Add quantize-stats command for testing quantization (#728)
Command that calculates some statistics over the errors introduced by quantization, like mean square error, max error and some percentile errors for layer weights. Should be useful for testing quantization improvements. Exposes some internal state from ggml and llama for testingpull/847/head master-62cfc54
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1 year ago
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parent
698f7b5d63
commit
62cfc54f77
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set(TARGET quantize-stats)
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add_executable(${TARGET} quantize-stats.cpp)
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target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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@ -0,0 +1,355 @@
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#include "ggml.h"
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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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static const char * type_strs[] = { "q4_0", "q4_1", "i8", "i16", "i32", "f16", "f32" };
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static_assert(sizeof(type_strs) == GGML_TYPE_COUNT * sizeof(char *), "Incomplete type list");
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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], type_strs[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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// Sort tensors for consistent output
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const auto tensors = llama_internal_get_tensor_map(ctx);
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std::map<std::string, struct ggml_tensor *> tensors_sorted { tensors.begin(), tensors.end() };
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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_sorted) {
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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(), type_strs[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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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", type_strs[i]);
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}
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error_stats global_stats {};
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for (const auto& kv_tensor : tensors_sorted) {
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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 { type_strs[i] };
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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(type_strs[i], 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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