mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-14 08:59:45 +00:00
355 lines
13 KiB
C++
355 lines
13 KiB
C++
#include "ggml.h"
|
|
|
|
#define LLAMA_API_INTERNAL
|
|
#include "llama.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>
|
|
|
|
static const char * type_strs[] = { "q4_0", "q4_1", "i8", "i16", "i32", "f16", "f32" };
|
|
static_assert(sizeof(type_strs) == GGML_TYPE_COUNT * sizeof(char *), "Incomplete type list");
|
|
|
|
struct quantize_stats_params {
|
|
std::string model = "models/7B/ggml-model-f16.bin";
|
|
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;
|
|
};
|
|
|
|
const int64_t SCRATCH_ELEMENTS = 32*32;
|
|
const size_t HISTOGRAM_BUCKETS = 150;
|
|
const double HISTOGRAM_RANGE = 0.03;
|
|
|
|
struct error_stats {
|
|
size_t num_samples;
|
|
double total_error;
|
|
double max_error;
|
|
uint64_t error_histogram[HISTOGRAM_BUCKETS];
|
|
};
|
|
|
|
|
|
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
|
|
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
|
|
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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
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];
|
|
}
|
|
|
|
// Run quantization function for a single layer and update error stats
|
|
void test_roundtrip_on_layer(
|
|
std::string & name,
|
|
bool print_layer_stats,
|
|
const quantize_fns_t & qfns,
|
|
bool use_reference,
|
|
const ggml_tensor * layer,
|
|
float * input_scratch,
|
|
char *quantized_scratch,
|
|
float * output_scratch,
|
|
error_stats & total_error) {
|
|
|
|
assert(tensor_is_contiguous(layer));
|
|
error_stats layer_error {};
|
|
int64_t nelements = ggml_nelements(layer);
|
|
|
|
for (int64_t offset = 0; offset < nelements; offset += SCRATCH_ELEMENTS) {
|
|
int64_t chunk_size = std::min(SCRATCH_ELEMENTS, nelements - offset);
|
|
|
|
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.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
|
|
} else {
|
|
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
|
|
}
|
|
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
|
|
|
|
update_error_stats(chunk_size, input_scratch, output_scratch, total_error);
|
|
if (print_layer_stats) {
|
|
update_error_stats(chunk_size, input_scratch, output_scratch, layer_error);
|
|
}
|
|
}
|
|
if (print_layer_stats) {
|
|
print_error_stats(name, layer_error, false);
|
|
}
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
ggml_time_init();
|
|
|
|
quantize_stats_params params;
|
|
|
|
// read command line
|
|
|
|
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.push_back(argv[i]);
|
|
} else if (arg == "-L" || arg == "--exclude-layer") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.exclude_layers.push_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 && strcmp(argv[i], type_strs[j]) != 0; j++) {
|
|
// find match
|
|
}
|
|
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 {
|
|
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;
|
|
}
|
|
|
|
// load the model
|
|
fprintf(stderr, "Loading model\n");
|
|
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
llama_context * ctx;
|
|
|
|
{
|
|
auto lparams = llama_context_default_params();
|
|
|
|
lparams.n_ctx = 256;
|
|
lparams.n_parts = 1;
|
|
lparams.seed = 1;
|
|
lparams.f16_kv = false;
|
|
lparams.use_mlock = false;
|
|
|
|
ctx = llama_init_from_file(params.model.c_str(), lparams);
|
|
|
|
if (ctx == NULL) {
|
|
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
|
|
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 (params.verbose) {
|
|
printf("%s: type %s, size %" PRId64 "\n", kv_tensor.first.c_str(), type_strs[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);
|
|
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(SCRATCH_ELEMENTS);
|
|
std::vector<char> quantized_scratch(SCRATCH_ELEMENTS*4);
|
|
std::vector<float> output_scratch(SCRATCH_ELEMENTS);
|
|
|
|
// loop throught quantization types
|
|
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
|
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
|
|
continue;
|
|
}
|
|
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
|
|
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
|
|
if (params.verbose) {
|
|
printf("testing %s ...\n", type_strs[i]);
|
|
}
|
|
|
|
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 { type_strs[i] };
|
|
layer_name += "::" + kv_tensor.first;
|
|
test_roundtrip_on_layer(
|
|
layer_name,
|
|
params.per_layer_stats,
|
|
qfns,
|
|
params.reference,
|
|
kv_tensor.second,
|
|
input_scratch.data(),
|
|
quantized_scratch.data(),
|
|
output_scratch.data(),
|
|
global_stats
|
|
);
|
|
}
|
|
|
|
print_error_stats(type_strs[i], global_stats, params.print_histogram);
|
|
}
|
|
}
|
|
|
|
|
|
llama_free(ctx);
|
|
// 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;
|
|
}
|