator: tools/xngen
4//
5// Copyright 2019 Google LLC
6//
7// This source code is licensed under the BSD-style license found in the
8// LICENSE file in the root directory of this source tree.
9
10#include <assert.h>
11
12#include <emmintrin.h>
13
14#include <xnnpack/common.h>
15#include <xnnpack/raddstoreexpminusmax.h>
16
17
18void xnn_f32_raddstoreexpminusmax_ukernel__sse2_p5_x12_acc3(
19 size_t elements,
20 const float* input,
21 float* output,
22 float* sum,
Marat Dukhanb2217dd2020-05-28 17:30:28 -070023 float max) XNN_DISABLE_TSAN
Marat Dukhanb39689d2020-01-24 13:32:20 -080024{
25 assert(elements % sizeof(float) == 0);
26
27 const __m128 vmagic_bias = _mm_set1_ps(0x1.8000FEp23f);
28 // The smallest x for which expf(x) is normalized.
29 const __m128 vdenorm_cutoff = _mm_set1_ps(-0x1.5D589Ep6f);
30 const __m128 vlog2e = _mm_set1_ps(0x1.715476p+0f);
31 // Last 7 bits are zeroes
32 const __m128 vminus_ln2_hi = _mm_set1_ps(-0x1.62E400p-1f);
33 const __m128 vminus_ln2_lo = _mm_set1_ps(-0x1.7F7D1Cp-20f);
34
35 const __m128 vc1 = _mm_set1_ps(0x1.FFFFF6p-1f);
36 const __m128 vc2 = _mm_set1_ps(0x1.FFFDC6p-2f);
37 const __m128 vc3 = _mm_set1_ps(0x1.555A80p-3f);
38 const __m128 vc4 = _mm_set1_ps(0x1.573A1Ap-5f);
39 const __m128 vc5 = _mm_set1_ps(0x1.0F9F9Cp-7f);
40
41 const __m128 vi_max = _mm_set1_ps(max);
42
43 __m128 vacc0 = _mm_setzero_ps();
44 __m128 vacc1 = _mm_setzero_ps();
45 __m128 vacc2 = _mm_setzero_ps();
46 for (; elements >= 12 * sizeof(float); elements -= 12 * sizeof(float)) {
47 // Load 12 (3x4) inputs at a time.
48 const __m128 vi0123 = _mm_loadu_ps(input);
49 const __m128 vi4567 = _mm_loadu_ps(input + 4);
50 const __m128 vi89AB = _mm_loadu_ps(input + 8);
51 input += 12;
52
53 // Subtract maximum input x := i - i_max. This implies x <= 0.
54 const __m128 vx0123 = _mm_sub_ps(vi0123, vi_max);
55 const __m128 vx4567 = _mm_sub_ps(vi4567, vi_max);
56 const __m128 vx89AB = _mm_sub_ps(vi89AB, vi_max);
57
58 // Compute reduced argument elements := round(x / log(2)).
59 __m128 vn0123 = _mm_add_ps(_mm_mul_ps(vx0123, vlog2e), vmagic_bias);
60 __m128 vn4567 = _mm_add_ps(_mm_mul_ps(vx4567, vlog2e), vmagic_bias);
61 __m128 vn89AB = _mm_add_ps(_mm_mul_ps(vx89AB, vlog2e), vmagic_bias);
62
63 // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e.
64 // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly.
65 const __m128 vs0123 = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn0123), 23));
66 const __m128 vs4567 = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn4567), 23));
67 const __m128 vs89AB = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn89AB), 23));
68
69 // Subtract the large number back to get final elements := round(x / log(2)).
70 vn0123 = _mm_sub_ps(vn0123, vmagic_bias);
71 vn4567 = _mm_sub_ps(vn4567, vmagic_bias);
72 vn89AB = _mm_sub_ps(vn89AB, vmagic_bias);
73
74 // Compute reduced argument t := x - elements * log(2).
75 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
76 __m128 vt0123 = _mm_add_ps(_mm_mul_ps(vn0123, vminus_ln2_hi), vx0123);
77 __m128 vt4567 = _mm_add_ps(_mm_mul_ps(vn4567, vminus_ln2_hi), vx4567);
78 __m128 vt89AB = _mm_add_ps(_mm_mul_ps(vn89AB, vminus_ln2_hi), vx89AB);
79
80 vt0123 = _mm_add_ps(_mm_mul_ps(vn0123, vminus_ln2_lo), vt0123);
81 vt4567 = _mm_add_ps(_mm_mul_ps(vn4567, vminus_ln2_lo), vt4567);
82 vt89AB = _mm_add_ps(_mm_mul_ps(vn89AB, vminus_ln2_lo), vt89AB);
83
Marat Dukhan102a7392020-11-20 01:18:10 -080084 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
Marat Dukhanb39689d2020-01-24 13:32:20 -080085 __m128 vp0123 = _mm_add_ps(_mm_mul_ps(vc5, vt0123), vc4);
86 __m128 vp4567 = _mm_add_ps(_mm_mul_ps(vc5, vt4567), vc4);
87 __m128 vp89AB = _mm_add_ps(_mm_mul_ps(vc5, vt89AB), vc4);
88
89 vp0123 = _mm_add_ps(_mm_mul_ps(vp0123, vt0123), vc3);
90 vp4567 = _mm_add_ps(_mm_mul_ps(vp4567, vt4567), vc3);
91 vp89AB = _mm_add_ps(_mm_mul_ps(vp89AB, vt89AB), vc3);
92
93 vp0123 = _mm_add_ps(_mm_mul_ps(vp0123, vt0123), vc2);
94 vp4567 = _mm_add_ps(_mm_mul_ps(vp4567, vt4567), vc2);
95 vp89AB = _mm_add_ps(_mm_mul_ps(vp89AB, vt89AB), vc2);
96
97 vp0123 = _mm_add_ps(_mm_mul_ps(vp0123, vt0123), vc1);
98 vp4567 = _mm_add_ps(_mm_mul_ps(vp4567, vt4567), vc1);
99 vp89AB = _mm_add_ps(_mm_mul_ps(vp89AB, vt89AB), vc1);
100
101 // Reconstruct the final f value:
102 // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
103 // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
104 // = s + (t * s) * p
105 vt0123 = _mm_mul_ps(vt0123, vs0123);
106 vt4567 = _mm_mul_ps(vt4567, vs4567);
107 vt89AB = _mm_mul_ps(vt89AB, vs89AB);
108
109 __m128 vf0123 = _mm_add_ps(_mm_mul_ps(vt0123, vp0123), vs0123);
110 __m128 vf4567 = _mm_add_ps(_mm_mul_ps(vt4567, vp4567), vs4567);
111 __m128 vf89AB = _mm_add_ps(_mm_mul_ps(vt89AB, vp89AB), vs89AB);
112
113 // For inputs below zero cutoff, replace output with +0.0f.
114 // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
115 vf0123 = _mm_andnot_ps(_mm_cmplt_ps(vx0123, vdenorm_cutoff), vf0123);
116 vf4567 = _mm_andnot_ps(_mm_cmplt_ps(vx4567, vdenorm_cutoff), vf4567);
117 vf89AB = _mm_andnot_ps(_mm_cmplt_ps(vx89AB, vdenorm_cutoff), vf89AB);
118
119 // Store 12 (3x4) outputs at a time.
120 _mm_storeu_ps(output, vf0123);
121 _mm_storeu_ps(output + 4, vf4567);
122 _mm_storeu_ps(output + 8, vf89AB);
123 output += 12;
124
125 // Accumulate computed exponents.
126 vacc0 = _mm_add_ps(vacc0, vf0123);
127 vacc1 = _mm_add_ps(vacc1, vf4567);
128 vacc2 = _mm_add_ps(vacc2, vf89AB);
129 }
130 // Add up all accumulators to vacc0
131 vacc0 = _mm_add_ps(vacc0, vacc1);
132 vacc0 = _mm_add_ps(vacc0, vacc2);
133
134 __m128 vacc = vacc0;
135 for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) {
136 // Load 4 inputs at a time.
137 const __m128 vi = _mm_loadu_ps(input);
138 input += 4;
139
140 // Subtract maximum input x := i - i_max. This implies x <= 0.
141 const __m128 vx = _mm_sub_ps(vi, vi_max);
142
143 // Compute reduced argument elements := round(x / log(2)).
144 __m128 vn = _mm_add_ps(_mm_mul_ps(vx, vlog2e), vmagic_bias);
145
146 // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e.
147 // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly.
148 const __m128 vs = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn), 23));
149
150 // Subtract the large number back to get final elements := round(x / log(2)).
151 vn = _mm_sub_ps(vn, vmagic_bias);
152
153 // Compute reduced argument t := x - elements * log(2).
154 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
155 __m128 vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_hi), vx);
156 vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_lo), vt);
157
Marat Dukhan102a7392020-11-20 01:18:10 -0800158 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
Marat Dukhanb39689d2020-01-24 13:32:20 -0800159 __m128 vp = _mm_add_ps(_mm_mul_ps(vc5, vt), vc4);
160 vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc3);
161 vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc2);
162 vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc1);
163
164 // Reconstruct the final f value:
165 // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
166 // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
167 // = s + (t * s) * p
168 vt = _mm_mul_ps(vt, vs);
169 __m128 vf = _mm_add_ps(_mm_mul_ps(vt, vp), vs);
170
171 // For inputs below zero cutoff, replace output with +0.0f.
172 // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
173 vf = _mm_andnot_ps(_mm_cmplt_ps(vx, vdenorm_cutoff), vf);
174
175 // Store 4 outputs at a time.
176 _mm_storeu_ps(output, vf);
177 output += 4;
178
179 // Accumulate computed exponents.
180 vacc = _mm_add_ps(vacc, vf);
181 }
182 if (elements != 0) {
183 assert(elements >= 1 * sizeof(float));
184 assert(elements <= 3 * sizeof(float));
185 // Load 4 inputs at a time.
Marat Dukhanb2217dd2020-05-28 17:30:28 -0700186 const __m128 vi = _mm_loadu_ps(input);
Marat Dukhanb39689d2020-01-24 13:32:20 -0800187
188 // Subtract maximum input x := i - i_max. This implies x <= 0.
189 const __m128 vx = _mm_sub_ps(vi, vi_max);
190
191 // Compute reduced argument elements := round(x / log(2)).
192 __m128 vn = _mm_add_ps(_mm_mul_ps(vx, vlog2e), vmagic_bias);
193
194 // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e.
195 // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly.
196 const __m128 vs = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn), 23));
197
198 // Subtract the large number back to get final elements := round(x / log(2)).
199 vn = _mm_sub_ps(vn, vmagic_bias);
200
201 // Compute reduced argument t := x - elements * log(2).
202 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
203 __m128 vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_hi), vx);
204 vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_lo), vt);
205
Marat Dukhan102a7392020-11-20 01:18:10 -0800206 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
Marat Dukhanb39689d2020-01-24 13:32:20 -0800207 __m128 vp = _mm_add_ps(_mm_mul_ps(vc5, vt), vc4);
208 vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc3);
209 vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc2);
210 vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc1);
211
212 // Reconstruct the final f value:
213 // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
214 // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
215 // = s + (t * s) * p
216 vt = _mm_mul_ps(vt, vs);
217 __m128 vf = _mm_add_ps(_mm_mul_ps(vt, vp), vs);
218
219 // For inputs below zero cutoff, replace output with +0.0f.
220 // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
221 vf = _mm_andnot_ps(_mm_cmplt_ps(vx, vdenorm_cutoff), vf);
222
223 if (elements & (2 * sizeof(float))) {
224 // Store 2 outputs at a time.
225 _mm_storel_pi((__m64*) output, vf);
226 output += 2;
227
228 // Accumulate 2 computed exponents.
229 vacc = _mm_add_ps(vacc, _mm_movelh_ps(vf, _mm_setzero_ps()));
230
231 vf = _mm_movehl_ps(vf, vf);
232 }
233 if (elements & (1 * sizeof(float))) {
234 // Store 1 output at a time.
235 _mm_store_ss(output, vf);
236
237 // Accumulate 1 computed exponent.
238 vacc = _mm_add_ss(vacc, vf);
239 }
240 }
241 // Reduce 4 elements in the SIMD register
242 vacc = _mm_add_ps(vacc, _mm_movehl_ps(vacc, vacc));
243 vacc = _mm_add_ss(vacc, _mm_shuffle_ps(vacc, vacc, _MM_SHUFFLE(2, 3, 0, 1)));
244 _mm_store_ss(sum, vacc);
245}