src/f32-raddstoreexpminusmax/gen/psimd-p5-x8.c - platform/external/XNNPACK - Gitiles

 // Auto-generated file. Do not edit!
 //   Template: src/f32-raddstoreexpminusmax/psimd-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 <assert.h>

 #include <psimd.h>

 #include <xnnpack/common.h>
 #include <xnnpack/raddstoreexpminusmax.h>


 void xnn_f32_raddstoreexpminusmax_ukernel__psimd_p5_x8(
     size_t elements,
     const float* input,
     float* output,
     float* sum,
     float max)
 {
   assert(elements % sizeof(float) == 0);

   const psimd_f32 vmagic_bias = psimd_splat_f32(0x1.8000FEp23f);
   // The smallest x for which expf(x) is normalized.
   const psimd_f32 vdenorm_cutoff = psimd_splat_f32(-0x1.5D589Ep6f);
   const psimd_f32 vlog2e = psimd_splat_f32(0x1.715476p+0f);
   // Last 7 bits are zeroes
   const psimd_f32 vminus_ln2_hi = psimd_splat_f32(-0x1.62E400p-1f);
   const psimd_f32 vminus_ln2_lo = psimd_splat_f32(-0x1.7F7D1Cp-20f);

   const psimd_f32 vc1 = psimd_splat_f32(0x1.FFFFF6p-1f);
   const psimd_f32 vc2 = psimd_splat_f32(0x1.FFFDC6p-2f);
   const psimd_f32 vc3 = psimd_splat_f32(0x1.555A80p-3f);
   const psimd_f32 vc4 = psimd_splat_f32(0x1.573A1Ap-5f);
   const psimd_f32 vc5 = psimd_splat_f32(0x1.0F9F9Cp-7f);

   const psimd_f32 vi_max = psimd_splat_f32(max);

   psimd_f32 vacc0 = psimd_zero_f32();
   for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
     // Load 8 (2x4) inputs at a time.
     const psimd_f32 vi0123 = psimd_load_f32(input);
     const psimd_f32 vi4567 = psimd_load_f32(input + 4);
     input += 8;

     // Subtract maximum input x := i - i_max. This implies x <= 0.
     const psimd_f32 vx0123 = psimd_sub_f32(vi0123, vi_max);
     const psimd_f32 vx4567 = psimd_sub_f32(vi4567, vi_max);

     // Compute reduced argument elements := round(x / log(2)).
     psimd_f32 vn0123 = psimd_qfma_f32(vmagic_bias, vx0123, vlog2e);
     psimd_f32 vn4567 = psimd_qfma_f32(vmagic_bias, vx4567, vlog2e);

     // 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 psimd_f32 vs0123 = (psimd_f32) ((psimd_u32) vn0123 << 23);
     const psimd_f32 vs4567 = (psimd_f32) ((psimd_u32) vn4567 << 23);

     // Subtract the large number back to get final elements := round(x / log(2)).
     vn0123 = psimd_sub_f32(vn0123, vmagic_bias);
     vn4567 = psimd_sub_f32(vn4567, 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.
     psimd_f32 vt0123 = psimd_qfma_f32(vx0123, vn0123, vminus_ln2_hi);
     psimd_f32 vt4567 = psimd_qfma_f32(vx4567, vn4567, vminus_ln2_hi);

     vt0123 = psimd_qfma_f32(vt0123, vn0123, vminus_ln2_lo);
     vt4567 = psimd_qfma_f32(vt4567, vn4567, vminus_ln2_lo);

     // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
     psimd_f32 vp0123 = psimd_qfma_f32(vc4, vc5, vt0123);
     psimd_f32 vp4567 = psimd_qfma_f32(vc4, vc5, vt4567);

     vp0123 = psimd_qfma_f32(vc3, vp0123, vt0123);
     vp4567 = psimd_qfma_f32(vc3, vp4567, vt4567);

     vp0123 = psimd_qfma_f32(vc2, vp0123, vt0123);
     vp4567 = psimd_qfma_f32(vc2, vp4567, vt4567);

     vp0123 = psimd_qfma_f32(vc1, vp0123, vt0123);
     vp4567 = psimd_qfma_f32(vc1, vp4567, vt4567);

     // 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 = psimd_mul_f32(vt0123, vs0123);
     vt4567 = psimd_mul_f32(vt4567, vs4567);

     psimd_f32 vf0123 = psimd_qfma_f32(vs0123, vt0123, vp0123);
     psimd_f32 vf4567 = psimd_qfma_f32(vs4567, vt4567, vp4567);

     // 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 = psimd_andnotmask_f32(vx0123 < vdenorm_cutoff, vf0123);
     vf4567 = psimd_andnotmask_f32(vx4567 < vdenorm_cutoff, vf4567);

     // Store 8 (2x4) outputs at a time.
     psimd_store_f32(output, vf0123);
     psimd_store_f32(output + 4, vf4567);
     output += 8;

     // Accumulate computed exponents.
     vacc0 = psimd_add_f32(vacc0, vf0123);
     vacc0 = psimd_add_f32(vacc0, vf4567);
   }

   psimd_f32 vacc = vacc0;
   for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) {
     // Load 4 inputs at a time.
     const psimd_f32 vi = psimd_load_f32(input);
     input += 4;

     // Subtract maximum input x := i - i_max. This implies x <= 0.
     const psimd_f32 vx = psimd_sub_f32(vi, vi_max);

     // Compute reduced argument elements := round(x / log(2)).
     psimd_f32 vn = psimd_qfma_f32(vmagic_bias, vx, vlog2e);

     // 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 psimd_f32 vs = (psimd_f32) ((psimd_u32) vn << 23);

     // Subtract the large number back to get final elements := round(x / log(2)).
     vn = psimd_sub_f32(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.
     psimd_f32 vt = psimd_qfma_f32(vx, vn, vminus_ln2_hi);
     vt = psimd_qfma_f32(vt, vn, vminus_ln2_lo);

     // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
     psimd_f32 vp = psimd_qfma_f32(vc4, vc5, vt);
     vp = psimd_qfma_f32(vc3, vp, vt);
     vp = psimd_qfma_f32(vc2, vp, vt);
     vp = psimd_qfma_f32(vc1, vp, vt);

     // 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 = psimd_mul_f32(vt, vs);
     psimd_f32 vf = psimd_qfma_f32(vs, vt, vp);

     // 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 = psimd_andnotmask_f32(vx < vdenorm_cutoff, vf);

     // Store 4 outputs at a time.
     psimd_store_f32(output, vf);
     output += 4;

     // Accumulate computed exponents.
     vacc = psimd_add_f32(vacc, vf);
   }
   if (elements != 0) {
     assert(elements >= 1 * sizeof(float));
     assert(elements <= 3 * sizeof(float));
     // Load 4 inputs at a time.
     const psimd_f32 vi = psimd_load_f32(input);

     // Subtract maximum input x := i - i_max. This implies x <= 0.
     const psimd_f32 vx = psimd_sub_f32(vi, vi_max);

     // Compute reduced argument elements := round(x / log(2)).
     psimd_f32 vn = psimd_qfma_f32(vmagic_bias, vx, vlog2e);

     // 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 psimd_f32 vs = (psimd_f32) ((psimd_u32) vn << 23);

     // Subtract the large number back to get final elements := round(x / log(2)).
     vn = psimd_sub_f32(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.
     psimd_f32 vt = psimd_qfma_f32(vx, vn, vminus_ln2_hi);
     vt = psimd_qfma_f32(vt, vn, vminus_ln2_lo);

     // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
     psimd_f32 vp = psimd_qfma_f32(vc4, vc5, vt);
     vp = psimd_qfma_f32(vc3, vp, vt);
     vp = psimd_qfma_f32(vc2, vp, vt);
     vp = psimd_qfma_f32(vc1, vp, vt);

     // 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 = psimd_mul_f32(vt, vs);
     psimd_f32 vf = psimd_qfma_f32(vs, vt, vp);

     // 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 = psimd_andnotmask_f32(vx < vdenorm_cutoff, vf);

     if (elements & (2 * sizeof(float))) {
       // Store 2 outputs at a time.
       psimd_store2_f32(output, vf);
       output += 2;

       // Accumulate 2 computed exponents.
       vacc = psimd_add_f32(vacc, psimd_concat_lo_f32(vf, psimd_zero_f32()));

       vf = psimd_concat_hi_f32(vf, vf);
     }
     if (elements & (1 * sizeof(float))) {
       // Store 1 output at a time.
       psimd_store1_f32(output, vf);

       // Accumulate 1 computed exponent.
       const psimd_f32 vzero = psimd_zero_f32();
       vf = psimd_concat_lo_f32(vf, vzero);
       vf = psimd_concat_even_f32(vf, vzero);
       vacc = psimd_add_f32(vacc, vf);
     }
   }
   // Reduce 4 elements in the SIMD register
   *sum = psimd_reduce_sum_f32(vacc);
 }
	// Auto-generated file. Do not edit!
	// Template: src/f32-raddstoreexpminusmax/psimd-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 <assert.h>

	#include <psimd.h>

	#include <xnnpack/common.h>
	#include <xnnpack/raddstoreexpminusmax.h>


	void xnn_f32_raddstoreexpminusmax_ukernel__psimd_p5_x8(
	size_t elements,
	const float* input,
	float* output,
	float* sum,
	float max)
	{
	assert(elements % sizeof(float) == 0);

	const psimd_f32 vmagic_bias = psimd_splat_f32(0x1.8000FEp23f);
	// The smallest x for which expf(x) is normalized.
	const psimd_f32 vdenorm_cutoff = psimd_splat_f32(-0x1.5D589Ep6f);
	const psimd_f32 vlog2e = psimd_splat_f32(0x1.715476p+0f);
	// Last 7 bits are zeroes
	const psimd_f32 vminus_ln2_hi = psimd_splat_f32(-0x1.62E400p-1f);
	const psimd_f32 vminus_ln2_lo = psimd_splat_f32(-0x1.7F7D1Cp-20f);

	const psimd_f32 vc1 = psimd_splat_f32(0x1.FFFFF6p-1f);
	const psimd_f32 vc2 = psimd_splat_f32(0x1.FFFDC6p-2f);
	const psimd_f32 vc3 = psimd_splat_f32(0x1.555A80p-3f);
	const psimd_f32 vc4 = psimd_splat_f32(0x1.573A1Ap-5f);
	const psimd_f32 vc5 = psimd_splat_f32(0x1.0F9F9Cp-7f);

	const psimd_f32 vi_max = psimd_splat_f32(max);

	psimd_f32 vacc0 = psimd_zero_f32();
	for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) {
	// Load 8 (2x4) inputs at a time.
	const psimd_f32 vi0123 = psimd_load_f32(input);
	const psimd_f32 vi4567 = psimd_load_f32(input + 4);
	input += 8;

	// Subtract maximum input x := i - i_max. This implies x <= 0.
	const psimd_f32 vx0123 = psimd_sub_f32(vi0123, vi_max);
	const psimd_f32 vx4567 = psimd_sub_f32(vi4567, vi_max);

	// Compute reduced argument elements := round(x / log(2)).
	psimd_f32 vn0123 = psimd_qfma_f32(vmagic_bias, vx0123, vlog2e);
	psimd_f32 vn4567 = psimd_qfma_f32(vmagic_bias, vx4567, vlog2e);

	// 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 psimd_f32 vs0123 = (psimd_f32) ((psimd_u32) vn0123 << 23);
	const psimd_f32 vs4567 = (psimd_f32) ((psimd_u32) vn4567 << 23);

	// Subtract the large number back to get final elements := round(x / log(2)).
	vn0123 = psimd_sub_f32(vn0123, vmagic_bias);
	vn4567 = psimd_sub_f32(vn4567, 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.
	psimd_f32 vt0123 = psimd_qfma_f32(vx0123, vn0123, vminus_ln2_hi);
	psimd_f32 vt4567 = psimd_qfma_f32(vx4567, vn4567, vminus_ln2_hi);

	vt0123 = psimd_qfma_f32(vt0123, vn0123, vminus_ln2_lo);
	vt4567 = psimd_qfma_f32(vt4567, vn4567, vminus_ln2_lo);

	// Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
	psimd_f32 vp0123 = psimd_qfma_f32(vc4, vc5, vt0123);
	psimd_f32 vp4567 = psimd_qfma_f32(vc4, vc5, vt4567);

	vp0123 = psimd_qfma_f32(vc3, vp0123, vt0123);
	vp4567 = psimd_qfma_f32(vc3, vp4567, vt4567);

	vp0123 = psimd_qfma_f32(vc2, vp0123, vt0123);
	vp4567 = psimd_qfma_f32(vc2, vp4567, vt4567);

	vp0123 = psimd_qfma_f32(vc1, vp0123, vt0123);
	vp4567 = psimd_qfma_f32(vc1, vp4567, vt4567);

	// 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 = psimd_mul_f32(vt0123, vs0123);
	vt4567 = psimd_mul_f32(vt4567, vs4567);

	psimd_f32 vf0123 = psimd_qfma_f32(vs0123, vt0123, vp0123);
	psimd_f32 vf4567 = psimd_qfma_f32(vs4567, vt4567, vp4567);

	// 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 = psimd_andnotmask_f32(vx0123 < vdenorm_cutoff, vf0123);
	vf4567 = psimd_andnotmask_f32(vx4567 < vdenorm_cutoff, vf4567);

	// Store 8 (2x4) outputs at a time.
	psimd_store_f32(output, vf0123);
	psimd_store_f32(output + 4, vf4567);
	output += 8;

	// Accumulate computed exponents.
	vacc0 = psimd_add_f32(vacc0, vf0123);
	vacc0 = psimd_add_f32(vacc0, vf4567);
	}

	psimd_f32 vacc = vacc0;
	for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) {
	// Load 4 inputs at a time.
	const psimd_f32 vi = psimd_load_f32(input);
	input += 4;

	// Subtract maximum input x := i - i_max. This implies x <= 0.
	const psimd_f32 vx = psimd_sub_f32(vi, vi_max);

	// Compute reduced argument elements := round(x / log(2)).
	psimd_f32 vn = psimd_qfma_f32(vmagic_bias, vx, vlog2e);

	// 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 psimd_f32 vs = (psimd_f32) ((psimd_u32) vn << 23);

	// Subtract the large number back to get final elements := round(x / log(2)).
	vn = psimd_sub_f32(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.
	psimd_f32 vt = psimd_qfma_f32(vx, vn, vminus_ln2_hi);
	vt = psimd_qfma_f32(vt, vn, vminus_ln2_lo);

	// Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
	psimd_f32 vp = psimd_qfma_f32(vc4, vc5, vt);
	vp = psimd_qfma_f32(vc3, vp, vt);
	vp = psimd_qfma_f32(vc2, vp, vt);
	vp = psimd_qfma_f32(vc1, vp, vt);

	// 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 = psimd_mul_f32(vt, vs);
	psimd_f32 vf = psimd_qfma_f32(vs, vt, vp);

	// 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 = psimd_andnotmask_f32(vx < vdenorm_cutoff, vf);

	// Store 4 outputs at a time.
	psimd_store_f32(output, vf);
	output += 4;

	// Accumulate computed exponents.
	vacc = psimd_add_f32(vacc, vf);
	}
	if (elements != 0) {
	assert(elements >= 1 * sizeof(float));
	assert(elements <= 3 * sizeof(float));
	// Load 4 inputs at a time.
	const psimd_f32 vi = psimd_load_f32(input);

	// Subtract maximum input x := i - i_max. This implies x <= 0.
	const psimd_f32 vx = psimd_sub_f32(vi, vi_max);

	// Compute reduced argument elements := round(x / log(2)).
	psimd_f32 vn = psimd_qfma_f32(vmagic_bias, vx, vlog2e);

	// 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 psimd_f32 vs = (psimd_f32) ((psimd_u32) vn << 23);

	// Subtract the large number back to get final elements := round(x / log(2)).
	vn = psimd_sub_f32(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.
	psimd_f32 vt = psimd_qfma_f32(vx, vn, vminus_ln2_hi);
	vt = psimd_qfma_f32(vt, vn, vminus_ln2_lo);

	// Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
	psimd_f32 vp = psimd_qfma_f32(vc4, vc5, vt);
	vp = psimd_qfma_f32(vc3, vp, vt);
	vp = psimd_qfma_f32(vc2, vp, vt);
	vp = psimd_qfma_f32(vc1, vp, vt);

	// 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 = psimd_mul_f32(vt, vs);
	psimd_f32 vf = psimd_qfma_f32(vs, vt, vp);

	// 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 = psimd_andnotmask_f32(vx < vdenorm_cutoff, vf);

	if (elements & (2 * sizeof(float))) {
	// Store 2 outputs at a time.
	psimd_store2_f32(output, vf);
	output += 2;

	// Accumulate 2 computed exponents.
	vacc = psimd_add_f32(vacc, psimd_concat_lo_f32(vf, psimd_zero_f32()));

	vf = psimd_concat_hi_f32(vf, vf);
	}
	if (elements & (1 * sizeof(float))) {
	// Store 1 output at a time.
	psimd_store1_f32(output, vf);

	// Accumulate 1 computed exponent.
	const psimd_f32 vzero = psimd_zero_f32();
	vf = psimd_concat_lo_f32(vf, vzero);
	vf = psimd_concat_even_f32(vf, vzero);
	vacc = psimd_add_f32(vacc, vf);
	}
	}
	// Reduce 4 elements in the SIMD register
	*sum = psimd_reduce_sum_f32(vacc);
	}