// Auto-generated file. Do not edit! // Template: src/f32-raddstoreexpminusmax/sse2-p5.c.in // Generator: tools/xngen // // 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 #include #include #include void xnn_f32_raddstoreexpminusmax_ukernel__sse2_p5_x4( size_t elements, const float* input, float* output, float* sum, float max) XNN_DISABLE_TSAN { assert(elements % sizeof(float) == 0); const __m128 vmagic_bias = _mm_set1_ps(0x1.8000FEp23f); // The smallest x for which expf(x) is normalized. const __m128 vdenorm_cutoff = _mm_set1_ps(-0x1.5D589Ep6f); 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 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); const __m128 vi_max = _mm_set1_ps(max); __m128 vacc0 = _mm_setzero_ps(); for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) { // Load 4 (1x4) inputs at a time. const __m128 vi0123 = _mm_loadu_ps(input); input += 4; // Subtract maximum input x := i - i_max. This implies x <= 0. const __m128 vx0123 = _mm_sub_ps(vi0123, vi_max); // Compute reduced argument elements := round(x / log(2)). __m128 vn0123 = _mm_add_ps(_mm_mul_ps(vx0123, vlog2e), vmagic_bias); // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e. // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly. const __m128 vs0123 = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn0123), 23)); // Subtract the large number back to get final elements := round(x / log(2)). vn0123 = _mm_sub_ps(vn0123, vmagic_bias); // Compute reduced argument t := x - elements * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m128 vt0123 = _mm_add_ps(_mm_mul_ps(vn0123, vminus_ln2_hi), vx0123); vt0123 = _mm_add_ps(_mm_mul_ps(vn0123, vminus_ln2_lo), vt0123); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m128 vp0123 = _mm_add_ps(_mm_mul_ps(vc5, vt0123), vc4); vp0123 = _mm_add_ps(_mm_mul_ps(vp0123, vt0123), vc3); vp0123 = _mm_add_ps(_mm_mul_ps(vp0123, vt0123), vc2); vp0123 = _mm_add_ps(_mm_mul_ps(vp0123, vt0123), vc1); // Reconstruct the final f value: // f = 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 vt0123 = _mm_mul_ps(vt0123, vs0123); __m128 vf0123 = _mm_add_ps(_mm_mul_ps(vt0123, vp0123), vs0123); // For inputs below zero cutoff, replace output with +0.0f. // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. vf0123 = _mm_andnot_ps(_mm_cmplt_ps(vx0123, vdenorm_cutoff), vf0123); // Store 4 (1x4) outputs at a time. _mm_storeu_ps(output, vf0123); output += 4; // Accumulate computed exponents. vacc0 = _mm_add_ps(vacc0, vf0123); } __m128 vacc = vacc0; for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) { // Load 4 inputs at a time. const __m128 vi = _mm_loadu_ps(input); input += 4; // Subtract maximum input x := i - i_max. This implies x <= 0. const __m128 vx = _mm_sub_ps(vi, vi_max); // Compute reduced argument elements := round(x / log(2)). __m128 vn = _mm_add_ps(_mm_mul_ps(vx, vlog2e), vmagic_bias); // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e. // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly. const __m128 vs = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn), 23)); // Subtract the large number back to get final elements := round(x / log(2)). vn = _mm_sub_ps(vn, vmagic_bias); // Compute reduced argument t := x - elements * 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), vx); vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_lo), vt); // Compute degree-5 polynomial approximation 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 final f value: // f = 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 vf = _mm_add_ps(_mm_mul_ps(vt, vp), vs); // For inputs below zero 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(vx, vdenorm_cutoff), vf); // Store 4 outputs at a time. _mm_storeu_ps(output, vf); output += 4; // Accumulate computed exponents. vacc = _mm_add_ps(vacc, vf); } if (elements != 0) { assert(elements >= 1 * sizeof(float)); assert(elements <= 3 * sizeof(float)); // Load 4 inputs at a time. const __m128 vi = _mm_loadu_ps(input); // Subtract maximum input x := i - i_max. This implies x <= 0. const __m128 vx = _mm_sub_ps(vi, vi_max); // Compute reduced argument elements := round(x / log(2)). __m128 vn = _mm_add_ps(_mm_mul_ps(vx, vlog2e), vmagic_bias); // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e. // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly. const __m128 vs = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn), 23)); // Subtract the large number back to get final elements := round(x / log(2)). vn = _mm_sub_ps(vn, vmagic_bias); // Compute reduced argument t := x - elements * 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), vx); vt = _mm_add_ps(_mm_mul_ps(vn, vminus_ln2_lo), vt); // Compute degree-5 polynomial approximation 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 final f value: // f = 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 vf = _mm_add_ps(_mm_mul_ps(vt, vp), vs); // For inputs below zero 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(vx, vdenorm_cutoff), vf); if (elements & (2 * sizeof(float))) { // Store 2 outputs at a time. _mm_storel_pi((__m64*) output, vf); output += 2; // Accumulate 2 computed exponents. vacc = _mm_add_ps(vacc, _mm_movelh_ps(vf, _mm_setzero_ps())); vf = _mm_movehl_ps(vf, vf); } if (elements & (1 * sizeof(float))) { // Store 1 output at a time. _mm_store_ss(output, vf); // Accumulate 1 computed exponent. vacc = _mm_add_ss(vacc, vf); } } // Reduce 4 elements in the SIMD register vacc = _mm_add_ps(vacc, _mm_movehl_ps(vacc, vacc)); vacc = _mm_add_ss(vacc, _mm_shuffle_ps(vacc, vacc, _MM_SHUFFLE(2, 3, 0, 1))); _mm_store_ss(sum, vacc); }