| // Auto-generated file. Do not edit! |
| // Template: src/f32-raddstoreexpminusmax/wasmsimd-p5.c.in |
| // Generator: tools/xngen |
| // |
| // 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 <assert.h> |
| |
| #include <wasm_simd128.h> |
| |
| #include <xnnpack/common.h> |
| #include <xnnpack/raddstoreexpminusmax.h> |
| |
| |
| void xnn_f32_raddstoreexpminusmax_ukernel__wasmsimd_p5_x16_acc4( |
| size_t elements, |
| const float* input, |
| float* output, |
| float* sum, |
| float max) XNN_DISABLE_TSAN |
| { |
| assert(elements % sizeof(float) == 0); |
| |
| const v128_t vmagic_bias = wasm_f32x4_splat(0x1.8000FEp23f); |
| // The smallest x for which expf(x) is normalized. |
| const v128_t vdenorm_cutoff = wasm_f32x4_splat(-0x1.5D589Ep6f); |
| const v128_t vlog2e = wasm_f32x4_splat(0x1.715476p+0f); |
| // Last 7 bits are zeroes |
| const v128_t vminus_ln2_hi = wasm_f32x4_splat(-0x1.62E400p-1f); |
| const v128_t vminus_ln2_lo = wasm_f32x4_splat(-0x1.7F7D1Cp-20f); |
| |
| const v128_t vc1 = wasm_f32x4_splat(0x1.FFFFF6p-1f); |
| const v128_t vc2 = wasm_f32x4_splat(0x1.FFFDC6p-2f); |
| const v128_t vc3 = wasm_f32x4_splat(0x1.555A80p-3f); |
| const v128_t vc4 = wasm_f32x4_splat(0x1.573A1Ap-5f); |
| const v128_t vc5 = wasm_f32x4_splat(0x1.0F9F9Cp-7f); |
| |
| const v128_t vi_max = wasm_f32x4_splat(max); |
| |
| v128_t vacc0 = wasm_f32x4_splat(0.0f); |
| v128_t vacc1 = vacc0; |
| v128_t vacc2 = vacc0; |
| v128_t vacc3 = vacc0; |
| for (; elements >= 16 * sizeof(float); elements -= 16 * sizeof(float)) { |
| // Load 16 (4x4) inputs at a time. |
| const v128_t vi0123 = wasm_v128_load(input); |
| const v128_t vi4567 = wasm_v128_load(input + 4); |
| const v128_t vi89AB = wasm_v128_load(input + 8); |
| const v128_t viCDEF = wasm_v128_load(input + 12); |
| input += 16; |
| |
| // Subtract maximum input x := i - i_max. This implies x <= 0. |
| const v128_t vx0123 = wasm_f32x4_sub(vi0123, vi_max); |
| const v128_t vx4567 = wasm_f32x4_sub(vi4567, vi_max); |
| const v128_t vx89AB = wasm_f32x4_sub(vi89AB, vi_max); |
| const v128_t vxCDEF = wasm_f32x4_sub(viCDEF, vi_max); |
| |
| // Compute reduced argument elements := round(x / log(2)). |
| v128_t vn0123 = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vx0123, vlog2e)); |
| v128_t vn4567 = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vx4567, vlog2e)); |
| v128_t vn89AB = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vx89AB, vlog2e)); |
| v128_t vnCDEF = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vxCDEF, vlog2e)); |
| |
| // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e. |
| // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly. |
| const v128_t vs0123 = wasm_i32x4_shl(vn0123, 23); |
| const v128_t vs4567 = wasm_i32x4_shl(vn4567, 23); |
| const v128_t vs89AB = wasm_i32x4_shl(vn89AB, 23); |
| const v128_t vsCDEF = wasm_i32x4_shl(vnCDEF, 23); |
| |
| // Subtract the large number back to get final elements := round(x / log(2)). |
| vn0123 = wasm_f32x4_sub(vn0123, vmagic_bias); |
| vn4567 = wasm_f32x4_sub(vn4567, vmagic_bias); |
| vn89AB = wasm_f32x4_sub(vn89AB, vmagic_bias); |
| vnCDEF = wasm_f32x4_sub(vnCDEF, vmagic_bias); |
| |
| // Compute reduced argument t := x - elements * log(2). |
| // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. |
| v128_t vt0123 = wasm_f32x4_add(vx0123, wasm_f32x4_mul(vn0123, vminus_ln2_hi)); |
| v128_t vt4567 = wasm_f32x4_add(vx4567, wasm_f32x4_mul(vn4567, vminus_ln2_hi)); |
| v128_t vt89AB = wasm_f32x4_add(vx89AB, wasm_f32x4_mul(vn89AB, vminus_ln2_hi)); |
| v128_t vtCDEF = wasm_f32x4_add(vxCDEF, wasm_f32x4_mul(vnCDEF, vminus_ln2_hi)); |
| |
| vt0123 = wasm_f32x4_add(vt0123, wasm_f32x4_mul(vn0123, vminus_ln2_lo)); |
| vt4567 = wasm_f32x4_add(vt4567, wasm_f32x4_mul(vn4567, vminus_ln2_lo)); |
| vt89AB = wasm_f32x4_add(vt89AB, wasm_f32x4_mul(vn89AB, vminus_ln2_lo)); |
| vtCDEF = wasm_f32x4_add(vtCDEF, wasm_f32x4_mul(vnCDEF, vminus_ln2_lo)); |
| |
| // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. |
| v128_t vp0123 = wasm_f32x4_add(vc4, wasm_f32x4_mul(vc5, vt0123)); |
| v128_t vp4567 = wasm_f32x4_add(vc4, wasm_f32x4_mul(vc5, vt4567)); |
| v128_t vp89AB = wasm_f32x4_add(vc4, wasm_f32x4_mul(vc5, vt89AB)); |
| v128_t vpCDEF = wasm_f32x4_add(vc4, wasm_f32x4_mul(vc5, vtCDEF)); |
| |
| vp0123 = wasm_f32x4_add(vc3, wasm_f32x4_mul(vp0123, vt0123)); |
| vp4567 = wasm_f32x4_add(vc3, wasm_f32x4_mul(vp4567, vt4567)); |
| vp89AB = wasm_f32x4_add(vc3, wasm_f32x4_mul(vp89AB, vt89AB)); |
| vpCDEF = wasm_f32x4_add(vc3, wasm_f32x4_mul(vpCDEF, vtCDEF)); |
| |
| vp0123 = wasm_f32x4_add(vc2, wasm_f32x4_mul(vp0123, vt0123)); |
| vp4567 = wasm_f32x4_add(vc2, wasm_f32x4_mul(vp4567, vt4567)); |
| vp89AB = wasm_f32x4_add(vc2, wasm_f32x4_mul(vp89AB, vt89AB)); |
| vpCDEF = wasm_f32x4_add(vc2, wasm_f32x4_mul(vpCDEF, vtCDEF)); |
| |
| vp0123 = wasm_f32x4_add(vc1, wasm_f32x4_mul(vp0123, vt0123)); |
| vp4567 = wasm_f32x4_add(vc1, wasm_f32x4_mul(vp4567, vt4567)); |
| vp89AB = wasm_f32x4_add(vc1, wasm_f32x4_mul(vp89AB, vt89AB)); |
| vpCDEF = wasm_f32x4_add(vc1, wasm_f32x4_mul(vpCDEF, vtCDEF)); |
| |
| // Reconstruct the final f value: |
| // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) |
| // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) |
| // = s + (t * s) * p |
| vt0123 = wasm_f32x4_mul(vt0123, vs0123); |
| vt4567 = wasm_f32x4_mul(vt4567, vs4567); |
| vt89AB = wasm_f32x4_mul(vt89AB, vs89AB); |
| vtCDEF = wasm_f32x4_mul(vtCDEF, vsCDEF); |
| |
| v128_t vf0123 = wasm_f32x4_add(vs0123, wasm_f32x4_mul(vt0123, vp0123)); |
| v128_t vf4567 = wasm_f32x4_add(vs4567, wasm_f32x4_mul(vt4567, vp4567)); |
| v128_t vf89AB = wasm_f32x4_add(vs89AB, wasm_f32x4_mul(vt89AB, vp89AB)); |
| v128_t vfCDEF = wasm_f32x4_add(vsCDEF, wasm_f32x4_mul(vtCDEF, vpCDEF)); |
| |
| // For inputs below zero cutoff, replace output with +0.0f. |
| // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. |
| vf0123 = wasm_v128_andnot(vf0123, wasm_f32x4_lt(vx0123, vdenorm_cutoff)); |
| vf4567 = wasm_v128_andnot(vf4567, wasm_f32x4_lt(vx4567, vdenorm_cutoff)); |
| vf89AB = wasm_v128_andnot(vf89AB, wasm_f32x4_lt(vx89AB, vdenorm_cutoff)); |
| vfCDEF = wasm_v128_andnot(vfCDEF, wasm_f32x4_lt(vxCDEF, vdenorm_cutoff)); |
| |
| // Store 16 (4x4) outputs at a time. |
| wasm_v128_store(output, vf0123); |
| wasm_v128_store(output + 4, vf4567); |
| wasm_v128_store(output + 8, vf89AB); |
| wasm_v128_store(output + 12, vfCDEF); |
| output += 16; |
| |
| // Accumulate computed exponents. |
| vacc0 = wasm_f32x4_add(vacc0, vf0123); |
| vacc0 = wasm_f32x4_add(vacc0, vf4567); |
| vacc0 = wasm_f32x4_add(vacc0, vf89AB); |
| vacc0 = wasm_f32x4_add(vacc0, vfCDEF); |
| } |
| // Add up all accumulators to vacc0 |
| vacc0 = wasm_f32x4_add(vacc0, vacc1); |
| vacc2 = wasm_f32x4_add(vacc2, vacc3); |
| vacc0 = wasm_f32x4_add(vacc0, vacc2); |
| |
| v128_t vacc = vacc0; |
| for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) { |
| // Load 4 inputs at a time. |
| const v128_t vi = wasm_v128_load(input); |
| input += 4; |
| |
| // Subtract maximum input x := i - i_max. This implies x <= 0. |
| const v128_t vx = wasm_f32x4_sub(vi, vi_max); |
| |
| // Compute reduced argument elements := round(x / log(2)). |
| v128_t vn = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vx, vlog2e)); |
| |
| // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e. |
| // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly. |
| const v128_t vs = wasm_i32x4_shl(vn, 23); |
| |
| // Subtract the large number back to get final elements := round(x / log(2)). |
| vn = wasm_f32x4_sub(vn, vmagic_bias); |
| |
| // Compute reduced argument t := x - elements * log(2). |
| // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. |
| v128_t vt = wasm_f32x4_add(vx, wasm_f32x4_mul(vn, vminus_ln2_hi)); |
| vt = wasm_f32x4_add(vt, wasm_f32x4_mul(vn, vminus_ln2_lo)); |
| |
| // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. |
| v128_t vp = wasm_f32x4_add(vc4, wasm_f32x4_mul(vc5, vt)); |
| vp = wasm_f32x4_add(vc3, wasm_f32x4_mul(vp, vt)); |
| vp = wasm_f32x4_add(vc2, wasm_f32x4_mul(vp, vt)); |
| vp = wasm_f32x4_add(vc1, wasm_f32x4_mul(vp, vt)); |
| |
| // Reconstruct the final f value: |
| // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) |
| // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) |
| // = s + (t * s) * p |
| vt = wasm_f32x4_mul(vt, vs); |
| v128_t vf = wasm_f32x4_add(vs, wasm_f32x4_mul(vt, vp)); |
| |
| // For inputs below zero cutoff, replace output with +0.0f. |
| // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. |
| vf = wasm_v128_andnot(vf, wasm_f32x4_lt(vx, vdenorm_cutoff)); |
| |
| // Store 4 outputs at a time. |
| wasm_v128_store(output, vf); |
| output += 4; |
| |
| // Accumulate computed exponents. |
| vacc = wasm_f32x4_add(vacc, vf); |
| } |
| vacc = wasm_f32x4_add(vacc, wasm_v32x4_shuffle(vacc, vacc, 2, 3, 2, 3)); |
| float vsum = wasm_f32x4_extract_lane(vacc, 0) + wasm_f32x4_extract_lane(vacc, 1); |
| if (elements != 0) { |
| assert(elements >= 1 * sizeof(float)); |
| assert(elements <= 3 * sizeof(float)); |
| // Load 4 inputs at a time. |
| const v128_t vi = wasm_v128_load(input); |
| |
| // Subtract maximum input x := i - i_max. This implies x <= 0. |
| const v128_t vx = wasm_f32x4_sub(vi, vi_max); |
| |
| // Compute reduced argument elements := round(x / log(2)). |
| v128_t vn = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vx, vlog2e)); |
| |
| // Create a floating-point number s (scale) such that s == 2**elements for inputs which don't cause underflow, i.e. |
| // -87.33642 <= x <= 0.0, and -126 <= elements <= 0 accordingly. |
| const v128_t vs = wasm_i32x4_shl(vn, 23); |
| |
| // Subtract the large number back to get final elements := round(x / log(2)). |
| vn = wasm_f32x4_sub(vn, vmagic_bias); |
| |
| // Compute reduced argument t := x - elements * log(2). |
| // Use Cody-Waite range reduction method (note two constants to represent log(2)) to improve accuracy. |
| v128_t vt = wasm_f32x4_add(vx, wasm_f32x4_mul(vn, vminus_ln2_hi)); |
| vt = wasm_f32x4_add(vt, wasm_f32x4_mul(vn, vminus_ln2_lo)); |
| |
| // Compute degree-5 polynomial approximation for exp(t) on [-log(2)/2, log(2)/2]. |
| v128_t vp = wasm_f32x4_add(vc4, wasm_f32x4_mul(vc5, vt)); |
| vp = wasm_f32x4_add(vc3, wasm_f32x4_mul(vp, vt)); |
| vp = wasm_f32x4_add(vc2, wasm_f32x4_mul(vp, vt)); |
| vp = wasm_f32x4_add(vc1, wasm_f32x4_mul(vp, vt)); |
| |
| // Reconstruct the final f value: |
| // f = s * (1 + t * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5))))) |
| // = s + (t * s) * (c1 + t * (c2 + t * (c3 + t * (c4 + t * c5)))) |
| // = s + (t * s) * p |
| vt = wasm_f32x4_mul(vt, vs); |
| v128_t vf = wasm_f32x4_add(vs, wasm_f32x4_mul(vt, vp)); |
| |
| // For inputs below zero cutoff, replace output with +0.0f. |
| // Note that for NaN inputs, comparison result is false, and outputs are left unchanged. |
| vf = wasm_v128_andnot(vf, wasm_f32x4_lt(vx, vdenorm_cutoff)); |
| |
| if (elements & (2 * sizeof(float))) { |
| // Store and accumulate 2 outputs at a time. |
| const float vf0 = wasm_f32x4_extract_lane(vf, 0); |
| output[0] = vf0; |
| vsum += vf0; |
| |
| const float vf1 = wasm_f32x4_extract_lane(vf, 1); |
| output[1] = vf1; |
| vsum += vf1; |
| |
| vf = wasm_v32x4_shuffle(vf, vf, 2, 3, 2, 3); |
| output += 2; |
| } |
| if (elements & (1 * sizeof(float))) { |
| // Store 1 output at a time. |
| const float vf0 = wasm_f32x4_extract_lane(vf, 0); |
| *output = vf0; |
| vsum += vf0; |
| } |
| } |
| // Reduce 4 elements in the SIMD register |
| *sum = vsum; |
| } |