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_x24(size_t elements,const float * x,float * y,float scale_value,float scale_exp)20 void xnn_f32_vscaleextexp_ukernel__avx2_p5_x24(
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 >= 24 * sizeof(float); elements -= 24 * sizeof(float)) {
49 // Load 24 (3x8) 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 x += 24;
54
55 // Compute reduced argument elements := round(x / log(2)).
56 const __m256 vn0 = _mm256_round_ps(_mm256_mul_ps(vx0, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
57 const __m256 vn1 = _mm256_round_ps(_mm256_mul_ps(vx1, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
58 const __m256 vn2 = _mm256_round_ps(_mm256_mul_ps(vx2, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
59
60 // Compute reduced argument t := x - elements * log(2).
61 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
62 __m256 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_hi, vx0);
63 __m256 vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_hi, vx1);
64 __m256 vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_hi, vx2);
65
66 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_lo, vt0);
67 vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_lo, vt1);
68 vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_lo, vt2);
69
70 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
71 __m256 vp0 = _mm256_fmadd_ps(vc5, vt0, vc4);
72 __m256 vp1 = _mm256_fmadd_ps(vc5, vt1, vc4);
73 __m256 vp2 = _mm256_fmadd_ps(vc5, vt2, vc4);
74
75 vp0 = _mm256_fmadd_ps(vp0, vt0, vc3);
76 vp1 = _mm256_fmadd_ps(vp1, vt1, vc3);
77 vp2 = _mm256_fmadd_ps(vp2, vt2, vc3);
78
79 vp0 = _mm256_fmadd_ps(vp0, vt0, vc2);
80 vp1 = _mm256_fmadd_ps(vp1, vt1, vc2);
81 vp2 = _mm256_fmadd_ps(vp2, vt2, vc2);
82
83 vp0 = _mm256_fmadd_ps(vp0, vt0, vc1);
84 vp1 = _mm256_fmadd_ps(vp1, vt1, vc1);
85 vp2 = _mm256_fmadd_ps(vp2, vt2, vc1);
86
87 vp0 = _mm256_fmadd_ps(vp0, vt0, vc0);
88 vp1 = _mm256_fmadd_ps(vp1, vt1, vc0);
89 vp2 = _mm256_fmadd_ps(vp2, vt2, vc0);
90
91 // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation where
92 // - vnX is "exponent"
93 // - vpX is "mantissa"
94 //
95 // exp2(ae) * av * exp2(be) * bv =
96 // = exp2(ae + be) * (av * bv)
97 __m256 vf0 = _mm256_mul_ps(vp0, vscalev);
98 __m256 vf1 = _mm256_mul_ps(vp1, vscalev);
99 __m256 vf2 = _mm256_mul_ps(vp2, vscalev);
100
101 __m256 ve0 = _mm256_add_ps(vn0, vscalee);
102 __m256 ve1 = _mm256_add_ps(vn1, vscalee);
103 __m256 ve2 = _mm256_add_ps(vn2, vscalee);
104
105 // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
106 // This replacement is done in two steps:
107 // 1. Clamp minimum e at -127.0.
108 // 2. Map e to scale factor 0.0 when e == -127.0
109 ve0 = _mm256_max_ps(ve0, vmin_exponent);
110 ve1 = _mm256_max_ps(ve1, vmin_exponent);
111 ve2 = _mm256_max_ps(ve2, vmin_exponent);
112
113 // Convert exponents into scale factors:
114 // - s = exp2(e) when e > -127.0
115 // - s = 0.0 when e <= -127.0
116 const __m256 vs0 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve0, vmagic_bias)), 23));
117 const __m256 vs1 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve1, vmagic_bias)), 23));
118 const __m256 vs2 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve2, vmagic_bias)), 23));
119
120 // Multiply "mantissa" by the scale factor.
121 vf0 = _mm256_mul_ps(vf0, vs0);
122 vf1 = _mm256_mul_ps(vf1, vs1);
123 vf2 = _mm256_mul_ps(vf2, vs2);
124
125 // Store 24 (3x8) outputs at a time.
126 _mm256_storeu_ps(y, vf0);
127 _mm256_storeu_ps(y + 8, vf1);
128 _mm256_storeu_ps(y + 16, vf2);
129 y += 24;
130 }
131
132 for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
133 // Load 8 inputs at a time.
134 const __m256 vx = _mm256_loadu_ps(x);
135 x += 8;
136
137 // Compute reduced argument elements := round(x / log(2)).
138 const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
139
140 // Compute reduced argument t := x - elements * log(2).
141 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
142 __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
143 vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
144
145 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
146 __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
147 vp = _mm256_fmadd_ps(vp, vt, vc3);
148 vp = _mm256_fmadd_ps(vp, vt, vc2);
149 vp = _mm256_fmadd_ps(vp, vt, vc1);
150 vp = _mm256_fmadd_ps(vp, vt, vc0);
151
152 // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation.
153 __m256 vf = _mm256_mul_ps(vp, vscalev);
154 __m256 ve = _mm256_add_ps(vn, vscalee);
155
156 // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
157 ve = _mm256_max_ps(ve, vmin_exponent);
158
159 // Convert exponents into scale factors.
160 const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23));
161
162 // Multiply "mantissa" by the scale factor.
163 vf = _mm256_mul_ps(vf, vs);
164
165 // Store 8 results at a time.
166 _mm256_storeu_ps(y, vf);
167 y += 8;
168 }
169 if XNN_UNLIKELY(elements != 0) {
170 assert(elements >= 1 * sizeof(float));
171 assert(elements <= 7 * sizeof(float));
172 const __m256i vmask = _mm256_loadu_si256((const __m256i*) ((uintptr_t) &mask_table[7] - elements));
173
174 // Load up to 7 inputs at a time.
175 const __m256 vx = _mm256_maskload_ps(x, vmask);
176
177 // Compute reduced argument elements := round(x / log(2)).
178 const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
179
180 // Compute reduced argument t := x - elements * log(2).
181 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
182 __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
183 vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
184
185 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
186 __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
187 vp = _mm256_fmadd_ps(vp, vt, vc3);
188 vp = _mm256_fmadd_ps(vp, vt, vc2);
189 vp = _mm256_fmadd_ps(vp, vt, vc1);
190 vp = _mm256_fmadd_ps(vp, vt, vc0);
191
192 // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation.
193 __m256 vf = _mm256_mul_ps(vp, vscalev);
194 __m256 ve = _mm256_add_ps(vn, vscalee);
195
196 // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
197 ve = _mm256_max_ps(ve, vmin_exponent);
198
199 // Convert exponents into scale factors.
200 const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23));
201
202 // Multiply "mantissa" by the scale factor.
203 vf = _mm256_mul_ps(vf, vs);
204
205 // Store up to 7 inputs at a time.
206 _mm256_maskstore_ps(y, vmask, vf);
207 }
208 _mm256_zeroupper();
209 }
210