1 // Auto-generated file. Do not edit!
2 // Template: src/f32-raddexpminusmax/avx2-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 <immintrin.h>
13
14 #include <xnnpack/raddexpminusmax.h>
15
16
17 static const int32_t mask_table[14] = {-1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0};
18
xnn_f32_raddexpminusmax_ukernel__avx2_p5_x80_acc2(size_t elements,const float * input,float * sum,float max)19 void xnn_f32_raddexpminusmax_ukernel__avx2_p5_x80_acc2(
20 size_t elements,
21 const float* input,
22 float* sum,
23 float max)
24 {
25 assert(elements % sizeof(float) == 0);
26
27 const __m256 vmagic_bias = _mm256_set1_ps(0x1.8000FEp23f);
28 // The smallest x for which expf(x) is normalized.
29 const __m256 vdenorm_cutoff = _mm256_set1_ps(-0x1.5D589Ep6f);
30 const __m256 vlog2e = _mm256_set1_ps(0x1.715476p+0f);
31 const __m256 vminus_ln2_hi = _mm256_set1_ps(-0x1.62E43p-1f);
32 const __m256 vminus_ln2_lo = _mm256_set1_ps(0x1.05C61p-29f);
33
34 const __m256 vc1 = _mm256_set1_ps(0x1.FFFFF6p-1f);
35 const __m256 vc2 = _mm256_set1_ps(0x1.FFFDC6p-2f);
36 const __m256 vc3 = _mm256_set1_ps(0x1.555A80p-3f);
37 const __m256 vc4 = _mm256_set1_ps(0x1.573A1Ap-5f);
38 const __m256 vc5 = _mm256_set1_ps(0x1.0F9F9Cp-7f);
39
40 const __m256 vi_max = _mm256_set1_ps(max);
41
42 __m256 vacc0 = _mm256_setzero_ps();
43 __m256 vacc1 = _mm256_setzero_ps();
44 for (; elements >= 80 * sizeof(float); elements -= 80 * sizeof(float)) {
45 // Load 80 (10x8) inputs at a time.
46 const __m256 vi0 = _mm256_loadu_ps(input);
47 const __m256 vi1 = _mm256_loadu_ps(input + 8);
48 const __m256 vi2 = _mm256_loadu_ps(input + 16);
49 const __m256 vi3 = _mm256_loadu_ps(input + 24);
50 const __m256 vi4 = _mm256_loadu_ps(input + 32);
51 const __m256 vi5 = _mm256_loadu_ps(input + 40);
52 const __m256 vi6 = _mm256_loadu_ps(input + 48);
53 const __m256 vi7 = _mm256_loadu_ps(input + 56);
54 const __m256 vi8 = _mm256_loadu_ps(input + 64);
55 const __m256 vi9 = _mm256_loadu_ps(input + 72);
56 input += 80;
57
58 // Subtract maximum input x := i - i_max. This implies x <= 0.
59 const __m256 vx0 = _mm256_sub_ps(vi0, vi_max);
60 const __m256 vx1 = _mm256_sub_ps(vi1, vi_max);
61 const __m256 vx2 = _mm256_sub_ps(vi2, vi_max);
62 const __m256 vx3 = _mm256_sub_ps(vi3, vi_max);
63 const __m256 vx4 = _mm256_sub_ps(vi4, vi_max);
64 const __m256 vx5 = _mm256_sub_ps(vi5, vi_max);
65 const __m256 vx6 = _mm256_sub_ps(vi6, vi_max);
66 const __m256 vx7 = _mm256_sub_ps(vi7, vi_max);
67 const __m256 vx8 = _mm256_sub_ps(vi8, vi_max);
68 const __m256 vx9 = _mm256_sub_ps(vi9, vi_max);
69
70 // Compute reduced argument elements := round(x / log(2)).
71 __m256 vn0 = _mm256_fmadd_ps(vx0, vlog2e, vmagic_bias);
72 __m256 vn1 = _mm256_fmadd_ps(vx1, vlog2e, vmagic_bias);
73 __m256 vn2 = _mm256_fmadd_ps(vx2, vlog2e, vmagic_bias);
74 __m256 vn3 = _mm256_fmadd_ps(vx3, vlog2e, vmagic_bias);
75 __m256 vn4 = _mm256_fmadd_ps(vx4, vlog2e, vmagic_bias);
76 __m256 vn5 = _mm256_fmadd_ps(vx5, vlog2e, vmagic_bias);
77 __m256 vn6 = _mm256_fmadd_ps(vx6, vlog2e, vmagic_bias);
78 __m256 vn7 = _mm256_fmadd_ps(vx7, vlog2e, vmagic_bias);
79 __m256 vn8 = _mm256_fmadd_ps(vx8, vlog2e, vmagic_bias);
80 __m256 vn9 = _mm256_fmadd_ps(vx9, vlog2e, vmagic_bias);
81
82 // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e.
83 // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly.
84 const __m256 vs0 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn0), 23));
85 const __m256 vs1 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn1), 23));
86 const __m256 vs2 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn2), 23));
87 const __m256 vs3 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn3), 23));
88 const __m256 vs4 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn4), 23));
89 const __m256 vs5 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn5), 23));
90 const __m256 vs6 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn6), 23));
91 const __m256 vs7 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn7), 23));
92 const __m256 vs8 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn8), 23));
93 const __m256 vs9 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn9), 23));
94
95 // Subtract the large number back to get final elements := round(x / log(2)).
96 vn0 = _mm256_sub_ps(vn0, vmagic_bias);
97 vn1 = _mm256_sub_ps(vn1, vmagic_bias);
98 vn2 = _mm256_sub_ps(vn2, vmagic_bias);
99 vn3 = _mm256_sub_ps(vn3, vmagic_bias);
100 vn4 = _mm256_sub_ps(vn4, vmagic_bias);
101 vn5 = _mm256_sub_ps(vn5, vmagic_bias);
102 vn6 = _mm256_sub_ps(vn6, vmagic_bias);
103 vn7 = _mm256_sub_ps(vn7, vmagic_bias);
104 vn8 = _mm256_sub_ps(vn8, vmagic_bias);
105 vn9 = _mm256_sub_ps(vn9, vmagic_bias);
106
107 // Compute reduced argument t := x - elements * log(2).
108 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
109 __m256 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_hi, vx0);
110 __m256 vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_hi, vx1);
111 __m256 vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_hi, vx2);
112 __m256 vt3 = _mm256_fmadd_ps(vn3, vminus_ln2_hi, vx3);
113 __m256 vt4 = _mm256_fmadd_ps(vn4, vminus_ln2_hi, vx4);
114 __m256 vt5 = _mm256_fmadd_ps(vn5, vminus_ln2_hi, vx5);
115 __m256 vt6 = _mm256_fmadd_ps(vn6, vminus_ln2_hi, vx6);
116 __m256 vt7 = _mm256_fmadd_ps(vn7, vminus_ln2_hi, vx7);
117 __m256 vt8 = _mm256_fmadd_ps(vn8, vminus_ln2_hi, vx8);
118 __m256 vt9 = _mm256_fmadd_ps(vn9, vminus_ln2_hi, vx9);
119
120 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_lo, vt0);
121 vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_lo, vt1);
122 vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_lo, vt2);
123 vt3 = _mm256_fmadd_ps(vn3, vminus_ln2_lo, vt3);
124 vt4 = _mm256_fmadd_ps(vn4, vminus_ln2_lo, vt4);
125 vt5 = _mm256_fmadd_ps(vn5, vminus_ln2_lo, vt5);
126 vt6 = _mm256_fmadd_ps(vn6, vminus_ln2_lo, vt6);
127 vt7 = _mm256_fmadd_ps(vn7, vminus_ln2_lo, vt7);
128 vt8 = _mm256_fmadd_ps(vn8, vminus_ln2_lo, vt8);
129 vt9 = _mm256_fmadd_ps(vn9, vminus_ln2_lo, vt9);
130
131 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
132 __m256 vp0 = _mm256_fmadd_ps(vc5, vt0, vc4);
133 __m256 vp1 = _mm256_fmadd_ps(vc5, vt1, vc4);
134 __m256 vp2 = _mm256_fmadd_ps(vc5, vt2, vc4);
135 __m256 vp3 = _mm256_fmadd_ps(vc5, vt3, vc4);
136 __m256 vp4 = _mm256_fmadd_ps(vc5, vt4, vc4);
137 __m256 vp5 = _mm256_fmadd_ps(vc5, vt5, vc4);
138 __m256 vp6 = _mm256_fmadd_ps(vc5, vt6, vc4);
139 __m256 vp7 = _mm256_fmadd_ps(vc5, vt7, vc4);
140 __m256 vp8 = _mm256_fmadd_ps(vc5, vt8, vc4);
141 __m256 vp9 = _mm256_fmadd_ps(vc5, vt9, vc4);
142
143 vp0 = _mm256_fmadd_ps(vp0, vt0, vc3);
144 vp1 = _mm256_fmadd_ps(vp1, vt1, vc3);
145 vp2 = _mm256_fmadd_ps(vp2, vt2, vc3);
146 vp3 = _mm256_fmadd_ps(vp3, vt3, vc3);
147 vp4 = _mm256_fmadd_ps(vp4, vt4, vc3);
148 vp5 = _mm256_fmadd_ps(vp5, vt5, vc3);
149 vp6 = _mm256_fmadd_ps(vp6, vt6, vc3);
150 vp7 = _mm256_fmadd_ps(vp7, vt7, vc3);
151 vp8 = _mm256_fmadd_ps(vp8, vt8, vc3);
152 vp9 = _mm256_fmadd_ps(vp9, vt9, vc3);
153
154 vp0 = _mm256_fmadd_ps(vp0, vt0, vc2);
155 vp1 = _mm256_fmadd_ps(vp1, vt1, vc2);
156 vp2 = _mm256_fmadd_ps(vp2, vt2, vc2);
157 vp3 = _mm256_fmadd_ps(vp3, vt3, vc2);
158 vp4 = _mm256_fmadd_ps(vp4, vt4, vc2);
159 vp5 = _mm256_fmadd_ps(vp5, vt5, vc2);
160 vp6 = _mm256_fmadd_ps(vp6, vt6, vc2);
161 vp7 = _mm256_fmadd_ps(vp7, vt7, vc2);
162 vp8 = _mm256_fmadd_ps(vp8, vt8, vc2);
163 vp9 = _mm256_fmadd_ps(vp9, vt9, vc2);
164
165 vp0 = _mm256_fmadd_ps(vp0, vt0, vc1);
166 vp1 = _mm256_fmadd_ps(vp1, vt1, vc1);
167 vp2 = _mm256_fmadd_ps(vp2, vt2, vc1);
168 vp3 = _mm256_fmadd_ps(vp3, vt3, vc1);
169 vp4 = _mm256_fmadd_ps(vp4, vt4, vc1);
170 vp5 = _mm256_fmadd_ps(vp5, vt5, vc1);
171 vp6 = _mm256_fmadd_ps(vp6, vt6, vc1);
172 vp7 = _mm256_fmadd_ps(vp7, vt7, vc1);
173 vp8 = _mm256_fmadd_ps(vp8, vt8, vc1);
174 vp9 = _mm256_fmadd_ps(vp9, vt9, vc1);
175
176 // Reconstruct the final f value:
177 // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
178 // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
179 // = s + (t * s) * p
180 vt0 = _mm256_mul_ps(vt0, vs0);
181 vt1 = _mm256_mul_ps(vt1, vs1);
182 vt2 = _mm256_mul_ps(vt2, vs2);
183 vt3 = _mm256_mul_ps(vt3, vs3);
184 vt4 = _mm256_mul_ps(vt4, vs4);
185 vt5 = _mm256_mul_ps(vt5, vs5);
186 vt6 = _mm256_mul_ps(vt6, vs6);
187 vt7 = _mm256_mul_ps(vt7, vs7);
188 vt8 = _mm256_mul_ps(vt8, vs8);
189 vt9 = _mm256_mul_ps(vt9, vs9);
190
191 __m256 vf0 = _mm256_fmadd_ps(vt0, vp0, vs0);
192 __m256 vf1 = _mm256_fmadd_ps(vt1, vp1, vs1);
193 __m256 vf2 = _mm256_fmadd_ps(vt2, vp2, vs2);
194 __m256 vf3 = _mm256_fmadd_ps(vt3, vp3, vs3);
195 __m256 vf4 = _mm256_fmadd_ps(vt4, vp4, vs4);
196 __m256 vf5 = _mm256_fmadd_ps(vt5, vp5, vs5);
197 __m256 vf6 = _mm256_fmadd_ps(vt6, vp6, vs6);
198 __m256 vf7 = _mm256_fmadd_ps(vt7, vp7, vs7);
199 __m256 vf8 = _mm256_fmadd_ps(vt8, vp8, vs8);
200 __m256 vf9 = _mm256_fmadd_ps(vt9, vp9, vs9);
201
202 // For inputs below zero cutoff, replace output with +0.0f.
203 // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
204 vf0 = _mm256_andnot_ps(_mm256_cmp_ps(vx0, vdenorm_cutoff, _CMP_LT_OS), vf0);
205 vf1 = _mm256_andnot_ps(_mm256_cmp_ps(vx1, vdenorm_cutoff, _CMP_LT_OS), vf1);
206 vf2 = _mm256_andnot_ps(_mm256_cmp_ps(vx2, vdenorm_cutoff, _CMP_LT_OS), vf2);
207 vf3 = _mm256_andnot_ps(_mm256_cmp_ps(vx3, vdenorm_cutoff, _CMP_LT_OS), vf3);
208 vf4 = _mm256_andnot_ps(_mm256_cmp_ps(vx4, vdenorm_cutoff, _CMP_LT_OS), vf4);
209 vf5 = _mm256_andnot_ps(_mm256_cmp_ps(vx5, vdenorm_cutoff, _CMP_LT_OS), vf5);
210 vf6 = _mm256_andnot_ps(_mm256_cmp_ps(vx6, vdenorm_cutoff, _CMP_LT_OS), vf6);
211 vf7 = _mm256_andnot_ps(_mm256_cmp_ps(vx7, vdenorm_cutoff, _CMP_LT_OS), vf7);
212 vf8 = _mm256_andnot_ps(_mm256_cmp_ps(vx8, vdenorm_cutoff, _CMP_LT_OS), vf8);
213 vf9 = _mm256_andnot_ps(_mm256_cmp_ps(vx9, vdenorm_cutoff, _CMP_LT_OS), vf9);
214
215 // Accumulate computed exponents.
216 vacc0 = _mm256_add_ps(vacc0, vf0);
217 vacc1 = _mm256_add_ps(vacc1, vf1);
218 vacc0 = _mm256_add_ps(vacc0, vf2);
219 vacc1 = _mm256_add_ps(vacc1, vf3);
220 vacc0 = _mm256_add_ps(vacc0, vf4);
221 vacc1 = _mm256_add_ps(vacc1, vf5);
222 vacc0 = _mm256_add_ps(vacc0, vf6);
223 vacc1 = _mm256_add_ps(vacc1, vf7);
224 vacc0 = _mm256_add_ps(vacc0, vf8);
225 vacc1 = _mm256_add_ps(vacc1, vf9);
226 }
227 // Add up all accumulators to vacc0
228 vacc0 = _mm256_add_ps(vacc0, vacc1);
229
230 __m256 vacc = vacc0;
231 for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
232 // Load 8 inputs at a time.
233 const __m256 vi = _mm256_loadu_ps(input);
234 input += 8;
235
236 // Subtract maximum input x := i - i_max. This implies x <= 0.
237 const __m256 vx = _mm256_sub_ps(vi, vi_max);
238
239 // Compute reduced argument elements := round(x / log(2)).
240 __m256 vn = _mm256_fmadd_ps(vx, vlog2e, vmagic_bias);
241
242 // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e.
243 // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly.
244 const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn), 23));
245
246 // Subtract the large number back to get final elements := round(x / log(2)).
247 vn = _mm256_sub_ps(vn, vmagic_bias);
248
249 // Compute reduced argument t := x - elements * log(2).
250 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
251 __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
252 vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
253
254 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
255 __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
256 vp = _mm256_fmadd_ps(vp, vt, vc3);
257 vp = _mm256_fmadd_ps(vp, vt, vc2);
258 vp = _mm256_fmadd_ps(vp, vt, vc1);
259
260 // Reconstruct the final f value:
261 // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
262 // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
263 // = s + (t * s) * p
264 vt = _mm256_mul_ps(vt, vs);
265 __m256 vf = _mm256_fmadd_ps(vt, vp, vs);
266
267 // For inputs below zero cutoff, replace output with +0.0f.
268 // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
269 vf = _mm256_andnot_ps(_mm256_cmp_ps(vx, vdenorm_cutoff, _CMP_LT_OS), vf);
270
271 // Accumulate computed exponents.
272 vacc = _mm256_add_ps(vacc, vf);
273 }
274 if (elements != 0) {
275 assert(elements >= 1 * sizeof(float));
276 assert(elements <= 7 * sizeof(float));
277 const __m256i vmask = _mm256_loadu_si256((const __m256i*) ((uintptr_t) &mask_table[7] - elements));
278
279 // Load up to 7 inputs at a time.
280 const __m256 vi = _mm256_maskload_ps(input, vmask);
281
282 // Subtract maximum input x := i - i_max. This implies x <= 0.
283 const __m256 vx = _mm256_sub_ps(vi, vi_max);
284
285 // Compute reduced argument elements := round(x / log(2)).
286 __m256 vn = _mm256_fmadd_ps(vx, vlog2e, vmagic_bias);
287
288 // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e.
289 // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly.
290 const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(vn), 23));
291
292 // Subtract the large number back to get final elements := round(x / log(2)).
293 vn = _mm256_sub_ps(vn, vmagic_bias);
294
295 // Compute reduced argument t := x - elements * log(2).
296 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
297 __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
298 vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
299
300 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
301 __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
302 vp = _mm256_fmadd_ps(vp, vt, vc3);
303 vp = _mm256_fmadd_ps(vp, vt, vc2);
304 vp = _mm256_fmadd_ps(vp, vt, vc1);
305
306 // Reconstruct the final f value:
307 // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
308 // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
309 // = s + (t * s) * p
310 vt = _mm256_mul_ps(vt, vs);
311 __m256 vf = _mm256_fmadd_ps(vt, vp, vs);
312
313 // For inputs below zero cutoff, replace output with +0.0f.
314 // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
315 vf = _mm256_andnot_ps(_mm256_cmp_ps(vx, vdenorm_cutoff, _CMP_LT_OS), vf);
316
317 // Accumulate computed exponents. And addend with mask to leave unmasked 32-bit lanes unchanged.
318 vacc = _mm256_add_ps(vacc, _mm256_and_ps(vf, _mm256_castsi256_ps(vmask)));
319 }
320 // Reduce 8 elements in the SIMD register
321 __m128 vacc_lo = _mm_add_ps(_mm256_castps256_ps128(vacc), _mm256_extractf128_ps(vacc, 1));
322 vacc_lo = _mm_add_ps(vacc_lo, _mm_movehl_ps(vacc_lo, vacc_lo));
323 vacc_lo = _mm_add_ss(vacc_lo, _mm_movehdup_ps(vacc_lo));
324 _mm_store_ss(sum, vacc_lo);
325 _mm256_zeroupper();
326 }
327