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.
253 lines
9.7 KiB
C
253 lines
9.7 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_1.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_1.h"
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// clang-format off
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static_assert(sizeof(block_v2_q4_1) == 2 * sizeof(float) + V2_QK4_1 / 2,
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"wrong q4_1 block size/padding");
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void dequantize_row_v2_q4_1(const void * restrict x_, float * restrict y, int k) {
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const block_v2_q4_1 * restrict x = x_;
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static const int qk = V2_QK4_1;
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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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const float m = x[i].m;
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for (int j = 0; j < qk/2; ++j) {
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const int x0 = (x[i].qs[j] & 0x0F);
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const int x1 = (x[i].qs[j] >> 4);
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y[i*qk + j + 0 ] = x0*d + m;
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y[i*qk + j + qk/2] = x1*d + m;
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}
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}
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}
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size_t ggml_quantize_v2_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) {
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assert(k % V2_QK4_1 == 0);
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const int nb = k / V2_QK4_1;
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for (int b = 0; b < n; b += k) {
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block_v2_q4_1 * restrict y = (block_v2_q4_1 *) dst + b/V2_QK4_1;
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quantize_row_v2_q4_1_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_1; 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_1*sizeof(block_v2_q4_1));
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}
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void quantize_row_v2_q4_1(const float * restrict x, void * restrict y, int k) {
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quantize_row_v2_q4_1_reference(x, y, k);
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}
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void ggml_vec_dot_v2_q4_1_q8_1(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_1;
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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_1 * restrict x = vx;
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const block_v2_q8_1 * restrict y = vy;
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// TODO: add WASM SIMD
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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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float summs = 0;
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for (int i = 0; i < nb; i += 2) {
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const block_v2_q4_1 * restrict x0 = &x[i + 0];
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const block_v2_q4_1 * restrict x1 = &x[i + 1];
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const block_v2_q8_1 * restrict y0 = &y[i + 0];
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const block_v2_q8_1 * restrict y1 = &y[i + 1];
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summs += x0->m * y0->s + x1->m * y1->s;
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const uint8x16_t m4b = vdupq_n_u8(0x0F);
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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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// 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_0l, v1_0l), v0_0h, v1_0h);
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const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, 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_0l), vget_low_s8 (v1_0l));
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const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
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const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h));
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const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h));
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const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l));
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const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l));
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const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h));
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const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), 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) + summs;
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#elif defined(__AVX2__) || defined(__AVX__)
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// Initialize accumulator with zeros
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__m256 acc = _mm256_setzero_ps();
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float summs = 0;
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// Main loop
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for (int i = 0; i < nb; ++i) {
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const float * d0 = &x[i].d;
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const float * d1 = &y[i].d;
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summs += x[i].m * y[i].s;
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const __m256 d0v = _mm256_broadcast_ss( d0 );
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const __m256 d1v = _mm256_broadcast_ss( d1 );
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// Compute combined scales
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const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
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// Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
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const __m256i bx = bytes_from_nibbles_32(x[i].qs);
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const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
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const __m256 xy = mul_sum_us8_pairs_float(bx, by);
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// Accumulate d0*d1*x*y
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#if defined(__AVX2__)
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acc = _mm256_fmadd_ps( d0d1, xy, acc );
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#else
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acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc );
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#endif
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}
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*s = hsum_float_8(acc) + summs;
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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);
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const int v1 = (x[i].qs[j] >> 4);
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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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sumf += (x[i].d*y[i].d)*sumi + x[i].m*y[i].s;
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}
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*s = sumf;
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#endif
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}
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void quantize_row_v2_q4_1_reference(const float * restrict x, void * restrict y_, int k) {
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block_v2_q4_1 * restrict y = y_;
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const int qk = V2_QK4_1;
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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 min = FLT_MAX;
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float max = -FLT_MAX;
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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 (v < min) min = v;
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if (v > max) max = v;
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}
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const float d = (max - min) / ((1 << 4) - 1);
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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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y[i].m = min;
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for (int j = 0; j < qk/2; ++j) {
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const float x0 = (x[i*qk + 0 + j] - min)*id;
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const float x1 = (x[i*qk + qk/2 + j] - min)*id;
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const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
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const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));
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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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