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1 // Copyright 2019 Google LLC
2 //
3 // This source code is licensed under the BSD-style license found in the
4 // LICENSE file in the root directory of this source tree.
5 
6 #include <assert.h>
7 #include <stddef.h>
8 
9 #include <xnnpack/common.h>
10 #include <xnnpack/math-stubs.h>
11 
12 #include <fp16/bitcasts.h>
13 
14 
15 // Table of exp2(k / 64) values decremented (as integer) by (k << 17), k = 0..63
16 extern XNN_INTERNAL const uint32_t xnn_table_exp2minus_k_over_64[64];
17 
xnn_math_f32_expminus__scalar_rr2_lut64_p2(size_t n,const float * input,float * output)18 void xnn_math_f32_expminus__scalar_rr2_lut64_p2(
19     size_t n,
20     const float* input,
21     float* output)
22 {
23   assert(n % sizeof(float) == 0);
24 
25   // Large number such that ulp(magic bias) == exp2(-6)
26   const float vmagic_bias = 0x1.800000p17f;
27   const float vlog2e  = 0x1.715476p0f;
28   // Mask for the lowest 6 bits
29   const uint32_t vindex_mask = UINT32_C(0x3F);
30   // Last 13 bits are zeroes
31   const float vminus_ln2_hi = -0x1.630000p-1f;
32   const float vminus_ln2_lo =  0x1.BD0106p-13f;
33   // Coefficient of polynomial approximation
34   //   exp(t) ~ 1 + t * (1 + t * c2)
35   // on [-log(2)/128, log(2)/128]
36   const float vc2 = 0x1.FFFF0Ap-2f;
37   // The smallest x for which expf(x) is normalized.
38   const float vdenorm_cutoff = -0x1.5D589Ep6f;
39 
40   for (; n != 0; n -= sizeof(float)) {
41     const float vx = *input++;
42 
43     // Compute reduced argument n := round(x / log(2), 6).
44     // We do it by adding a large number (magic bias), which cause rounding of the result to 6 fractional bits, then
45     // subtracing the large number back. The trick with adding large number is valid only within certain bounds
46     // (|x / log(2)| <= 2**16, i.e. |x| <= 0x1.62E43p+15 = 45426.09375), but that is acceptable, because inputs x
47     // outside of [-87.336544, 0] underflow expf(x). We fixup the result for such inputs at the very end of the
48     // algorithm.
49     float vn = vx * vlog2e + vmagic_bias;
50 
51     // Create a floating-point number s (scale) such that s := 2**n for such inputs that expf(x) is normalized, i.e.
52     // -87.336544 <= x <= 0. As n has 6 fractional bits, we split s == 2**n = 2**int(n) * 2**frac(n). We create s in
53     // two steps:
54     // 1. Fetch 2**frac(n) from the table using the 6 low bits of n, as integer. Note that the fetched values are in
55     //    the [1.0, 2.0) range, i.e. their floating-point exponent is 0.
56     // 2. Adjust fecthed value by addition of int(n) to its floating-point exponent. The result is always a normalized
57     //    number, because for -87.33642 <= x <= 0 (inputs for which expf(x) is normalized) we have -126 <= int(n) <= 0,
58     //    and thus the adjusted exponent is not lower than -126.
59     //
60     // Shift bits 6:14 into 23:31 (position of floating-point exponent).
61     const uint32_t ve = fp32_to_bits(vn) << 17;
62 
63     // Use bits 0:6 of n, as integer, as an index for table lookup of l := 2**frac(n).
64     const uint32_t vidx = fp32_to_bits(vn) & vindex_mask;
65     // Adjust exponent of the value l fetched from the table to get the final s value.
66     const float vs = fp32_from_bits(xnn_table_exp2minus_k_over_64[vidx] + ve);
67 
68     // Subtract the large number back to get the final n := round(x / log(2), 6) as a floating-point number.
69     vn -= vmagic_bias;
70 
71     // Compute reduced argument t := x - n * log(2)
72     // Use Cody-Waite range reduction method (note the two constants representing log(2)) to improve accuracy.
73     float vt = vn * vminus_ln2_hi + vx;
74     vt = vn * vminus_ln2_lo + vt;
75 
76     // Compute degree-2 polynomial approximation for exp(t) on [-log(2)/128, log(2)/128].
77     //   P(t) = 1 + t * (1 + t * c2) = 1 + (t + t * (t * c2)) = 1 + p
78     float vp = vt * vc2;
79     vp = vp * vt + vt;
80 
81     // Reconstruct the exp(x) value:
82     //   exp(x) = s * (1 + t * (1 + t * c2))
83     //          = s * (1 + p)
84     //          = s + s * p
85     float vf = vp * vs + vs;
86 
87     // For inputs below denormal cutoff, replace output with +0.0f.
88     // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
89     if XNN_UNPREDICTABLE(vx < vdenorm_cutoff) {
90       vf = 0.0f;
91     }
92 
93     *output++ = vf;
94   }
95 }
96