Remove sse2-p5-div.c.in Sigmoid template
Never used, remnant of the previous version of the sse-p5-div.c.in template
PiperOrigin-RevId: 286845365
diff --git a/src/f32-sigmoid/sse2-p5-div.c.in b/src/f32-sigmoid/sse2-p5-div.c.in
deleted file mode 100644
index ab02615..0000000
--- a/src/f32-sigmoid/sse2-p5-div.c.in
+++ /dev/null
@@ -1,286 +0,0 @@
-// 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.
-
-$assert BATCH_TILE % 4 == 0
-$assert BATCH_TILE >= 4
-$ABC = "0123456789ABCDEFGHIJKLMN"
-#include <assert.h>
-
-#include <emmintrin.h>
-
-#include <xnnpack/common.h>
-#include <xnnpack/vunary.h>
-
-
-void xnn_f32_sigmoid_ukernel__sse2_p5_div_x${BATCH_TILE}(
- size_t n,
- const float* x,
- float* y,
- const void* params)
-{
- assert(n % sizeof(float) == 0);
-
- const __m128 vmagic_bias = _mm_set1_ps(0x1.8000FEp23f);
- // The smallest x for which sigmoidf(x) is normalized.
- // This number is also the smallest x for which expf(x) is normalized.
- const __m128 vdenorm_cutoff = _mm_set1_ps(-0x1.5D589Ep+6f);
- // The largest x for which sigmoidf(x) is not equal 1.0.
- const __m128 vone_cutoff = _mm_set1_ps(0x1.154244p+4f);
- const __m128 vlog2e = _mm_set1_ps(0x1.715476p+0f);
- // Last 8 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 vone = _mm_set1_ps(1.0f);
- const __m128 vsign_mask = _mm_set1_ps(-0.0f);
-
- 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);
-
- for (; n >= ${BATCH_TILE} * sizeof(float); n -= ${BATCH_TILE} * sizeof(float)) {
- const __m128 vx${ABC[0:4]} = _mm_loadu_ps(x);
- $for N in range(4, BATCH_TILE, 4):
- const __m128 vx${ABC[N:N+4]} = _mm_loadu_ps(x + ${N});
-
- // General structure of the algorithm:
- // / exp(x) / (1 + exp(x)) if x <= 0
- // f[x] :=
- // \ 1 - f[-x] if x >= 0
- //
- // First we compute f[z] := exp(z) / (1 + exp(z)) where z = -abs(x),
- // then replace result with 1 - f[z] if x >= 0.
- $for N in range(0, BATCH_TILE, 4):
- const __m128 vz${ABC[N:N+4]} = _mm_or_ps(vx${ABC[N:N+4]}, vsign_mask);
-
- // Compute reduced argument n := round(z / log(2)).
- // We do it by adding a large number (magic bias) to the product z * (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 x outside of [-87.336544, 17.328678] (i.e. z outsize
- // [0, 87.336544]) underflow or saturate sigmoidf(x) anyway. We fixup the result for such inputs at the very end of
- // the algorithm.
- $for N in range(0, BATCH_TILE, 4):
- __m128 vn${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vz${ABC[N:N+4]}, 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 <= z <= 0.0, and -126 <= n <= 0 accordingly.
- $for N in range(0, BATCH_TILE, 4):
- const __m128 vs${ABC[N:N+4]} = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn${ABC[N:N+4]}), 23));
-
- // Subtract the large number back to get final n := round(z / log(2)).
- $for N in range(0, BATCH_TILE, 4):
- vn${ABC[N:N+4]} = _mm_sub_ps(vn${ABC[N:N+4]}, vmagic_bias);
-
- // Compute reduced argument t := z - n * log(2).
- // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy.
- $for N in range(0, BATCH_TILE, 4):
- __m128 vt${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vn${ABC[N:N+4]}, vminus_ln2_hi), vz${ABC[N:N+4]});
-
- $for N in range(0, BATCH_TILE, 4):
- vt${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vn${ABC[N:N+4]}, vminus_ln2_lo), vt${ABC[N:N+4]});
-
- // Compute degree-5 polynomial approxiatmion for exp(t) on [-log(2)/2, log(2)/2].
- $for N in range(0, BATCH_TILE, 4):
- __m128 vp${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vc5, vt${ABC[N:N+4]}), vc4);
-
- $for N in range(0, BATCH_TILE, 4):
- vp${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vp${ABC[N:N+4]}, vt${ABC[N:N+4]}), vc3);
-
- $for N in range(0, BATCH_TILE, 4):
- vp${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vp${ABC[N:N+4]}, vt${ABC[N:N+4]}), vc2);
-
- $for N in range(0, BATCH_TILE, 4):
- vp${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vp${ABC[N:N+4]}, vt${ABC[N:N+4]}), vc1);
-
- // Reconstruct the exp(z) value:
- // e = 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
- $for N in range(0, BATCH_TILE, 4):
- vt${ABC[N:N+4]} = _mm_mul_ps(vt${ABC[N:N+4]}, vs${ABC[N:N+4]});
-
- $for N in range(0, BATCH_TILE, 4):
- __m128 ve${ABC[N:N+4]} = _mm_add_ps(_mm_mul_ps(vt${ABC[N:N+4]}, vp${ABC[N:N+4]}), vs${ABC[N:N+4]});
-
- // Denominator of the sigmoid fraction: 1.0 + exp(z)
- $for N in range(0, BATCH_TILE, 4):
- __m128 vd${ABC[N:N+4]} = _mm_add_ps(ve${ABC[N:N+4]}, vone);
-
- // Reconstruct sigmoid(-z) = exp(z) / (1.0 + exp(z))
- $for N in range(0, BATCH_TILE, 4):
- __m128 vf${ABC[N:N+4]} = _mm_div_ps(ve${ABC[N:N+4]}, vd${ABC[N:N+4]});
-
- // Reconstruct sigmoid(x) = x < 0 ? sigmoid(z) : 1.0 - sigmoid(z)
- $for N in range(0, BATCH_TILE, 4):
- __m128 vm${ABC[N:N+4]} = _mm_castsi128_ps(_mm_cmpgt_epi32(_mm_setzero_si128(), _mm_castps_si128(vx${ABC[N:N+4]})));
-
- $for N in range(0, BATCH_TILE, 4):
- vf${ABC[N:N+4]} = _mm_or_ps(_mm_and_ps(vf${ABC[N:N+4]}, vm${ABC[N:N+4]}), _mm_andnot_ps(vm${ABC[N:N+4]}, _mm_sub_ps(vone, vf${ABC[N:N+4]})));
-
- // For inputs above 1.0 cutoff, replace output with 1.0.
- // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
- $for N in range(0, BATCH_TILE, 4):
- vm${ABC[N:N+4]} = _mm_cmpgt_ps(vx${ABC[N:N+4]}, vone_cutoff);
-
- $for N in range(0, BATCH_TILE, 4):
- vf${ABC[N:N+4]} = _mm_or_ps(_mm_and_ps(vone, vm${ABC[N:N+4]}), _mm_andnot_ps(vm${ABC[N:N+4]}, vf${ABC[N:N+4]}));
-
- // For inputs below denormal cutoff, replace output with +0.0f.
- // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
- $for N in range(0, BATCH_TILE, 4):
- vf${ABC[N:N+4]} = _mm_andnot_ps(_mm_cmplt_ps(vx${ABC[N:N+4]}, vdenorm_cutoff), vf${ABC[N:N+4]});
-
- _mm_storeu_ps(y, vf${ABC[0:4]});
- $for N in range(4, BATCH_TILE, 4):
- _mm_storeu_ps(y + ${N}, vf${ABC[N:N+4]});
-
- x += ${BATCH_TILE};
- y += ${BATCH_TILE};
- }
- $if BATCH_TILE > 4:
- for (; n >= 4 * sizeof(float); n -= 4 * sizeof(float)) {
- const __m128 vx0123 = _mm_loadu_ps(x);
-
- // General structure of the algorithm:
- // / exp(x) / (1 + exp(x)) if x <= 0
- // f[x] :=
- // \ 1 - f[-x] if x >= 0
- //
- // First we compute f[z] := exp(z) / (1 + exp(z)) where z = -abs(x),
- // then replace result with 1 - f[z] if x >= 0.
- const __m128 vz0123 = _mm_or_ps(vx0123, vsign_mask);
-
- // Compute reduced argument n := round(z / log(2)).
- // We do it by adding a large number (magic bias) to the product z * (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 x outside of [-87.336544, 17.328678] (i.e. z outsize
- // [0, 87.336544]) underflow or saturate sigmoidf(x) anyway. We fixup the result for such inputs at the very end of
- // the algorithm.
- __m128 vn0123 = _mm_add_ps(_mm_mul_ps(vz0123, 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 <= z <= 0.0, and -126 <= n <= 0 accordingly.
- const __m128 vs0123 = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn0123), 23));
-
- // Subtract the large number back to get final n := round(z / log(2)).
- vn0123 = _mm_sub_ps(vn0123, vmagic_bias);
-
- // Compute reduced argument t := z - n * 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), vz0123);
- vt0123 = _mm_add_ps(_mm_mul_ps(vn0123, vminus_ln2_lo), vt0123);
-
- // Compute degree-5 polynomial approxiatmion 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 exp(z) value:
- // e = 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 ve0123 = _mm_add_ps(_mm_mul_ps(vt0123, vp0123), vs0123);
-
- // Denominator of the sigmoid fraction: 1.0 + exp(z)
- __m128 vd0123 = _mm_add_ps(ve0123, vone);
-
- // Reconstruct sigmoid(-z) = exp(z) / (1.0 + exp(z))
- __m128 vf0123 = _mm_div_ps(ve0123, vd0123);
-
- // Reconstruct sigmoid(x) = x < 0 ? sigmoid(z) : 1.0 - sigmoid(z)
- __m128 vm0123 = _mm_castsi128_ps(_mm_cmpgt_epi32(_mm_setzero_si128(), _mm_castps_si128(vx0123)));
- vf0123 = _mm_or_ps(_mm_and_ps(vf0123, vm0123), _mm_andnot_ps(vm0123, _mm_sub_ps(vone, vf0123)));
-
- // For inputs above 1.0 cutoff, replace output with 1.0.
- // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
- vm0123 = _mm_cmpgt_ps(vx0123, vone_cutoff);
- vf0123 = _mm_or_ps(_mm_and_ps(vone, vm0123), _mm_andnot_ps(vm0123, vf0123));
-
- // For inputs below denormal 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);
-
- _mm_storeu_ps(y, vf0123);
-
- x += 4;
- y += 4;
- }
- if XNN_UNLIKELY(n != 0) {
- const __m128 vx0123 = _mm_loadu_ps(x);
-
- // General structure of the algorithm:
- // / exp(x) / (1 + exp(x)) if x <= 0
- // f[x] :=
- // \ 1 - f[-x] if x >= 0
- //
- // First we compute f[z] := exp(z) / (1 + exp(z)) where z = -abs(x),
- // then replace result with 1 - f[z] if x >= 0.
- const __m128 vz0123 = _mm_or_ps(vx0123, vsign_mask);
-
- // Compute reduced argument n := round(z / log(2)).
- // We do it by adding a large number (magic bias) to the product z * (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 x outside of [-87.336544, 17.328678] (i.e. z outsize
- // [0, 87.336544]) underflow or saturate sigmoidf(x) anyway. We fixup the result for such inputs at the very end of
- // the algorithm.
- __m128 vn0123 = _mm_add_ps(_mm_mul_ps(vz0123, 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 <= z <= 0.0, and -126 <= n <= 0 accordingly.
- const __m128 vs0123 = _mm_castsi128_ps(_mm_slli_epi32(_mm_castps_si128(vn0123), 23));
-
- // Subtract the large number back to get final n := round(z / log(2)).
- vn0123 = _mm_sub_ps(vn0123, vmagic_bias);
-
- // Compute reduced argument t := z - n * 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), vz0123);
- vt0123 = _mm_add_ps(_mm_mul_ps(vn0123, vminus_ln2_lo), vt0123);
-
- // Compute degree-5 polynomial approxiatmion 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 exp(z) value:
- // e = 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 ve0123 = _mm_add_ps(_mm_mul_ps(vt0123, vp0123), vs0123);
-
- // Denominator of the sigmoid fraction: 1.0 + exp(z)
- __m128 vd0123 = _mm_add_ps(ve0123, vone);
-
- // Reconstruct sigmoid(-z) = exp(z) / (1.0 + exp(z))
- __m128 vf0123 = _mm_div_ps(ve0123, vd0123);
-
- // Reconstruct sigmoid(x) = x < 0 ? sigmoid(z) : 1.0 - sigmoid(z)
- __m128 vm0123 = _mm_castsi128_ps(_mm_cmpgt_epi32(_mm_setzero_si128(), _mm_castps_si128(vx0123)));
- vf0123 = _mm_or_ps(_mm_and_ps(vf0123, vm0123), _mm_andnot_ps(vm0123, _mm_sub_ps(vone, vf0123)));
-
- // For inputs above 1.0 cutoff, replace output with 1.0.
- // Note that for NaN inputs, comparison result is false, and outputs are left unchanged.
- vm0123 = _mm_cmpgt_ps(vx0123, vone_cutoff);
- vf0123 = _mm_or_ps(_mm_and_ps(vone, vm0123), _mm_andnot_ps(vm0123, vf0123));
-
- // For inputs below denormal 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);
-
- if (n & (2 * sizeof(float))) {
- _mm_storel_pi((__m64*) y, vf0123);
- vf0123 = _mm_movehl_ps(vf0123, vf0123);
- y += 2;
- }
- if (n & (1 * sizeof(float))) {
- _mm_store_ss(y, vf0123);
- }
- }
-}
