blob: 7105c30cd087ce72519043db990814a8a45d28b5 [file] [log] [blame]
// 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 = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
$assert OP in ["ADD", "DIV", "MAX", "MIN", "MUL", "SUB", "SQRDIFF"]
$assert ACTIVATION in ["LINEAR", "MINMAX"]
#include <assert.h>
#include <xmmintrin.h>
#include <xnnpack/common.h>
#include <xnnpack/intrinsics-polyfill.h>
#include <xnnpack/vbinary.h>
$_MM_OP_PS = {
$ "ADD": lambda x, y: "_mm_add_ps(%s, %s)" % (x, y),
$ "DIV": lambda x, y: "_mm_div_ps(%s, %s)" % (x, y),
$ "MAX": lambda x, y: "_mm_max_ps(%s, %s)" % (x, y),
$ "MIN": lambda x, y: "_mm_min_ps(%s, %s)" % (x, y),
$ "MUL": lambda x, y: "_mm_mul_ps(%s, %s)" % (x, y),
$ "SUB": lambda x, y: "_mm_sub_ps(%s, %s)" % (x, y),
$ "SQRDIFF": lambda x, y: "_mm_sub_ps(%s, %s)" % (x, y),
$}[OP]
$SUFFIX = {"LINEAR": "", "MINMAX": "_minmax"}[ACTIVATION]
$PARAMS = {"LINEAR": "xnn_f32_default_params", "MINMAX": "xnn_f32_minmax_params"}[ACTIVATION]
void xnn_f32_v${OP.lower()}${SUFFIX}_ukernel__sse_x${BATCH_TILE}(
size_t n,
const float* a,
const float* b,
float* y,
const union ${PARAMS} params[restrict XNN_MIN_ELEMENTS(1)]) XNN_DISABLE_TSAN
{
assert(n != 0);
assert(n % sizeof(float) == 0);
assert(a != NULL);
assert(b != NULL);
assert(y != NULL);
$if ACTIVATION == "MINMAX":
const __m128 vy_min = _mm_load_ps(params->sse.min);
const __m128 vy_max = _mm_load_ps(params->sse.max);
for (; n >= ${BATCH_TILE} * sizeof(float); n -= ${BATCH_TILE} * sizeof(float)) {
const __m128 va${ABC[0:4]} = _mm_loadu_ps(a);
$for N in range(4, BATCH_TILE, 4):
const __m128 va${ABC[N:N+4]} = _mm_loadu_ps(a + ${N});
a += ${BATCH_TILE};
const __m128 vb${ABC[0:4]} = _mm_loadu_ps(b);
$for N in range(4, BATCH_TILE, 4):
const __m128 vb${ABC[N:N+4]} = _mm_loadu_ps(b + ${N});
b += ${BATCH_TILE};
$for N in range(0, BATCH_TILE, 4):
__m128 vy${ABC[N:N+4]} = ${_MM_OP_PS("va" + ABC[N:N+4], "vb" + ABC[N:N+4])};
$if OP == "SQRDIFF":
$for N in range(0, BATCH_TILE, 4):
vy${ABC[N:N+4]} = _mm_mul_ps(vy${ABC[N:N+4]}, vy${ABC[N:N+4]});
$if ACTIVATION == "MINMAX":
$for N in range(0, BATCH_TILE, 4):
vy${ABC[N:N+4]} = _mm_max_ps(vy${ABC[N:N+4]}, vy_min);
$for N in range(0, BATCH_TILE, 4):
vy${ABC[N:N+4]} = _mm_min_ps(vy${ABC[N:N+4]}, vy_max);
_mm_storeu_ps(y, vy${ABC[0:4]});
$for N in range(4, BATCH_TILE, 4):
_mm_storeu_ps(y + ${N}, vy${ABC[N:N+4]});
y += ${BATCH_TILE};
}
$if BATCH_TILE > 4:
for (; n >= 4 * sizeof(float); n -= 4 * sizeof(float)) {
const __m128 va0123 = _mm_loadu_ps(a);
a += 4;
const __m128 vb0123 = _mm_loadu_ps(b);
b += 4;
__m128 vy0123 = ${_MM_OP_PS("va0123", "vb0123")};
$if OP == "SQRDIFF":
vy0123 = _mm_mul_ps(vy0123, vy0123);
$if ACTIVATION == "MINMAX":
vy0123 = _mm_max_ps(vy0123, vy_min);
vy0123 = _mm_min_ps(vy0123, vy_max);
_mm_storeu_ps(y, vy0123);
y += 4;
}
if XNN_UNLIKELY(n != 0) {
const __m128 va0123 = _mm_loadu_ps(a);
const __m128 vb0123 = _mm_loadu_ps(b);
__m128 vy0123 = ${_MM_OP_PS("va0123", "vb0123")};
$if OP == "SQRDIFF":
vy0123 = _mm_mul_ps(vy0123, vy0123);
$if ACTIVATION == "MINMAX":
vy0123 = _mm_max_ps(vy0123, vy_min);
vy0123 = _mm_min_ps(vy0123, vy_max);
if (n & (2 * sizeof(float))) {
_mm_storel_pi((__m64*) y, vy0123);
vy0123 = _mm_movehl_ps(vy0123, vy0123);
y += 2;
}
if (n & (1 * sizeof(float))) {
_mm_store_ss(y, vy0123);
}
}
}