| // 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_x4( |
| 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); |
| for (; elements >= 4 * sizeof(float); elements -= 4 * sizeof(float)) { |
| // Load 4 (1x4) inputs at a time. |
| const v128_t vi0123 = wasm_v128_load(input); |
| input += 4; |
| |
| // Subtract maximum input x := i - i_max. This implies x <= 0. |
| const v128_t vx0123 = wasm_f32x4_sub(vi0123, vi_max); |
| |
| // Compute reduced argument elements := round(x / log(2)). |
| v128_t vn0123 = wasm_f32x4_add(vmagic_bias, wasm_f32x4_mul(vx0123, 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); |
| |
| // Subtract the large number back to get final elements := round(x / log(2)). |
| vn0123 = wasm_f32x4_sub(vn0123, 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)); |
| |
| vt0123 = wasm_f32x4_add(vt0123, wasm_f32x4_mul(vn0123, 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)); |
| |
| vp0123 = wasm_f32x4_add(vc3, wasm_f32x4_mul(vp0123, vt0123)); |
| |
| vp0123 = wasm_f32x4_add(vc2, wasm_f32x4_mul(vp0123, vt0123)); |
| |
| vp0123 = wasm_f32x4_add(vc1, wasm_f32x4_mul(vp0123, vt0123)); |
| |
| // 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); |
| |
| v128_t vf0123 = wasm_f32x4_add(vs0123, wasm_f32x4_mul(vt0123, vp0123)); |
| |
| // 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)); |
| |
| // Store 4 (1x4) outputs at a time. |
| wasm_v128_store(output, vf0123); |
| output += 4; |
| |
| // Accumulate computed exponents. |
| vacc0 = wasm_f32x4_add(vacc0, vf0123); |
| } |
| |
| 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; |
| } |