1673 lines
60 KiB
C++
1673 lines
60 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "gguf.h"
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#include "llama.h"
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#include "log.h"
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#include <algorithm>
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#include <chrono>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <map>
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#include <mutex>
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#include <numeric>
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#include <regex>
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#include <thread>
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#include <unordered_map>
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#include <valarray>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static void print_usage(int, char ** argv) {
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LOG("\nexample usage:\n");
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LOG("\n %s \\\n"
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" -m model.gguf -f some-text.txt [-o imatrix.gguf] [--output-format {gguf,dat}] [--no-ppl] \\\n"
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" [--process-output] [--chunk 123] [--save-frequency 0] [--output-frequency 10] \\\n"
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" [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...] [--parse-special] \\\n"
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" [--output-format gguf|dat] [--show-statistics] [...]\n" , argv[0]);
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LOG("\n");
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}
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static const char * const LLM_KV_IMATRIX_DATASETS = "imatrix.datasets";
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static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
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static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
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struct Stats {
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std::vector<float> activations;
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std::vector<float> values;
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std::vector<int64_t> counts;
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};
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struct tensor_statistics {
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std::string tensor;
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Stats stats;
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float sum_values = 0.0f;
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float mean_values = 0.0f;
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float max_values = 0.0f;
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float min_values = 0.0f;
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int elements = 0;
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float std_deviation = 0.0f;
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float entropy = 0.0f;
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float zd_score = 0.0f;
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float cossim = 0.0f;
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float pearson = 0.0f;
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float l2_dist = 0.0f;
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double dot_prod = 0.0;
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double norm1_sq = 0.0;
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double norm2_sq = 0.0;
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double l2_dist_sq = 0.0;
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};
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class IMatrixCollector {
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public:
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IMatrixCollector() = default;
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void set_params(common_params params) { m_params = std::move(params); }
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bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
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void save_imatrix_legacy(int32_t ncall = -1) const;
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void save_imatrix(int32_t n_chunk = -1) const;
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bool load_imatrix_legacy(const char * fname);
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bool load_imatrix(const char * file_name);
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const std::unordered_map<std::string, Stats> & get_mstats() const { return m_stats; }
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private:
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std::unordered_map<std::string, Stats> m_stats;
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common_params m_params;
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std::mutex m_mutex;
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std::vector<std::string> m_datasets;
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int32_t m_last_chunk = 0;
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std::vector<char> m_src1_data;
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std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
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};
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// remove any prefix and suffixes from the name
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// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
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static std::string filter_tensor_name(const char * name) {
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std::string wname;
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const char * p = strchr(name, '#');
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if (p != NULL) {
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p = p + 1;
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const char * q = strchr(p, '#');
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if (q != NULL) {
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wname = std::string(p, q - p);
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} else {
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wname = p;
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}
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} else {
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wname = name;
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}
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return wname;
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}
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static void process_tensor_name(const std::string & input, std::string & layer, std::string & tensor) {
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layer.clear();
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tensor.clear();
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std::vector<std::string> name;
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std::istringstream stream(input);
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std::string item;
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while (std::getline(stream, item, '.')) { name.push_back(item); }
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for (size_t i = 0; i < name.size(); ++i) {
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if (name[i] == "blk" && i + 1 < name.size()) {
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layer = name[i + 1];
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break;
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}
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}
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for (size_t i = 0; i < name.size(); ++i) {
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if (name[i] == "weight" && i > 0) {
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tensor = name[i - 1];
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break;
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}
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}
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if (tensor.empty()) { tensor = input; }
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if (layer.empty()) { layer = "-"; }
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}
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static std::vector<float> compute_tensor_averages(const Stats & tstats) {
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if (tstats.counts.empty()) { return {}; }
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const size_t n_mat = tstats.counts.size();
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const size_t len = !tstats.activations.empty() ? tstats.activations.size() : tstats.values.size();
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if (len == 0 || n_mat == 0 || len % n_mat != 0) { return {}; }
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const size_t row = len / n_mat;
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std::vector<float> vec;
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vec.resize(len);
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bool has_valid = false;
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const bool use_activations = !tstats.activations.empty();
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for (size_t m = 0; m < n_mat; ++m) {
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const auto c = (float) tstats.counts[m];
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const size_t off = m * row;
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if (c <= 0.0f) { continue; }
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has_valid = true;
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const float scale = 1.0f / c;
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const float * src = use_activations ? &tstats.activations[off] : &tstats.values[off];
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float * dst = & vec[off];
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for (size_t j = 0; j < row; ++j) { dst[j] = src[j] * scale; }
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}
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if (!has_valid) { return {}; }
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return vec;
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}
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static bool compute_vector_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e, bool & legacy) {
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legacy = e.activations.empty();
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const size_t n_mat = e.counts.size();
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const size_t len = legacy ? e.values.size() : e.activations.size();
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if (n_mat == 0 || len == 0 || len % n_mat != 0) {
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LOG_ERR("%s: data size mismatch or empty for tensor %s\n", __func__, name.c_str());
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return false;
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}
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if (!legacy && e.values.size() != len) {
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LOG_ERR("%s: activations/values size mismatch for %s\n", __func__, name.c_str());
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return false;
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}
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const size_t row_size = len / n_mat;
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double mean = 0.0;
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double M2 = 0.0;
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double sum = 0.0;
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float vmin = std::numeric_limits<float>::max();
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float vmax = -std::numeric_limits<float>::max();
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double energy_sum = 0.0;
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size_t valid_n = 0;
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// Pass 1: Mean, Min, Max, Std Dev
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for (size_t i = 0; i < n_mat; ++i) {
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const auto c = (float)e.counts[i];
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if (c <= 0.0f) { continue; }
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const double inv_c = 1.0 / (double)c;
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const size_t off = i * row_size;
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for (size_t j = 0; j < row_size; ++j) {
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const double v_act = legacy ? 0.0 : (double)e.activations[off + j] * inv_c;
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const double v_val = (double)e.values[off + j] * inv_c;
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const double v = legacy ? v_val : v_act; // Use activation average for non-legacy
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sum += v_val;
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if ((float)v < vmin) { vmin = (float)v; }
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if ((float)v > vmax) { vmax = (float)v; }
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valid_n++;
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const double delta = v - mean;
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mean += delta / (double)valid_n;
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M2 += delta * (v - mean);
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if (v_val > 0.0) { energy_sum += v_val; }
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}
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}
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if (valid_n == 0) { return false; }
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float std_deviation = 0.0f;
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float entropy = 0.0f;
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double zd_count = 0.0;
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const double variance = valid_n > 1 ? M2 / ((double)valid_n - 1) : 0.0;
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std_deviation = std::sqrt((float)std::max(variance, 0.0));
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// Pass 2: Entropy and Z-Score
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const double inv_energy_sum = energy_sum > 0.0 ? 1.0 / energy_sum : 0.0;
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const float inv_std = std_deviation > 0.0f ? 1.0f / std_deviation : 0.0f;
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const float fmean = (float)mean;
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const float log2_val = 1 / std::log2f(2);
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for (size_t i = 0; i < n_mat; ++i) {
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const auto c = (float)e.counts[i];
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if (c <= 0.0f) { continue; }
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const double inv_c = 1.0 / (double)c;
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const size_t off = i * row_size;
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for (size_t j = 0; j < row_size; ++j) {
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// Entropy (Distribution of Energy)
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if (inv_energy_sum > 0.0) {
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const double v_energy = (double)e.values[off + j] * inv_c;
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const double p = std::max(0.0, v_energy) * inv_energy_sum;
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if (p > 1e-9) { entropy -= (float)(p * std::log(p) * log2_val); }
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}
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// Z-Score Density (Outlier detection)
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if (std_deviation > 0.0f) {
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const double v_act = legacy ? 0.0 : (double)e.activations[off + j] * inv_c;
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const double v_val = (double)e.values[off + j] * inv_c;
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const float v = (float)(legacy ? v_val : v_act);
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if (std::fabs((v - fmean) * inv_std) > 1.0f) { zd_count += 1.0; }
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}
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}
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}
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auto & ts = tstats.emplace_back();
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ts.tensor = name;
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ts.stats = e;
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ts.sum_values = (float)sum;
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ts.mean_values = (float)mean;
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ts.max_values = vmax;
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ts.min_values = vmin;
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ts.elements = (int)valid_n;
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ts.std_deviation = std_deviation;
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ts.entropy = std::abs(entropy);
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ts.zd_score = (float)(zd_count / (double)valid_n);
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ts.cossim = std::numeric_limits<float>::quiet_NaN();
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ts.pearson = std::numeric_limits<float>::quiet_NaN();
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ts.l2_dist = std::numeric_limits<float>::quiet_NaN();
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return true;
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}
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static void compute_tensor_statistics(std::vector<tensor_statistics> & tstats) {
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std::unordered_map<std::string, size_t> tensor_map;
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tensor_map.reserve(tstats.size());
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for (size_t i = 0; i < tstats.size(); ++i) { tensor_map[tstats[i].tensor] = i; }
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for (auto & ts : tstats) {
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std::string layer_str;
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std::string dummy_tensor;
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process_tensor_name(ts.tensor, layer_str, dummy_tensor);
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int blk = -1;
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try { blk = std::stoi(layer_str); } catch (...) { continue; }
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if (blk <= 0) { continue; }
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const size_t blk_start_pos = ts.tensor.find("blk." + layer_str);
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if (blk_start_pos == std::string::npos) { continue; }
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std::string tname = ts.tensor;
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tname.replace(blk_start_pos, layer_str.length() + 4, "blk." + std::to_string(blk - 1));
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auto it = tensor_map.find(tname);
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if (it == tensor_map.end()) {
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LOG_WRN("%s: missing previous-layer tensor '%s'\n", __func__, tname.c_str());
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continue;
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}
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const auto & prev_ts = tstats[it->second];
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const auto curr_avg = compute_tensor_averages(ts.stats);
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const auto prev_avg = compute_tensor_averages(prev_ts.stats);
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if (curr_avg.empty() || curr_avg.size() != prev_avg.size()) { continue; }
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double dot_prod = 0.0;
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double norm1_sq = 0.0;
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double norm2_sq = 0.0;
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double l2_dist_sq = 0.0;
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double sum_c = 0.0;
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double sum_p = 0.0;
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const size_t n = curr_avg.size();
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// Pass 1: Sums for Means
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for (size_t i = 0; i < n; ++i) {
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sum_c += curr_avg[i];
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sum_p += prev_avg[i];
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}
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const double mean_c = sum_c / n;
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const double mean_p = sum_p / n;
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double cov_sum = 0.0;
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double var_c_sum = 0.0;
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double var_p_sum = 0.0;
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// Pass 2: Metrics
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for (size_t i = 0; i < n; ++i) {
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const double c_val = curr_avg[i];
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const double p_val = prev_avg[i];
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// Cosine Similarity & L2 Distance
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dot_prod += c_val * p_val;
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norm1_sq += c_val * c_val;
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norm2_sq += p_val * p_val;
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const double diff = c_val - p_val;
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l2_dist_sq += diff * diff;
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// Pearson (Centered stats)
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const double dc = c_val - mean_c;
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const double dp = p_val - mean_p;
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cov_sum += dc * dp;
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var_c_sum += dc * dc;
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var_p_sum += dp * dp;
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}
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ts.dot_prod = dot_prod;
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ts.norm1_sq = norm1_sq;
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ts.norm2_sq = norm2_sq;
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ts.l2_dist_sq = l2_dist_sq;
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ts.l2_dist = (float)std::sqrt(l2_dist_sq);
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if (norm1_sq > 0.0 && norm2_sq > 0.0) {
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ts.cossim = (float)(dot_prod / (std::sqrt(norm1_sq) * std::sqrt(norm2_sq)));
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ts.cossim = std::clamp(ts.cossim, -1.0f, 1.0f);
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} else {
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ts.cossim = (norm1_sq == 0.0 && norm2_sq == 0.0) ? std::numeric_limits<float>::quiet_NaN() : 0.0f;
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}
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if (var_c_sum > 0.0 && var_p_sum > 0.0) {
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ts.pearson = (float)(cov_sum / (std::sqrt(var_c_sum) * std::sqrt(var_p_sum)));
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ts.pearson = std::clamp(ts.pearson, -1.0f, 1.0f);
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} else {
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ts.pearson = (var_c_sum == 0.0 && var_p_sum == 0.0) ? std::numeric_limits<float>::quiet_NaN() : 0.0f;
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}
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}
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}
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static void compute_layer_statistics(const std::vector<tensor_statistics> & tstats,
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std::map<int, float> & layer_cossim,
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std::map<int, float> & layer_l2_dist,
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std::map<int, float> & layer_pearson) {
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struct layer_aggregation {
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double sum_dot_prod = 0.0;
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double sum_norm1_sq = 0.0;
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double sum_norm2_sq = 0.0;
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double sum_l2_dist_sq = 0.0;
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double sum_pearson = 0.0;
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int n_tensors = 0;
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};
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std::map<int, layer_aggregation> laggr;
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for (const auto & ts : tstats) {
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std::string layer_str;
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std::string dummy;
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process_tensor_name(ts.tensor, layer_str, dummy);
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int blk = -1;
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try { blk = std::stoi(layer_str); } catch(...) { continue; }
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if (blk <= 0) { continue; }
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if (ts.norm1_sq == 0.0 && ts.norm2_sq == 0.0 && ts.l2_dist_sq == 0.0) { continue; }
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auto & entry = laggr[blk];
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entry.sum_dot_prod += ts.dot_prod;
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entry.sum_norm1_sq += ts.norm1_sq;
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entry.sum_norm2_sq += ts.norm2_sq;
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entry.sum_l2_dist_sq += ts.l2_dist_sq;
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entry.sum_pearson += ts.pearson;
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entry.n_tensors++;
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}
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for (const auto & [layer, agg] : laggr) {
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if (agg.n_tensors == 0) { continue; }
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float cossim = 0.0f;
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if (agg.sum_norm1_sq > 0.0 && agg.sum_norm2_sq > 0.0) {
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cossim = (float)(agg.sum_dot_prod / (std::sqrt(agg.sum_norm1_sq) * std::sqrt(agg.sum_norm2_sq)));
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cossim = std::clamp(cossim, -1.0f, 1.0f);
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} else if (agg.sum_norm1_sq == 0.0 && agg.sum_norm2_sq == 0.0) {
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cossim = std::numeric_limits<float>::quiet_NaN();
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}
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layer_cossim[layer] = cossim;
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layer_l2_dist[layer] = (float)std::sqrt(agg.sum_l2_dist_sq);
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layer_pearson[layer] = (float)(agg.sum_pearson / agg.n_tensors);
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}
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}
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bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
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GGML_UNUSED(user_data);
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const struct ggml_tensor * src0 = t->src[0];
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const struct ggml_tensor * src1 = t->src[1];
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std::string wname = filter_tensor_name(src0->name);
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const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
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// when ask is true, the scheduler wants to know if we are interested in data from this tensor
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// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
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if (ask) {
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if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
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if (t->op != GGML_OP_MUL_MAT) return false;
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// why are small batches ignored (<16 tokens)?
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if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
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if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false;
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return true;
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}
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std::lock_guard<std::mutex> lock(m_mutex);
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// copy the data from the GPU memory if needed
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const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
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|
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if (!is_host) {
|
|
const size_t src1_nbytes = ggml_nbytes(src1);
|
|
m_src1_data.resize(src1_nbytes);
|
|
ggml_backend_tensor_get(src1, m_src1_data.data(), 0, src1_nbytes);
|
|
}
|
|
|
|
const char * data = is_host ? (const char *) src1->data : m_src1_data.data();
|
|
GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
|
|
|
|
// this has been adapted to the new format of storing merged experts in a single 3d tensor
|
|
// ref: https://github.com/ggml-org/llama.cpp/pull/6387
|
|
if (t->op == GGML_OP_MUL_MAT_ID) {
|
|
// ids -> [n_experts_used, n_tokens]
|
|
// src1 -> [cols, n_expert_used, n_tokens]
|
|
const ggml_tensor * ids = t->src[2];
|
|
const int64_t n_as = src0->ne[2];
|
|
const int64_t n_ids = ids->ne[0];
|
|
|
|
// the top-k selected expert ids are stored in the ids tensor
|
|
// for simplicity, always copy ids to host, because it is small
|
|
// take into account that ids is not contiguous!
|
|
|
|
GGML_ASSERT(ids->ne[1] == src1->ne[2]);
|
|
|
|
// the extra dimension would need to be stored somewhere to be reflected in the imatrix file
|
|
if (ggml_nrows(src1) != src1->ne[1] * src1->ne[2]) {
|
|
LOG_ERR("%s: tensor has more than 3 dimensions: %s", __func__, wname.c_str());
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
m_ids.resize(ggml_nbytes(ids));
|
|
ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
|
|
|
|
auto & e = m_stats[wname];
|
|
|
|
if (e.counts.size() == 1 && n_as > 1) {
|
|
// broadcast, when loading an old imatrix
|
|
e.counts.resize(n_as, e.counts[0]);
|
|
}
|
|
if (e.values.empty()) {
|
|
e.activations.resize(src1->ne[0]*n_as, 0);
|
|
e.values.resize(src1->ne[0]*n_as, 0);
|
|
e.counts.resize(n_as, 0);
|
|
}
|
|
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
|
|
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)(src1->ne[0]*n_as));
|
|
exit(1); //GGML_ABORT("fatal error");
|
|
}
|
|
else if (e.counts.size() != (size_t)n_as) {
|
|
LOG_ERR("%s: inconsistent expert count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), (int)n_as);
|
|
exit(1); //GGML_ABORT("fatal error");
|
|
}
|
|
LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
|
|
// loop over all possible experts, regardless if they are used or not in the batch
|
|
for (int64_t ex = 0; ex < n_as; ++ex) {
|
|
size_t e_start = ex*src1->ne[0];
|
|
|
|
for (int64_t idx = 0; idx < n_ids; ++idx) {
|
|
for (int64_t row = 0; row < src1->ne[2]; ++row) {
|
|
const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]);
|
|
|
|
GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
|
|
|
|
if (excur != ex) continue;
|
|
|
|
const int64_t i11 = idx % src1->ne[1];
|
|
const int64_t i12 = row;
|
|
const float * x = (const float *)(data + i11*src1->nb[1] + i12*src1->nb[2]);
|
|
|
|
e.counts[ex]++;
|
|
|
|
for (int64_t j = 0; j < src1->ne[0]; ++j) {
|
|
e.activations[e_start + j] += x[j];
|
|
e.values[e_start + j] += x[j] * x[j];
|
|
if (!std::isfinite((float)e.values[e_start + j])) {
|
|
LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
|
|
exit(1);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
const int32_t n_chunk = e.counts[ex] / chunk_size;
|
|
if (n_chunk > m_last_chunk) {
|
|
const int32_t chunk_step = n_chunk - m_last_chunk;
|
|
m_last_chunk = n_chunk;
|
|
if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
|
|
save_imatrix();
|
|
}
|
|
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
|
|
save_imatrix(m_last_chunk);
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
auto & e = m_stats[wname];
|
|
const int64_t n_mat = src0->ne[2] * src0->ne[3];
|
|
|
|
// use a single count per dense tensor
|
|
// (necessary when merging older GGUF-imatrix files with 3d tensors)
|
|
if (e.counts.size() > 1) {
|
|
bool all_equal = true;
|
|
for (size_t i = 1; i < e.counts.size(); ++i) {
|
|
if (e.counts[0] != e.counts[i]) {
|
|
all_equal = false;
|
|
break;
|
|
}
|
|
}
|
|
if (all_equal) {
|
|
e.counts.resize(1);
|
|
}
|
|
}
|
|
if (e.values.empty()) {
|
|
e.activations.resize(src1->ne[0] * n_mat, 0);
|
|
e.values.resize(src1->ne[0] * n_mat, 0);
|
|
e.counts.resize(1, 0);
|
|
}
|
|
else if (e.values.size() != (size_t)(src1->ne[0] * n_mat)) {
|
|
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)(src1->ne[0] * n_mat));
|
|
exit(1); //GGML_ABORT("fatal error");
|
|
}
|
|
LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->ne[2], (int)src1->type);
|
|
|
|
for (int64_t i3 = 0; i3 < src1->ne[3]; ++i3) {
|
|
for (int64_t i2 = 0; i2 < src1->ne[2]; ++i2) {
|
|
// handle 3D+ tensors, but flatten 3D+ activations when model tensor is 2D
|
|
const int64_t mat_id = (i3 % src0->ne[3]) * src0->ne[2] + (i2 % src0->ne[2]);
|
|
const int64_t mat_start = mat_id * src1->ne[0];
|
|
|
|
for (int64_t row = 0; row < src1->ne[1]; ++row) {
|
|
const float * x = (const float *) (data + row * src1->nb[1] + i2 * src1->nb[2] + i3 * src1->nb[3]);
|
|
for (int64_t j = 0; j < src1->ne[0]; ++j) {
|
|
e.activations[mat_start + j] += x[j];
|
|
e.values[mat_start + j] += x[j] * x[j];
|
|
if (!std::isfinite((float)e.values[j])) {
|
|
LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str());
|
|
exit(1);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
// only 1 count in practice, except when a tensor is used for both MUL_MAT_ID and MUL_MAT
|
|
for (size_t i = 0; i < e.counts.size(); ++i) {
|
|
e.counts[i] += ggml_nrows(src1) / n_mat;
|
|
const int32_t n_chunk = e.counts[i] / chunk_size;
|
|
if (n_chunk > m_last_chunk) {
|
|
const int32_t chunk_step = n_chunk - m_last_chunk;
|
|
m_last_chunk = n_chunk;
|
|
if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
|
|
save_imatrix();
|
|
}
|
|
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
|
|
save_imatrix(m_last_chunk);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const {
|
|
auto fname = m_params.out_file;
|
|
|
|
if (ncall > 0) {
|
|
fname += ".at_";
|
|
fname += std::to_string(ncall);
|
|
}
|
|
|
|
// warn when writing imatrix entries that do not have full data
|
|
// this can happen with MoE models where some of the experts end up not being exercised by the provided training data
|
|
|
|
int n_entries = 0;
|
|
std::vector<std::string> to_store;
|
|
|
|
bool is_first = true; // for printing
|
|
for (const auto & kv : m_stats) {
|
|
const int n_all = kv.second.counts.size();
|
|
|
|
if (n_all == 0) {
|
|
continue;
|
|
}
|
|
|
|
int n_zeros = 0;
|
|
for (const int c : kv.second.counts) {
|
|
if (c == 0) {
|
|
n_zeros++;
|
|
}
|
|
}
|
|
|
|
if (n_zeros != 0 && is_first) {
|
|
LOG_INF("\n");
|
|
is_first = false;
|
|
}
|
|
|
|
if (n_zeros == n_all) {
|
|
LOG_WRN("%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
|
|
continue;
|
|
}
|
|
|
|
if (n_zeros > 0) {
|
|
LOG_WRN("%s: entry '%40s' has partial data (%.2f%%)\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
|
|
}
|
|
|
|
n_entries++;
|
|
to_store.push_back(kv.first);
|
|
}
|
|
|
|
if (to_store.size() < m_stats.size()) {
|
|
LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
|
|
}
|
|
|
|
// deterministic tensor name order
|
|
std::sort(to_store.begin(), to_store.end());
|
|
|
|
const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
|
|
|
|
std::ofstream out(fname, std::ios::binary);
|
|
out.write((const char *) &n_entries, sizeof(n_entries));
|
|
for (const auto & name : to_store) {
|
|
const auto & stat = m_stats.at(name);
|
|
const int32_t len = name.size();
|
|
out.write((const char *) &len, sizeof(len));
|
|
out.write(name.c_str(), len);
|
|
// ceiling division to avoid accidental zeros
|
|
const int32_t ncall = (*std::max_element(stat.counts.begin(), stat.counts.end()) + (chunk_size - 1)) / chunk_size;
|
|
out.write((const char *) &ncall, sizeof(ncall));
|
|
const int32_t nval = stat.values.size();
|
|
const int32_t nmat = stat.counts.size();
|
|
out.write((const char *) &nval, sizeof(nval));
|
|
if (nval > 0 && nmat > 0) {
|
|
std::vector<float> tmp(nval);
|
|
for (int32_t i = 0; i < nval; i++) {
|
|
float count = static_cast<float>(stat.counts[i / (nval / nmat)]);
|
|
float value = stat.values[i];
|
|
if (count == 0.0f) {
|
|
// store 1 for partial data
|
|
value = 1.0f;
|
|
count = 1.0f;
|
|
}
|
|
tmp[i] = (value / count) * static_cast<float>(ncall);
|
|
}
|
|
out.write((const char *) tmp.data(), nval * sizeof(float));
|
|
}
|
|
}
|
|
|
|
// Write the number of call the matrix was computed with
|
|
out.write((const char *) &m_last_chunk, sizeof(m_last_chunk));
|
|
|
|
// Write the input filename at the end of the file to later on specify it in quantize
|
|
{
|
|
const char * dataset_file = m_params.prompt_file.c_str();
|
|
int32_t len = m_params.prompt_file.size();
|
|
// When there is no prompt but there were other imatrix files loaded, use the last dataset
|
|
if (m_params.prompt_file.empty() && !m_datasets.empty()) {
|
|
const std::string & dataset_str = m_datasets[m_datasets.size() - 1];
|
|
dataset_file = dataset_str.c_str();
|
|
len = dataset_str.size();
|
|
}
|
|
out.write((const char *) &len, sizeof(len));
|
|
out.write(dataset_file, len);
|
|
}
|
|
|
|
LOGV(1, "\n");
|
|
LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
|
|
}
|
|
|
|
void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
|
|
auto fname = m_params.out_file;
|
|
int8_t use_legacy_format = m_params.imat_dat;
|
|
|
|
if (use_legacy_format > 0) {
|
|
this->save_imatrix_legacy(n_chunk);
|
|
return;
|
|
}
|
|
// only warn when `--output-format gguf` is not specified
|
|
if (use_legacy_format == 0 && !string_ends_with(fname, ".gguf")) {
|
|
LOG_WRN("\n%s: saving imatrix using GGUF format with a different suffix than .gguf\n", __func__);
|
|
LOG_WRN("%s: if you want the previous imatrix format, use --output-format dat\n", __func__);
|
|
}
|
|
|
|
if (n_chunk > 0) {
|
|
fname += ".at_";
|
|
fname += std::to_string(n_chunk);
|
|
}
|
|
|
|
// write imatrix entries even if they don't have full data. (can be corrected when reading)
|
|
// this can happen with MoE models where some of the experts end up not being exercised by the provided training data
|
|
|
|
std::vector<std::string> to_store;
|
|
size_t data_size = 0;
|
|
|
|
bool is_first = true; // for printing
|
|
for (const auto & kv : m_stats) {
|
|
const int n_all = kv.second.counts.size();
|
|
|
|
int n_zeros = 0;
|
|
for (const auto c : kv.second.counts) {
|
|
if (c == 0) {
|
|
n_zeros++;
|
|
}
|
|
}
|
|
|
|
if (n_zeros != 0 && is_first) {
|
|
LOG_INF("\n");
|
|
is_first = false;
|
|
}
|
|
|
|
if (n_zeros > 0) {
|
|
LOG_WRN("%s: entry '%40s' has partial data (%.2f%%)\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
|
|
}
|
|
|
|
to_store.push_back(kv.first);
|
|
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.activations.size(), GGML_MEM_ALIGN);
|
|
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN);
|
|
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
|
|
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * 10, GGML_MEM_ALIGN);
|
|
}
|
|
|
|
// deterministic tensor name order
|
|
std::sort(to_store.begin(), to_store.end());
|
|
|
|
// Compute per-tensor statistics
|
|
std::vector<tensor_statistics> tstats;
|
|
tstats.reserve(m_stats.size());
|
|
bool legacy;
|
|
for (const auto & kv : m_stats) {
|
|
compute_vector_statistics(tstats, kv.first, kv.second, legacy);
|
|
}
|
|
if (!tstats.empty()) { compute_tensor_statistics(tstats); }
|
|
|
|
// index by tensor name
|
|
std::unordered_map<std::string, const tensor_statistics *> tstat_index;
|
|
tstat_index.reserve(tstats.size());
|
|
for (const auto & ts : tstats) { tstat_index[ts.tensor] = &ts; }
|
|
|
|
struct ggml_init_params params = {
|
|
/* .mem_size = */ data_size,
|
|
/* .mem_buffer = */ NULL,
|
|
/* .no_alloc = */ false,
|
|
};
|
|
struct ggml_context * ctx = ggml_init(params);
|
|
struct gguf_context * ctx_gguf = gguf_init_empty();
|
|
|
|
{
|
|
std::vector<const char *> datasets;
|
|
datasets.reserve(m_datasets.size() + 1);
|
|
for (size_t i = 0; i < m_datasets.size(); ++i) {
|
|
datasets.push_back(m_datasets[i].c_str());
|
|
}
|
|
if (!m_params.prompt_file.empty()) {
|
|
datasets.push_back(m_params.prompt_file.c_str());
|
|
}
|
|
|
|
gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
|
|
// Write the dataset paths
|
|
gguf_set_arr_str(ctx_gguf, LLM_KV_IMATRIX_DATASETS, datasets.data(), datasets.size());
|
|
// Write the number of chunks the matrix was computed with
|
|
gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk);
|
|
gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel);
|
|
}
|
|
|
|
for (const auto & name : to_store) {
|
|
const auto & stat = m_stats.at(name);
|
|
const int32_t nval = (int32_t) stat.values.size();
|
|
const int32_t nmat = (int32_t) stat.counts.size();
|
|
if (nval > 0 && nmat > 0) {
|
|
struct ggml_tensor * in_sum2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat);
|
|
struct ggml_tensor * counts = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat);
|
|
ggml_format_name(in_sum2, "%s.in_sum2", name.c_str());
|
|
ggml_format_name(counts, "%s.counts", name.c_str());
|
|
|
|
for (int32_t j = 0; j < nval; ++j) {
|
|
((float *) in_sum2->data)[j] = (float) stat.values[j];
|
|
}
|
|
for (int32_t j = 0; j < nmat; ++j) {
|
|
((float *) counts->data)[j] = (float) stat.counts[j];
|
|
}
|
|
|
|
gguf_add_tensor(ctx_gguf, in_sum2);
|
|
gguf_add_tensor(ctx_gguf, counts);
|
|
|
|
if (!stat.activations.empty()) {
|
|
const int32_t nact = (int32_t) stat.activations.size();
|
|
struct ggml_tensor * in_sum = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nact / nmat, nmat);
|
|
ggml_format_name(in_sum, "%s.in_sum", name.c_str());
|
|
for (int32_t j = 0; j < nact; ++j) {
|
|
((float *) in_sum->data)[j] = (float) stat.activations[j];
|
|
}
|
|
gguf_add_tensor(ctx_gguf, in_sum);
|
|
}
|
|
}
|
|
|
|
// Store per-tensor statistics as a small 1D tensor
|
|
{
|
|
float nan = std::numeric_limits<float>::quiet_NaN();
|
|
float min = 0.0f;
|
|
float max = 0.0f;
|
|
float mean = 0.0f;
|
|
float stddev = 0.0f;
|
|
float h_norm = 0.0f;
|
|
float zd_score = 0.0f;
|
|
float sum_sq = 0.0f;
|
|
float l2_dist = 0.0f;
|
|
float cossim = 0.0f;
|
|
float pcc = 0.0f;
|
|
auto it_ts = tstat_index.find(name);
|
|
if (it_ts != tstat_index.end() && it_ts->second != nullptr) {
|
|
sum_sq = it_ts->second->sum_values;
|
|
h_norm = it_ts->second->elements > 0 ? 100.0f * (it_ts->second->entropy / std::log2f((float)it_ts->second->elements)) : nan;
|
|
zd_score = it_ts->second->zd_score;
|
|
l2_dist = it_ts->second->l2_dist;
|
|
cossim = it_ts->second->cossim;
|
|
pcc = it_ts->second->pearson;
|
|
min = it_ts->second->min_values;
|
|
max = it_ts->second->max_values;
|
|
mean = it_ts->second->mean_values;
|
|
stddev = it_ts->second->std_deviation;
|
|
}
|
|
|
|
struct ggml_tensor * stats_t = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 10);
|
|
ggml_format_name(stats_t, "%s.stats", name.c_str());
|
|
((float *)stats_t->data)[0] = sum_sq;
|
|
((float *)stats_t->data)[1] = h_norm;
|
|
((float *)stats_t->data)[2] = zd_score;
|
|
((float *)stats_t->data)[3] = l2_dist;
|
|
((float *)stats_t->data)[4] = cossim;
|
|
((float *)stats_t->data)[5] = pcc;
|
|
((float *)stats_t->data)[6] = min;
|
|
((float *)stats_t->data)[7] = max;
|
|
((float *)stats_t->data)[8] = mean;
|
|
((float *)stats_t->data)[9] = stddev;
|
|
gguf_add_tensor(ctx_gguf, stats_t);
|
|
}
|
|
}
|
|
|
|
gguf_write_to_file(ctx_gguf, fname.c_str(), false);
|
|
|
|
LOGV(1, "\n");
|
|
LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
|
|
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
|
|
std::ifstream in(fname, std::ios::binary);
|
|
if (!in) {
|
|
LOG_ERR("%s: failed to open %s\n", __func__, fname);
|
|
return false;
|
|
}
|
|
int n_entries;
|
|
in.read((char *) &n_entries, sizeof(n_entries));
|
|
if (in.fail() || n_entries < 1) {
|
|
LOG_ERR("%s: no data in file %s\n", __func__, fname);
|
|
return false;
|
|
}
|
|
// Guess the chunk size because it's not stored in the file
|
|
const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
|
|
|
|
for (int i = 0; i < n_entries; ++i) {
|
|
int32_t len = 0;
|
|
in.read((char *) &len, sizeof(len));
|
|
std::vector<char> name_as_vec(len + 1);
|
|
in.read((char *) name_as_vec.data(), len);
|
|
if (in.fail()) {
|
|
LOG_ERR("%s: failed reading name for entry %d from %s\n", __func__, i + 1, fname);
|
|
return false;
|
|
}
|
|
name_as_vec[len] = 0;
|
|
std::string name{ name_as_vec.data() };
|
|
auto & e = m_stats[std::move(name)];
|
|
int32_t ncall = 0;
|
|
in.read((char *) &ncall, sizeof(ncall));
|
|
int32_t nval = 0;
|
|
in.read((char *) &nval, sizeof(nval));
|
|
if (in.fail() || nval < 1) {
|
|
LOG_ERR("%s: failed reading number of values for entry %d\n", __func__, i);
|
|
m_stats = {};
|
|
return false;
|
|
}
|
|
|
|
if (e.values.empty()) {
|
|
e.values.resize(nval, 0.0f);
|
|
e.counts.resize(1, 0);
|
|
}
|
|
|
|
std::vector<float> tmp(nval);
|
|
in.read((char *) tmp.data(), nval * sizeof(float));
|
|
if (in.fail()) {
|
|
LOG_ERR("%s: failed reading data for entry %d\n", __func__, i);
|
|
m_stats = {};
|
|
return false;
|
|
}
|
|
|
|
// Recreate the state as expected by save_imatrix(), and correct for weighted sum.
|
|
for (int i = 0; i < nval; i++) {
|
|
e.values[i] += tmp[i] * chunk_size;
|
|
}
|
|
// The legacy format doesn't distinguish the counts for different experts
|
|
for (size_t j = 0; j < e.counts.size(); ++j) {
|
|
e.counts[j] += ncall * chunk_size;
|
|
}
|
|
}
|
|
|
|
{
|
|
// TODO: extract into its own method; this is also used by the GGUF-based format
|
|
// Calculate the last chunk count
|
|
int64_t max_count = 0;
|
|
for (const auto & stats : m_stats) {
|
|
for (int64_t count : stats.second.counts) {
|
|
if (count > max_count) {
|
|
max_count = count;
|
|
}
|
|
}
|
|
}
|
|
m_last_chunk = max_count / (chunk_size);
|
|
}
|
|
|
|
{
|
|
// Read the number of calls the matrix was computed with
|
|
int32_t n_calls;
|
|
in.read((char *) &n_calls, sizeof(n_calls));
|
|
// ignore it because it's not important
|
|
}
|
|
|
|
// Read the dataset path to include it when writing to GGUF
|
|
if (!in.fail()){
|
|
int32_t len = 0;
|
|
in.read((char *) &len, sizeof(len));
|
|
if (!in.fail()) {
|
|
std::vector<char> dataset;
|
|
dataset.resize(len + 1, 0);
|
|
in.read(dataset.data(), len);
|
|
if (!in.fail()) {
|
|
m_datasets.push_back(dataset.data());
|
|
}
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// Using GGUF as the file format, for greater extensibility
|
|
bool IMatrixCollector::load_imatrix(const char * file_name) {
|
|
struct ggml_context * ctx = nullptr;
|
|
struct gguf_init_params meta_gguf_params = {
|
|
/* .no_alloc = */ false, // the data is needed
|
|
/* .ctx = */ &ctx,
|
|
};
|
|
struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params);
|
|
if (!ctx_gguf) {
|
|
return this->load_imatrix_legacy(file_name);
|
|
}
|
|
const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
|
|
if (n_entries < 1) {
|
|
LOG_ERR("%s: no data in file %s\n", __func__, file_name);
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
return false;
|
|
}
|
|
|
|
const int64_t datasets_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS);
|
|
if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
|
|
const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key);
|
|
m_datasets.reserve(m_datasets.size() + n);
|
|
for (int64_t i = 0; i < n; ++i) {
|
|
m_datasets.push_back(gguf_get_arr_str(ctx_gguf, datasets_key, i));
|
|
}
|
|
}
|
|
|
|
const std::string in_sum_suffix{ ".in_sum" };
|
|
const std::string in_sum2_suffix{ ".in_sum2" };
|
|
const std::string counts_suffix{ ".counts" };
|
|
|
|
// Could re-use m_stats instead, but this allows
|
|
// checking for completeness of *each* loaded imatrix file
|
|
// and also makes it easier to re-use a similar implementation in quantize.cpp
|
|
// Using an ordered map to get a deterministic iteration order.
|
|
std::map<std::string, std::tuple<struct ggml_tensor *, struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
|
|
|
|
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
|
|
std::string name = cur->name;
|
|
|
|
if (name.empty()) { continue; }
|
|
|
|
if (string_remove_suffix(name, in_sum2_suffix)) {
|
|
// in_sum2
|
|
std::get<0>(sums_counts_for[std::move(name)]) = cur;
|
|
} else if (string_remove_suffix(name, counts_suffix)) {
|
|
// counts
|
|
std::get<1>(sums_counts_for[std::move(name)]) = cur;
|
|
} else if (string_remove_suffix(name, in_sum_suffix)) {
|
|
// in_sum
|
|
std::get<2>(sums_counts_for[std::move(name)]) = cur;
|
|
}
|
|
else {
|
|
// ignore other tensors
|
|
}
|
|
}
|
|
|
|
for (const auto & sc : sums_counts_for) {
|
|
const std::string & name = sc.first;
|
|
const struct ggml_tensor * in_sum = std::get<2>(sc.second);
|
|
const struct ggml_tensor * in_sum2 = std::get<0>(sc.second);
|
|
const struct ggml_tensor * counts = std::get<1>(sc.second);
|
|
|
|
if (!in_sum2 || !counts || (in_sum != nullptr && ggml_nelements(in_sum) != ggml_nelements(in_sum2))) {
|
|
LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
return false;
|
|
}
|
|
|
|
auto & e = m_stats[name];
|
|
|
|
int64_t nval = ggml_nelements(in_sum2);
|
|
if (e.values.empty()) {
|
|
e.values.resize(nval, 0.0f);
|
|
} else if ((size_t) nval != e.values.size()) {
|
|
LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size());
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
return false;
|
|
}
|
|
if (in_sum != nullptr) {
|
|
if (e.activations.empty()) {
|
|
e.activations.resize(nval, 0.0f);
|
|
} else if ((size_t) nval != e.activations.size()) {
|
|
LOG_ERR("%s: mismatched activations size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.activations.size());
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
int64_t ncounts = ggml_nelements(counts);
|
|
if (e.counts.empty()) {
|
|
e.counts.resize(ncounts, 0);
|
|
} else if (e.counts.size() == 1 && ncounts > 1) {
|
|
// broadcast, when loading an old imatrix
|
|
e.counts.resize(ncounts, e.counts[0]);
|
|
} else if ((size_t) ncounts != e.counts.size()) {
|
|
LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size());
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
return false;
|
|
}
|
|
|
|
// Recreate the state as expected by save_imatrix()
|
|
for (int64_t j = 0; j < nval; j++) {
|
|
if (in_sum != nullptr) { e.activations[j] += ((const float *) in_sum->data)[j]; }
|
|
e.values[j] += ((const float *) in_sum2->data)[j];
|
|
}
|
|
for (int64_t j = 0; j < ncounts; j++) {
|
|
e.counts[j] += std::lround(((const float *) counts->data)[j]);
|
|
}
|
|
}
|
|
|
|
// TODO: extract into its own method; this is also used by the legacy format
|
|
// Calculate the last chunk count
|
|
int64_t max_count = 0;
|
|
for (const auto & stats : m_stats) {
|
|
for (int64_t count : stats.second.counts) {
|
|
if (count > max_count) {
|
|
max_count = count;
|
|
}
|
|
}
|
|
}
|
|
m_last_chunk = max_count / (m_params.n_ctx / m_params.n_parallel);
|
|
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
return true;
|
|
}
|
|
|
|
static IMatrixCollector g_collector;
|
|
|
|
static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
|
|
return g_collector.collect_imatrix(t, ask, user_data);
|
|
}
|
|
|
|
struct results_log_softmax {
|
|
double log_softmax;
|
|
float logit;
|
|
float prob;
|
|
};
|
|
|
|
static std::vector<float> softmax(const std::vector<float> & logits) {
|
|
std::vector<float> probs(logits.size());
|
|
float max_logit = logits[0];
|
|
for (float v : logits) {
|
|
max_logit = std::max(max_logit, v);
|
|
}
|
|
double sum_exp = 0.0;
|
|
for (size_t i = 0; i < logits.size(); i++) {
|
|
// Subtract the maximum logit value from the current logit value for numerical stability
|
|
const float logit = logits[i] - max_logit;
|
|
const float exp_logit = expf(logit);
|
|
sum_exp += exp_logit;
|
|
probs[i] = exp_logit;
|
|
}
|
|
for (size_t i = 0; i < probs.size(); i++) {
|
|
probs[i] /= sum_exp;
|
|
}
|
|
return probs;
|
|
}
|
|
|
|
static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
|
|
float max_logit = logits[0];
|
|
for (int i = 1; i < n_vocab; ++i) {
|
|
max_logit = std::max(max_logit, logits[i]);
|
|
}
|
|
double sum_exp = 0.0;
|
|
for (int i = 0; i < n_vocab; ++i) {
|
|
sum_exp += expf(logits[i] - max_logit);
|
|
}
|
|
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
|
|
}
|
|
|
|
static void process_logits(
|
|
int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
|
|
double & nll, double & nll2, float * logit_history, float * prob_history) {
|
|
std::mutex mutex;
|
|
int counter = 0;
|
|
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
|
|
double local_nll = 0;
|
|
double local_nll2 = 0;
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex);
|
|
int i = counter++;
|
|
if (i >= n_token) {
|
|
nll += local_nll; nll2 += local_nll2;
|
|
break;
|
|
}
|
|
lock.unlock();
|
|
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
|
|
const double v = -results.log_softmax;
|
|
local_nll += v;
|
|
local_nll2 += v*v;
|
|
|
|
logit_history[i] = results.logit;
|
|
prob_history[i] = results.prob;
|
|
}
|
|
};
|
|
for (auto & w : workers) {
|
|
w = std::thread(compute);
|
|
}
|
|
compute();
|
|
for (auto & w : workers) {
|
|
w.join();
|
|
}
|
|
}
|
|
|
|
static bool compute_imatrix(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
const bool add_bos = llama_vocab_get_add_bos(vocab);
|
|
|
|
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
|
|
|
|
auto tim1 = std::chrono::high_resolution_clock::now();
|
|
LOG_INF("%s: tokenizing the input ..\n", __func__);
|
|
|
|
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true, params.parse_special);
|
|
|
|
auto tim2 = std::chrono::high_resolution_clock::now();
|
|
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
|
|
|
|
if (params.i_chunk > 0) {
|
|
if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) {
|
|
LOG_ERR("%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk);
|
|
return false;
|
|
}
|
|
LOG_INF("%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx);
|
|
tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx);
|
|
}
|
|
|
|
if (int(tokens.size()) < 2*n_ctx) {
|
|
LOG_ERR("%s: you need at least %d tokens for a context of %d tokens\n", __func__, 2*n_ctx, n_ctx);
|
|
LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n", __func__, tokens.size());
|
|
return false;
|
|
}
|
|
|
|
std::vector<float> logit_history;
|
|
std::vector<float> prob_history;
|
|
|
|
if (params.compute_ppl) {
|
|
logit_history.resize(tokens.size());
|
|
prob_history.resize(tokens.size());
|
|
}
|
|
|
|
const int n_chunk_max = tokens.size() / n_ctx;
|
|
|
|
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
|
|
const int n_vocab = llama_vocab_n_tokens(vocab);
|
|
const int n_batch = params.n_batch;
|
|
|
|
int count = 0;
|
|
double nll = 0.0;
|
|
double nll2 = 0.0;
|
|
|
|
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
|
|
const int n_seq = std::max(1, n_batch / n_ctx);
|
|
|
|
GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
|
|
GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
|
|
|
|
llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
|
|
|
|
std::vector<float> logits;
|
|
if (params.compute_ppl && num_batches > 1) {
|
|
logits.reserve((size_t)n_ctx * n_vocab);
|
|
}
|
|
|
|
LOG_INF("%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
|
|
|
|
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
|
|
|
|
for (int i = 0; i < n_chunk; i += n_seq) {
|
|
const int start = i * n_ctx;
|
|
const int end = start + n_ctx;
|
|
|
|
const int n_seq_batch = std::min(n_seq, n_chunk - i);
|
|
|
|
const auto t_start = std::chrono::high_resolution_clock::now();
|
|
|
|
// clear the KV cache
|
|
llama_memory_clear(llama_get_memory(ctx), true);
|
|
|
|
for (int j = 0; j < num_batches; ++j) {
|
|
const int batch_start = start + j * n_batch;
|
|
const int batch_size = std::min(end - batch_start, n_batch);
|
|
|
|
// clear the batch
|
|
common_batch_clear(batch);
|
|
|
|
for (int seq = 0; seq < n_seq_batch; seq++) {
|
|
int seq_start = batch_start + seq*n_ctx;
|
|
|
|
// save original token and restore it after eval
|
|
const auto token_org = tokens[seq_start];
|
|
|
|
// add BOS token for the first batch of each chunk
|
|
if (add_bos && j == 0) {
|
|
tokens[seq_start] = llama_vocab_bos(vocab);
|
|
}
|
|
for (int k = 0; k < batch_size; ++k) {
|
|
// NOTE: specifying all logits to get activations for the output.weight tensor
|
|
// and also for the perplexity calculation.
|
|
// TODO: only get outputs when (params.process_output || params.compute_ppl)
|
|
// (not possible when this skips FFN computation of the last layer)
|
|
common_batch_add(batch, tokens[seq_start + k], j*n_batch + k, { seq }, true);
|
|
}
|
|
|
|
// restore the original token in case it was set to BOS
|
|
tokens[seq_start] = token_org;
|
|
}
|
|
|
|
if (llama_decode(ctx, batch)) {
|
|
LOG_ERR("%s : failed to eval\n", __func__);
|
|
llama_batch_free(batch);
|
|
return false;
|
|
}
|
|
|
|
if (params.compute_ppl && num_batches > 1) {
|
|
const auto * batch_logits = llama_get_logits(ctx);
|
|
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
|
|
}
|
|
}
|
|
|
|
|
|
if (i == 0) {
|
|
llama_synchronize(ctx);
|
|
const auto t_end = std::chrono::high_resolution_clock::now();
|
|
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
|
|
LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
|
int total_seconds = (int)(t_total * n_chunk / n_seq);
|
|
if (total_seconds >= 60*60) {
|
|
LOG("%d hours ", total_seconds / (60*60));
|
|
total_seconds = total_seconds % (60*60);
|
|
}
|
|
LOG("%.2f minutes\n", total_seconds / 60.0);
|
|
}
|
|
|
|
if (params.compute_ppl) {
|
|
const int first = n_ctx/2;
|
|
for (int seq = 0; seq < n_seq_batch; seq++) {
|
|
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
|
|
|
|
llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
|
|
|
|
process_logits(n_vocab, all_logits + first*n_vocab,
|
|
tokens_data, n_ctx - 1 - first,
|
|
workers, nll, nll2,
|
|
logit_history.data() + start + seq*n_ctx + first,
|
|
prob_history.data() + start + seq*n_ctx + first);
|
|
|
|
count += n_ctx - first - 1;
|
|
|
|
LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
|
|
}
|
|
fflush(stdout);
|
|
|
|
logits.clear();
|
|
}
|
|
}
|
|
|
|
LOG("\n");
|
|
|
|
if (params.compute_ppl) {
|
|
nll2 /= count;
|
|
nll /= count;
|
|
const double ppl = exp(nll);
|
|
nll2 -= nll * nll;
|
|
if (nll2 > 0) {
|
|
nll2 = sqrt(nll2/(count-1));
|
|
LOG("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
|
|
} else {
|
|
LOG("Unexpected negative standard deviation of log(prob)\n");
|
|
}
|
|
}
|
|
|
|
llama_batch_free(batch);
|
|
|
|
return true;
|
|
}
|
|
|
|
static bool show_statistics(const common_params & params) {
|
|
g_collector.set_params(params);
|
|
std::vector<tensor_statistics> ts;
|
|
|
|
if (params.in_files.empty()) { return false; }
|
|
|
|
// Load and process data
|
|
if (g_collector.load_imatrix(params.in_files[0].c_str())) {
|
|
ts.reserve(g_collector.get_mstats().size());
|
|
for (const auto & [name, stats] : g_collector.get_mstats()) {
|
|
bool legacy_imatrix = true;
|
|
if (!compute_vector_statistics(ts, name, stats, legacy_imatrix)) { continue; }
|
|
}
|
|
} else {
|
|
return false;
|
|
}
|
|
|
|
if (ts.empty()) { return false; }
|
|
|
|
bool legacy = ts.empty() ? true : ts[0].stats.activations.empty();
|
|
compute_tensor_statistics(ts);
|
|
|
|
// Sorting logic (Layer index -> Tensor Name)
|
|
struct tensor_comparer {
|
|
bool operator()(const tensor_statistics & a, const tensor_statistics & b) const {
|
|
std::string lay_a;
|
|
std::string lay_b;
|
|
std::string name_a;
|
|
std::string name_b;
|
|
process_tensor_name(a.tensor, lay_a, name_a);
|
|
process_tensor_name(b.tensor, lay_b, name_b);
|
|
|
|
// Handle non-numeric layers (e.g., "output")
|
|
int blk_a = 9999;
|
|
int blk_b = 9999;
|
|
try {
|
|
blk_a = std::stoi(lay_a);
|
|
} catch(...) {
|
|
if (lay_a == "output") { blk_a = 10000; }
|
|
}
|
|
try {
|
|
blk_b = std::stoi(lay_b);
|
|
} catch(...) {
|
|
if (lay_b == "output") { blk_b = 10000; }
|
|
}
|
|
|
|
if (blk_a != blk_b) { return blk_a < blk_b; }
|
|
return name_a < name_b;
|
|
}
|
|
};
|
|
std::sort(ts.begin(), ts.end(), tensor_comparer());
|
|
|
|
struct layer_stats {
|
|
float layer_sum = 0.0f;
|
|
float layer_zd = 0.0f;
|
|
int n = 0;
|
|
};
|
|
std::map<int, layer_stats> ls;
|
|
|
|
// Shorten names for table formatting
|
|
auto label_fmt = [](std::string s, size_t w) -> std::string {
|
|
if (s.length() <= w) { return s; }
|
|
return ".." + s.substr(s.length() - (w - 2));
|
|
};
|
|
|
|
constexpr int w_lay = 6;
|
|
constexpr int w_nam = 40; // Should be wide enough for most tensor names
|
|
const auto * sep = " | ";
|
|
|
|
LOG_INF("\nComputing tensor statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast<int>(ts.size()));
|
|
|
|
if (legacy) {
|
|
LOG_INF("\n%*s%s%-*s%s%10s %12s %10s %10s%s %8s %8s%s%17s %8s %8s\n",
|
|
w_lay, "Layer", sep,
|
|
w_nam, "Tensor", sep,
|
|
"Min", "Max", "Mean", "StdDev", sep,
|
|
"H Norm", "ZD", sep,
|
|
"∑ E[A²]", "CosSim", "PCC");
|
|
LOG_INF("%s\n", std::string(154, '-').c_str());
|
|
} else {
|
|
LOG_INF("\n%*s%s%-*s%s%10s %12s %10s %10s%s %8s %8s%s%17s %12s %8s %8s\n",
|
|
w_lay, "Layer", sep,
|
|
w_nam, "Tensor", sep,
|
|
"Min", "Max", "Mean", "StdDev", sep,
|
|
"H Norm", "ZD", sep,
|
|
"∑ E[A²]", "L2 Dist", "CosSim", "PCC");
|
|
LOG_INF("%s\n", std::string(167, '-').c_str());
|
|
}
|
|
|
|
// Tensor Statistics
|
|
for (const auto & tstat : ts) {
|
|
std::string layer;
|
|
std::string name;
|
|
|
|
process_tensor_name(tstat.tensor, layer, name);
|
|
const float h_norm = tstat.elements > 1 ? 100.0f * (tstat.entropy / std::log2f((float)tstat.elements)) : std::numeric_limits<float>::quiet_NaN();
|
|
|
|
int blk;
|
|
try { blk = std::stoi(layer); } catch (...) { blk = -1; }
|
|
|
|
if (legacy) {
|
|
LOG_INF("%*s%s%-*s%s%10.4f %12.4f %10.4f %10.4f%s%8.2f%% %8.2f%%%s%14.4f %8.4f %8.4f\n",
|
|
w_lay, layer.c_str(), sep,
|
|
w_nam, label_fmt(tstat.tensor, w_nam).c_str(), sep,
|
|
tstat.min_values, tstat.max_values, tstat.mean_values, tstat.std_deviation, sep,
|
|
h_norm, 100.0f * tstat.zd_score, sep,
|
|
tstat.sum_values, tstat.cossim, tstat.pearson
|
|
);
|
|
} else {
|
|
LOG_INF("%*s%s%-*s%s%10.4f %12.4f %10.4f %10.4f%s%8.2f%% %8.2f%%%s%14.4f %12.4f %8.4f %8.4f\n",
|
|
w_lay, layer.c_str(), sep,
|
|
w_nam, label_fmt(tstat.tensor, w_nam).c_str(), sep,
|
|
tstat.min_values, tstat.max_values, tstat.mean_values, tstat.std_deviation, sep,
|
|
h_norm, 100.0f * tstat.zd_score, sep,
|
|
tstat.sum_values, tstat.l2_dist, tstat.cossim, tstat.pearson
|
|
);
|
|
}
|
|
|
|
// Aggregate Layer Stats
|
|
const float zd = (float)tstat.elements * tstat.zd_score;
|
|
auto & l = ls[blk];
|
|
l.layer_sum += tstat.sum_values;
|
|
l.layer_zd += zd;
|
|
l.n += tstat.elements;
|
|
}
|
|
|
|
// Layer Statistics
|
|
std::map<int, float> layer_cossim;
|
|
std::map<int, float> layer_l2_dist;
|
|
std::map<int, float> layer_pearson;
|
|
compute_layer_statistics(ts, layer_cossim, layer_l2_dist, layer_pearson);
|
|
|
|
size_t layers = 0;
|
|
int min = std::numeric_limits<int>::max();
|
|
int max = -1;
|
|
|
|
for (const auto & [layer, stats] : ls) {
|
|
if (layer >= 0 && stats.n > 0) {
|
|
layers++;
|
|
min = std::min(layer, min);
|
|
max = std::max(layer, max);
|
|
}
|
|
}
|
|
|
|
if (layers > 0) {
|
|
const auto expected = (size_t)(max - min + 1);
|
|
if (layers != expected) {
|
|
LOG_WRN("\n%s: layer sequence gap detected (found %zu layers in range %d-%d, expected %zu); layer statistics will not be shown\n",
|
|
__func__, layers, min, max, expected);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
LOG_INF("\n\nComputing layer statistics for %s (%zu layers)\n\n", params.in_files[0].c_str(), layers);
|
|
|
|
if (legacy) {
|
|
LOG_INF("%*s%s%9s%s%17s %10s %10s\n",
|
|
w_lay, "Layer", sep,
|
|
"ZD", sep,
|
|
"∑ E[A²]", "CosSim", "PCC");
|
|
LOG_INF("%s\n", std::string(57, '-').c_str());
|
|
} else {
|
|
LOG_INF("%*s%s%9s%s%17s %14s %10s %10s\n",
|
|
w_lay, "Layer", sep,
|
|
"ZD", sep,
|
|
"∑ E[A²]", "L2 Dist", "CosSim", "PCC");
|
|
LOG_INF("%s\n", std::string(72, '-').c_str());
|
|
}
|
|
|
|
for (const auto & [layer, stats] : ls) {
|
|
if (layer < 0 || stats.n == 0) { continue; }
|
|
|
|
float lcs = layer == 0 ? std::numeric_limits<float>::quiet_NaN() : layer_cossim[layer];
|
|
float ll2 = layer == 0 ? std::numeric_limits<float>::quiet_NaN() : layer_l2_dist[layer];
|
|
float lpc = layer == 0 ? std::numeric_limits<float>::quiet_NaN() : layer_pearson[layer];
|
|
|
|
if (legacy) {
|
|
LOG_INF("%*d%s%8.2f%%%s%14.4f %10.4f %10.4f\n",
|
|
w_lay, layer, sep,
|
|
100.0f * stats.layer_zd / stats.n, sep,
|
|
stats.layer_sum, lcs, lpc);
|
|
} else {
|
|
LOG_INF("%*d%s%8.2f%%%s%14.4f %14.4f %10.4f %10.4f\n",
|
|
w_lay, layer, sep,
|
|
100.0f * stats.layer_zd / stats.n, sep,
|
|
stats.layer_sum, ll2, lcs, lpc);
|
|
}
|
|
}
|
|
LOG_INF("\n");
|
|
return true;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
common_params params;
|
|
|
|
params.out_file = "imatrix.gguf";
|
|
|
|
params.n_ctx = 512;
|
|
params.escape = false;
|
|
|
|
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
|
|
return 1;
|
|
}
|
|
|
|
if (params.show_statistics) {
|
|
if (!show_statistics(params)) {
|
|
return 1;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
common_init();
|
|
|
|
const int32_t n_ctx = params.n_ctx;
|
|
|
|
if (n_ctx <= 0) {
|
|
LOG_ERR("%s: imatrix tool requires '--ctx-size' > 0\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
{
|
|
const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
|
|
const int32_t n_kv = n_seq * n_ctx;
|
|
|
|
params.n_parallel = n_seq;
|
|
params.n_ctx = n_kv;
|
|
|
|
params.n_batch = std::min(params.n_batch, n_kv);
|
|
}
|
|
|
|
g_collector.set_params(params);
|
|
|
|
for (const auto & in_file : params.in_files) {
|
|
LOG_INF("%s : loading imatrix from '%s'\n", __func__, in_file.c_str());
|
|
if (!g_collector.load_imatrix(in_file.c_str())) {
|
|
LOG_ERR("%s : failed to load %s\n", __func__, in_file.c_str());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
if (params.prompt.empty()) {
|
|
LOG_INF("No prompt provided; combining precomputed matrices only.\n");
|
|
|
|
if (params.in_files.empty()) {
|
|
LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n");
|
|
return 1;
|
|
}
|
|
|
|
if (params.in_files.size() == 1) {
|
|
LOG_INF("%s : saving imatrix to '%s'\n", __func__, params.out_file.c_str());
|
|
} else if (params.in_files.size() > 1) {
|
|
LOG_INF("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
|
|
}
|
|
|
|
g_collector.save_imatrix();
|
|
|
|
return 0;
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
// pass the callback to the backend scheduler
|
|
// it will be executed for each node during the graph computation
|
|
params.cb_eval = ik_collect_imatrix;
|
|
params.cb_eval_user_data = NULL;
|
|
params.warmup = false;
|
|
|
|
// init
|
|
auto llama_init = common_init_from_params(params);
|
|
|
|
auto * model = llama_init->model();
|
|
auto * ctx = llama_init->context();
|
|
|
|
if (model == nullptr || ctx == nullptr) {
|
|
LOG_ERR("%s : failed to init\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
const int n_ctx_train = llama_model_n_ctx_train(model);
|
|
if (params.n_ctx > n_ctx_train) {
|
|
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
|
|
__func__, n_ctx_train, params.n_ctx);
|
|
}
|
|
|
|
// print system information
|
|
{
|
|
LOG_INF("\n");
|
|
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
|
|
}
|
|
|
|
if (!compute_imatrix(ctx, params, n_ctx)) {
|
|
return 1;
|
|
}
|
|
|
|
g_collector.save_imatrix();
|
|
|
|
LOG("\n");
|
|
llama_perf_context_print(ctx);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|