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// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <assert.h>
#include <stddef.h>
#include <emmintrin.h>
#include <xnnpack/math-stubs.h>
void xnn_math_f32_sigmoid__sse2_p5_div(
size_t n,
const float* input,
float* output)
{
assert(n % (4 * sizeof(float)) == 0);
const __m128 vmagic_bias = _mm_set1_ps(0x1.8000FEp23f);
// The smallest x for which sigmoidf(x) is normalized.
// This number is also the smallest x for which expf(x) is normalized.
const __m128 vdenorm_cutoff = _mm_set1_ps(-0x1.5D589Ep+6f);
const __m128 vlog2e = _mm_set1_ps(0x1.715476p+0f);
// Last 7 bits are zeroes
const __m128 vminus_ln2_hi = _mm_set1_ps(-0x1.62E400p-1f);
const __m128 vminus_ln2_lo = _mm_set1_ps(-0x1.7F7D1Cp-20f);
const __m128 vone = _mm_set1_ps(1.0f);
const __m128 vsign_mask = _mm_set1_ps(-0.0f);
const __m128 vc1 = _mm_set1_ps(0x1.FFFFF6p-1f);
const __m128 vc2 = _mm_set1_ps(0x1.FFFDC6p-2f);
const __m128 vc3 = _mm_set1_ps(0x1.555A80p-3f);
const __m128 vc4 = _mm_set1_ps(0x1.573A1Ap-5f);
const __m128 vc5 = _mm_set1_ps(0x1.0F9F9Cp-7f);
for (; n != 0; n -= 4 * sizeof(float)) {
const __m128 vx = _mm_loadu_ps(input);
// General structure of the algorithm:
// / exp(x) / (1 + exp(x)) if x <= 0
// f[x] :=
// \ 1 - f[-x] if x >= 0
//
// First we compute f[z] := exp(z) / (1 + exp(z)) where z = -abs(x),
// then replace result with 1 - f[z] if x >= 0.
const __m128 vz = _mm_or_ps(vx, vsign_mask);
// Compute reduced argument n := round(z / log(2)).
// We do it by adding a large number (magic bias) to the product z * (1/log(2)), which cause rounding of the result
// to an integer, then subtracing the large number back. The trick with adding large number is valid only within
// certain bounds (|x| <= 2**22), but thats ok, because inputs x outside of [-87.336544, 17.328678] (i.e. z outsize
// [0, 87.336544]) underflow or saturate sigmoidf(x) anyway. We fixup the result for such inputs at the very end of
// the algorithm.
__m128 vn = _mm_add_ps(_mm_mul_ps(vz, vlog2e), vmagic_bias);
// Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e.
// -87.33642 <= z <= 0.0, and -126 <= n <= 0 accordingly.
const __m128 vs = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn), 23));
// Subtract the large number back to get final n := round(z / log(2)).
vn = _mm_sub_ps(vn, vmagic_bias);
// Compute reduced argument t := z - n * log(2).
// Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
__m128 vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_hi), vz);
vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_lo), vt);
// Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
__m128 vp = _mm_add_ps(_mm_mul_ps(vc5, vt), vc4);
vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc3);
vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc2);
vp = _mm_add_ps(_mm_mul_ps(vp, vt), vc1);
// Reconstruct the exp(z) value:
// e = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))))
// = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))
// = s + (t * s) * p
vt = _mm_mul_ps(vt, vs);
__m128 ve = _mm_add_ps(_mm_mul_ps(vt, vp), vs);
// Denominator of the sigmoid fraction: 1.0 + exp(z)
__m128 vd = _mm_add_ps(ve, vone);
// Reconstruct sigmoid(-z) = exp(z) / (1.0 + exp(z))
__m128 vf = _mm_div_ps(ve, vd);
// For inputs below denormal cutoff, replace output with +0.0f.
// Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
vf = _mm_andnot_ps(_mm_cmplt_ps(vz, vdenorm_cutoff), vf);
// Reconstruct sigmoid(x) = x < 0 ? sigmoid(z) : 1.0 - sigmoid(z)
__m128 vm = _mm_castsi128_ps(_mm_cmpgt_epi32(_mm_setzero_si128(), _mm_castps_si128(vx)));
vf = _mm_or_ps(_mm_and_ps(vf, vm), _mm_andnot_ps(vm, _mm_sub_ps(vone, vf)));
_mm_storeu_ps(output, vf);
input += 4;
output += 4;
}
}