mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 03:44:35 +00:00
354 lines
13 KiB
C++
354 lines
13 KiB
C++
#include "ggml.h"
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#define LLAMA_API_INTERNAL
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#include "llama.h"
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#include <algorithm>
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <map>
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#include <numeric>
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#include <regex>
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#include <string>
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#include <unordered_map>
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#include <vector>
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struct quantize_stats_params {
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std::string model = "models/7B/ggml-model-f16.bin";
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bool verbose = false;
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bool per_layer_stats = false;
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bool print_histogram = false;
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bool reference = false;
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std::vector<std::string> include_layers;
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std::vector<std::string> exclude_layers;
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std::vector<enum ggml_type> include_types;
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};
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const int64_t SCRATCH_ELEMENTS = 32*32;
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const size_t HISTOGRAM_BUCKETS = 150;
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const double HISTOGRAM_RANGE = 0.03;
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struct error_stats {
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size_t num_samples;
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double total_error;
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double max_error;
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uint64_t error_histogram[HISTOGRAM_BUCKETS];
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};
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void quantize_stats_print_usage(int /*argc*/, char ** argv) {
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quantize_stats_params params;
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -m FNAME, --model FNAME\n");
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fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
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fprintf(stderr, " -r, --reference\n");
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fprintf(stderr, " use reference implementation (default: false)\n");
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fprintf(stderr, " -v, --verbose\n");
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fprintf(stderr, " verbose output (default: false)\n");
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fprintf(stderr, " -p, --per-layer-stats\n");
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fprintf(stderr, " print stats per layer (default: false)\n");
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fprintf(stderr, " --histogram\n");
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fprintf(stderr, " print error histogram (default: false)\n");
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fprintf(stderr, " -l LAYER, --include-layer LAYER\n");
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fprintf(stderr, " only test layers matching pattern\n");
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fprintf(stderr, " -L LAYER, --exclude-layer LAYER\n");
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fprintf(stderr, " exclude layers matching pattern\n");
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fprintf(stderr, " -t TYPE, --type TYPE\n");
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fprintf(stderr, " only test given type (q4_0, q4_1)\n");
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fprintf(stderr, "\n");
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}
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// Check if a layer is included/excluded by command line
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bool layer_included(const quantize_stats_params params, const std::string & layer) {
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for (const auto& excluded : params.exclude_layers) {
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if (std::regex_search(layer, std::regex(excluded))) {
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return false;
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}
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}
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for (const auto& included : params.include_layers) {
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if (std::regex_search(layer, std::regex(included))) {
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return true;
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}
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}
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return params.include_layers.empty();
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}
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// Update error statistics given vectors with the before/after result of quantization
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void update_error_stats(int64_t nelements, const float * input, const float * output, error_stats & stats) {
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for (int64_t i = 0; i < nelements; i++) {
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double diff = input[i] - output[i];
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stats.total_error += diff * diff;
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stats.max_error = fmax(fabs(diff), stats.max_error);
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stats.error_histogram[std::max(std::min((size_t) floor(fabs(diff) / HISTOGRAM_RANGE * HISTOGRAM_BUCKETS), HISTOGRAM_BUCKETS-1), (size_t) 0)]++;
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}
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stats.num_samples += nelements;
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}
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double find_quantile(const error_stats & stats, double quantile) {
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double sum = std::accumulate(std::begin(stats.error_histogram), std::end(stats.error_histogram), 0.0);
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double accum = 0;
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for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {
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accum += stats.error_histogram[i];
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if (accum >= sum*quantile) {
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return (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
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}
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}
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return INFINITY;
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}
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void print_error_stats(const std::string & name, const error_stats & stats, bool print_histogram) {
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double rmse = sqrt(stats.total_error / (double) stats.num_samples);
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double median = find_quantile(stats, .5);
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double pct95 = find_quantile(stats, .95);
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printf("%-50s: rmse %.8f, maxerr %.8f, 95pct<%.4f, median<%.4f\n", name.c_str(), rmse, stats.max_error, pct95, median);
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if (print_histogram) {
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printf("Error distribution:\n");
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for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {
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double lower = i * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
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double upper = (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
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if (i == HISTOGRAM_BUCKETS -1) upper = INFINITY;
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printf("[%3.4f, %3.4f): %11" PRIu64 "\n", lower, upper, stats.error_histogram[i]);
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}
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}
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}
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// copied from ggml.h - verify that we can access this as a flat array
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static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
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static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
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return
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tensor->nb[0] == ggml_type_size(tensor->type) &&
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tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
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tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
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tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
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}
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// Run quantization function for a single layer and update error stats
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void test_roundtrip_on_layer(
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std::string & name,
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bool print_layer_stats,
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const quantize_fns_t & qfns,
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bool use_reference,
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const ggml_tensor * layer,
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float * input_scratch,
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char *quantized_scratch,
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float * output_scratch,
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error_stats & total_error) {
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assert(tensor_is_contiguous(layer));
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error_stats layer_error {};
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int64_t nelements = ggml_nelements(layer);
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for (int64_t offset = 0; offset < nelements; offset += SCRATCH_ELEMENTS) {
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int64_t chunk_size = std::min(SCRATCH_ELEMENTS, nelements - offset);
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if (layer->type == GGML_TYPE_F16) {
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for (int i = 0; i < chunk_size; i++) {
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input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
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}
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} else {
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input_scratch = ggml_get_data_f32(layer) + offset;
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}
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if (use_reference) {
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qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
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} else {
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qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
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}
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qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
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update_error_stats(chunk_size, input_scratch, output_scratch, total_error);
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if (print_layer_stats) {
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update_error_stats(chunk_size, input_scratch, output_scratch, layer_error);
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}
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}
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if (print_layer_stats) {
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print_error_stats(name, layer_error, false);
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}
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}
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int main(int argc, char ** argv) {
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ggml_time_init();
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quantize_stats_params params;
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// read command line
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bool invalid_param = false;
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std::string arg;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg == "-h" || arg == "--help") {
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quantize_stats_print_usage(argc, argv);
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exit(0);
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} else if (arg == "-r" || arg == "--reference") {
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params.reference = true;
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} else if (arg == "-v") {
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params.verbose = true;
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} else if (arg == "-p" || arg == "--per-layer-stats") {
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params.per_layer_stats = true;
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} else if (arg == "--histogram") {
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params.print_histogram = true;
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} else if (arg == "-m" || arg == "--model") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.model = argv[i];
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} else if (arg == "-l" || arg == "--include-layer") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.include_layers.push_back(argv[i]);
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} else if (arg == "-L" || arg == "--exclude-layer") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.exclude_layers.push_back(argv[i]);
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} else if (arg == "-t" || arg == "--type") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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int j;
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for (j = 0; j < GGML_TYPE_COUNT && strcmp(argv[i], ggml_type_name((ggml_type) j)) != 0; j++) {
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// find match
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}
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if (j < GGML_TYPE_COUNT) {
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params.include_types.push_back((ggml_type) j);
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} else {
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fprintf(stderr, "error: %s not in list of types\n", argv[i]);
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invalid_param = true;
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}
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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quantize_stats_print_usage(argc, argv);
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return 1;
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}
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}
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if (invalid_param) {
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fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
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quantize_stats_print_usage(argc, argv);
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return 1;
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}
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// load the model
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fprintf(stderr, "Loading model\n");
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const int64_t t_main_start_us = ggml_time_us();
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llama_context * ctx;
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{
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auto lparams = llama_context_default_params();
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lparams.n_ctx = 256;
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lparams.n_parts = 1;
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lparams.seed = 1;
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lparams.f16_kv = false;
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lparams.use_mlock = false;
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ctx = llama_init_from_file(params.model.c_str(), lparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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return 1;
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}
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}
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const auto &tensors = llama_internal_get_tensor_map(ctx);
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// check layer tensors
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int included_layers = 0;
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int64_t max_nelements = 0;
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bool is_f16 = false;
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for (const auto& kv_tensor : tensors) {
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if (!layer_included(params, kv_tensor.first)) {
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continue;
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}
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if (params.verbose) {
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printf("%s: type %s, size %" PRId64 "\n", kv_tensor.first.c_str(), ggml_type_name(kv_tensor.second->type), ggml_nelements(kv_tensor.second));
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}
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if (kv_tensor.second->type == GGML_TYPE_F16) {
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is_f16 = true;
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} else if (kv_tensor.second->type != GGML_TYPE_F32) {
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fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
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"this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
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llama_free(ctx);
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return 1;
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}
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included_layers++;
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max_nelements = std::max(max_nelements, ggml_nelements(kv_tensor.second));
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}
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if (is_f16) {
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printf("note: source model is f16\n");
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}
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printf("testing %d layers with max size %" PRId64 "\n", included_layers, max_nelements);
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// allocate scratch space
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std::vector<float> input_scratch(SCRATCH_ELEMENTS);
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std::vector<char> quantized_scratch(SCRATCH_ELEMENTS*4);
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std::vector<float> output_scratch(SCRATCH_ELEMENTS);
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// loop throught quantization types
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for (int i = 0; i < GGML_TYPE_COUNT; i++) {
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const ggml_type type = (ggml_type) i;
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if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
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continue;
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}
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quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
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if (qfns.quantize_row_q && qfns.dequantize_row_q) {
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if (params.verbose) {
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printf("testing %s ...\n", ggml_type_name(type));
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}
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error_stats global_stats {};
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for (const auto& kv_tensor : tensors) {
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if (!layer_included(params, kv_tensor.first)) {
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continue;
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}
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if (params.verbose) {
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printf(" %s ...\n", kv_tensor.first.c_str());
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}
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std::string layer_name { ggml_type_name(type) };
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layer_name += "::" + kv_tensor.first;
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test_roundtrip_on_layer(
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layer_name,
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params.per_layer_stats,
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qfns,
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params.reference,
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kv_tensor.second,
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input_scratch.data(),
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quantized_scratch.data(),
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output_scratch.data(),
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global_stats
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);
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}
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print_error_stats(ggml_type_name(type), global_stats, params.print_histogram);
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}
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}
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llama_free(ctx);
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// report timing
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{
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const int64_t t_main_end_us = ggml_time_us();
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printf("\n");
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
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}
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return 0;
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}
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