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_x8(size_t elements,const float * x,float * y,float scale_value,float scale_exp)20 void xnn_f32_vscaleextexp_ukernel__avx2_p5_x8(
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 >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
49 // Load 8 (1x8) inputs at a time.
50 const __m256 vx0 = _mm256_loadu_ps(x);
51 x += 8;
52
53 // Compute reduced argument elements := round(x / log(2)).
54 const __m256 vn0 = _mm256_round_ps(_mm256_mul_ps(vx0, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
55
56 // Compute reduced argument t := x - elements * log(2).
57 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
58 __m256 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_hi, vx0);
59
60 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_lo, vt0);
61
62 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
63 __m256 vp0 = _mm256_fmadd_ps(vc5, vt0, vc4);
64
65 vp0 = _mm256_fmadd_ps(vp0, vt0, vc3);
66
67 vp0 = _mm256_fmadd_ps(vp0, vt0, vc2);
68
69 vp0 = _mm256_fmadd_ps(vp0, vt0, vc1);
70
71 vp0 = _mm256_fmadd_ps(vp0, vt0, vc0);
72
73 // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation where
74 // - vnX is "exponent"
75 // - vpX is "mantissa"
76 //
77 // exp2(ae) * av * exp2(be) * bv =
78 // = exp2(ae + be) * (av * bv)
79 __m256 vf0 = _mm256_mul_ps(vp0, vscalev);
80
81 __m256 ve0 = _mm256_add_ps(vn0, vscalee);
82
83 // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
84 // This replacement is done in two steps:
85 // 1. Clamp minimum e at -127.0.
86 // 2. Map e to scale factor 0.0 when e == -127.0
87 ve0 = _mm256_max_ps(ve0, vmin_exponent);
88
89 // Convert exponents into scale factors:
90 // - s = exp2(e) when e > -127.0
91 // - s = 0.0 when e <= -127.0
92 const __m256 vs0 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve0, vmagic_bias)), 23));
93
94 // Multiply "mantissa" by the scale factor.
95 vf0 = _mm256_mul_ps(vf0, vs0);
96
97 // Store 8 (1x8) outputs at a time.
98 _mm256_storeu_ps(y, vf0);
99 y += 8;
100 }
101
102 for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
103 // Load 8 inputs at a time.
104 const __m256 vx = _mm256_loadu_ps(x);
105 x += 8;
106
107 // Compute reduced argument elements := round(x / log(2)).
108 const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
109
110 // Compute reduced argument t := x - elements * log(2).
111 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
112 __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
113 vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
114
115 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
116 __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
117 vp = _mm256_fmadd_ps(vp, vt, vc3);
118 vp = _mm256_fmadd_ps(vp, vt, vc2);
119 vp = _mm256_fmadd_ps(vp, vt, vc1);
120 vp = _mm256_fmadd_ps(vp, vt, vc0);
121
122 // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation.
123 __m256 vf = _mm256_mul_ps(vp, vscalev);
124 __m256 ve = _mm256_add_ps(vn, vscalee);
125
126 // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
127 ve = _mm256_max_ps(ve, vmin_exponent);
128
129 // Convert exponents into scale factors.
130 const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23));
131
132 // Multiply "mantissa" by the scale factor.
133 vf = _mm256_mul_ps(vf, vs);
134
135 // Store 8 results at a time.
136 _mm256_storeu_ps(y, vf);
137 y += 8;
138 }
139 if XNN_UNLIKELY(elements != 0) {
140 assert(elements >= 1 * sizeof(float));
141 assert(elements <= 7 * sizeof(float));
142 const __m256i vmask = _mm256_loadu_si256((const __m256i*) ((uintptr_t) &mask_table[7] - elements));
143
144 // Load up to 7 inputs at a time.
145 const __m256 vx = _mm256_maskload_ps(x, vmask);
146
147 // Compute reduced argument elements := round(x / log(2)).
148 const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
149
150 // Compute reduced argument t := x - elements * log(2).
151 // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
152 __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx);
153 vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt);
154
155 // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
156 __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4);
157 vp = _mm256_fmadd_ps(vp, vt, vc3);
158 vp = _mm256_fmadd_ps(vp, vt, vc2);
159 vp = _mm256_fmadd_ps(vp, vt, vc1);
160 vp = _mm256_fmadd_ps(vp, vt, vc0);
161
162 // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation.
163 __m256 vf = _mm256_mul_ps(vp, vscalev);
164 __m256 ve = _mm256_add_ps(vn, vscalee);
165
166 // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0.
167 ve = _mm256_max_ps(ve, vmin_exponent);
168
169 // Convert exponents into scale factors.
170 const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23));
171
172 // Multiply "mantissa" by the scale factor.
173 vf = _mm256_mul_ps(vf, vs);
174
175 // Store up to 7 inputs at a time.
176 _mm256_maskstore_ps(y, vmask, vf);
177 }
178 _mm256_zeroupper();
179 }
180