Marat Dukhan | 8137e4c | 2020-01-25 12:56:58 -0800 | [diff] [blame] | 1 | // Auto-generated file. Do not edit! |
| 2 | // Template: src/f32-raddstoreexpminusmax/neon-p5.c.in |
| 3 | // Generator: tools/xngen |
| 4 | // |
| 5 | // Copyright 2020 Google LLC |
| 6 | // |
| 7 | // This source code is licensed under the BSD-style license found in the |
| 8 | // LICENSE file in the root directory of this source tree. |
| 9 | |
| 10 | #include <assert.h> |
| 11 | |
| 12 | #include <arm_neon.h> |
| 13 | |
| 14 | #include <xnnpack/common.h> |
| 15 | #include <xnnpack/raddstoreexpminusmax.h> |
| 16 | |
| 17 | |
| 18 | void xnn_f32_raddstoreexpminusmax_ukernel__neonfma_p5_x8_acc2( |
| 19 | size_t elements, |
| 20 | const float* input, |
| 21 | float* output, |
| 22 | float* sum, |
Marat Dukhan | b2217dd | 2020-05-28 17:30:28 -0700 | [diff] [blame] | 23 | float max) XNN_DISABLE_TSAN |
Marat Dukhan | 8137e4c | 2020-01-25 12:56:58 -0800 | [diff] [blame] | 24 | { |
| 25 | assert(elements % sizeof(float) == 0); |
| 26 | |
| 27 | const float32x4_t vmagic_bias = vmovq_n_f32(0x1.8000FEp23f); |
| 28 | // The smallest x for which expf(x) is normalized. |
| 29 | const float32x4_t vdenorm_cutoff = vmovq_n_f32(-0x1.5D589Ep6f); |
| 30 | const float32x4_t vlog2e = vmovq_n_f32(0x1.715476p+0f); |
| 31 | const float32x4_t vminus_ln2_hi = vmovq_n_f32(-0x1.62E43p-1f); |
| 32 | const float32x4_t vminus_ln2_lo = vmovq_n_f32(0x1.05C61p-29f); |
| 33 | |
| 34 | const float32x4_t vc1 = vmovq_n_f32(0x1.FFFFF6p-1f); |
| 35 | const float32x4_t vc2 = vmovq_n_f32(0x1.FFFDC6p-2f); |
| 36 | const float32x4_t vc3 = vmovq_n_f32(0x1.555A80p-3f); |
| 37 | const float32x4_t vc4 = vmovq_n_f32(0x1.573A1Ap-5f); |
| 38 | const float32x4_t vc5 = vmovq_n_f32(0x1.0F9F9Cp-7f); |
| 39 | |
| 40 | const float32x4_t vi_max = vdupq_n_f32(max); |
| 41 | |
| 42 | float32x4_t vacc0 = vmovq_n_f32(0.0f); |
| 43 | float32x4_t vacc1 = vmovq_n_f32(0.0f); |
| 44 | for (; elements >= 8 * sizeof(float); elements -= 8 * sizeof(float)) { |
| 45 | // Load 8 (2x4) inputs at a time. |
| 46 | const float32x4_t vi0123 = vld1q_f32(input); input += 4; |
| 47 | const float32x4_t vi4567 = vld1q_f32(input); input += 4; |
| 48 | |
| 49 | // Subtract maximum input x := i - i_max. This implies x <= 0. |
| 50 | const float32x4_t vx0123 = vsubq_f32(vi0123, vi_max); |
| 51 | const float32x4_t vx4567 = vsubq_f32(vi4567, vi_max); |
| 52 | |
| 53 | // Compute reduced argument n := round(x / log(2)). |
| 54 | // We do it by adding a large number (magic bias), which cause rounding of result to an integer, then subtracing the |
| 55 | // large number back. The first addition is combined with multiplication by log2e into a single FMA instruction. |
| 56 | // The trick with adding large number is valid only within certain bounds (|x| <= 2**22), but thats ok, because |
| 57 | // inputs outside of [-87.336540, 0.0] underflow expf(x) anyway. We fixup the result for such inputs at the very end |
| 58 | // of the algorithm. |
| 59 | float32x4_t vn0123 = vfmaq_f32(vmagic_bias, vx0123, vlog2e); |
| 60 | float32x4_t vn4567 = vfmaq_f32(vmagic_bias, vx4567, vlog2e); |
| 61 | |
| 62 | // Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e. |
| 63 | // -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly. |
| 64 | const float32x4_t vs0123 = vreinterpretq_f32_s32(vshlq_n_s32(vreinterpretq_s32_f32(vn0123), 23)); |
| 65 | const float32x4_t vs4567 = vreinterpretq_f32_s32(vshlq_n_s32(vreinterpretq_s32_f32(vn4567), 23)); |
| 66 | |
| 67 | // Subtract the large number back to get final n := round(x / log(2)). |
| 68 | vn0123 = vsubq_f32(vn0123, vmagic_bias); |
| 69 | vn4567 = vsubq_f32(vn4567, vmagic_bias); |
| 70 | |
| 71 | // Compute reduced argument t := z - n * log(2). |
| 72 | // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. |
| 73 | float32x4_t vt0123 = vfmaq_f32(vx0123, vn0123, vminus_ln2_hi); |
| 74 | float32x4_t vt4567 = vfmaq_f32(vx4567, vn4567, vminus_ln2_hi); |
| 75 | |
| 76 | vt0123 = vfmaq_f32(vt0123, vn0123, vminus_ln2_lo); |
| 77 | vt4567 = vfmaq_f32(vt4567, vn4567, vminus_ln2_lo); |
| 78 | |
Marat Dukhan | 102a739 | 2020-11-20 01:18:10 -0800 | [diff] [blame^] | 79 | // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. |
Marat Dukhan | 8137e4c | 2020-01-25 12:56:58 -0800 | [diff] [blame] | 80 | float32x4_t vp0123 = vfmaq_f32(vc4, vc5, vt0123); |
| 81 | float32x4_t vp4567 = vfmaq_f32(vc4, vc5, vt4567); |
| 82 | |
| 83 | vp0123 = vfmaq_f32(vc3, vp0123, vt0123); |
| 84 | vp4567 = vfmaq_f32(vc3, vp4567, vt4567); |
| 85 | |
| 86 | vp0123 = vfmaq_f32(vc2, vp0123, vt0123); |
| 87 | vp4567 = vfmaq_f32(vc2, vp4567, vt4567); |
| 88 | |
| 89 | vp0123 = vfmaq_f32(vc1, vp0123, vt0123); |
| 90 | vp4567 = vfmaq_f32(vc1, vp4567, vt4567); |
| 91 | |
| 92 | // Reconstruct the final f value: |
| 93 | // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) |
| 94 | // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) |
| 95 | // = s + (t * s) * p |
| 96 | vt0123 = vmulq_f32(vt0123, vs0123); |
| 97 | vt4567 = vmulq_f32(vt4567, vs4567); |
| 98 | |
| 99 | float32x4_t vf0123 = vfmaq_f32(vs0123, vp0123, vt0123); |
| 100 | float32x4_t vf4567 = vfmaq_f32(vs4567, vp4567, vt4567); |
| 101 | |
| 102 | // For inputs below denormal cutoff, replace output with +0.0f. |
| 103 | // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. |
| 104 | vf0123 = vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(vf0123), vcltq_f32(vx0123, vdenorm_cutoff))); |
| 105 | vf4567 = vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(vf4567), vcltq_f32(vx4567, vdenorm_cutoff))); |
| 106 | |
| 107 | // Store 8 (2x4) outputs at a time. |
| 108 | vst1q_f32(output, vf0123); output += 4; |
| 109 | vst1q_f32(output, vf4567); output += 4; |
| 110 | |
| 111 | // Accumulate computed exponents. |
| 112 | vacc0 = vaddq_f32(vacc0, vf0123); |
| 113 | vacc0 = vaddq_f32(vacc0, vf4567); |
| 114 | } |
| 115 | // Add up all accumulators to vacc0 |
| 116 | vacc0 = vaddq_f32(vacc0, vacc1); |
| 117 | |
| 118 | float32x4_t vacc = vacc0; |
| 119 | for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) { |
| 120 | // Load 4 inputs at a time. |
| 121 | const float32x4_t vi = vld1q_f32(input); input += 4; |
| 122 | |
| 123 | // Subtract maximum input x := i - i_max. This implies x <= 0. |
| 124 | const float32x4_t vx = vsubq_f32(vi, vi_max); |
| 125 | |
| 126 | // Compute reduced argument n := round(x / log(2)). |
| 127 | // We do it by adding a large number (magic bias), which cause rounding of result to an integer, then subtracing the |
| 128 | // large number back. The first addition is combined with multiplication by log2e into a single FMA instruction. |
| 129 | // The trick with adding large number is valid only within certain bounds (|x| <= 2**22), but thats ok, because |
| 130 | // inputs outside of [-87.336540, 0.0] underflow expf(x) anyway. We fixup the result for such inputs at the very end |
| 131 | // of the algorithm. |
| 132 | float32x4_t vn = vfmaq_f32(vmagic_bias, vx, vlog2e); |
| 133 | |
| 134 | // Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e. |
| 135 | // -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly. |
| 136 | const float32x4_t vs = vreinterpretq_f32_s32(vshlq_n_s32(vreinterpretq_s32_f32(vn), 23)); |
| 137 | |
| 138 | // Subtract the large number back to get final n := round(x / log(2)). |
| 139 | vn = vsubq_f32(vn, vmagic_bias); |
| 140 | |
| 141 | // Compute reduced argument t := z - n * log(2). |
| 142 | // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. |
| 143 | float32x4_t vt = vfmaq_f32(vx, vn, vminus_ln2_hi); |
| 144 | vt = vfmaq_f32(vt, vn, vminus_ln2_lo); |
| 145 | |
Marat Dukhan | 102a739 | 2020-11-20 01:18:10 -0800 | [diff] [blame^] | 146 | // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. |
Marat Dukhan | 8137e4c | 2020-01-25 12:56:58 -0800 | [diff] [blame] | 147 | float32x4_t vp = vfmaq_f32(vc4, vc5, vt); |
| 148 | vp = vfmaq_f32(vc3, vp, vt); |
| 149 | vp = vfmaq_f32(vc2, vp, vt); |
| 150 | vp = vfmaq_f32(vc1, vp, vt); |
| 151 | |
| 152 | // Reconstruct the final f value: |
| 153 | // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) |
| 154 | // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) |
| 155 | // = s + (t * s) * p |
| 156 | vt = vmulq_f32(vt, vs); |
| 157 | float32x4_t vf = vfmaq_f32(vs, vp, vt); |
| 158 | |
| 159 | // For inputs below denormal cutoff, replace output with +0.0f. |
| 160 | // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. |
| 161 | vf = vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(vf), vcltq_f32(vx, vdenorm_cutoff))); |
| 162 | |
| 163 | // Store 4 outputs at a time. |
| 164 | vst1q_f32(output, vf); output += 4; |
| 165 | |
| 166 | // Accumulate computed exponents. |
| 167 | vacc = vaddq_f32(vacc, vf); |
| 168 | } |
| 169 | #if XNN_ARCH_ARM64 |
| 170 | float vacc_lo = vaddvq_f32(vacc); |
| 171 | #else |
| 172 | float32x2_t vacc_lo = vadd_f32(vget_high_f32(vacc), vget_low_f32(vacc)); |
| 173 | #endif |
| 174 | if (elements != 0) { |
| 175 | assert(elements >= 1 * sizeof(float)); |
| 176 | assert(elements <= 3 * sizeof(float)); |
| 177 | // Load 4 inputs at a time. |
| 178 | const float32x4_t vi = vld1q_f32(input); input += 4; |
| 179 | |
| 180 | // Subtract maximum input x := i - i_max. This implies x <= 0. |
| 181 | const float32x4_t vx = vsubq_f32(vi, vi_max); |
| 182 | |
| 183 | // Compute reduced argument n := round(x / log(2)). |
| 184 | // We do it by adding a large number (magic bias), which cause rounding of result to an integer, then subtracing the |
| 185 | // large number back. The first addition is combined with multiplication by log2e into a single FMA instruction. |
| 186 | // The trick with adding large number is valid only within certain bounds (|x| <= 2**22), but thats ok, because |
| 187 | // inputs outside of [-87.336540, 0.0] underflow expf(x) anyway. We fixup the result for such inputs at the very end |
| 188 | // of the algorithm. |
| 189 | float32x4_t vn = vfmaq_f32(vmagic_bias, vx, vlog2e); |
| 190 | |
| 191 | // Create a floating-point number s (scale) such that s == 2**n for inputs which don't cause underflow, i.e. |
| 192 | // -87.33642 <= x <= 0.0, and -126 <= n <= 0 accordingly. |
| 193 | const float32x4_t vs = vreinterpretq_f32_s32(vshlq_n_s32(vreinterpretq_s32_f32(vn), 23)); |
| 194 | |
| 195 | // Subtract the large number back to get final n := round(x / log(2)). |
| 196 | vn = vsubq_f32(vn, vmagic_bias); |
| 197 | |
| 198 | // Compute reduced argument t := z - n * log(2). |
| 199 | // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. |
| 200 | float32x4_t vt = vfmaq_f32(vx, vn, vminus_ln2_hi); |
| 201 | vt = vfmaq_f32(vt, vn, vminus_ln2_lo); |
| 202 | |
Marat Dukhan | 102a739 | 2020-11-20 01:18:10 -0800 | [diff] [blame^] | 203 | // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. |
Marat Dukhan | 8137e4c | 2020-01-25 12:56:58 -0800 | [diff] [blame] | 204 | float32x4_t vp = vfmaq_f32(vc4, vc5, vt); |
| 205 | vp = vfmaq_f32(vc3, vp, vt); |
| 206 | vp = vfmaq_f32(vc2, vp, vt); |
| 207 | vp = vfmaq_f32(vc1, vp, vt); |
| 208 | |
| 209 | // Reconstruct the final f value: |
| 210 | // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) |
| 211 | // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) |
| 212 | // = s + (t * s) * p |
| 213 | vt = vmulq_f32(vt, vs); |
| 214 | float32x4_t vf = vfmaq_f32(vs, vp, vt); |
| 215 | |
| 216 | // For inputs below denormal cutoff, replace output with +0.0f. |
| 217 | // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. |
| 218 | vf = vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(vf), vcltq_f32(vx, vdenorm_cutoff))); |
| 219 | |
| 220 | float32x2_t vf_lo = vget_low_f32(vf); |
| 221 | if (elements & (2 * sizeof(float))) { |
| 222 | // Store 2 outputs at a time. |
| 223 | vst1_f32(output, vf_lo); output += 2; |
| 224 | |
| 225 | // Accumulate 2 computed exponents. |
| 226 | #if XNN_ARCH_ARM64 |
| 227 | vacc_lo += vaddv_f32(vf_lo); |
| 228 | #else |
| 229 | vacc_lo = vadd_f32(vacc_lo, vf_lo); |
| 230 | #endif |
| 231 | |
| 232 | vf_lo = vget_high_f32(vf); |
| 233 | } |
| 234 | if (elements & (1 * sizeof(float))) { |
| 235 | // Store 1 output at a time. |
| 236 | vst1_lane_f32(output, vf_lo, 0); |
| 237 | |
| 238 | // Accumulate 1 computed exponent. |
| 239 | #if XNN_ARCH_ARM64 |
| 240 | vacc_lo += vget_lane_f32(vf_lo, 0); |
| 241 | #else |
| 242 | vacc_lo = vadd_f32(vacc_lo, vreinterpret_f32_u64(vshl_n_u64(vreinterpret_u64_f32(vf_lo), 32))); |
| 243 | #endif |
| 244 | } |
| 245 | } |
| 246 | // Reduce 4 elements in the SIMD register |
| 247 | #if XNN_ARCH_ARM64 |
| 248 | *sum = vacc_lo; |
| 249 | #else |
| 250 | vst1_lane_f32(sum, vpadd_f32(vacc_lo, vacc_lo), 0); |
| 251 | #endif |
| 252 | } |