mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-27 01:29:51 +00:00
52ad5764dd
It seems it is enough to have 4 scales per row for Q8. I get PPL = 8.5470 with this, which is slightly higher than the 8.5430 we get with 1 scale per 128 activations, but still OK, I think. With this, we get the following performance: Systema | quant | PP-512 | TG-128a | quant | PP-512 | TG-12s | M2 Max | iq2bn 229.02 ± 0.37 78.75 ± 0.61 | iq1bn | 146.67 ± 2.85 33.12 ± 0.03 Ryzen7950| iq2bn 379.36 ± 1.03 49.08 ± 0.18 | iq1bn | 247.12 ± 1.53 32.80 ± 0.02 Ryzen5975| iq2bn 465.28 ± 0.57 39.17 ± 0.02 | iq1bn | 325.86 ± 0.46 26.60 ± 0.10
403 lines
12 KiB
C++
403 lines
12 KiB
C++
//
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// Copyright 2024 Iwan Kawrakow
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "iqk-quantize.h"
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#include "ggml-quants.h"
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#include "ggml-impl.h"
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#define GGML_COMMON_IMPL_C
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#include "ggml-common.h"
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#include <vector>
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#include <utility>
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#include <cstdint>
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#include <cmath>
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#include <array>
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#include <algorithm>
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#include <cstring>
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#include <mutex>
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namespace {
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inline int nearest_int(float fval) {
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assert(fval <= 4194303.f);
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float val = fval + 12582912.f;
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int i; memcpy(&i, &val, sizeof(int));
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return (i & 0x007fffff) - 0x00400000;
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}
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struct IQ1BNData {
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IQ1BNData();
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std::vector<std::pair<int16_t, bool>> map;
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std::vector<uint16_t> rmap;
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};
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const IQ1BNData& get_iq1bn_data() {
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static std::mutex mutex;
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std::lock_guard<std::mutex> lock(mutex);
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static IQ1BNData iq1bn;
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return iq1bn;
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}
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IQ1BNData::IQ1BNData() {
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map.resize(1 << 16, {int16_t(-1), false});
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uint64_t aux64;
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uint8_t * aux8 = (uint8_t *)&aux64;
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std::vector<uint64_t> values;
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values.reserve(6561);
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rmap.reserve(6561);
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for (int i = 0; i < (1 << 16); ++i) {
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bool is_good = true;
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for (int j = 0; j < 8; ++j) {
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aux8[j] = (i >> 2*j) & 3;
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if (aux8[j] == 3u) { is_good = false; break; }
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}
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if (!is_good) continue;
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auto orig = aux64;
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for (int j = 0; j < 8; ++j) aux8[j] = 2 - aux8[j];
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int k = 0;
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for (; k < int(values.size()); ++k) {
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if (values[k] == aux64) break;
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}
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if (k < int(values.size())) {
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map[i] = {k, true};
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} else {
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map[i].first = values.size();
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values.push_back(orig);
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rmap.push_back(i);
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}
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}
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printf("==================== %s: initialized %d grid points\n", __func__, int(rmap.size()));
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}
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struct IQ1BNQuantizer {
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constexpr static int block_size = QK_IQ1BN;
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int8_t L[QK_IQ1BN];
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void quantize_one_row_1bn(const float * src, block_iq1_bn * y, int n_per_row, const float * imatrix);
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void quantize_one_row_2bn(const float * src, block_iq2_bn * y, int n_per_row, const float * imatrix);
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static inline float row_max(int n_per_row, const float * src) {
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float max_in_row = 0;
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for (int j = 0; j < n_per_row; ++j) {
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float ax = fabsf(src[j]);
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max_in_row = std::max(max_in_row, ax);
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}
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return max_in_row;
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}
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static uint16_t quantize_one_block_1bn(const IQ1BNData& iq1l, const float * xb, int8_t * L, uint8_t * ql, uint8_t * qh);
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};
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uint16_t IQ1BNQuantizer::quantize_one_block_1bn(const IQ1BNData& iq1bn, const float * xb, int8_t * L, uint8_t * ql, uint8_t * qh) {
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for (int j = 0; j < QK_IQ1BN; ++j) {
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L[j] = fabsf(xb[j]) < 1e-6f ? 1 : xb[j] < 0 ? 0 : 2;
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}
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uint16_t extra = 0;
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for (int k = 0; k < QK_IQ1BN/8; ++k) {
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auto Lk = L + 8*k;
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uint16_t u = 0;
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for (int j = 0; j < 8; ++j) u |= (Lk[j] << 2*j);
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auto& val = iq1bn.map[u];
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GGML_ASSERT(val.first >= 0);
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ql[k] = val.first & 255;
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qh[k/2] |= (val.first >> 8) << 4*(k%2);
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if (val.second) extra |= (1 << k);
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}
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return extra;
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}
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void IQ1BNQuantizer::quantize_one_row_1bn(const float * src, block_iq1_bn * y, int n_per_row, const float * imatrix) {
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(void)imatrix;
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const int nblock = n_per_row/QK_IQ1BN;
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const auto& iq1bn = get_iq1bn_data();
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for (int ib = 0; ib < nblock; ++ib) {
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std::memset(&y[ib], 0, sizeof(block_iq1_bn));
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auto xb = src + QK_IQ1BN*ib;
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y[ib].extra = quantize_one_block_1bn(iq1bn, xb, L, y[ib].ql, y[ib].qh);
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}
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}
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void IQ1BNQuantizer::quantize_one_row_2bn(const float * src, block_iq2_bn * y, int n_per_row, const float * imatrix) {
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(void)imatrix;
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const int nblock = n_per_row/QK_IQ1BN;
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//auto max_in_row = row_max(n_per_row, src);
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//printf("%s: max = %g\n", __func__, max_in_row);
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constexpr int Nj = QK_IQ1BN/4;
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for (int ib = 0; ib < nblock; ++ib) {
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auto xb = src + QK_IQ1BN*ib;
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for (int j = 0; j < QK_IQ1BN; ++j) {
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L[j] = fabsf(xb[j]) < 1e-6f ? 1 : xb[j] < 0 ? 0 : 2;
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}
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for (int j = 0; j < Nj; ++j) {
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y[ib].qs[j] = L[j] | (L[j + Nj] << 2) | (L[j + 2*Nj] << 4) | (L[j + 3*Nj] << 6);
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}
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}
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}
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}
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void iq1bn_init_impl(void) {
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get_iq1bn_data();
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}
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size_t quantize_iq1_bn(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
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IQ1BNQuantizer iq1bn;
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int nblock = n_per_row/QK_IQ1BN;
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block_iq1_bn * y = (block_iq1_bn *)dst;
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for (int row = 0; row < nrows; ++row) {
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iq1bn.quantize_one_row_1bn(src + row*n_per_row, y, n_per_row, imatrix);
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y += nblock;
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}
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return sizeof(block_iq1_bn)*nblock*nrows;
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}
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void quantize_row_iq1_bn_reference(const float * x, block_iq1_bn * y, int64_t k) {
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quantize_iq1_bn(x, y, 1, k, nullptr);
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}
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void quantize_row_iq1_bn(const float * x, void * y, int64_t k) {
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quantize_iq1_bn(x, y, 1, k, nullptr);
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}
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void dequantize_row_iq1_bn(const block_iq1_bn * x, float * y, int64_t k) {
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assert(k%QK_IQ1BN == 0);
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int nblock = k / QK_IQ1BN;
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for (int i = 0; i < nblock; ++i) {
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float d = iq1bn_fp8_to_float(x[i].extra & 0xff);
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uint8_t extra = x[i].extra >> 8;
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auto qh = x[i].qh;
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auto ql = x[i].ql;
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for (int k = 0; k < QK_IQ1BN/8; ++k) {
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uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
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uint16_t val = iq1bn_grid_u16[idx];
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float dls = extra & (1 << k) ? -d : d;
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for (int j = 0; j < 8; ++j) y[j] = dls * (((val >> 2*j) & 3) - 1);
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y += 8;
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}
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}
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}
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size_t quantize_iq2_bn(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
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IQ1BNQuantizer iq1bn;
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int nblock = n_per_row/QK_IQ1BN;
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block_iq2_bn * y = (block_iq2_bn *)dst;
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for (int row = 0; row < nrows; ++row) {
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iq1bn.quantize_one_row_2bn(src + row*n_per_row, y, n_per_row, imatrix);
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y += nblock;
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}
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return sizeof(block_iq2_bn)*nblock*nrows;
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}
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void quantize_row_iq2_bn_reference(const float * x, block_iq2_bn * y, int64_t k) {
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quantize_iq2_bn(x, y, 1, k, nullptr);
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}
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void quantize_row_iq2_bn(const float * x, void * y, int64_t k) {
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quantize_iq2_bn(x, y, 1, k, nullptr);
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}
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void dequantize_row_iq2_bn(const block_iq2_bn * x, float * y, int64_t k) {
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assert(k%QK_IQ1BN == 0);
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int nblock = k / QK_IQ1BN;
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auto d1 = 1.f, d2 = 0.25f, d3 = d2*0.25f, d4 = d3*0.25f;
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auto m = -1.f;
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constexpr int Nj = QK_IQ1BN/4;
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for (int i = 0; i < nblock; ++i) {
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for (int j = 0; j < Nj; ++j) {
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y[j+ 0] = d1*(x[i].qs[j] & 0x03) + m;
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y[j+1*Nj] = d2*(x[i].qs[j] & 0x0c) + m;
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y[j+2*Nj] = d3*(x[i].qs[j] & 0x30) + m;
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y[j+3*Nj] = d4*(x[i].qs[j] & 0xc0) + m;
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}
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y += QK_IQ1BN;
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}
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}
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void ggml_vec_dot_iq1_bn_q8_0 (int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
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GGML_UNUSED(bs);
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GGML_UNUSED(bx);
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GGML_UNUSED(by);
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GGML_UNUSED(nrc);
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static_assert(QK_IQ1BN == 64, "This dot product implementation for iq1_bn requires a block size of 64");
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const block_iq1_bn * x = (const block_iq1_bn *)vx;
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const block_q8_0 * y = (const block_q8_0 *)vy;
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int nblock = n / QK_IQ1BN;
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float sumf = 0;
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for (int i = 0; i < nblock; ++i) {
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auto qh = x[i].qh;
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auto ql = x[i].ql;
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auto q8 = y[2*i+0].qs;
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int16_t sumi1 = 0;
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for (int k = 0; k < 4; ++k) {
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uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
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uint16_t val = iq1bn_grid_u16[idx];
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int16_t sl = 0;
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for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
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sumi1 += x[i].extra & (1 << k) ? -sl : sl;
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q8 += 8;
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}
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q8 = y[2*i+1].qs;
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int16_t sumi2 = 0;
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for (int k = 4; k < 8; ++k) {
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uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
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uint16_t val = iq1bn_grid_u16[idx];
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int16_t sl = 0;
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for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
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sumi2 += x[i].extra & (1 << k) ? -sl : sl;
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q8 += 8;
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}
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sumf += GGML_FP16_TO_FP32(y[2*i+0].d) * sumi1 + GGML_FP16_TO_FP32(y[2*i+1].d) * sumi2;
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}
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*s = sumf;
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}
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void ggml_vec_dot_iq1_bn_q8_K64(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
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GGML_UNUSED(bs);
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GGML_UNUSED(bx);
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GGML_UNUSED(by);
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GGML_UNUSED(nrc);
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static_assert(QK_IQ1BN == 64, "This dot product implementation for iq1_bn requires a block size of 64");
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const block_iq1_bn * x = (const block_iq1_bn *)vx;
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const block_q8_K64 * y = (const block_q8_K64 *)vy;
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int nblock = n / QK_IQ1BN;
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float sumf = 0;
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for (int i = 0; i < nblock; ++i) {
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auto qh = x[i].qh;
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auto ql = x[i].ql;
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auto q8 = y[i].qs;
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int sumi = 0;
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for (int k = 0; k < 4; ++k) {
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uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
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uint16_t val = iq1bn_grid_u16[idx];
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int16_t sl = 0;
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for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
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sumi += x[i].extra & (1 << k) ? -sl : sl;
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q8 += 8;
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}
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for (int k = 4; k < 8; ++k) {
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uint16_t idx = ql[k] | ((qh[k/2] << (8 - 4*(k%2))) & 0x0f00);
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uint16_t val = iq1bn_grid_u16[idx];
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int16_t sl = 0;
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for (int j = 0; j < 8; ++j) sl += q8[j] * (((val >> 2*j) & 3) - 1);
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sumi += x[i].extra & (1 << k) ? -sl : sl;
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q8 += 8;
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}
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sumf += y[i].d * sumi;
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}
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*s = sumf;
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}
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void ggml_vec_dot_iq2_bn_q8_K64(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
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GGML_UNUSED(bs);
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GGML_UNUSED(bx);
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GGML_UNUSED(by);
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GGML_UNUSED(nrc);
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static_assert(QK_IQ1BN == 64, "This dot product implementation for iq2_bn requires a block size of 64");
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constexpr int Nj = QK_IQ1BN/4;
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const block_iq2_bn * x = (const block_iq2_bn *)vx;
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const block_q8_K64 * y = (const block_q8_K64 *)vy;
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int nblock = n / QK_IQ1BN;
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float sumf = 0;
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for (int i = 0; i < nblock; ++i) {
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auto q8 = y[i].qs;
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int s0 = 0, s1 = 0, s2 = 0, s3 = 0, s4 = 0;
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for (int j = 0; j < Nj; ++j) {
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s1 += q8[j+ 0] * (x[i].qs[j] & 0x03);
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s2 += q8[j+1*Nj] * (x[i].qs[j] & 0x0c);
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s3 += q8[j+2*Nj] * (x[i].qs[j] & 0x30);
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s4 += q8[j+3*Nj] * (x[i].qs[j] & 0xc0);
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s0 += q8[j] + q8[j+1*Nj] + q8[j+2*Nj] + q8[j+3*Nj];
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}
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sumf += y[i].d * (s1 + 0.25f*s2 + 0.0625*s3 + 0.015625*s4 - s0);
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}
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*s = sumf;
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}
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void quantize_row_q8_K64_reference(const float * x, block_q8_K64 * y, int64_t k) {
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//assert(k % 64 == 0);
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//const int64_t nb = k / 64;
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// Check if a row-wise scale works. It almost does, PPL is only ~0.02 higher
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//float amax = 0;
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//for (int j = 0; j < k; ++j) {
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// float ax = fabsf(x[j]);
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// amax = MAX(ax, amax);
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//}
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//float d = amax/127;
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//float id = d ? 1/d : 0.f;
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//for (int i = 0; i < nb; i++) {
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// for (int j = 0; j < 64; ++j) y[i].qs[j] = nearest_int(id*x[j]);
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// y[i].d = d;
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// x += 64;
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//}
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float aux[4] = {0.f, 0.f, 0.f, 0.f};
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for (int j = 0; j < k; j += 16) {
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for (int i = 0; i < 4; ++i) {
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for (int l = 0; l < 4; ++l) {
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float ax = fabsf(x[j+4*i+l]);
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aux[i] = std::max(aux[i], ax);
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}
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}
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}
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float * dptr = (float *)y;
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for (int i = 0; i < 4; ++i) {
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dptr[i] = aux[i]/127;
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aux[i] = dptr[i] > 0 ? 1/dptr[i] : 0.f;
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}
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auto qs = (int8_t *)(dptr + 4);
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for (int j = 0; j < k; j += 16) {
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for (int i = 0; i < 4; ++i) {
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for (int l = 0; l < 4; ++l) qs[j+4*i+l] = nearest_int(aux[i]*x[j+4*i+l]);
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}
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}
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}
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void quantize_row_q8_K64(const float * x, void * y, int64_t k) {
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quantize_row_q8_K64_reference(x, (block_q8_K64 *)y, k);
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}
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