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1 // Auto-generated file. Do not edit!
2 //   Template: src/f32-vscaleextexp/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/common.h>
15 #include <xnnpack/vscaleextexp.h>
16 
17 
18 static const int32_t mask_table[14] = {-1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0};
19 
xnn_f32_vscaleextexp_ukernel__avx2_p5_x80(size_t elements,const float * x,float * y,float scale_value,float scale_exp)20 void xnn_f32_vscaleextexp_ukernel__avx2_p5_x80(
21     size_t elements,
22     const float* x,
23     float* y,
24     float scale_value,
25     float scale_exp)
26 {
27   assert(elements % sizeof(float) == 0);
28 
29   const __m256 vlog2e = _mm256_set1_ps(0x1.715476p+0f);
30   const __m256 vminus_ln2_hi = _mm256_set1_ps(-0x1.62E43p-1f);
31   const __m256 vminus_ln2_lo = _mm256_set1_ps(0x1.05C61p-29f);
32 
33   // The smallest elements such that 2**elements is considered non-negligible.
34   // For smaller elements, 2**elements is replaced with zero.
35   const __m256 vmin_exponent = _mm256_set1_ps(-127.0f);
36   const __m256 vmagic_bias = _mm256_set1_ps(0x1.8000FEp23f);
37 
38   const __m256 vc0 = _mm256_set1_ps(1.0f);
39   const __m256 vc1 = _mm256_set1_ps(0x1.FFFFF6p-1f);
40   const __m256 vc2 = _mm256_set1_ps(0x1.FFFDC6p-2f);
41   const __m256 vc3 = _mm256_set1_ps(0x1.555A80p-3f);
42   const __m256 vc4 = _mm256_set1_ps(0x1.573A1Ap-5f);
43   const __m256 vc5 = _mm256_set1_ps(0x1.0F9F9Cp-7f);
44 
45   const __m256 vscalev = _mm256_set1_ps(scale_value);
46   const __m256 vscalee = _mm256_set1_ps(scale_exp);
47 
48   for (; elements >= 80 * sizeof(float); elements -= 80 * sizeof(float)) {
49     // Load 80 (10x8) inputs at a time.
50     const __m256 vx0 = _mm256_loadu_ps(x);
51     const __m256 vx1 = _mm256_loadu_ps(x + 8);
52     const __m256 vx2 = _mm256_loadu_ps(x + 16);
53     const __m256 vx3 = _mm256_loadu_ps(x + 24);
54     const __m256 vx4 = _mm256_loadu_ps(x + 32);
55     const __m256 vx5 = _mm256_loadu_ps(x + 40);
56     const __m256 vx6 = _mm256_loadu_ps(x + 48);
57     const __m256 vx7 = _mm256_loadu_ps(x + 56);
58     const __m256 vx8 = _mm256_loadu_ps(x + 64);
59     const __m256 vx9 = _mm256_loadu_ps(x + 72);
60     x += 80;
61 
62     // Compute reduced argument elements := round(x / log(2)).
63     const __m256 vn0 = _mm256_round_ps(_mm256_mul_ps(vx0, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
64     const __m256 vn1 = _mm256_round_ps(_mm256_mul_ps(vx1, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
65     const __m256 vn2 = _mm256_round_ps(_mm256_mul_ps(vx2, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
66     const __m256 vn3 = _mm256_round_ps(_mm256_mul_ps(vx3, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
67     const __m256 vn4 = _mm256_round_ps(_mm256_mul_ps(vx4, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
68     const __m256 vn5 = _mm256_round_ps(_mm256_mul_ps(vx5, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
69     const __m256 vn6 = _mm256_round_ps(_mm256_mul_ps(vx6, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
70     const __m256 vn7 = _mm256_round_ps(_mm256_mul_ps(vx7, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
71     const __m256 vn8 = _mm256_round_ps(_mm256_mul_ps(vx8, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
72     const __m256 vn9 = _mm256_round_ps(_mm256_mul_ps(vx9, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
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     __m256 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_hi, vx0);
77     __m256 vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_hi, vx1);
78     __m256 vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_hi, vx2);
79     __m256 vt3 = _mm256_fmadd_ps(vn3, vminus_ln2_hi, vx3);
80     __m256 vt4 = _mm256_fmadd_ps(vn4, vminus_ln2_hi, vx4);
81     __m256 vt5 = _mm256_fmadd_ps(vn5, vminus_ln2_hi, vx5);
82     __m256 vt6 = _mm256_fmadd_ps(vn6, vminus_ln2_hi, vx6);
83     __m256 vt7 = _mm256_fmadd_ps(vn7, vminus_ln2_hi, vx7);
84     __m256 vt8 = _mm256_fmadd_ps(vn8, vminus_ln2_hi, vx8);
85     __m256 vt9 = _mm256_fmadd_ps(vn9, vminus_ln2_hi, vx9);
86 
87     vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_lo, vt0);
88     vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_lo, vt1);
89     vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_lo, vt2);
90     vt3 = _mm256_fmadd_ps(vn3, vminus_ln2_lo, vt3);
91     vt4 = _mm256_fmadd_ps(vn4, vminus_ln2_lo, vt4);
92     vt5 = _mm256_fmadd_ps(vn5, vminus_ln2_lo, vt5);
93     vt6 = _mm256_fmadd_ps(vn6, vminus_ln2_lo, vt6);
94     vt7 = _mm256_fmadd_ps(vn7, vminus_ln2_lo, vt7);
95     vt8 = _mm256_fmadd_ps(vn8, vminus_ln2_lo, vt8);
96     vt9 = _mm256_fmadd_ps(vn9, vminus_ln2_lo, vt9);
97 
98     // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
99     __m256 vp0 = _mm256_fmadd_ps(vc5, vt0, vc4);
100     __m256 vp1 = _mm256_fmadd_ps(vc5, vt1, vc4);
101     __m256 vp2 = _mm256_fmadd_ps(vc5, vt2, vc4);
102     __m256 vp3 = _mm256_fmadd_ps(vc5, vt3, vc4);
103     __m256 vp4 = _mm256_fmadd_ps(vc5, vt4, vc4);
104     __m256 vp5 = _mm256_fmadd_ps(vc5, vt5, vc4);
105     __m256 vp6 = _mm256_fmadd_ps(vc5, vt6, vc4);
106     __m256 vp7 = _mm256_fmadd_ps(vc5, vt7, vc4);
107     __m256 vp8 = _mm256_fmadd_ps(vc5, vt8, vc4);
108     __m256 vp9 = _mm256_fmadd_ps(vc5, vt9, vc4);
109 
110     vp0 = _mm256_fmadd_ps(vp0, vt0, vc3);
111     vp1 = _mm256_fmadd_ps(vp1, vt1, vc3);
112     vp2 = _mm256_fmadd_ps(vp2, vt2, vc3);
113     vp3 = _mm256_fmadd_ps(vp3, vt3, vc3);
114     vp4 = _mm256_fmadd_ps(vp4, vt4, vc3);
115     vp5 = _mm256_fmadd_ps(vp5, vt5, vc3);
116     vp6 = _mm256_fmadd_ps(vp6, vt6, vc3);
117     vp7 = _mm256_fmadd_ps(vp7, vt7, vc3);
118     vp8 = _mm256_fmadd_ps(vp8, vt8, vc3);
119     vp9 = _mm256_fmadd_ps(vp9, vt9, vc3);
120 
121     vp0 = _mm256_fmadd_ps(vp0, vt0, vc2);
122     vp1 = _mm256_fmadd_ps(vp1, vt1, vc2);
123     vp2 = _mm256_fmadd_ps(vp2, vt2, vc2);
124     vp3 = _mm256_fmadd_ps(vp3, vt3, vc2);
125     vp4 = _mm256_fmadd_ps(vp4, vt4, vc2);
126     vp5 = _mm256_fmadd_ps(vp5, vt5, vc2);
127     vp6 = _mm256_fmadd_ps(vp6, vt6, vc2);
128     vp7 = _mm256_fmadd_ps(vp7, vt7, vc2);
129     vp8 = _mm256_fmadd_ps(vp8, vt8, vc2);
130     vp9 = _mm256_fmadd_ps(vp9, vt9, vc2);
131 
132     vp0 = _mm256_fmadd_ps(vp0, vt0, vc1);
133     vp1 = _mm256_fmadd_ps(vp1, vt1, vc1);
134     vp2 = _mm256_fmadd_ps(vp2, vt2, vc1);
135     vp3 = _mm256_fmadd_ps(vp3, vt3, vc1);
136     vp4 = _mm256_fmadd_ps(vp4, vt4, vc1);
137     vp5 = _mm256_fmadd_ps(vp5, vt5, vc1);
138     vp6 = _mm256_fmadd_ps(vp6, vt6, vc1);
139     vp7 = _mm256_fmadd_ps(vp7, vt7, vc1);
140     vp8 = _mm256_fmadd_ps(vp8, vt8, vc1);
141     vp9 = _mm256_fmadd_ps(vp9, vt9, vc1);
142 
143     vp0 = _mm256_fmadd_ps(vp0, vt0, vc0);
144     vp1 = _mm256_fmadd_ps(vp1, vt1, vc0);
145     vp2 = _mm256_fmadd_ps(vp2, vt2, vc0);
146     vp3 = _mm256_fmadd_ps(vp3, vt3, vc0);
147     vp4 = _mm256_fmadd_ps(vp4, vt4, vc0);
148     vp5 = _mm256_fmadd_ps(vp5, vt5, vc0);
149     vp6 = _mm256_fmadd_ps(vp6, vt6, vc0);
150     vp7 = _mm256_fmadd_ps(vp7, vt7, vc0);
151     vp8 = _mm256_fmadd_ps(vp8, vt8, vc0);
152     vp9 = _mm256_fmadd_ps(vp9, vt9, vc0);
153 
154     // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation where
155     //  - vnX is "exponent"
156     //  - vpX is "mantissa"
157     //
158     // exp2(ae) * av * exp2(be) * bv =
159     //   = exp2(ae + be) * (av * bv)
160     __m256 vf0 = _mm256_mul_ps(vp0, vscalev);
161     __m256 vf1 = _mm256_mul_ps(vp1, vscalev);
162     __m256 vf2 = _mm256_mul_ps(vp2, vscalev);
163     __m256 vf3 = _mm256_mul_ps(vp3, vscalev);
164     __m256 vf4 = _mm256_mul_ps(vp4, vscalev);
165     __m256 vf5 = _mm256_mul_ps(vp5, vscalev);
166     __m256 vf6 = _mm256_mul_ps(vp6, vscalev);
167     __m256 vf7 = _mm256_mul_ps(vp7, vscalev);
168     __m256 vf8 = _mm256_mul_ps(vp8, vscalev);
169     __m256 vf9 = _mm256_mul_ps(vp9, vscalev);
170 
171     __m256 ve0 = _mm256_add_ps(vn0, vscalee);
172     __m256 ve1 = _mm256_add_ps(vn1, vscalee);
173     __m256 ve2 = _mm256_add_ps(vn2, vscalee);
174     __m256 ve3 = _mm256_add_ps(vn3, vscalee);
175     __m256 ve4 = _mm256_add_ps(vn4, vscalee);
176     __m256 ve5 = _mm256_add_ps(vn5, vscalee);
177     __m256 ve6 = _mm256_add_ps(vn6, vscalee);
178     __m256 ve7 = _mm256_add_ps(vn7, vscalee);
179     __m256 ve8 = _mm256_add_ps(vn8, vscalee);
180     __m256 ve9 = _mm256_add_ps(vn9, vscalee);
181 
182     // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
183     // This replacement is done in two steps:
184     // 1. Clamp minimum e at -127.0.
185     // 2. Map e to scale factor 0.0 when e == -127.0
186     ve0 = _mm256_max_ps(ve0, vmin_exponent);
187     ve1 = _mm256_max_ps(ve1, vmin_exponent);
188     ve2 = _mm256_max_ps(ve2, vmin_exponent);
189     ve3 = _mm256_max_ps(ve3, vmin_exponent);
190     ve4 = _mm256_max_ps(ve4, vmin_exponent);
191     ve5 = _mm256_max_ps(ve5, vmin_exponent);
192     ve6 = _mm256_max_ps(ve6, vmin_exponent);
193     ve7 = _mm256_max_ps(ve7, vmin_exponent);
194     ve8 = _mm256_max_ps(ve8, vmin_exponent);
195     ve9 = _mm256_max_ps(ve9, vmin_exponent);
196 
197     // Convert exponents into scale factors:
198     // - s = exp2(e) when e > -127.0
199     // - s = 0.0 when e <= -127.0
200     const __m256 vs0 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve0, vmagic_bias)), 23));
201     const __m256 vs1 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve1, vmagic_bias)), 23));
202     const __m256 vs2 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve2, vmagic_bias)), 23));
203     const __m256 vs3 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve3, vmagic_bias)), 23));
204     const __m256 vs4 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve4, vmagic_bias)), 23));
205     const __m256 vs5 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve5, vmagic_bias)), 23));
206     const __m256 vs6 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve6, vmagic_bias)), 23));
207     const __m256 vs7 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve7, vmagic_bias)), 23));
208     const __m256 vs8 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve8, vmagic_bias)), 23));
209     const __m256 vs9 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve9, vmagic_bias)), 23));
210 
211     // Multiply "mantissa" by the scale factor.
212     vf0 = _mm256_mul_ps(vf0, vs0);
213     vf1 = _mm256_mul_ps(vf1, vs1);
214     vf2 = _mm256_mul_ps(vf2, vs2);
215     vf3 = _mm256_mul_ps(vf3, vs3);
216     vf4 = _mm256_mul_ps(vf4, vs4);
217     vf5 = _mm256_mul_ps(vf5, vs5);
218     vf6 = _mm256_mul_ps(vf6, vs6);
219     vf7 = _mm256_mul_ps(vf7, vs7);
220     vf8 = _mm256_mul_ps(vf8, vs8);
221     vf9 = _mm256_mul_ps(vf9, vs9);
222 
223     // Store 80 (10x8) outputs at a time.
224     _mm256_storeu_ps(y, vf0);
225     _mm256_storeu_ps(y + 8, vf1);
226     _mm256_storeu_ps(y + 16, vf2);
227     _mm256_storeu_ps(y + 24, vf3);
228     _mm256_storeu_ps(y + 32, vf4);
229     _mm256_storeu_ps(y + 40, vf5);
230     _mm256_storeu_ps(y + 48, vf6);
231     _mm256_storeu_ps(y + 56, vf7);
232     _mm256_storeu_ps(y + 64, vf8);
233     _mm256_storeu_ps(y + 72, vf9);
234     y += 80;
235   }
236 
237   for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
238     // Load 8 inputs at a time.
239     const __m256 vx = _mm256_loadu_ps(x);
240     x += 8;
241 
242     // Compute reduced argument elements := round(x / log(2)).
243     const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
244 
245     // Compute reduced argument t := x - elements * log(2).
246     // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
247     __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
248     vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
249 
250     // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
251     __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
252     vp = _mm256_fmadd_ps(vp, vt, vc3);
253     vp = _mm256_fmadd_ps(vp, vt, vc2);
254     vp = _mm256_fmadd_ps(vp, vt, vc1);
255     vp = _mm256_fmadd_ps(vp, vt, vc0);
256 
257     // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation.
258     __m256 vf = _mm256_mul_ps(vp, vscalev);
259     __m256 ve = _mm256_add_ps(vn, vscalee);
260 
261     // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
262     ve = _mm256_max_ps(ve, vmin_exponent);
263 
264     // Convert exponents into scale factors.
265     const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23));
266 
267     // Multiply "mantissa" by the scale factor.
268     vf = _mm256_mul_ps(vf, vs);
269 
270     // Store 8 results at a time.
271     _mm256_storeu_ps(y, vf);
272     y += 8;
273   }
274   if XNN_UNLIKELY(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 vx = _mm256_maskload_ps(x, vmask);
281 
282     // Compute reduced argument elements := round(x / log(2)).
283     const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
284 
285     // Compute reduced argument t := x - elements * log(2).
286     // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
287     __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
288     vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
289 
290     // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
291     __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
292     vp = _mm256_fmadd_ps(vp, vt, vc3);
293     vp = _mm256_fmadd_ps(vp, vt, vc2);
294     vp = _mm256_fmadd_ps(vp, vt, vc1);
295     vp = _mm256_fmadd_ps(vp, vt, vc0);
296 
297     // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation.
298     __m256 vf = _mm256_mul_ps(vp, vscalev);
299     __m256 ve = _mm256_add_ps(vn, vscalee);
300 
301     // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
302     ve = _mm256_max_ps(ve, vmin_exponent);
303 
304     // Convert exponents into scale factors.
305     const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23));
306 
307     // Multiply "mantissa" by the scale factor.
308     vf = _mm256_mul_ps(vf, vs);
309 
310     // Store up to 7 inputs at a time.
311     _mm256_maskstore_ps(y, vmask, vf);
312   }
313   _mm256_zeroupper();
314 }
315