mirror of
https://github.com/jart/cosmopolitan.git
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8fdb31681a
llama.com can now load weights that use the new file format which was introduced a few weeks ago. Note that, unlike llama.cpp, we will keep support for old file formats in our tool so you don't need to convert your weights when the upstream project makes breaking changes. Please note that using ggjt v3 does make avx2 inference go 5% faster for me.
396 lines
16 KiB
C
396 lines
16 KiB
C
/*-*- mode:c;indent-tabs-mode:nil;c-basic-offset:4;tab-width:8;coding:utf-8 -*-│
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│vi: set net ft=c ts=4 sts=4 sw=4 fenc=utf-8 :vi│
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╚──────────────────────────────────────────────────────────────────────────────╝
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│ │
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│ GGML │
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│ Copyright (c) 2023 Georgi Gerganov │
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│ │
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│ Permission is hereby granted, free of charge, to any person obtaining │
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│ a copy of this software and associated documentation files (the │
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│ "Software"), to deal in the Software without restriction, including │
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│ without limitation the rights to use, copy, modify, merge, publish, │
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│ distribute, sublicense, and/or sell copies of the Software, and to │
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│ permit persons to whom the Software is furnished to do so, subject to │
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│ the following conditions: │
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│ │
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│ The above copyright notice and this permission notice shall be │
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│ included in all copies or substantial portions of the Software. │
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│ │
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│ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, │
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│ EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF │
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│ MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. │
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│ IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY │
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│ CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, │
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│ TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE │
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│ SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. │
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│ │
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╚─────────────────────────────────────────────────────────────────────────────*/
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#include "third_party/ggml/ggjt.v2.q4_0.h"
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#include "libc/assert.h"
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#include "libc/macros.internal.h"
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#include "libc/math.h"
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#include "third_party/ggml/ggjt.v2.internal.h"
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#include "third_party/ggml/ggjt.v2.q8_0.h"
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// clang-format off
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static_assert(sizeof(block_v2_q4_0) == sizeof(float) + V2_QK4_0 / 2,
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"wrong q4_0 block size/padding");
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void dequantize_row_v2_q4_0(const void * restrict x_, float * restrict y, int k) {
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const block_v2_q4_0 * restrict x = x_;
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static const int qk = V2_QK4_0;
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assert(k % qk == 0);
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const int nb = k / qk;
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for (int i = 0; i < nb; i++) {
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const float d = x[i].d;
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for (int j = 0; j < qk/2; ++j) {
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const int x0 = (x[i].qs[j] & 0x0F) - 8;
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const int x1 = (x[i].qs[j] >> 4) - 8;
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y[i*qk + j + 0 ] = x0*d;
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y[i*qk + j + qk/2] = x1*d;
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}
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}
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}
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size_t ggml_quantize_v2_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
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assert(k % V2_QK4_0 == 0);
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const int nb = k / V2_QK4_0;
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for (int b = 0; b < n; b += k) {
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block_v2_q4_0 * restrict y = (block_v2_q4_0 *) dst + b/V2_QK4_0;
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quantize_row_v2_q4_0_reference(src + b, y, k);
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for (int i = 0; i < nb; i++) {
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for (int j = 0; j < V2_QK4_0; j += 2) {
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const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
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const uint8_t vi1 = y[i].qs[j/2] >> 4;
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hist[vi0]++;
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hist[vi1]++;
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}
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}
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}
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return (n/V2_QK4_0*sizeof(block_v2_q4_0));
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}
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void quantize_row_v2_q4_0(const float * restrict x, void * restrict y, int k) {
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quantize_row_v2_q4_0_reference(x, y, k);
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}
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// reference implementation for deterministic creation of model files
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void quantize_row_v2_q4_0_reference(const float * restrict x, block_v2_q4_0 * restrict y, int k) {
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static const int qk = V2_QK4_0;
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assert(k % qk == 0);
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const int nb = k / qk;
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for (int i = 0; i < nb; i++) {
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float amax = 0.0f; // absolute max
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float max = 0.0f;
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for (int j = 0; j < qk; j++) {
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const float v = x[i*qk + j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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max = v;
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}
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}
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const float d = max / -8;
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const float id = d ? 1.0f/d : 0.0f;
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y[i].d = d;
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for (int j = 0; j < qk/2; ++j) {
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const float x0 = x[i*qk + 0 + j]*id;
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const float x1 = x[i*qk + qk/2 + j]*id;
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const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
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const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));
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y[i].qs[j] = xi0;
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y[i].qs[j] |= xi1 << 4;
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}
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}
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}
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void ggml_vec_dot_v2_q4_0_q8_0(const int n,
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float * restrict s,
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const void * restrict vx,
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const void * restrict vy) {
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const int qk = V2_QK8_0;
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const int nb = n / qk;
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assert(n % qk == 0);
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assert(nb % 2 == 0);
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const block_v2_q4_0 * restrict x = vx;
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const block_v2_q8_0 * restrict y = vy;
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#if defined(__ARM_NEON)
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float32x4_t sumv0 = vdupq_n_f32(0.0f);
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float32x4_t sumv1 = vdupq_n_f32(0.0f);
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for (int i = 0; i < nb; i += 2) {
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const block_v2_q4_0 * restrict x0 = &x[i + 0];
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const block_v2_q4_0 * restrict x1 = &x[i + 1];
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const block_v2_q8_0 * restrict y0 = &y[i + 0];
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const block_v2_q8_0 * restrict y1 = &y[i + 1];
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const uint8x16_t m4b = vdupq_n_u8(0x0F);
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const int8x16_t s8b = vdupq_n_s8(0x8);
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const uint8x16_t v0_0 = vld1q_u8(x0->qs);
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const uint8x16_t v0_1 = vld1q_u8(x1->qs);
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// 4-bit -> 8-bit
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const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b));
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const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
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const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b));
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const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
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// sub 8
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const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
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const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
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const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
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const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
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// load y
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const int8x16_t v1_0l = vld1q_s8(y0->qs);
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const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
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const int8x16_t v1_1l = vld1q_s8(y1->qs);
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const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
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#if defined(__ARM_FEATURE_DOTPROD)
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// dot product into int32x4_t
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const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
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const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
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sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d);
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sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d);
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#else
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const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
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const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
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const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h));
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const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
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const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l));
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const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
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const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h));
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const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));
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const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
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const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
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const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
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const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
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sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d);
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sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d);
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#endif
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}
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*s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
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#elif defined(__AVX2__)
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// Initialize accumulator with zeros
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__m256 acc = _mm256_setzero_ps();
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// Main loop
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for (int i = 0; i < nb; ++i) {
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/* Compute combined scale for the block */
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const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
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__m256i bx = bytes_from_nibbles_32(x[i].qs);
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// Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
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const __m256i off = _mm256_set1_epi8( 8 );
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bx = _mm256_sub_epi8( bx, off );
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__m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
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const __m256 q = mul_sum_i8_pairs_float(bx, by);
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/* Multiply q with scale and accumulate */
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acc = _mm256_fmadd_ps( d, q, acc );
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}
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*s = hsum_float_8(acc);
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#elif defined(__AVX__)
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// Initialize accumulator with zeros
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__m256 acc = _mm256_setzero_ps();
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// Main loop
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for (int i = 0; i < nb; ++i) {
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// Compute combined scale for the block
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const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
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const __m128i lowMask = _mm_set1_epi8(0xF);
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const __m128i off = _mm_set1_epi8(8);
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const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
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__m128i bx = _mm_and_si128(lowMask, tmp);
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__m128i by = _mm_loadu_si128((const __m128i *)y[i].qs);
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bx = _mm_sub_epi8(bx, off);
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const __m128i i32_0 = mul_sum_i8_pairs(bx, by);
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bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
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by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
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bx = _mm_sub_epi8(bx, off);
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const __m128i i32_1 = mul_sum_i8_pairs(bx, by);
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// Convert int32_t to float
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__m256 p = _mm256_cvtepi32_ps(_mm256_set_m128i(i32_0, i32_1));
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// Apply the scale, and accumulate
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acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc);
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}
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*s = hsum_float_8(acc);
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#elif defined(__SSSE3__)
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// set constants
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const __m128i lowMask = _mm_set1_epi8(0xF);
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const __m128i off = _mm_set1_epi8(8);
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// Initialize accumulator with zeros
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__m128 acc_0 = _mm_setzero_ps();
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__m128 acc_1 = _mm_setzero_ps();
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__m128 acc_2 = _mm_setzero_ps();
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__m128 acc_3 = _mm_setzero_ps();
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// First round without accumulation
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{
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_mm_prefetch(&x[0] + sizeof(block_v2_q4_0), _MM_HINT_T0);
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_mm_prefetch(&y[0] + sizeof(block_v2_q8_0), _MM_HINT_T0);
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// Compute combined scale for the block 0 and 1
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const __m128 d_0_1 = _mm_mul_ps( _mm_set1_ps( x[0].d ), _mm_set1_ps( y[0].d ) );
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const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
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__m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
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__m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
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bx_0 = _mm_sub_epi8(bx_0, off);
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const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
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__m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
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__m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
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bx_1 = _mm_sub_epi8(bx_1, off);
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const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
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_mm_prefetch(&x[1] + sizeof(block_v2_q4_0), _MM_HINT_T0);
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_mm_prefetch(&y[1] + sizeof(block_v2_q8_0), _MM_HINT_T0);
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// Compute combined scale for the block 2 and 3
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const __m128 d_2_3 = _mm_mul_ps( _mm_set1_ps( x[1].d ), _mm_set1_ps( y[1].d ) );
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const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
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__m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
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__m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
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bx_2 = _mm_sub_epi8(bx_2, off);
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const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
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__m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
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__m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
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bx_3 = _mm_sub_epi8(bx_3, off);
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const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
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// Convert int32_t to float
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__m128 p0 = _mm_cvtepi32_ps(i32_0);
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__m128 p1 = _mm_cvtepi32_ps(i32_1);
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__m128 p2 = _mm_cvtepi32_ps(i32_2);
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__m128 p3 = _mm_cvtepi32_ps(i32_3);
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// Apply the scale
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acc_0 = _mm_mul_ps( d_0_1, p0 );
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acc_1 = _mm_mul_ps( d_0_1, p1 );
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acc_2 = _mm_mul_ps( d_2_3, p2 );
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acc_3 = _mm_mul_ps( d_2_3, p3 );
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}
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// Main loop
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for (int i = 2; i < nb; i+=2) {
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_mm_prefetch(&x[i] + sizeof(block_v2_q4_0), _MM_HINT_T0);
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_mm_prefetch(&y[i] + sizeof(block_v2_q8_0), _MM_HINT_T0);
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// Compute combined scale for the block 0 and 1
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const __m128 d_0_1 = _mm_mul_ps( _mm_set1_ps( x[i].d ), _mm_set1_ps( y[i].d ) );
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const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
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__m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
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__m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
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bx_0 = _mm_sub_epi8(bx_0, off);
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const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
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__m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
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__m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
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bx_1 = _mm_sub_epi8(bx_1, off);
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const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
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_mm_prefetch(&x[i] + 2 * sizeof(block_v2_q4_0), _MM_HINT_T0);
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_mm_prefetch(&y[i] + 2 * sizeof(block_v2_q8_0), _MM_HINT_T0);
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// Compute combined scale for the block 2 and 3
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const __m128 d_2_3 = _mm_mul_ps( _mm_set1_ps( x[i + 1].d ), _mm_set1_ps( y[i + 1].d ) );
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const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
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__m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
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__m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
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bx_2 = _mm_sub_epi8(bx_2, off);
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const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
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__m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
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__m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
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bx_3 = _mm_sub_epi8(bx_3, off);
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const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
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// Convert int32_t to float
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__m128 p0 = _mm_cvtepi32_ps(i32_0);
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__m128 p1 = _mm_cvtepi32_ps(i32_1);
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__m128 p2 = _mm_cvtepi32_ps(i32_2);
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__m128 p3 = _mm_cvtepi32_ps(i32_3);
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// Apply the scale
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__m128 p0_d = _mm_mul_ps( d_0_1, p0 );
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__m128 p1_d = _mm_mul_ps( d_0_1, p1 );
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__m128 p2_d = _mm_mul_ps( d_2_3, p2 );
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__m128 p3_d = _mm_mul_ps( d_2_3, p3 );
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// Acummulate
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acc_0 = _mm_add_ps(p0_d, acc_0);
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acc_1 = _mm_add_ps(p1_d, acc_1);
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acc_2 = _mm_add_ps(p2_d, acc_2);
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acc_3 = _mm_add_ps(p3_d, acc_3);
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}
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*s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
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#else
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// scalar
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float sumf = 0.0;
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for (int i = 0; i < nb; i++) {
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int sumi = 0;
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for (int j = 0; j < qk/2; ++j) {
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const int v0 = (x[i].qs[j] & 0x0F) - 8;
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const int v1 = (x[i].qs[j] >> 4) - 8;
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|
|
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sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
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}
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|
|
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sumf += (x[i].d*y[i].d)*sumi;
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}
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|
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*s = sumf;
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#endif
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}
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