src/f32-raddstoreexpminusmax/gen/scalar-p5-x4.c - platform/external/XNNPACK - Gitiles

 // Auto-generated file. Do not edit!
 //   Template: src/f32-raddstoreexpminusmax/scalar-p5.c.in
 //   Generator: tools/xngen
 //
 // 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 <assert.h>

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

 #include <fp16/bitcasts.h>


 void xnn_f32_raddstoreexpminusmax_ukernel__scalar_p5_x4(
     size_t elements,
     const float* input,
     float* output,
     float* sum,
     float vi_max)
 {
   assert(elements % sizeof(float) == 0);

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

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

   float vacc0 = 0.0f;
   for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) {
     // Load 4 inputs at a time.
     const float vi0 = input[0];
     const float vi1 = input[1];
     const float vi2 = input[2];
     const float vi3 = input[3];
     input += 4;

     // Subtract maximum input x := i - i_max. This implies x <= 0.
     const float vx0 = vi0 - vi_max;
     const float vx1 = vi1 - vi_max;
     const float vx2 = vi2 - vi_max;
     const float vx3 = vi3 - vi_max;

     // Compute reduced argument n := round(x / log(2)).
     // We do it by adding a large number (magic bias) to the product x * (1/log(2)), which cause rounding of the result
     // to an integer, then subtracing the large number back. The trick with adding large number is valid only within
     // certain bounds (|x| <= 2**22), but thats ok, because inputs outside of [-87.336540, 0.0] underflow expf(x)
     // anyway. We fixup the result for such inputs at the very end of the algorithm.
     float vn0 = vx0 * vlog2e + vmagic_bias;
     float vn1 = vx1 * vlog2e + vmagic_bias;
     float vn2 = vx2 * vlog2e + vmagic_bias;
     float vn3 = vx3 * vlog2e + vmagic_bias;

     // Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e.
     // -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly.
     const float vs0 = fp32_from_bits(fp32_to_bits(vn0) << 23);
     const float vs1 = fp32_from_bits(fp32_to_bits(vn1) << 23);
     const float vs2 = fp32_from_bits(fp32_to_bits(vn2) << 23);
     const float vs3 = fp32_from_bits(fp32_to_bits(vn3) << 23);

     // Subtract the large number back to get final n := round(x / log(2)).
     vn0 -= vmagic_bias;
     vn1 -= vmagic_bias;
     vn2 -= vmagic_bias;
     vn3 -= 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 vt0 = vn0 * vminus_ln2_hi + vx0;
     float vt1 = vn1 * vminus_ln2_hi + vx1;
     float vt2 = vn2 * vminus_ln2_hi + vx2;
     float vt3 = vn3 * vminus_ln2_hi + vx3;

     vt0 = vn0 * vminus_ln2_lo + vt0;
     vt1 = vn1 * vminus_ln2_lo + vt1;
     vt2 = vn2 * vminus_ln2_lo + vt2;
     vt3 = vn3 * vminus_ln2_lo + vt3;

     // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
     float vp0 = vc5 * vt0 + vc4;
     float vp1 = vc5 * vt1 + vc4;
     float vp2 = vc5 * vt2 + vc4;
     float vp3 = vc5 * vt3 + vc4;

     vp0 = vp0 * vt0 + vc3;
     vp1 = vp1 * vt1 + vc3;
     vp2 = vp2 * vt2 + vc3;
     vp3 = vp3 * vt3 + vc3;

     vp0 = vp0 * vt0 + vc2;
     vp1 = vp1 * vt1 + vc2;
     vp2 = vp2 * vt2 + vc2;
     vp3 = vp3 * vt3 + vc2;

     vp0 = vp0 * vt0 + vc1;
     vp1 = vp1 * vt1 + vc1;
     vp2 = vp2 * vt2 + vc1;
     vp3 = vp3 * vt3 + 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
     vt0 *= vs0;
     vt1 *= vs1;
     vt2 *= vs2;
     vt3 *= vs3;

     float vf0 = vt0 * vp0 + vs0;
     float vf1 = vt1 * vp1 + vs1;
     float vf2 = vt2 * vp2 + vs2;
     float vf3 = vt3 * vp3 + vs3;

     // For inputs below denormal cutoff, replace output with +0.0f.
     // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
     if XNN_UNPREDICTABLE(vx0 < vdenorm_cutoff) {
       vf0 = 0.0f;
     }
     if XNN_UNPREDICTABLE(vx1 < vdenorm_cutoff) {
       vf1 = 0.0f;
     }
     if XNN_UNPREDICTABLE(vx2 < vdenorm_cutoff) {
       vf2 = 0.0f;
     }
     if XNN_UNPREDICTABLE(vx3 < vdenorm_cutoff) {
       vf3 = 0.0f;
     }

     // Store 4 outputs at a time.
     output[0] = vf0;
     output[1] = vf1;
     output[2] = vf2;
     output[3] = vf3;
     output += 4;

     // Accumulate computed exponents.
     vacc0 += vf0;
     vacc0 += vf1;
     vacc0 += vf2;
     vacc0 += vf3;
   }

   float vacc = vacc0;
   for (; elements >= sizeof(float); elements -= sizeof(float)) {
     // Load 1 input at a time.
     const float vi = *input++;

     // Subtract maximum input x := i - i_max. This implies x <= 0.
     const float vx = vi - vi_max;

     // Compute reduced argument n := round(x / log(2)).
     // We do it by adding a large number (magic bias) to the product x * (1/log(2)), which cause rounding of the result
     // to an integer, then subtracing the large number back. The trick with adding large number is valid only within
     // certain bounds (|x| <= 2**22), but thats ok, because inputs outside of [-87.336540, 0.0] underflow expf(x)
     // anyway. We fixup the result for such inputs at the very end of the algorithm.
     float vn = vx * vlog2e + vmagic_bias;

     // Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e.
     // -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly.
     const 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;

     // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
     float vp = vc5 * vt + vc4;
     vp = vp * vt + vc3;
     vp = vp * vt + vc2;
     vp = 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 *= vs;
     float vf = vt * vp + vs;

     // For inputs below denormal cutoff, replace output with +0.0f.
     // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
     if XNN_UNPREDICTABLE(vx < vdenorm_cutoff) {
       vf = 0.0f;
     }

     // Store 1 output at a time.
     *output++ = vf;

     // Accumulate computed exponents.
     vacc += vf;
   }
   *sum = vacc;
 }
	// Auto-generated file. Do not edit!
	// Template: src/f32-raddstoreexpminusmax/scalar-p5.c.in
	// Generator: tools/xngen
	//
	// 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 <assert.h>

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

	#include <fp16/bitcasts.h>


	void xnn_f32_raddstoreexpminusmax_ukernel__scalar_p5_x4(
	size_t elements,
	const float* input,
	float* output,
	float* sum,
	float vi_max)
	{
	assert(elements % sizeof(float) == 0);

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

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

	float vacc0 = 0.0f;
	for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) {
	// Load 4 inputs at a time.
	const float vi0 = input[0];
	const float vi1 = input[1];
	const float vi2 = input[2];
	const float vi3 = input[3];
	input += 4;

	// Subtract maximum input x := i - i_max. This implies x <= 0.
	const float vx0 = vi0 - vi_max;
	const float vx1 = vi1 - vi_max;
	const float vx2 = vi2 - vi_max;
	const float vx3 = vi3 - vi_max;

	// Compute reduced argument n := round(x / log(2)).
	// We do it by adding a large number (magic bias) to the product x * (1/log(2)), which cause rounding of the result
	// to an integer, then subtracing the large number back. The trick with adding large number is valid only within
	// certain bounds (\|x\| <= 2**22), but thats ok, because inputs outside of [-87.336540, 0.0] underflow expf(x)
	// anyway. We fixup the result for such inputs at the very end of the algorithm.
	float vn0 = vx0 * vlog2e + vmagic_bias;
	float vn1 = vx1 * vlog2e + vmagic_bias;
	float vn2 = vx2 * vlog2e + vmagic_bias;
	float vn3 = vx3 * vlog2e + vmagic_bias;

	// Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e.
	// -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly.
	const float vs0 = fp32_from_bits(fp32_to_bits(vn0) << 23);
	const float vs1 = fp32_from_bits(fp32_to_bits(vn1) << 23);
	const float vs2 = fp32_from_bits(fp32_to_bits(vn2) << 23);
	const float vs3 = fp32_from_bits(fp32_to_bits(vn3) << 23);

	// Subtract the large number back to get final n := round(x / log(2)).
	vn0 -= vmagic_bias;
	vn1 -= vmagic_bias;
	vn2 -= vmagic_bias;
	vn3 -= 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 vt0 = vn0 * vminus_ln2_hi + vx0;
	float vt1 = vn1 * vminus_ln2_hi + vx1;
	float vt2 = vn2 * vminus_ln2_hi + vx2;
	float vt3 = vn3 * vminus_ln2_hi + vx3;

	vt0 = vn0 * vminus_ln2_lo + vt0;
	vt1 = vn1 * vminus_ln2_lo + vt1;
	vt2 = vn2 * vminus_ln2_lo + vt2;
	vt3 = vn3 * vminus_ln2_lo + vt3;

	// Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
	float vp0 = vc5 * vt0 + vc4;
	float vp1 = vc5 * vt1 + vc4;
	float vp2 = vc5 * vt2 + vc4;
	float vp3 = vc5 * vt3 + vc4;

	vp0 = vp0 * vt0 + vc3;
	vp1 = vp1 * vt1 + vc3;
	vp2 = vp2 * vt2 + vc3;
	vp3 = vp3 * vt3 + vc3;

	vp0 = vp0 * vt0 + vc2;
	vp1 = vp1 * vt1 + vc2;
	vp2 = vp2 * vt2 + vc2;
	vp3 = vp3 * vt3 + vc2;

	vp0 = vp0 * vt0 + vc1;
	vp1 = vp1 * vt1 + vc1;
	vp2 = vp2 * vt2 + vc1;
	vp3 = vp3 * vt3 + 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
	vt0 *= vs0;
	vt1 *= vs1;
	vt2 *= vs2;
	vt3 *= vs3;

	float vf0 = vt0 * vp0 + vs0;
	float vf1 = vt1 * vp1 + vs1;
	float vf2 = vt2 * vp2 + vs2;
	float vf3 = vt3 * vp3 + vs3;

	// For inputs below denormal cutoff, replace output with +0.0f.
	// Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
	if XNN_UNPREDICTABLE(vx0 < vdenorm_cutoff) {
	vf0 = 0.0f;
	}
	if XNN_UNPREDICTABLE(vx1 < vdenorm_cutoff) {
	vf1 = 0.0f;
	}
	if XNN_UNPREDICTABLE(vx2 < vdenorm_cutoff) {
	vf2 = 0.0f;
	}
	if XNN_UNPREDICTABLE(vx3 < vdenorm_cutoff) {
	vf3 = 0.0f;
	}

	// Store 4 outputs at a time.
	output[0] = vf0;
	output[1] = vf1;
	output[2] = vf2;
	output[3] = vf3;
	output += 4;

	// Accumulate computed exponents.
	vacc0 += vf0;
	vacc0 += vf1;
	vacc0 += vf2;
	vacc0 += vf3;
	}

	float vacc = vacc0;
	for (; elements >= sizeof(float); elements -= sizeof(float)) {
	// Load 1 input at a time.
	const float vi = *input++;

	// Subtract maximum input x := i - i_max. This implies x <= 0.
	const float vx = vi - vi_max;

	// Compute reduced argument n := round(x / log(2)).
	// We do it by adding a large number (magic bias) to the product x * (1/log(2)), which cause rounding of the result
	// to an integer, then subtracing the large number back. The trick with adding large number is valid only within
	// certain bounds (\|x\| <= 2**22), but thats ok, because inputs outside of [-87.336540, 0.0] underflow expf(x)
	// anyway. We fixup the result for such inputs at the very end of the algorithm.
	float vn = vx * vlog2e + vmagic_bias;

	// Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e.
	// -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly.
	const 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;

	// Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2].
	float vp = vc5 * vt + vc4;
	vp = vp * vt + vc3;
	vp = vp * vt + vc2;
	vp = 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 *= vs;
	float vf = vt * vp + vs;

	// For inputs below denormal cutoff, replace output with +0.0f.
	// Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
	if XNN_UNPREDICTABLE(vx < vdenorm_cutoff) {
	vf = 0.0f;
	}

	// Store 1 output at a time.
	*output++ = vf;

	// Accumulate computed exponents.
	vacc += vf;
	}
	*sum = vacc;
	}