test / src /f16-raddstoreexpminusmax /neonfp16arith-rr2-p2.c.in
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// Copyright 2022 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 % 8 == 0
$assert BATCH_TILE >= 8
$SIMD_TILE = BATCH_TILE // 8
$ABC = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
#include <assert.h>
#include <arm_neon.h>
#include <xnnpack/common.h>
#include <xnnpack/raddstoreexpminusmax.h>
void xnn_f16_raddstoreexpminusmax_ukernel__neonfp16arith_rr2_p2_x${BATCH_TILE}${"" if ACCUMULATORS == 1 else "_acc%d" % ACCUMULATORS}(
size_t batch,
const void* input,
const void* max,
void* output,
void* sum,
const union xnn_f16_expminus_params params[restrict XNN_MIN_ELEMENTS(1)]) XNN_OOB_READS
{
assert(batch != 0);
assert(batch % sizeof(uint16_t) == 0);
assert(input != NULL);
assert(max != NULL);
assert(output != NULL);
assert(sum != NULL);
const float16x8_t vi_max = vreinterpretq_f16_u16(vld1q_dup_u16(max));
const float16x8_t vlog2e = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.log2e));
const float16x8_t vmagic_bias = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.magic_bias));
const float16x8_t vminus_ln2_hi = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.minus_ln2_hi));
const float16x8_t vminus_ln2_lo = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.minus_ln2_lo));
const float16x8_t vc2 = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.c2));
const float16x8_t vc1 = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.c1));
const float16x8_t vdenorm_cutoff = vreinterpretq_f16_u16(vld1q_dup_u16(&params->fp16arith_rr2_p2.denorm_cutoff));
const uint16_t* i = (const uint16_t*) input;
uint16_t* o = (uint16_t*) output;
$if BATCH_TILE > 8:
$for K in range(ACCUMULATORS):
float16x8_t vacc${K} = vreinterpretq_f16_u16(vmovq_n_u16(0));
for (; batch >= ${BATCH_TILE} * sizeof(uint16_t); batch -= ${BATCH_TILE} * sizeof(uint16_t)) {
$for N in range(SIMD_TILE):
const float16x8_t vi${ABC[N]} = vreinterpretq_f16_u16(vld1q_u16(i)); i += 8;
$for N in range(SIMD_TILE):
const float16x8_t vx${ABC[N]} = vsubq_f16(vi${ABC[N]}, vi_max);
$for N in range(SIMD_TILE):
float16x8_t vn${ABC[N]} = vfmaq_f16(vmagic_bias, vx${ABC[N]}, vlog2e);
$for N in range(SIMD_TILE):
const float16x8_t vs${ABC[N]} = vreinterpretq_f16_s16(vshlq_n_s16(vreinterpretq_s16_f16(vn${ABC[N]}), 10));
$for N in range(SIMD_TILE):
vn${ABC[N]} = vsubq_f16(vn${ABC[N]}, vmagic_bias);
$for N in range(SIMD_TILE):
float16x8_t vt${ABC[N]} = vfmaq_f16(vx${ABC[N]}, vn${ABC[N]}, vminus_ln2_hi);
$for N in range(SIMD_TILE):
vt${ABC[N]} = vfmaq_f16(vt${ABC[N]}, vn${ABC[N]}, vminus_ln2_lo);
$for N in range(SIMD_TILE):
const float16x8_t vp${ABC[N]} = vfmaq_f16(vc1, vc2, vt${ABC[N]});
$for N in range(SIMD_TILE):
vt${ABC[N]} = vmulq_f16(vt${ABC[N]}, vs${ABC[N]});
$for N in range(SIMD_TILE):
float16x8_t vf${ABC[N]} = vfmaq_f16(vs${ABC[N]}, vp${ABC[N]}, vt${ABC[N]});
const uint16x8_t vm${ABC[N]} = vcltq_f16(vx${ABC[N]}, vdenorm_cutoff);
$for N in range(SIMD_TILE):
vf${ABC[N]} = vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(vf${ABC[N]}), vm${ABC[N]}));
$for N in range(SIMD_TILE):
vst1q_u16(o, vreinterpretq_u16_f16(vf${ABC[N]})); o += 8;
$for N in range(SIMD_TILE):
vacc${N % ACCUMULATORS} = vaddq_f16(vacc${N % ACCUMULATORS}, vf${ABC[N]});
}
$if ACCUMULATORS > 1:
$ACC_SLICE = 1
$while ACC_SLICE < ACCUMULATORS:
$for A in range(0, ACCUMULATORS, ACC_SLICE * 2):
$if A + ACC_SLICE < ACCUMULATORS:
vacc${A} = vaddq_f16(vacc${A}, vacc${A + ACC_SLICE});
$ACC_SLICE *= 2
float16x8_t vacc = vacc0;
$else:
float16x8_t vacc = vreinterpretq_f16_u16(vmovq_n_u16(0));
for (; batch >= 8 * sizeof(uint16_t); batch -= 8 * sizeof(uint16_t)) {
const float16x8_t vi = vreinterpretq_f16_u16(vld1q_u16(i)); i += 8;
const float16x8_t vx = vsubq_f16(vi, vi_max);
float16x8_t vn = vfmaq_f16(vmagic_bias, vx, vlog2e);
const float16x8_t vs = vreinterpretq_f16_s16(vshlq_n_s16(vreinterpretq_s16_f16(vn), 10));
vn = vsubq_f16(vn, vmagic_bias);
float16x8_t vt = vfmaq_f16(vx, vn, vminus_ln2_hi);
vt = vfmaq_f16(vt, vn, vminus_ln2_lo);
const float16x8_t vp = vfmaq_f16(vc1, vc2, vt);
vt = vmulq_f16(vt, vs);
float16x8_t vf = vfmaq_f16(vs, vp, vt);
const uint16x8_t vm = vcltq_f16(vx, vdenorm_cutoff);
vf = vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(vf), vm));
vst1q_u16(o, vreinterpretq_u16_f16(vf)); o += 8;
vacc = vaddq_f16(vacc, vf);
}
float16x4_t vacc_lo = vadd_f16(vget_low_f16(vacc), vget_high_f16(vacc));
if (batch != 0) {
assert(batch >= 1 * sizeof(uint16_t));
assert(batch <= 7 * sizeof(uint16_t));
const float16x8_t vi = vreinterpretq_f16_u16(vld1q_u16(i));
const float16x8_t vx = vsubq_f16(vi, vi_max);
float16x8_t vn = vfmaq_f16(vmagic_bias, vx, vlog2e);
const float16x8_t vs = vreinterpretq_f16_s16(vshlq_n_s16(vreinterpretq_s16_f16(vn), 10));
vn = vsubq_f16(vn, vmagic_bias);
float16x8_t vt = vfmaq_f16(vx, vn, vminus_ln2_hi);
vt = vfmaq_f16(vt, vn, vminus_ln2_lo);
const float16x8_t vp = vfmaq_f16(vc1, vc2, vt);
vt = vmulq_f16(vt, vs);
float16x8_t vf = vfmaq_f16(vs, vp, vt);
const uint16x8_t vm = vcltq_f16(vx, vdenorm_cutoff);
vf = vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(vf), vm));
float16x4_t vf_lo = vget_low_f16(vf);
if (batch & (4 * sizeof(uint16_t))) {
vst1_u16(o, vreinterpret_u16_f16(vf_lo)); o += 4;
vacc_lo = vadd_f16(vacc_lo, vf_lo);
vf_lo = vget_high_f16(vf);
}
if (batch & (2 * sizeof(uint16_t))) {
vst1_lane_u32((void*) o, vreinterpret_u32_f16(vf_lo), 0); o += 2;
vacc_lo = vadd_f16(vacc_lo, vreinterpret_f16_u64(vshl_n_u64(vreinterpret_u64_f16(vf_lo), 32)));
vf_lo = vext_f16(vf_lo, vf_lo, 2);
}
if (batch & (1 * sizeof(uint16_t))) {
vst1_lane_u16(o, vreinterpret_u16_f16(vf_lo), 0);
vacc_lo = vadd_f16(vacc_lo, vreinterpret_f16_u64(vshl_n_u64(vreinterpret_u64_f16(vf_lo), 48)));
}
}
vacc_lo = vpadd_f16(vacc_lo, vacc_lo);
vacc_lo = vpadd_f16(vacc_lo, vacc_lo);
vst1_lane_u16(sum, vreinterpret_u16_f16(vacc_lo), 0);
}