// Auto-generated file. Do not edit! // Template: src/f32-vscaleextexp/avx512f-p5-scalef.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 "xnnpack/common.h" #include "xnnpack/intrinsics-polyfill.h" #include "xnnpack/vscaleextexp.h" void xnn_f32_vscaleextexp_ukernel__avx512f_p5_scalef_u48( size_t batch, const float* input, float* output, float scale_value, float scale_exp) { assert(batch % sizeof(float) == 0); const __m512 vlog2e = _mm512_set1_ps(0x1.715476p+0f); const __m512 vminus_ln2_hi = _mm512_set1_ps(-0x1.62E43p-1f); const __m512 vminus_ln2_lo = _mm512_set1_ps(0x1.05C61p-29f); const __m512 vc0 = _mm512_set1_ps(1.0f); const __m512 vc1 = _mm512_set1_ps(0x1.FFFFF6p-1f); const __m512 vc2 = _mm512_set1_ps(0x1.FFFDC6p-2f); const __m512 vc3 = _mm512_set1_ps(0x1.555A80p-3f); const __m512 vc4 = _mm512_set1_ps(0x1.573A1Ap-5f); const __m512 vc5 = _mm512_set1_ps(0x1.0F9F9Cp-7f); const __m512 vscalev = _mm512_set1_ps(scale_value); const __m512 vscalee = _mm512_set1_ps(scale_exp); for (; batch >= 48 * sizeof(float); batch -= 48 * sizeof(float)) { // Load 48 (3x16) inputs at a time. const __m512 vx0 = _mm512_loadu_ps(input); const __m512 vx1 = _mm512_loadu_ps(input + 16); const __m512 vx2 = _mm512_loadu_ps(input + 32); input += 48; // Compute reduced argument batch := round(input / log(2)). const __m512 vn0 = _mm512_roundscale_ps(_mm512_mul_ps(vx0, vlog2e), 0); const __m512 vn1 = _mm512_roundscale_ps(_mm512_mul_ps(vx1, vlog2e), 0); const __m512 vn2 = _mm512_roundscale_ps(_mm512_mul_ps(vx2, vlog2e), 0); // Compute reduced argument t := input - batch * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m512 vt0 = _mm512_fmadd_ps(vn0, vminus_ln2_hi, vx0); __m512 vt1 = _mm512_fmadd_ps(vn1, vminus_ln2_hi, vx1); __m512 vt2 = _mm512_fmadd_ps(vn2, vminus_ln2_hi, vx2); vt0 = _mm512_fmadd_ps(vn0, vminus_ln2_lo, vt0); vt1 = _mm512_fmadd_ps(vn1, vminus_ln2_lo, vt1); vt2 = _mm512_fmadd_ps(vn2, vminus_ln2_lo, vt2); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m512 vp0 = _mm512_fmadd_ps(vc5, vt0, vc4); __m512 vp1 = _mm512_fmadd_ps(vc5, vt1, vc4); __m512 vp2 = _mm512_fmadd_ps(vc5, vt2, vc4); vp0 = _mm512_fmadd_ps(vp0, vt0, vc3); vp1 = _mm512_fmadd_ps(vp1, vt1, vc3); vp2 = _mm512_fmadd_ps(vp2, vt2, vc3); vp0 = _mm512_fmadd_ps(vp0, vt0, vc2); vp1 = _mm512_fmadd_ps(vp1, vt1, vc2); vp2 = _mm512_fmadd_ps(vp2, vt2, vc2); vp0 = _mm512_fmadd_ps(vp0, vt0, vc1); vp1 = _mm512_fmadd_ps(vp1, vt1, vc1); vp2 = _mm512_fmadd_ps(vp2, vt2, vc1); vp0 = _mm512_fmadd_ps(vp0, vt0, vc0); vp1 = _mm512_fmadd_ps(vp1, vt1, vc0); vp2 = _mm512_fmadd_ps(vp2, vt2, 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) __m512 vf0 = _mm512_mul_ps(vp0, vscalev); __m512 vf1 = _mm512_mul_ps(vp1, vscalev); __m512 vf2 = _mm512_mul_ps(vp2, vscalev); const __m512 ve0 = _mm512_add_ps(vn0, vscalee); const __m512 ve1 = _mm512_add_ps(vn1, vscalee); const __m512 ve2 = _mm512_add_ps(vn2, vscalee); // Multiply "mantissa" by the exp2("exponent"). vf0 = _mm512_scalef_ps(vf0, ve0); vf1 = _mm512_scalef_ps(vf1, ve1); vf2 = _mm512_scalef_ps(vf2, ve2); // Store 128 (8x16) results at a time. _mm512_storeu_ps(output, vf0); _mm512_storeu_ps(output + 0, vf0); _mm512_storeu_ps(output + 16, vf1); _mm512_storeu_ps(output + 32, vf2); output += 48; } for (; batch >= 16 * sizeof(float); batch -= 16 * sizeof(float)) { // Load 16 inputs at a time. const __m512 vx = _mm512_loadu_ps(input); input += 16; // Compute reduced argument batch := round(input / log(2)). const __m512 vn = _mm512_roundscale_ps(_mm512_mul_ps(vx, vlog2e), 0); // Compute reduced argument t := input - batch * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m512 vt = _mm512_fmadd_ps(vn, vminus_ln2_hi, vx); vt = _mm512_fmadd_ps(vn, vminus_ln2_lo, vt); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m512 vp = _mm512_fmadd_ps(vc5, vt, vc4); vp = _mm512_fmadd_ps(vp, vt, vc3); vp = _mm512_fmadd_ps(vp, vt, vc2); vp = _mm512_fmadd_ps(vp, vt, vc1); vp = _mm512_fmadd_ps(vp, vt, vc0); // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation. __m512 vf = _mm512_mul_ps(vp, vscalev); const __m512 ve = _mm512_add_ps(vn, vscalee); // Multiply "mantissa" by the exp2("exponent"). vf = _mm512_scalef_ps(vf, ve); // Store 16 results at a time. _mm512_storeu_ps(output, vf); output += 16; } if XNN_UNLIKELY(batch != 0) { // Prepare mask for valid 32-bit batch (depends on batch). batch >>= XNN_LOG2_SIZEOF_FLOAT; const __mmask16 vmask = _cvtu32_mask16((uint32_t) ((UINT32_C(1) << batch) - UINT32_C(1))); // Load up to 15 inputs at a time. const __m512 vx = _mm512_maskz_loadu_ps(vmask, input); // Compute reduced argument batch := round(input / log(2)). const __m512 vn = _mm512_roundscale_ps(_mm512_mul_ps(vx, vlog2e), 0); // Compute reduced argument t := input - batch * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m512 vt = _mm512_fmadd_ps(vn, vminus_ln2_hi, vx); vt = _mm512_fmadd_ps(vn, vminus_ln2_lo, vt); // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. __m512 vp = _mm512_fmadd_ps(vc5, vt, vc4); vp = _mm512_fmadd_ps(vp, vt, vc3); vp = _mm512_fmadd_ps(vp, vt, vc2); vp = _mm512_fmadd_ps(vp, vt, vc1); vp = _mm512_fmadd_ps(vp, vt, vc0); // Multiply "extended" floating-point numbers in ("mantissa", "exponent") representation. __m512 vf = _mm512_mul_ps(vp, vscalev); const __m512 ve = _mm512_add_ps(vn, vscalee); // Multiply "mantissa" by the exp2("exponent"). vf = _mm512_scalef_ps(vf, ve); // Store up to 15 results at a time. _mm512_mask_storeu_ps(output, vmask, vf); } }