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#include "llama.h"
#include "build-info.h"
#include "common.h"
#include "../src/llama-model.h"
#include "ggml.h"
#include "ggml-cpu.h"
#include <algorithm>
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <numeric>
#include <regex>
#include <string>
#include <vector>
#include <thread>
#include <mutex>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.gguf";
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;
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.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;
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 median = find_quantile(stats, .5);
double pct95 = find_quantile(stats, .95);
printf("%-50s: rmse %.8f, maxerr %.8f, 95pct<%.4f, median<%.4f\n", name.c_str(), rmse, stats.max_error, 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 & qfns, const ggml_type_traits_cpu & qfns_cpu, 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_cpu.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 & qfns, const ggml_type_traits_cpu & qfns_cpu, 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, qfns_cpu, 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, &qfns_cpu, 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, qfns_cpu, 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);
}
}
int main(int argc, char ** argv) {
ggml_time_init();
quantize_stats_params params;
// read command line
int max_thread = 0;
bool invalid_param = 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 == "--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;
}
llama_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_model_load_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;
ctx = llama_init_from_model(model, cparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_model_free(model);
return 1;
}
}
const auto & tensors = llama_internal_get_tensor_map(model);
// 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 (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_model_free(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;
// 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;
}
const auto * qfns = ggml_get_type_traits(type);
const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
if (qfns_cpu->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 (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, *qfns_cpu,
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_model_free(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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