xref: /aosp_15_r20/external/XNNPACK/src/qs8-vlrelu/avx2.c.in (revision 4bdc94577ba0e567308109d787f7fec7b531ce36)
1// Copyright 2022 Google LLC
2//
3// This source code is licensed under the BSD-style license found in the
4// LICENSE file in the root directory of this source tree.
5
6$assert BATCH_TILE >= 16
7$assert BATCH_TILE == 16 or BATCH_TILE % 32 == 0
8$SIMD_TILE = BATCH_TILE // 32
9$ABC = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
10#include <assert.h>
11
12#include <immintrin.h>
13
14#include <xnnpack/common.h>
15#include <xnnpack/intrinsics-polyfill.h>
16#include <xnnpack/vlrelu.h>
17
18
19$XINT8_T = {"QS8": "int8_t", "QU8": "uint8_t"}[DATATYPE]
20$_MM256_CVTEPX8_EPI16 = {"QS8": "_mm256_cvtepi8_epi16", "QU8": "_mm256_cvtepu8_epi16"}[DATATYPE]
21$_MM256_PACKXS_EPI16 = {"QS8": "_mm256_packs_epi16", "QU8": "_mm256_packus_epi16"}[DATATYPE]
22$_MM_PACKXS_EPI16 = {"QS8": "_mm_packs_epi16", "QU8": "_mm_packus_epi16"}[DATATYPE]
23void xnn_${DATATYPE.lower()}_vlrelu_ukernel__avx2_x${BATCH_TILE}(
24    size_t n,
25    const ${XINT8_T}* x,
26    ${XINT8_T}* y,
27    const union xnn_${DATATYPE.lower()}_lrelu_params params[restrict XNN_MIN_ELEMENTS(1)]) XNN_OOB_READS
28{
29  assert(n != 0);
30  assert(n % sizeof(${XINT8_T}) == 0);
31  assert(x != NULL);
32  assert(y != NULL);
33
34  const __m256i vinput_zero_point = _mm256_load_si256((const __m256i*) params->avx2.input_zero_point);
35  const __m256i vpositive_multiplier = _mm256_load_si256((const __m256i*) params->avx2.positive_multiplier);
36  const __m256i vnegative_multiplier = _mm256_load_si256((const __m256i*) params->avx2.negative_multiplier);
37  const __m256i voutput_zero_point = _mm256_load_si256((const __m256i*) params->avx2.output_zero_point);
38  $if BATCH_TILE > 16:
39    for (; n >= ${BATCH_TILE} * sizeof(${XINT8_T}); n -= ${BATCH_TILE} * sizeof(${XINT8_T})) {
40      __m256i vacc${ABC[0]} = ${_MM256_CVTEPX8_EPI16}(_mm_loadu_si128((const __m128i*) x));
41      $for N in range(1, 2*SIMD_TILE):
42        __m256i vacc${ABC[N]} = ${_MM256_CVTEPX8_EPI16}(_mm_loadu_si128((const __m128i*) (x + ${N * 16})));
43      x += ${BATCH_TILE};
44
45      $for N in range(2*SIMD_TILE):
46        __m256i vmultiplier${ABC[N]} = _mm256_cmpgt_epi16(vacc${ABC[N]}, vinput_zero_point);
47        vacc${ABC[N]} = _mm256_sub_epi16(vinput_zero_point, vacc${ABC[N]});
48
49      $for N in range(2*SIMD_TILE):
50        vmultiplier${ABC[N]} = _mm256_blendv_epi8(vnegative_multiplier, vpositive_multiplier, vmultiplier${ABC[N]});
51        vacc${ABC[N]} = _mm256_slli_epi16(vacc${ABC[N]}, 7);
52
53      $for N in range(2*SIMD_TILE):
54        vacc${ABC[N]} = _mm256_mulhrs_epi16(vacc${ABC[N]}, vmultiplier${ABC[N]});
55
56      $for N in range(2*SIMD_TILE):
57        vacc${ABC[N]} = _mm256_adds_epi16(vacc${ABC[N]}, voutput_zero_point);
58
59      $for N in range(SIMD_TILE):
60        __m256i vy${ABC[N]} = ${_MM256_PACKXS_EPI16}(vacc${ABC[2*N]}, vacc${ABC[2*N+1]});
61
62      $for N in range(SIMD_TILE):
63        vy${ABC[N]} = _mm256_permute4x64_epi64(vy${ABC[N]}, _MM_SHUFFLE(3, 1, 2, 0));
64
65      _mm256_storeu_si256((__m256i*) y, vy${ABC[0]});
66      $for N in range(1, SIMD_TILE):
67        _mm256_storeu_si256((__m256i*) (y + ${N * 32}), vy${ABC[N]});
68      y += ${BATCH_TILE};
69    }
70  for (; n >= 16 * sizeof(${XINT8_T}); n -= 16 * sizeof(${XINT8_T})) {
71    __m256i vacc = ${_MM256_CVTEPX8_EPI16}(_mm_loadu_si128((const __m128i*) x));
72    __m256i vmultiplier = _mm256_cmpgt_epi16(vacc, vinput_zero_point);
73    vacc = _mm256_sub_epi16(vinput_zero_point, vacc);
74    vmultiplier = _mm256_blendv_epi8(vnegative_multiplier, vpositive_multiplier, vmultiplier);
75    vacc = _mm256_slli_epi16(vacc, 7);
76    vacc = _mm256_mulhrs_epi16(vacc, vmultiplier);
77    vacc = _mm256_adds_epi16(vacc, voutput_zero_point);
78    x += 16;
79
80    const __m128i vacc_hi = _mm256_extracti128_si256(vacc, 1);
81    const __m128i vy = ${_MM_PACKXS_EPI16}(_mm256_castsi256_si128(vacc), vacc_hi);
82    _mm_storeu_si128((__m128i*) y, vy);
83    y += 16;
84  }
85  if XNN_UNLIKELY(n != 0) {
86    assert(n >= 1 * sizeof(${XINT8_T}));
87    assert(n <= 15 * sizeof(${XINT8_T}));
88
89    __m256i vacc = ${_MM256_CVTEPX8_EPI16}(_mm_loadu_si128((const __m128i*) x));
90    __m256i vmultiplier = _mm256_cmpgt_epi16(vacc, vinput_zero_point);
91    vacc = _mm256_sub_epi16(vinput_zero_point, vacc);
92    vmultiplier = _mm256_blendv_epi8(vnegative_multiplier, vpositive_multiplier, vmultiplier);
93    vacc = _mm256_slli_epi16(vacc, 7);
94    vacc = _mm256_mulhrs_epi16(vacc, vmultiplier);
95    vacc = _mm256_adds_epi16(vacc, voutput_zero_point);
96
97    const __m128i vacc_hi = _mm256_extracti128_si256(vacc, 1);
98    __m128i vy = ${_MM_PACKXS_EPI16}(_mm256_castsi256_si128(vacc), vacc_hi);
99    if (n & (8 * sizeof(${XINT8_T}))) {
100      _mm_storel_epi64((__m128i*) y, vy);
101      vy = _mm_unpackhi_epi64(vy, vy);
102      y += 8;
103    }
104    if (n & (4 * sizeof(${XINT8_T}))) {
105      _mm_storeu_si32(y, vy);
106      vy = _mm_srli_epi64(vy, 32);
107      y += 4;
108    }
109    if (n & (2 * sizeof(${XINT8_T}))) {
110      _mm_storeu_si16(y, vy);
111      vy = _mm_srli_epi32(vy, 16);
112      y += 2;
113    }
114    if (n & (1 * sizeof(${XINT8_T}))) {
115      *y = (${XINT8_T}) _mm_extract_epi8(vy, 0);
116    }
117  }
118}
119