// Copyright 2020 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 #include #include #include #include void xnn_math_f32_expm1minus__scalar_rr2_p5( size_t n, const float* input, float* output) { assert(n % (4 * sizeof(float)) == 0); // Large number such that ulp(magic bias) == 1 and magic bias === 127 mod 2**22. const float vmagic_bias = 0x1.8000FEp23f; const float vlog2e = 0x1.715476p+0f; // The largest x for which expm1f(x) is saturated at -1.0f. const float vsat_cutoff = -0x1.154246p+4f; // Last 5 bits are zeroes const float vminus_ln2_hi = -0x1.62E440p-1f; const float vminus_ln2_lo = 0x1.0105C6p-21f; // Coefficient of polynomial approximation // exp(t) - 1 ~ t * (1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) // on [-log(2)/2, log(2)/2] const float vc5 = 0x1.113780p-7f; const float vc4 = 0x1.5704DCp-5f; const float vc3 = 0x1.555634p-3f; const float vc2 = 0x1.FFFE70p-2f; const float vone = 1.0f; for (; n != 0; n -= sizeof(float)) { float vx = *input++; // Compute reduced argument n := round(x / log(2)). // We do it by adding a large number (magic bias), which cause rounding of the result to integer, then subtracing // the large number back. The trick with adding large number is valid only within certain bounds // (|x / log(2)| <= 2**22, i.e. |x| <= 0x1.62E43p+21 = 2907270.0), but that is acceptable, because inputs x are // restricted to [-17.328680, 0]. // Note that addition-subtraction of the large number doesn't cause overflow for inputs in this range. float vn = vx * vlog2e + vmagic_bias; // Create a floating-point number s (scale) such that s == 2**n for valid inputs, i.e. // -17.328680 <= x <= 0.0, and -25 <= n <= 0 accordingly. float vs = fp32_from_bits(fp32_to_bits(vn) << 23); // Subtract the large number back to get final n := round(x / log(2)). vn -= vmagic_bias; // Compute reduced argument t := x - n * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. float vt = vn * vminus_ln2_hi + vx; vt = vn * vminus_ln2_lo + vt; // The function saturates at -1 for large negative inputs: expm1f(x) == -1.0f for x <= sat_cutoff ~= -17.328680. // To guarantee this behaviour, we zero out s (scale) and t (reduced argument) for x <= sat_cutoff. if XNN_UNPREDICTABLE(vx <= vsat_cutoff) { vs = 0.0f; vt = 0.0f; } // Compute degree-5 polynomial approximation for exp(t) - 1 on [-log(2)/2, log(2)/2]. // P(t) = t * (1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) // = t + t * (t * (c2 + t * (c3 + t * (c4 + t * c5)))) = t + t * p float vp = vc5 * vt + vc4; vp = vp * vt + vc3; vp = vp * vt + vc2; vp *= vt; // Reconstruct the exp(x) - 1 value: // exp(x) - 1 = s * (1 + t * (1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) - 1 // = (s - 1) + s * (t + t * p) // = ((t * s) + (t * s) * p) + (s - 1) vt *= vs; const float vsm1 = vs - vone; vp = vp * vt + vt; const float vf = vp + vsm1; *output++ = vf; } }