ikawrakow/ik_llama.cpp
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ik_llama.cpp/examples/quantize-stats/quantize-stats.cpp
Kawrakow 2440eca319 Fix MSVC compilation (#448) 
* Fix MSVC compilation

* MSVC cannot capture constexpr in lambdas

* Arghhh

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-23 16:46:27 +03:00

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//
// Copyright (C) 2023-2025 The llama.cpp authors
// Copyright (C) 2024-2025 Iwan Kawrakow
// MIT license
// SPDX-License-Identifier: MIT
//
#define LLAMA_API_INTERNAL
#include "common.h"
#include "ggml.h"
#include "llama.h"
#define GGML_COMMON_DECL_C
#define GGML_COMMON_IMPL_C
#include "../ggml/src/ggml-common.h"
#include <algorithm>
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <map>
#include <numeric>
#include <regex>
#include <string>
#include <unordered_map>
#include <vector>
#include <thread>
#include <mutex>
#include <array>
#include <random>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#include <intrin.h>
#include <ammintrin.h>
#include <nmmintrin.h>
#include <immintrin.h>
#include <stdlib.h>
inline int popcount(uint8_t x) { return __popcnt(x); }
inline int popcount(uint16_t x) { return __popcnt(x); }
inline int popcount(uint32_t x) { return __popcnt(x); }
inline int popcount(uint64_t x) { return _mm_popcnt_u64(x); }
#else
constexpr int popcount(uint8_t x) { return __builtin_popcount(x); }
constexpr int popcount(uint16_t x) { return __builtin_popcount(x); }
constexpr int popcount(uint32_t x) { return __builtin_popcount(x); }
constexpr int popcount(uint64_t x) { return __builtin_popcountll(x); }
#endif
#ifdef __AVX2__
#include <immintrin.h>
#endif
struct quantize_stats_params {
std::string model = DEFAULT_MODEL_PATH;
bool verbose = false;
bool per_layer_stats = false;
bool print_histogram = false;
bool reference = false;
std::vector<std::string> include_layers;
std::vector<std::string> exclude_layers;
std::vector<enum ggml_type> include_types;
};
constexpr size_t HISTOGRAM_BUCKETS = 150;
constexpr double HISTOGRAM_RANGE = 0.03;
struct error_stats {
size_t num_samples;
double total_error;
double max_error;
double sum_x2;
uint64_t error_histogram[HISTOGRAM_BUCKETS];
};
static void quantize_stats_print_usage(int /*argc*/, char ** argv) {
quantize_stats_params params;
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, " -r, --reference\n");
fprintf(stderr, " use reference implementation (default: false)\n");
fprintf(stderr, " -v, --verbose\n");
fprintf(stderr, " verbose output (default: false)\n");
fprintf(stderr, " -p, --per-layer-stats\n");
fprintf(stderr, " print stats per layer (default: false)\n");
fprintf(stderr, " --histogram\n");
fprintf(stderr, " print error histogram (default: false)\n");
fprintf(stderr, " -l LAYER, --include-layer LAYER\n");
fprintf(stderr, " only test layers matching pattern\n");
fprintf(stderr, " -L LAYER, --exclude-layer LAYER\n");
fprintf(stderr, " exclude layers matching pattern\n");
fprintf(stderr, " -t TYPE, --type TYPE\n");
fprintf(stderr, " only test given type (q4_0, q4_1)\n");
fprintf(stderr, "\n");
}
// Check if a layer is included/excluded by command line
static bool layer_included(const quantize_stats_params & params, const std::string & layer) {
for (const auto& excluded : params.exclude_layers) {
if (std::regex_search(layer, std::regex(excluded))) {
return false;
}
}
for (const auto& included : params.include_layers) {
if (std::regex_search(layer, std::regex(included))) {
return true;
}
}
return params.include_layers.empty();
}
// Update error statistics given vectors with the before/after result of quantization
static void update_error_stats(int64_t nelements, const float * input, const float * output, error_stats & stats) {
for (int64_t i = 0; i < nelements; i++) {
double diff = input[i] - output[i];
stats.total_error += diff * diff;
stats.max_error = fmax(fabs(diff), stats.max_error);
stats.sum_x2 += input[i]*input[i];
stats.error_histogram[std::max(std::min((size_t) floor(fabs(diff) / HISTOGRAM_RANGE * HISTOGRAM_BUCKETS), HISTOGRAM_BUCKETS-1), (size_t) 0)]++;
}
stats.num_samples += nelements;
}
static void combine_error_stats(error_stats & into, const error_stats & from) {
into.num_samples += from.num_samples;
into.total_error += from.total_error;
into.sum_x2 += from.sum_x2;
if (from.max_error > into.max_error) into.max_error = from.max_error;
for (size_t i=0; i<HISTOGRAM_BUCKETS; ++i) into.error_histogram[i] += from.error_histogram[i];
}
static double find_quantile(const error_stats & stats, double quantile) {
double sum = std::accumulate(std::begin(stats.error_histogram), std::end(stats.error_histogram), 0.0);
double accum = 0;
for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {
accum += stats.error_histogram[i];
if (accum >= sum*quantile) {
return (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
}
}
return INFINITY;
}
static void print_error_stats(const std::string & name, const error_stats & stats, bool print_histogram) {
double rmse = sqrt(stats.total_error / (double) stats.num_samples);
double av_x = sqrt(stats.sum_x2 / (double) stats.num_samples);
double median = find_quantile(stats, .5);
double pct95 = find_quantile(stats, .95);
printf("%-40s: rmse %.8f, %.6f maxerr %.8f, %.6f 95pct<%.4f, median<%.4f\n", name.c_str(), rmse, rmse/av_x,
stats.max_error, stats.max_error/av_x, pct95, median);
if (print_histogram) {
printf("Error distribution:\n");
for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {
double lower = i * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
double upper = (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
if (i == HISTOGRAM_BUCKETS -1) upper = INFINITY;
printf("[%3.4f, %3.4f): %11" PRIu64 "\n", lower, upper, stats.error_histogram[i]);
}
}
}
// copied from ggml.h - verify that we can access this as a flat array
static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return
tensor->nb[0] == ggml_type_size(tensor->type) &&
tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
static void test_roundtrip_on_chunk(
const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits_t & qfns, bool use_reference,
float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats
) {
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.from_float(input_scratch, quantized_scratch, chunk_size);
}
qfns.to_float(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, stats);
}
// Run quantization function for a single layer and update error stats
static void test_roundtrip_on_layer(
std::string & name, bool print_layer_stats, const ggml_type_traits_t & qfns, bool use_reference,
const ggml_tensor * layer, std::vector<float> & input_scratch, std::vector<char> & quantized_scratch,
std::vector<float> & output_scratch, error_stats & total_error, int max_thread = 0
) {
assert(tensor_is_contiguous(layer));
error_stats layer_error {};
uint64_t nelements = ggml_nelements(layer);
float* input_scratch_ptr = nullptr;
if (layer->type == GGML_TYPE_F16) {
if (input_scratch.size() < nelements) input_scratch.resize(nelements);
input_scratch_ptr = input_scratch.data();
}
if (quantized_scratch.size() < 4*nelements) quantized_scratch.resize(4*nelements);
if (output_scratch.size() < nelements) output_scratch.resize(nelements);
if (max_thread < 1) max_thread = std::thread::hardware_concurrency();
int chunk_size = 32*512;
int num_chunks = (nelements + chunk_size - 1)/chunk_size;
if (num_chunks < 2 || max_thread < 2) {
test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(),
output_scratch.data(), print_layer_stats ? layer_error : total_error);
} else {
auto & stats = print_layer_stats ? layer_error : total_error;
std::mutex mutex;
uint64_t counter = 0;
auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr,
&quantized_scratch, &output_scratch, chunk_size] () {
error_stats local_stats {};
while (true) {
std::unique_lock<std::mutex> lock(mutex);
uint64_t offset = counter; counter += chunk_size;
if (offset >= nelements) {
combine_error_stats(stats, local_stats);
break;
}
lock.unlock();
uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset;
test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset,
quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats);
}
};
int nthread = std::min(num_chunks, max_thread);
std::vector<std::thread> workers(nthread-1);
for (auto& w : workers) w = std::thread(compute);
compute();
for (auto& w : workers) w.join();
}
if (print_layer_stats) {
print_error_stats(name, layer_error, false);
combine_error_stats(total_error, layer_error);
}
}
static inline int nearest_int(float fval) {
assert(fval <= 4194303.f);
float val = fval + 12582912.f;
int i; memcpy(&i, &val, sizeof(int));
return (i & 0x007fffff) - 0x00400000;
}
static const int8_t scale_values[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
static std::vector<float> make_values(int nval, int n_per_val, float scale = 16.f) {
std::vector<float> result(nval*n_per_val);
uint16_t m16 = ggml_fp32_to_fp16(0.922f);
uint32_t m32 = (uint32_t(m16) << 16) | m16;
const uint32_t a = 89226354, b = 64248484;
float * data = result.data();
for (int i = 0; i < nval; ++i) {
uint32_t x = i + 4096;
for (int k = 0; k < n_per_val; ++k) {
x = a*x + b;
uint32_t s = (x & 0b10001111111111111000111111111111) ^ m32;
float val = ggml_fp16_to_fp32(s & 65535) + ggml_fp16_to_fp32(s >> 16);
int ival = nearest_int(scale*val);
data[k] = ival;
}
data += n_per_val;
}
return result;
}
#ifdef __AVX2__
static inline float hsum_float_4(__m128 x) {
x = _mm_add_ps(x, _mm_movehl_ps(x, x));
x = _mm_add_ss(x, _mm_movehdup_ps(x));
return _mm_cvtss_f32(x);
}
static inline float hsum_float_8(__m256 x) {
return hsum_float_4(_mm_add_ps(_mm256_castps256_ps128(x), _mm256_extractf128_ps(x, 1)));
}
static __m256 hsum_float_8x8(__m256 * accm) {
for (int i = 0; i < 4; ++i) {
accm[i] = _mm256_set_m128(_mm_add_ps(_mm256_castps256_ps128(accm[i+4]), _mm256_extractf128_ps(accm[i+4], 1)),
_mm_add_ps(_mm256_castps256_ps128(accm[i+0]), _mm256_extractf128_ps(accm[i+0], 1)));
}
for (int i = 0; i < 2; ++i) accm[i] = _mm256_add_ps(_mm256_unpacklo_ps(accm[i], accm[i+2]), _mm256_unpackhi_ps(accm[i], accm[i+2]));
return _mm256_add_ps(_mm256_unpacklo_ps(accm[0], accm[1]), _mm256_unpackhi_ps(accm[0], accm[1]));
}
#endif
const int8_t scale_index[241] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 16, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 17, 17, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 18, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 19, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 20, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 21, 21, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 22, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 23, 23, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 24, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 25, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 26, 26,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 27, 27, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 28, 13, 13, 13,
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 29, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 30, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15
};
inline int best_index_scale(const int8_t * values, float x) {
int ix = (int)x - values[0];
if (ix < 0 || ix >= 241) return ix < 0 ? 0 : 15;
ix = scale_index[ix];
return ix < 16 ? ix : x - values[ix-16] < values[ix-15] - x ? ix-16 : ix-15;
}
inline int best_index_iq4nl(const int8_t * values, float x) { return best_index_scale(values, x); }
static float find_best_scale(int block_size, const float * xb, const float * weight, const int8_t * values, int ntry) {
float amax = 0, max = 0;
for (int j = 0; j < block_size; ++j) {
float ax = fabsf(xb[j]);
if (ax > amax) {
amax = ax; max = xb[j];
}
}
return amax/96.f; //120.f; //127.f;
if (!amax) return 0.f;
float d = ntry > 0 ? -max/values[0] : max/values[0];
float id = 1/d;
float sumqx_p = 0, sumq2_p = 0;
float sumqx_m = 0, sumq2_m = 0;
for (int j = 0; j < block_size; ++j) {
float w = weight[j];
float al = id*xb[j];
int l = best_index_iq4nl(values, al);
float q = values[l];
sumqx_p += w*q*xb[j];
sumq2_p += w*q*q;
l = best_index_iq4nl(values, -al);
q = values[l];
sumqx_m += w*q*xb[j];
sumq2_m += w*q*q;
}
d = sumqx_p/sumq2_p;
float best = d*sumqx_p;
if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
d = sumqx_m/sumq2_m; best = d*sumqx_m;
}
for (int itry = -ntry; itry <= ntry; ++itry) {
id = (itry + values[0])/max;
sumqx_p = sumq2_p = 0;
sumqx_m = sumq2_m = 0;
for (int j = 0; j < block_size; ++j) {
float w = weight[j];
float al = id*xb[j];
int l = best_index_iq4nl(values, al);
float q = values[l];
sumqx_p += w*q*xb[j];
sumq2_p += w*q*q;
l = best_index_iq4nl(values, -al);
q = values[l];
sumqx_m += w*q*xb[j];
sumq2_m += w*q*q;
}
if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
d = sumqx_p/sumq2_p; best = d * sumqx_p;
}
if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
d = sumqx_m/sumq2_m; best = d * sumqx_m;
}
}
return d;
}
static std::vector<float> cluster_points(const std::vector<float>& points, int ndim, int ncluster, int niter) {
if (points.size() % ndim != 0) {
printf("%s: bad input\n", __func__); return {};
}
int npoint = points.size() / ndim;
if (npoint < 2*ncluster) {
printf("%s: bad input\n", __func__); return {};
}
std::vector<std::pair<float, float>> range(ndim, std::make_pair(INFINITY, -INFINITY));
double Fo = 0;
for (int i = 0; i < npoint; ++i) {
auto v = points.data() + i*ndim;
for (int k = 0; k < ndim; ++k) {
Fo += v[k]*v[k];
range[k].first = std::min(range[k].first, v[k]);
range[k].second = std::max(range[k].second, v[k]);
}
}
printf("%s (ndim = %d, npoint = %d): Fo = %g\n", __func__, ndim, npoint, Fo/points.size());
std::mt19937 rndm(1234);
float scale = 1.f/4294967296.f;
std::vector<float> result(ncluster*ndim);
for (int i = 0; i < ncluster; ++i) {
auto v = result.data() + i*ndim;
for (int k = 0; k < ndim; ++k) v[k] = range[k].first + (range[k].second - range[k].first)*scale*rndm();
}
std::vector<float> sump(ncluster*ndim);
std::vector<int> counts(ncluster);
std::vector<int> which_cluster(npoint, -1);
double Flast = Fo;
for (int iter = 0; iter < niter; ++iter) {
std::memset(sump.data(), 0, sump.size()*sizeof(float));
std::memset(counts.data(), 0, counts.size()*sizeof(int));
int nchanged = 0;
double F = 0;
for (int ip = 0; ip < npoint; ++ip) {
auto vp = points.data() + ndim*ip;
float best = INFINITY; int ibest = -1;
for (int ic = 0; ic < ncluster; ++ic) {
auto vc = result.data() + ndim*ic;
float dist2 = 0;
for (int k = 0; k < ndim; ++k) {
float d = vp[k] - vc[k]; dist2 += d*d;
}
if (dist2 < best) {
best = dist2; ibest = ic;
}
}
if (ibest < 0) { printf("Oops.\n"); exit(1); }
F += best;
if (which_cluster[ip] != ibest) ++nchanged;
which_cluster[ip] = ibest;
++counts[ibest];
auto vc = sump.data() + ndim*ibest;
for (int k = 0; k < ndim; ++k) vc[k] += vp[k];
}
if (nchanged == 0) break;
for (int ic = 0; ic < ncluster; ++ic) {
float norm = counts[ic] > 0 ? 1.f/counts[ic] : 0.f;
auto vc = sump.data() + ndim*ic;
auto r = result.data() + ndim*ic;
for (int k = 0; k < ndim; ++k) r[k] = vc[k]*norm;
}
printf("%s(iteration %d): F = %g, nchanged = %d\n", __func__, iter+1, F/points.size(), nchanged);
if (iter > 1 && Flast/F - 1 < 1e-6) break;
Flast = F;
}
return result;
}
static void analyze_x_v2(const char * name, int nrows, int n_per_row, const float * values, float& tot_mse, float& tot_mse_q, float& tot_elements) {
constexpr int kNumVal = 1 << 15;
constexpr int kBlockSize = 32;
constexpr int kGroupSize = 8;
constexpr int kNg = kBlockSize/kGroupSize;
constexpr int kSuperBlockSize = 256;
static_assert(kNumVal%8 == 0);
static std::vector<float> codes, clusters;
static std::vector<std::vector<int>> p_in_cluster;
if (codes.empty()) {
codes = make_values(kNumVal, kGroupSize, 31.75f);
clusters = cluster_points(codes, kGroupSize, kNumVal/512, 200);
if (clusters.empty()) { printf("Oops\n"); exit(1); }
int ncluster = clusters.size()/kGroupSize;
p_in_cluster.resize(ncluster);
std::vector<int> which_cluster(4*kNumVal);
GGML_ASSERT(ncluster%8 == 0);
for (int ip = 0; ip < kNumVal; ++ip) {
auto vp = codes.data() + ip*kGroupSize;
float best[4] = {INFINITY, INFINITY, INFINITY, INFINITY};
int ibest[4] = {-1, -1, -1, -1};
for (int ic = 0; ic < ncluster; ++ic) {
auto vc = clusters.data() + ic*kGroupSize;
float dist2 = 0;
for (int k = 0; k < kGroupSize; ++k) {
float d = vp[k] - vc[k]; dist2 += d*d;
}
if (dist2 < best[0]) {
best[3] = best[2]; ibest[3] = ibest[2];
best[2] = best[1]; ibest[2] = ibest[1];
best[1] = best[0]; ibest[1] = ibest[0];
best[0] = dist2; ibest[0] = ic;
}
else if (dist2 < best[1]) {
best[3] = best[2]; ibest[3] = ibest[2];
best[2] = best[1]; ibest[2] = ibest[1];
best[1] = dist2; ibest[1] = ic;
}
else if (dist2 < best[2]) {
best[3] = best[2]; ibest[3] = ibest[2];
best[2] = dist2; ibest[2] = ic;
}
else if (dist2 < best[3]) {
best[3] = dist2; ibest[3] = ic;
}
}
GGML_ASSERT(ibest[0] >= 0 && ibest[1] >= 0 && ibest[2] >= 0 && ibest[3] >= 0);
p_in_cluster[ibest[0]].push_back(ip);
p_in_cluster[ibest[1]].push_back(ip);
p_in_cluster[ibest[2]].push_back(ip);
p_in_cluster[ibest[3]].push_back(ip);
std::memcpy(which_cluster.data() + 4*ip, ibest, 4*sizeof(int));
}
std::vector<std::pair<float, int>> extra;
extra.reserve(kNumVal);
for (int ic = 0; ic < ncluster; ++ic) {
auto& points = p_in_cluster[ic];
if (!points.empty() && points.size()%8 == 0) continue;
extra.clear();
auto vc = clusters.data() + ic*kGroupSize;
for (int ip = 0; ip < kNumVal; ++ip) {
if (which_cluster[4*ip] == ic || which_cluster[4*ip+1] == ic || which_cluster[4*ip+2] == ic || which_cluster[4*ip+3] == ic) continue;
auto vp = codes.data() + ip*kGroupSize;
float dist2 = 0;
for (int k = 0; k < kGroupSize; ++k) {
float d = vp[k] - vc[k]; dist2 += d*d;
}
extra.push_back(std::make_pair(dist2, ip));
}
std::sort(extra.begin(), extra.end());
int nadd = 8*((points.size()+7)/8) - points.size();
for (int i = 0; i < nadd; ++i) points.push_back(extra[i].second);
GGML_ASSERT(points.size()%8 == 0);
}
auto min = p_in_cluster.front().size(), max = p_in_cluster.front().size();
int nzero = 0;
for (auto& points : p_in_cluster) {
min = std::min(min, points.size());
max = std::max(max, points.size());
if (points.empty()) ++nzero;
}
printf("%s: prepared %d clusters\n", __func__, ncluster);
printf(" min number of points in a cluster: %d\n", int(min));
printf(" max number of points in a cluster: %d\n", int(max));
if (nzero > 0) {
printf(" there are %d empty clusters\n", nzero);
for (auto& points : p_in_cluster) {
if (!points.empty()) continue;
points.reserve(kNumVal);
for (int j = 0; j < kNumVal; ++j) points.push_back(j); // i.e., if we end iup picking an empty cluster, we just check all points
}
}
}
int nthread = std::max(1, int(std::thread::hardware_concurrency()/2));
int chunk = (nrows + 8*nthread - 1)/(8*nthread);
std::mutex mutex;
int counter = 0;
float mse = 0, mse_q = 0;
auto compute = [&mutex, &counter, &mse, &mse_q, values, nrows, n_per_row, chunk] () {
constexpr int kNumVal = 1 << 15;
constexpr int kBlockSize = 32;
constexpr int kGroupSize = 8;
constexpr int kNg = kBlockSize/kGroupSize;
double lmse = 0, lmse_q = 0;
std::vector<float> scales(n_per_row/kBlockSize);
std::vector<int> best_idx(n_per_row/kGroupSize);
std::vector<float> weight(kBlockSize, 1.f);
int ncluster = clusters.size() / kGroupSize;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int first = counter; counter += chunk;
if (first >= nrows) {
mse += lmse; mse_q += lmse_q;
return;
}
lock.unlock();
int last = std::min(first + chunk, nrows);
#ifdef __AVX2__
__m256 sqx[8];
__m256i add_idx = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
float sx[8];
int index[8];
#endif
for (int row = first; row < last; ++row) {
auto xr = values + row*n_per_row;
float sigma2 = 0;
for (int j = 0; j < n_per_row; ++j) sigma2 += xr[j]*xr[j];
sigma2 /= n_per_row;
for (int ib = 0; ib < n_per_row/kBlockSize; ++ib) {
auto xb = xr + kBlockSize*ib;
//for (int i = 0; i < kBlockSize; ++i) weight[i] = 0.25f*sigma2 + xb[i]*xb[i];
float d = find_best_scale(kBlockSize, xb, weight.data(), iq4k_values, 5);
float id = d ? 1/d : 0.f;
#ifdef __AVX2__
auto vid = _mm256_set1_ps(id);
for (int l = 0; l < kNg; ++l) {
auto xl = xb + 8*l;
auto wl = weight.data() + 8*l;
auto vx = _mm256_mul_ps(vid, _mm256_loadu_ps(xl));
auto vw = _mm256_loadu_ps(wl);
auto vbest = _mm256_set1_ps(INFINITY);
auto best_index = _mm256_set1_epi32(-1);
float best = INFINITY; int jbest = -1;
for (int j = 0; j < ncluster; j += 8) {
auto idx = _mm256_add_epi32(_mm256_set1_epi32(j), add_idx);
for (int i = 0; i < 8; ++i) {
auto vq = _mm256_loadu_ps(clusters.data() + kGroupSize*(j+i));
auto vdiff = _mm256_sub_ps(vq, vx);
sqx[i] = _mm256_mul_ps(vw, _mm256_mul_ps(vdiff, vdiff));
}
auto score = hsum_float_8x8(sqx);
auto mask = _mm256_cmp_ps(score, vbest, _CMP_LT_OQ);
best_index = _mm256_or_si256(_mm256_and_si256(_mm256_castps_si256(mask), idx),
_mm256_andnot_si256(_mm256_castps_si256(mask), best_index));
vbest = _mm256_min_ps(vbest, score);
}
_mm256_store_ps(sx, vbest);
_mm256_store_si256((__m256i *)index, best_index);
for (int i = 0; i < 8; ++i) {
if (sx[i] < best) { best = sx[i]; jbest = index[i]; }
}
auto& points = p_in_cluster[jbest];
if (points.empty()) {
printf("Oops: empty cluster %d\n", jbest);
auto vc = clusters.data() + kGroupSize*jbest;
printf("Cluster:\n");
for (int j = 0; j < kGroupSize; ++j) printf("%d %g %g\n", j, vc[j], xl[j]);
GGML_ASSERT(false);
}
int jbest_cluster = jbest;
vbest = _mm256_set1_ps(INFINITY);
best_index = _mm256_set1_epi32(-1);
best = INFINITY; jbest = -1;
for (int j = 0; j < int(points.size()); j += 8) {
auto idx = _mm256_loadu_si256((const __m256i*)(points.data() + j));
for (int i = 0; i < 8; ++i) {
auto vq = _mm256_loadu_ps(codes.data() + kGroupSize*points[j+i]);
auto vdiff = _mm256_sub_ps(vq, vx);
sqx[i] = _mm256_mul_ps(vw, _mm256_mul_ps(vdiff, vdiff));
}
auto score = hsum_float_8x8(sqx);
auto mask = _mm256_cmp_ps(score, vbest, _CMP_LT_OQ);
best_index = _mm256_or_si256(_mm256_and_si256(_mm256_castps_si256(mask), idx),
_mm256_andnot_si256(_mm256_castps_si256(mask), best_index));
vbest = _mm256_min_ps(vbest, score);
}
_mm256_store_ps(sx, vbest);
_mm256_store_si256((__m256i *)index, best_index);
for (int i = 0; i < 8; ++i) {
if (sx[i] < best) { best = sx[i]; jbest = index[i]; }
}
if (jbest < 0) {
printf("Oops: jbest = %d for cluster %d with %d points\n", jbest, jbest_cluster, int(points.size()));
GGML_ASSERT(false);
}
GGML_ASSERT(jbest >= 0);
best_idx[ib*kNg + l] = jbest;
}
auto vqx = _mm256_setzero_ps();
auto vq2 = _mm256_setzero_ps();
for (int l = 0; l < kNg; ++l) {
auto vx = _mm256_loadu_ps(xb+8*l);
auto vw = _mm256_loadu_ps(weight.data() + 8*l);
auto vq = _mm256_loadu_ps(codes.data() + kGroupSize*best_idx[ib*kNg + l]);
auto vqw = _mm256_mul_ps(vq, vw);
vqx = _mm256_fmadd_ps(vqw, vx, vqx);
vq2 = _mm256_fmadd_ps(vqw, vq, vq2);
}
auto sumqx = hsum_float_8(vqx);
auto sumq2 = hsum_float_8(vq2);
scales[ib] = sumq2 > 0 ? sumqx/sumq2 : 0.f;
#else
#endif
}
float amax_scale = std::abs(scales[0]);
float max_scale = scales[0];
for (int ib = 1; ib < n_per_row/kBlockSize; ++ib) {
float ax = std::abs(scales[ib]);
if (ax > amax_scale) {
amax_scale = ax;
max_scale = scales[ib];
}
}
float d = max_scale/scale_values[0];
float id = d ? 1/d : 0.f;
for (int ib = 0; ib < n_per_row/kBlockSize; ++ib) {
int ls = best_index_scale(scale_values, id*scales[ib]);
float dl = d * scale_values[ls];
auto xb = xr + kBlockSize*ib;
for (int l = 0; l < kNg; ++l) {
auto q = codes.data() + kGroupSize*best_idx[ib*kNg+l];
for (int k = 0; k < kGroupSize; ++k) {
float diff1 = xb[kGroupSize*l + k] - scales[ib]*q[k];
float diff2 = xb[kGroupSize*l + k] - dl*q[k];
lmse += diff1*diff1;
lmse_q += diff2*diff2;
}
}
}
}
}
};
std::vector<std::thread> workers(nthread);
for (auto& w : workers) w = std::thread(compute);
for (auto& w : workers) w.join();
tot_mse += mse;
tot_mse_q += mse_q;
tot_elements += n_per_row*nrows;
printf("%s: %g %g %g %g\n", name, sqrt(mse/(n_per_row*nrows)), sqrt(tot_mse/tot_elements),
sqrt(mse_q/(n_per_row*nrows)), sqrt(tot_mse_q/tot_elements));
}
static void analyze_x(const char * name, int nrows, int n_per_row, const float * values, float& tot_mse, float& tot_mse_q, float& tot_elements) {
constexpr int kNumVal = 1 << 12;
constexpr int kBlockSize = 8;
constexpr int kSuperBlockSize = 256;
static_assert(kNumVal%8 == 0);
auto codes = make_values(kNumVal, kBlockSize);
std::vector<float> sumq2i(kNumVal);
for (int j = 0; j < kNumVal; ++j) {
auto data = codes.data() + kBlockSize*j;
float sum = 0; for (int k = 0; k < kBlockSize; ++k) sum += data[k]*data[k];
sumq2i[j] = sum > 0 ? 1/sum : 0.f;;
}
int nthread = std::max(1, int(std::thread::hardware_concurrency()/2));
int chunk = (nrows + 8*nthread - 1)/(8*nthread);
std::mutex mutex;
int counter = 0;
float mse = 0, mse_q = 0;
auto compute = [&mutex, &counter, &mse, &mse_q, &codes, &sumq2i, values, nrows, n_per_row, chunk] () {
constexpr int kBlockSize = 8;
constexpr int kNumVal = 1 << 12;
float lmse = 0, lmse_q = 0;
std::vector<float> scales(n_per_row/kBlockSize);
std::vector<int> best_idx(n_per_row/kBlockSize);
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int first = counter; counter += chunk;
if (first >= nrows) {
mse += lmse; mse_q += lmse_q;
return;
}
lock.unlock();
int last = std::min(first + chunk, nrows);
#ifdef __AVX2__
__m256 vx[kBlockSize/8];
__m256 sqx[8];
__m256i add_idx = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
float sx[8];
int index[8];
#endif
for (int row = first; row < last; ++row) {
auto xr = values + row*n_per_row;
for (int ib = 0; ib < n_per_row/kBlockSize; ++ib) {
float best = 0, d = 0; int jbest = -1;
auto xb = xr + kBlockSize*ib;
#ifdef __AVX2__
for (int l = 0; l < kBlockSize/8; ++l) {
vx[l] = _mm256_loadu_ps(xb+8*l);
}
auto vbest = _mm256_set1_ps(0.f);
auto best_index = _mm256_set1_epi32(-1);
for (int j = 0; j < kNumVal; j += 8) {
auto idx = _mm256_add_epi32(_mm256_set1_epi32(j), add_idx);
for (int i = 0; i < 8; ++i) {
sqx[i] = _mm256_setzero_ps();
for (int l = 0; l < kBlockSize/8; ++l) {
auto qv = _mm256_loadu_ps(codes.data() + kBlockSize*(j+i) + 8*l);
sqx[i] = _mm256_fmadd_ps(vx[l], qv, sqx[i]);
}
}
auto sumqx = hsum_float_8x8(sqx);
auto score = _mm256_mul_ps(_mm256_mul_ps(sumqx, sumqx), _mm256_loadu_ps(sumq2i.data() + j));
auto mask = _mm256_cmp_ps(score, vbest, _CMP_GT_OQ);
best_index = _mm256_or_si256(_mm256_and_si256(idx, _mm256_castps_si256(mask)), _mm256_andnot_si256(_mm256_castps_si256(mask), best_index));
vbest = _mm256_max_ps(vbest, score);
}
_mm256_store_ps(sx, vbest);
_mm256_store_si256((__m256i *)index, best_index);
best = sx[0]; jbest = index[0];
for (int j = 1; j < 8; ++j) {
if (sx[j] > best) { best = sx[j]; jbest = index[j]; }
}
auto qv = codes.data() + kBlockSize*jbest;
float sumqx = 0;
for (int k = 0; k < kBlockSize; ++k) sumqx += xb[k]*qv[k];
d = sumqx*sumq2i[jbest];
#else
for (int j = 0; j < kNumVal; ++j) {
if (!sumq2i[j]) continue;
auto qv = codes.data() + kBlockSize*j;
float sumqx = 0;
for (int k = 0; k < kBlockSize; ++k) sumqx += qv[k]*xb[k];
if (sumqx*sumqx*sumq2i[j] > best) {
d = sumqx*sumq2i[j]; best = d*sumqx; jbest = j;
}
}
auto qv = codes.data() + kBlockSize*jbest;
#endif
scales[ib] = d;
best_idx[ib] = jbest;
for (int k = 0; k < kBlockSize; ++k) {
float diff = xb[k] - d*qv[k];
lmse += diff*diff;
}
}
float amax_scale = std::abs(scales[0]);
float max_scale = scales[0];
for (int ib = 1; ib < n_per_row/kBlockSize; ++ib) {
float ax = std::abs(scales[ib]);
if (ax > amax_scale) {
amax_scale = ax;
max_scale = scales[ib];
}
}
float d = max_scale/scale_values[0];
float id = d ? 1/d : 0.f;
for (int ib = 0; ib < n_per_row/kBlockSize; ++ib) {
int ls = best_index_scale(scale_values, id*scales[ib]);
float dl = d * scale_values[ls];
auto xb = xr + kBlockSize*ib;
auto qv = codes.data() + kBlockSize*best_idx[ib];
for (int k = 0; k < kBlockSize; ++k) {
float diff = xb[k] - dl*qv[k];
lmse_q += diff*diff;
}
}
}
}
};
std::vector<std::thread> workers(nthread);
for (auto& w : workers) w = std::thread(compute);
for (auto& w : workers) w.join();
tot_mse += mse;
tot_mse_q += mse_q;
tot_elements += n_per_row*nrows;
printf("%s: %g %g %g %g\n", name, sqrt(mse/(n_per_row*nrows)), sqrt(tot_mse/tot_elements),
sqrt(mse_q/(n_per_row*nrows)), sqrt(tot_mse_q/tot_elements));
}
static void analyze_iq4ks(const char * name, int nrows, int n_per_row, const float * values, float& tot_mse, float& tot_elements) {
int row_size = ggml_row_size(GGML_TYPE_IQ4_KS, n_per_row);
int nblock = n_per_row/QK_K;
int nthread = std::max(1, int(std::thread::hardware_concurrency()/2));
int chunk = (nrows + 8*nthread - 1)/(8*nthread);
std::mutex mutex;
int counter = 0;
float mse0 = 0, mse = 0;
auto compute = [&mutex, &counter, &mse0, &mse, values, row_size, nblock, nrows, n_per_row, chunk] () {
std::vector<char> Q(row_size);
float diff[4];
float xv[4];
float lmse0 = 0, lmse = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int first = counter; counter += chunk;
if (first >= nrows) {
mse += lmse; mse0 += lmse0;
return;
}
lock.unlock();
int last = std::min(first + chunk, nrows);
for (int row = first; row < last; ++row) {
auto xr = values + row*n_per_row;
ggml_quantize_chunk(GGML_TYPE_IQ4_KS, xr, (void *)Q.data(), 0, 1, n_per_row, nullptr);
const float * dptr = (const float *)Q.data();
const float d = *dptr;
const block_iq4_ks * iq4 = (const block_iq4_ks *)(dptr + 1);
for (int ibl = 0; ibl < nblock; ++ibl) {
const float * xbl = xr + ibl*QK_K;
auto qs = iq4[ibl].qs;
for (int ib = 0; ib < QK_K/32; ++ib) {
const float * xb = xbl + 32*ib;
const float dl = d * ((iq4[ibl].scales[ib] & 254) - 127);
const int8_t * values = iq4k_values + ((iq4[ibl].scales[ib] & 1) << 4);
for (int j = 0; j < 16; j += 2) {
uint16_t v0 = *(const uint16_t *)(qs + j);
int non = popcount(v0);
xv[0] = xb[j+ 0]; xv[1] = xb[j+16]; xv[2] = xb[j+ 1]; xv[3] = xb[j+17];
diff[0] = xv[0] - dl*values[qs[j+0] & 0xf];
diff[1] = xv[1] - dl*values[qs[j+0] >> 4];
diff[2] = xv[2] - dl*values[qs[j+1] & 0xf];
diff[3] = xv[3] - dl*values[qs[j+1] >> 4];
float diff4 = diff[0]*diff[0] + diff[1]*diff[1] + diff[2]*diff[2] + diff[3]*diff[3];
lmse0 += diff4;
if (non%2 == 0) {
lmse += diff4;
} else {
float best = std::numeric_limits<float>::max();
for (int k = 0; k < 4; ++k) {
uint16_t v = (v0 >> 4*k) & 0xf;
auto pc = popcount(v);
if (v > 0 && popcount(v-1u) != pc) {
float this_diff = xv[k] - dl*values[v-1u];
float score = diff4 - diff[k]*diff[k] + this_diff*this_diff;
if (score < best) best = score;
}
if (v < 15 && popcount(v + 1u) != pc) {
float this_diff = xv[k] - dl*values[v+1u];
float score = diff4 - diff[k]*diff[k] + this_diff*this_diff;
if (score < best) best = score;
}
}
lmse += best;
}
}
qs += 16;
}
}
}
}
};
std::vector<std::thread> workers(nthread-1);
for (auto& w : workers) w = std::thread(compute);
compute();
for (auto& w : workers) w.join();
tot_mse += mse;
tot_elements += n_per_row*nrows;
printf("%s: %g %g %g\n", name, sqrt(mse0/(n_per_row*nrows)), sqrt(mse/(n_per_row*nrows)), sqrt(tot_mse/tot_elements));
}
static void analyze_iq4ks(const ggml_tensor * t, float& tot_mse, float& tot_mse_q, float& tot_elements) {
if (!ggml_is_contiguous(t) || (t->type != GGML_TYPE_F32 && t->type != GGML_TYPE_F16 && t->type != GGML_TYPE_BF16)) {
return;
}
if (t->type == GGML_TYPE_F32) {
analyze_x_v2(t->name, t->ne[1], t->ne[0], (const float *)t->data, tot_mse, tot_mse_q, tot_elements);
} else {
std::vector<float> aux(t->ne[0]*t->ne[1]);
if (t->type == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((const ggml_fp16_t *)t->data, aux.data(), aux.size());
} else {
ggml_bf16_to_fp32_row((const ggml_bf16_t *)t->data, aux.data(), aux.size());
}
analyze_x_v2(t->name, t->ne[1], t->ne[0], aux.data(), tot_mse, tot_mse_q, tot_elements);
}
}
static void print_fp_stats(const char * msg, const uint64_t * counts) {
printf("===== %s\n", msg);
uint64_t tot = 0; for (int i = 0; i < 32; ++i) tot += counts[i];
double norm = 1./tot;
for (int i = 0; i < 32; ++i) {
if (!counts[i]) continue;
uint16_t val = i << 10;
float f = ggml_fp16_to_fp32(val);
printf("%2d %f %g\n", i, norm*counts[i], f);
}
}
static void analyze_tensor_fp(const ggml_tensor * t, uint64_t * H) {
if (t->type != GGML_TYPE_F16) return;
if (!ggml_is_contiguous(t)) return;
int n = ggml_nelements(t);
const uint16_t * x = (const uint16_t *)t->data;
std::array<uint64_t, 32> counts = {};
for (int j = 0; j < n; ++j) {
++counts[(x[j] >> 10) & 31];
}
for (int i = 0; i < 32; ++i) H[i] += counts[i];
print_fp_stats(t->name, counts.data());
}
int main(int argc, char ** argv) {
ggml_time_init();
quantize_stats_params params;
// read command line
int max_thread = 0;
bool invalid_param = false;
bool analyze_fp = false;
bool analyze = false;
std::string arg;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg == "-h" || arg == "--help") {
quantize_stats_print_usage(argc, argv);
exit(0);
} else if (arg == "-r" || arg == "--reference") {
params.reference = true;
} else if (arg == "-v") {
params.verbose = true;
} else if (arg == "-p" || arg == "--per-layer-stats") {
params.per_layer_stats = true;
} else if (arg == "-afp" || arg == "--analyze-fp") {
analyze_fp = true;
} else if (arg == "-a" || arg == "--analyze") {
analyze = true;
} else if (arg == "--histogram") {
params.print_histogram = true;
} else if (arg == "-m" || arg == "--model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.model = argv[i];
} else if (arg == "-l" || arg == "--include-layer") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.include_layers.emplace_back(argv[i]);
} else if (arg == "-L" || arg == "--exclude-layer") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.exclude_layers.emplace_back(argv[i]);
} else if (arg == "-t" || arg == "--type") {
if (++i >= argc) {
invalid_param = true;
break;
}
int j;
for (j = 0; j < GGML_TYPE_COUNT; ++j) {
const auto * name = ggml_type_name((ggml_type) j);
if (name && strcmp(argv[i], name) == 0) break;
}
if (j < GGML_TYPE_COUNT) {
params.include_types.push_back((ggml_type) j);
} else {
fprintf(stderr, "error: %s not in list of types\n", argv[i]);
invalid_param = true;
}
} else if (arg == "-n" || arg == "--num-threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
max_thread = atoi(argv[i]);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
quantize_stats_print_usage(argc, argv);
return 1;
}
}
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
quantize_stats_print_usage(argc, argv);
return 1;
}
print_build_info();
// load the model
fprintf(stderr, "Loading model\n");
const int64_t t_main_start_us = ggml_time_us();
llama_model * model;
llama_context * ctx;
{
auto mparams = llama_model_default_params();
mparams.use_mlock = false;
model = llama_load_model_from_file(params.model.c_str(), mparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return 1;
}
auto cparams = llama_context_default_params();
cparams.n_ctx = 256;
cparams.seed = 1;
ctx = llama_new_context_with_model(model, cparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return 1;
}
}
const auto &tensors = llama_internal_get_tensor_map(ctx);
// check layer tensors
int included_layers = 0;
int64_t max_nelements = 0;
bool is_f16 = false;
for (const auto& kv_tensor : tensors) {
if (!layer_included(params, kv_tensor.first)) {
continue;
}
if (kv_tensor.second->ne[0] == 1 || kv_tensor.second->ne[1] == 1) {
// we never quantize those
continue;
}
if (params.verbose) {
printf("%s: type %s, size %" PRId64 "\n", kv_tensor.first.c_str(), ggml_type_name(kv_tensor.second->type), ggml_nelements(kv_tensor.second));
}
if (kv_tensor.second->type == GGML_TYPE_F16) {
is_f16 = true;
} else if (kv_tensor.second->type != GGML_TYPE_F32) {
fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
"this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
llama_free(ctx);
llama_free_model(model);
return 1;
}
included_layers++;
max_nelements = std::max(max_nelements, ggml_nelements(kv_tensor.second));
}
if (is_f16) {
printf("note: source model is f16\n");
}
printf("testing %d layers with max size %" PRId64 "\n", included_layers, max_nelements);
// allocate scratch space
std::vector<float> input_scratch;
std::vector<char> quantized_scratch;
std::vector<float> output_scratch;
if (analyze) {
float tot_mse = 0, tot_mse_q = 0, tot_elements = 0;
for (const auto& kv_tensor : tensors) {
if (!layer_included(params, kv_tensor.first)) {
continue;
}
if (kv_tensor.second->ne[0] == 1 || kv_tensor.second->ne[1] == 1) {
// we never quantize those
continue;
}
analyze_iq4ks(kv_tensor.second, tot_mse, tot_mse_q, tot_elements);
}
return 0;
}
if (analyze_fp) {
for (const auto& kv_tensor : tensors) {
if (!layer_included(params, kv_tensor.first)) {
continue;
}
if (kv_tensor.second->ne[0] == 1 || kv_tensor.second->ne[1] == 1) {
// we never quantize those
continue;
}
std::array<uint64_t, 32> H = {};
analyze_tensor_fp(kv_tensor.second, H.data());
print_fp_stats("Total", H.data());
}
return 0;
}
// loop throught quantization types
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
const ggml_type type = (ggml_type) i;
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
continue;
}
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (qfns.from_float && qfns.to_float) {
if (params.verbose) {
printf("testing %s ...\n", ggml_type_name(type));
}
ggml_quantize_init(type);
error_stats global_stats {};
for (const auto& kv_tensor : tensors) {
if (!layer_included(params, kv_tensor.first)) {
continue;
}
if (kv_tensor.second->ne[0] == 1 || kv_tensor.second->ne[1] == 1) {
// we never quantize those
continue;
}
if (params.verbose) {
printf(" %s ...\n", kv_tensor.first.c_str());
}
std::string layer_name { ggml_type_name(type) };
layer_name += "::" + kv_tensor.first;
test_roundtrip_on_layer(
layer_name,
params.per_layer_stats,
qfns,
params.reference,
kv_tensor.second,
input_scratch,
quantized_scratch,
output_scratch,
global_stats,
max_thread
);
}
print_error_stats(ggml_type_name(type), global_stats, params.print_histogram);
}
}
llama_free(ctx);
llama_free_model(model);
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n");
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
}
return 0;
}
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