// 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 % sizeof(__fp16) == 0);

  const float16x8_t vi_max = vld1q_dup_f16(max);
  const float16x8_t vlog2e = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.log2e));
  const float16x8_t vmagic_bias = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.magic_bias));
  const float16x8_t vminus_ln2_hi = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.minus_ln2_hi));
  const float16x8_t vminus_ln2_lo = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.minus_ln2_lo));
  const float16x8_t vc2 = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.c2));
  const float16x8_t vc1 = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.c1));
  const float16x8_t vdenorm_cutoff = vreinterpretq_f16_u16(vld1q_dup_u16(&params->neonfp16arith_rr2_p2.denorm_cutoff));

  const __fp16* i = (const __fp16*) input;
  __fp16* o = (__fp16*) output;
  $if BATCH_TILE > 8:
    $for K in range(ACCUMULATORS):
      float16x8_t vacc${K} = vmovq_n_f16(0.0f);
    for (; batch >= ${BATCH_TILE} * sizeof(__fp16); batch -= ${BATCH_TILE} * sizeof(__fp16)) {
      $for N in range(SIMD_TILE):
        const float16x8_t vi${ABC[N]} = vld1q_f16(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]}));

      $for N in range(SIMD_TILE):
        vst1q_f16(o, vf${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 = vmovq_n_f16(0.0f);
  for (; batch >= 8 * sizeof(__fp16); batch -= 8 * sizeof(__fp16)) {
    const float16x8_t vi = vld1q_f16(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_f16(o, 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(__fp16));
    assert(batch <= 7 * sizeof(__fp16));
    const float16x8_t vi = vld1q_f16(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(__fp16))) {
      vst1_f16(o, vf_lo); o += 4;
      vacc_lo = vadd_f16(vacc_lo, vf_lo);
      vf_lo = vget_high_f16(vf);
    }
    if (batch & (2 * sizeof(__fp16))) {
      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(__fp16))) {
      vst1_lane_f16(o, 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);
  *((__fp16*) sum) = vget_lane_f16(vacc_lo, 0) + vget_lane_f16(vacc_lo, 1);
}