// Auto-generated file. Do not edit! // Template: src/f32-vscaleextexp/avx2-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 static const int32_t mask_table[14] = {-1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0}; void xnn_f32_vscaleextexp_ukernel__avx2_p5_x56( size_t elements, const float* x, float* y, float scale_value, float scale_exp) { assert(elements % sizeof(float) == 0); const __m256 vlog2e = _mm256_set1_ps(0x1.715476p+0f); const __m256 vminus_ln2_hi = _mm256_set1_ps(-0x1.62E43p-1f); const __m256 vminus_ln2_lo = _mm256_set1_ps(0x1.05C61p-29f); // The smallest elements such that 2**elements is considered non-negligible. // For smaller elements, 2**elements is replaced with zero. const __m256 vmin_exponent = _mm256_set1_ps(-127.0f); const __m256 vmagic_bias = _mm256_set1_ps(0x1.8000FEp23f); const __m256 vc0 = _mm256_set1_ps(1.0f); const __m256 vc1 = _mm256_set1_ps(0x1.FFFFF6p-1f); const __m256 vc2 = _mm256_set1_ps(0x1.FFFDC6p-2f); const __m256 vc3 = _mm256_set1_ps(0x1.555A80p-3f); const __m256 vc4 = _mm256_set1_ps(0x1.573A1Ap-5f); const __m256 vc5 = _mm256_set1_ps(0x1.0F9F9Cp-7f); const __m256 vscalev = _mm256_set1_ps(scale_value); const __m256 vscalee = _mm256_set1_ps(scale_exp); for (; elements >= 56 * sizeof(float); elements -= 56 * sizeof(float)) { // Load 56 (7x8) inputs at a time. const __m256 vx0 = _mm256_loadu_ps(x); const __m256 vx1 = _mm256_loadu_ps(x + 8); const __m256 vx2 = _mm256_loadu_ps(x + 16); const __m256 vx3 = _mm256_loadu_ps(x + 24); const __m256 vx4 = _mm256_loadu_ps(x + 32); const __m256 vx5 = _mm256_loadu_ps(x + 40); const __m256 vx6 = _mm256_loadu_ps(x + 48); x += 56; // Compute reduced argument elements := round(x / log(2)). const __m256 vn0 = _mm256_round_ps(_mm256_mul_ps(vx0, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); const __m256 vn1 = _mm256_round_ps(_mm256_mul_ps(vx1, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); const __m256 vn2 = _mm256_round_ps(_mm256_mul_ps(vx2, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); const __m256 vn3 = _mm256_round_ps(_mm256_mul_ps(vx3, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); const __m256 vn4 = _mm256_round_ps(_mm256_mul_ps(vx4, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); const __m256 vn5 = _mm256_round_ps(_mm256_mul_ps(vx5, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); const __m256 vn6 = _mm256_round_ps(_mm256_mul_ps(vx6, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); // Compute reduced argument t := x - elements * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m256 vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_hi, vx0); __m256 vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_hi, vx1); __m256 vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_hi, vx2); __m256 vt3 = _mm256_fmadd_ps(vn3, vminus_ln2_hi, vx3); __m256 vt4 = _mm256_fmadd_ps(vn4, vminus_ln2_hi, vx4); __m256 vt5 = _mm256_fmadd_ps(vn5, vminus_ln2_hi, vx5); __m256 vt6 = _mm256_fmadd_ps(vn6, vminus_ln2_hi, vx6); vt0 = _mm256_fmadd_ps(vn0, vminus_ln2_lo, vt0); vt1 = _mm256_fmadd_ps(vn1, vminus_ln2_lo, vt1); vt2 = _mm256_fmadd_ps(vn2, vminus_ln2_lo, vt2); vt3 = _mm256_fmadd_ps(vn3, vminus_ln2_lo, vt3); vt4 = _mm256_fmadd_ps(vn4, vminus_ln2_lo, vt4); vt5 = _mm256_fmadd_ps(vn5, vminus_ln2_lo, vt5); vt6 = _mm256_fmadd_ps(vn6, vminus_ln2_lo, vt6); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m256 vp0 = _mm256_fmadd_ps(vc5, vt0, vc4); __m256 vp1 = _mm256_fmadd_ps(vc5, vt1, vc4); __m256 vp2 = _mm256_fmadd_ps(vc5, vt2, vc4); __m256 vp3 = _mm256_fmadd_ps(vc5, vt3, vc4); __m256 vp4 = _mm256_fmadd_ps(vc5, vt4, vc4); __m256 vp5 = _mm256_fmadd_ps(vc5, vt5, vc4); __m256 vp6 = _mm256_fmadd_ps(vc5, vt6, vc4); vp0 = _mm256_fmadd_ps(vp0, vt0, vc3); vp1 = _mm256_fmadd_ps(vp1, vt1, vc3); vp2 = _mm256_fmadd_ps(vp2, vt2, vc3); vp3 = _mm256_fmadd_ps(vp3, vt3, vc3); vp4 = _mm256_fmadd_ps(vp4, vt4, vc3); vp5 = _mm256_fmadd_ps(vp5, vt5, vc3); vp6 = _mm256_fmadd_ps(vp6, vt6, vc3); vp0 = _mm256_fmadd_ps(vp0, vt0, vc2); vp1 = _mm256_fmadd_ps(vp1, vt1, vc2); vp2 = _mm256_fmadd_ps(vp2, vt2, vc2); vp3 = _mm256_fmadd_ps(vp3, vt3, vc2); vp4 = _mm256_fmadd_ps(vp4, vt4, vc2); vp5 = _mm256_fmadd_ps(vp5, vt5, vc2); vp6 = _mm256_fmadd_ps(vp6, vt6, vc2); vp0 = _mm256_fmadd_ps(vp0, vt0, vc1); vp1 = _mm256_fmadd_ps(vp1, vt1, vc1); vp2 = _mm256_fmadd_ps(vp2, vt2, vc1); vp3 = _mm256_fmadd_ps(vp3, vt3, vc1); vp4 = _mm256_fmadd_ps(vp4, vt4, vc1); vp5 = _mm256_fmadd_ps(vp5, vt5, vc1); vp6 = _mm256_fmadd_ps(vp6, vt6, vc1); vp0 = _mm256_fmadd_ps(vp0, vt0, vc0); vp1 = _mm256_fmadd_ps(vp1, vt1, vc0); vp2 = _mm256_fmadd_ps(vp2, vt2, vc0); vp3 = _mm256_fmadd_ps(vp3, vt3, vc0); vp4 = _mm256_fmadd_ps(vp4, vt4, vc0); vp5 = _mm256_fmadd_ps(vp5, vt5, vc0); vp6 = _mm256_fmadd_ps(vp6, vt6, vc0); // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation where // - vnX is "exponent" // - vpX is "mantissa" // // exp2(ae) * av * exp2(be) * bv = // = exp2(ae + be) * (av * bv) __m256 vf0 = _mm256_mul_ps(vp0, vscalev); __m256 vf1 = _mm256_mul_ps(vp1, vscalev); __m256 vf2 = _mm256_mul_ps(vp2, vscalev); __m256 vf3 = _mm256_mul_ps(vp3, vscalev); __m256 vf4 = _mm256_mul_ps(vp4, vscalev); __m256 vf5 = _mm256_mul_ps(vp5, vscalev); __m256 vf6 = _mm256_mul_ps(vp6, vscalev); __m256 ve0 = _mm256_add_ps(vn0, vscalee); __m256 ve1 = _mm256_add_ps(vn1, vscalee); __m256 ve2 = _mm256_add_ps(vn2, vscalee); __m256 ve3 = _mm256_add_ps(vn3, vscalee); __m256 ve4 = _mm256_add_ps(vn4, vscalee); __m256 ve5 = _mm256_add_ps(vn5, vscalee); __m256 ve6 = _mm256_add_ps(vn6, vscalee); // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0. // This replacement is done in two steps: // 1. Clamp minimum e at -127.0. // 2. Map e to scale factor 0.0 when e == -127.0 ve0 = _mm256_max_ps(ve0, vmin_exponent); ve1 = _mm256_max_ps(ve1, vmin_exponent); ve2 = _mm256_max_ps(ve2, vmin_exponent); ve3 = _mm256_max_ps(ve3, vmin_exponent); ve4 = _mm256_max_ps(ve4, vmin_exponent); ve5 = _mm256_max_ps(ve5, vmin_exponent); ve6 = _mm256_max_ps(ve6, vmin_exponent); // Convert exponents into scale factors: // - s = exp2(e) when e > -127.0 // - s = 0.0 when e <= -127.0 const __m256 vs0 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve0, vmagic_bias)), 23)); const __m256 vs1 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve1, vmagic_bias)), 23)); const __m256 vs2 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve2, vmagic_bias)), 23)); const __m256 vs3 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve3, vmagic_bias)), 23)); const __m256 vs4 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve4, vmagic_bias)), 23)); const __m256 vs5 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve5, vmagic_bias)), 23)); const __m256 vs6 = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve6, vmagic_bias)), 23)); // Multiply "mantissa" by the scale factor. vf0 = _mm256_mul_ps(vf0, vs0); vf1 = _mm256_mul_ps(vf1, vs1); vf2 = _mm256_mul_ps(vf2, vs2); vf3 = _mm256_mul_ps(vf3, vs3); vf4 = _mm256_mul_ps(vf4, vs4); vf5 = _mm256_mul_ps(vf5, vs5); vf6 = _mm256_mul_ps(vf6, vs6); // Store 56 (7x8) outputs at a time. _mm256_storeu_ps(y, vf0); _mm256_storeu_ps(y + 8, vf1); _mm256_storeu_ps(y + 16, vf2); _mm256_storeu_ps(y + 24, vf3); _mm256_storeu_ps(y + 32, vf4); _mm256_storeu_ps(y + 40, vf5); _mm256_storeu_ps(y + 48, vf6); y += 56; } for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) { // Load 8 inputs at a time. const __m256 vx = _mm256_loadu_ps(x); x += 8; // Compute reduced argument elements := round(x / log(2)). const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); // Compute reduced argument t := x - elements * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx); vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4); vp = _mm256_fmadd_ps(vp, vt, vc3); vp = _mm256_fmadd_ps(vp, vt, vc2); vp = _mm256_fmadd_ps(vp, vt, vc1); vp = _mm256_fmadd_ps(vp, vt, vc0); // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation. __m256 vf = _mm256_mul_ps(vp, vscalev); __m256 ve = _mm256_add_ps(vn, vscalee); // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0. ve = _mm256_max_ps(ve, vmin_exponent); // Convert exponents into scale factors. const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23)); // Multiply "mantissa" by the scale factor. vf = _mm256_mul_ps(vf, vs); // Store 8 results at a time. _mm256_storeu_ps(y, vf); y += 8; } if XNN_UNLIKELY(elements != 0) { assert(elements >= 1 * sizeof(float)); assert(elements <= 7 * sizeof(float)); const __m256i vmask = _mm256_loadu_si256((const __m256i*) ((uintptr_t) &mask_table[7] - elements)); // Load up to 7 inputs at a time. const __m256 vx = _mm256_maskload_ps(x, vmask); // Compute reduced argument elements := round(x / log(2)). const __m256 vn = _mm256_round_ps(_mm256_mul_ps(vx, vlog2e), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC); // Compute reduced argument t := x - elements * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m256 vt = _mm256_fmadd_ps(vn, vminus_ln2_hi, vx); vt = _mm256_fmadd_ps(vn, vminus_ln2_lo, vt); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m256 vp = _mm256_fmadd_ps(vc5, vt, vc4); vp = _mm256_fmadd_ps(vp, vt, vc3); vp = _mm256_fmadd_ps(vp, vt, vc2); vp = _mm256_fmadd_ps(vp, vt, vc1); vp = _mm256_fmadd_ps(vp, vt, vc0); // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation. __m256 vf = _mm256_mul_ps(vp, vscalev); __m256 ve = _mm256_add_ps(vn, vscalee); // For computational efficiency, replace exp2(e) with 0.0f when e <= -127.0. ve = _mm256_max_ps(ve, vmin_exponent); // Convert exponents into scale factors. const __m256 vs = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_castps_si256(_mm256_add_ps(ve, vmagic_bias)), 23)); // Multiply "mantissa" by the scale factor. vf = _mm256_mul_ps(vf, vs); // Store up to 7 inputs at a time. _mm256_maskstore_ps(y, vmask, vf); } _mm256_zeroupper(); }