// Copyright 2020 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. #include #include #include #include #include // Table of exp2(k / 16) values decremented (as integer) by (k << 19), k = 0..15 extern XNN_INTERNAL const float xnn_table_exp2minus_k_over_16[16]; void xnn_math_f32_expm1minus__avx_rr2_lut16_p3( size_t n, const float* input, float* output) { assert(n % (8 * sizeof(float)) == 0); // The largest x for which expm1f(x) is saturated at -1.0f. const __m256 vsat_cutoff = _mm256_set1_ps(-0x1.154246p+4f); // Large number such that ulp(magic bias) == exp2(-4) const __m256 vmagic_bias = _mm256_set1_ps(0x1.800000p19f); const __m256 vlog2e = _mm256_set1_ps(0x1.715476p+0f); // Mask for the lowest 4 bits const __m256 vindex_mask = _mm256_castsi256_ps(_mm256_set1_epi32(0xF)); // Last 9 bits are zeroes const __m256 vminus_ln2_hi = _mm256_set1_ps(-0x1.62E400p-1f); const __m256 vminus_ln2_lo = _mm256_set1_ps(-0x1.7F7D1Cp-20f); // Coefficient of polynomial approximation // exp(t) - 1 ~ t * (1 + t * (c2 + t * c3)) // on [-log(2)/32, log(2)/32] const __m256 vc3 = _mm256_set1_ps(0x1.55561Cp-3f); const __m256 vc2 = _mm256_set1_ps(0x1.0001ECp-1f); const __m256 vone = _mm256_set1_ps(1.0f); for (; n != 0; n -= 8 * sizeof(float)) { __m256 vx = _mm256_loadu_ps(input); // The function saturates at -1 for large negative inputs: expm1f(x) == -1.0f for x <= sat_cutoff ~= -17.328680. // To guarantee this behaviour, we clip input at sat_cutoff, and leverage the fact that for our implementation // expm1f(sat_cutoff) == -1.0f. The order of operands in the [V]MAXPS instruction matters: it ensures that NaN // inputs are passed unchanged. vx = _mm256_max_ps(vsat_cutoff, vx); // Compute reduced argument n := round(x / log(2), 4). // We do it by adding a large number (magic bias), which cause rounding of the result to 4 fractional bits, then // subtracing the large number back. The trick with adding large number is valid only within certain bounds // (|x / log(2)| <= 2**18, i.e. |x| <= 0x1.62E43p+17 = 181704.375), but that is acceptable, because inputs x are // restricted to [-17.328680, 0]. // Note that addition-subtraction of the large number doesn't cause overflow for inputs in this range. __m256 vn = _mm256_add_ps(_mm256_mul_ps(vx, vlog2e), vmagic_bias); // Create a floating-point number s (scale) such that s := 2**n for valid inputs, i.e. -17.328680 <= x <= 0.0. As n // has 4 fractional bits, we split s == 2**n = 2**int(n) * 2**frac(n). We create s in two steps: // 1. Fetch 2**frac(n) from the table using the 4 low bits of n, as integer. Note that the fetched values are in // the [1.0, 2.0) range, i.e. their floating-point exponent is 0. // 2. Adjust fecthed value by addition of int(n) to its floating-point exponent. The result is always a normalized // number, because for -17.328680 <= x <= 0.0 we have -25 <= int(n) <= 0, and thus the adjusted exponent is not // lower than -25. // // Shift bits 4:12 into 23:31 (position of floating-point exponent). const __m128i ven_lo = _mm_slli_epi32(_mm_castps_si128(_mm256_castps256_ps128(vn)), 19); const __m128i ven_hi = _mm_slli_epi32(_mm_castps_si128(_mm256_extractf128_ps(vn, 1)), 19); // Use bits 0:4 bits of n, as integer, as an index for table lookup of l := 2**frac(n). const __m256 vidx = _mm256_and_ps(vn, vindex_mask); const __m128i vidx_lo = _mm_slli_epi32(_mm_castps_si128(_mm256_castps256_ps128(vidx)), 2); const __m128i vidx_hi = _mm_slli_epi32(_mm_castps_si128(_mm256_extractf128_ps(vidx, 1)), 2); #if XNN_ARCH_X86_64 const uint64_t vidx_ll = (uint64_t) _mm_cvtsi128_si64(vidx_lo); const uint64_t vidx_lh = (uint64_t) _mm_extract_epi64(vidx_lo, 1); const uint64_t vidx_hl = (uint64_t) _mm_cvtsi128_si64(vidx_hi); const uint64_t vidx_hh = (uint64_t) _mm_extract_epi64(vidx_hi, 1); __m128i vl_ll = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) vidx_ll))); __m128i vl_lh = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) vidx_lh))); __m128i vl_hl = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) vidx_hl))); __m128i vl_hh = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) vidx_hh))); vl_ll = _mm_insert_epi32(vl_ll, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) (vidx_ll >> 32))), 1); vl_lh = _mm_insert_epi32(vl_lh, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) (vidx_lh >> 32))), 1); vl_hl = _mm_insert_epi32(vl_hl, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) (vidx_hl >> 32))), 1); vl_hh = _mm_insert_epi32(vl_hh, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) (vidx_hh >> 32))), 1); #else __m128i vl_ll = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_cvtsi128_si32(vidx_lo)))); __m128i vl_lh = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_extract_epi32(vidx_lo, 2)))); __m128i vl_hl = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_cvtsi128_si32(vidx_hi)))); __m128i vl_hh = _mm_cvtsi32_si128(*((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_extract_epi32(vidx_hi, 2)))); vl_ll = _mm_insert_epi32(vl_ll, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_extract_epi32(vidx_lo, 1))), 1); vl_lh = _mm_insert_epi32(vl_lh, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_extract_epi32(vidx_lo, 3))), 1); vl_hl = _mm_insert_epi32(vl_hl, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_extract_epi32(vidx_hi, 1))), 1); vl_hh = _mm_insert_epi32(vl_hh, *((const int*) ((uintptr_t) xnn_table_exp2minus_k_over_16 + (uint32_t) _mm_extract_epi32(vidx_hi, 3))), 1); #endif const __m128i vl_lo = _mm_unpacklo_epi64(vl_ll, vl_lh); const __m128i vl_hi = _mm_unpacklo_epi64(vl_hl, vl_hh); // Adjust exponent of the value l fetched from the table to get the final s value. const __m128 vs_lo = _mm_castsi128_ps(_mm_add_epi32(vl_lo, ven_lo)); const __m128 vs_hi = _mm_castsi128_ps(_mm_add_epi32(vl_hi, ven_hi)); const __m256 vs = _mm256_insertf128_ps(_mm256_castps128_ps256(vs_lo), vs_hi, 1); // Subtract the large number back to get final n := round(x / log(2), 4). vn = _mm256_sub_ps(vn, vmagic_bias); // Compute reduced argument t := x - n * log(2). // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. __m256 vt = _mm256_add_ps(_mm256_mul_ps(vn, vminus_ln2_hi), vx); vt = _mm256_add_ps(_mm256_mul_ps(vn, vminus_ln2_lo), vt); // Compute degree-3 polynomial approximation for exp(t) - 1 on [-log(2)/32, log(2)/32]. // P(t) = t * (1 + t * (c2 + t * c3)) = t + t * (t * (c2 + t * c3)) = t + t * p __m256 vp = _mm256_add_ps(_mm256_mul_ps(vc3, vt), vc2); vp = _mm256_mul_ps(vp, vt); // Reconstruct the exp(x) - 1 value: // exp(x) - 1 = s * (1 + t * (1 + t * (c2 + t * c3))) - 1 // = (s - 1) + s * (t + t * p) // = ((t * s) + (t * s) * p) + (s - 1) vt = _mm256_mul_ps(vt, vs); const __m256 vsm1 = _mm256_sub_ps(vs, vone); vp = _mm256_add_ps(_mm256_mul_ps(vp, vt), vt); const __m256 vf = _mm256_add_ps(vp, vsm1); _mm256_storeu_ps(output, vf); input += 8; output += 8; } }