mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-27 11:43:35 -04:00
d1aa0cc5d1
* Add --show-statistics option * Add --show-statistics logic * Add tensor name parsing * Tidy output format * Fix typo in title * Improve tensor influence ranking * Add better statistics * Change statistics' sort order * Add Cosine Similarity * Add header search path * Change header search path to private * Add weighted statistics per layer * Update report title * Refactor compute_statistics out of main * Refactor compute_cossim out of load_imatrix * Refactor compute_statistics out of load_imatrix * Move imatrix statistics calculation into its own functions * Add checks and validations * Remove unnecessary include directory * Rename labels * Add m_stats getter and refactor compute_statistics out of load_imatrix * Refactor variable names * Minor cosmetic change * Retrigger checks (empty commit) * Rerun checks (empty commit) * Fix unnecessary type promotion Co-authored-by: compilade <git@compilade.net> * Reverting change to improve code readability * Rerun checks (empty commit) * Rerun checks (empty commit) * Rerun checks - third time's the Charm 🤞 (empty commit) * Minor cosmetic change * Update README * Fix typo * Update README * Rerun checks (empty commit) * Re-implement changes on top of #9400 * Update README.md * Update README * Update README.md Co-authored-by: compilade <git@compilade.net> * Update README.md Co-authored-by: compilade <git@compilade.net> * Update README.md * Remove duplicate option in print_usage() * Update README.md * Update README.md Co-authored-by: compilade <git@compilade.net> * Update README.md Co-authored-by: compilade <git@compilade.net> * Remove input check * Remove commented out code --------- Co-authored-by: compilade <git@compilade.net>
1285 lines
46 KiB
C++
1285 lines
46 KiB
C++
#include "arg.h"
|
||
#include "common.h"
|
||
#include "log.h"
|
||
#include "llama.h"
|
||
#include "gguf.h"
|
||
|
||
#include <algorithm>
|
||
#include <chrono>
|
||
#include <cmath>
|
||
#include <cstdio>
|
||
#include <cstring>
|
||
#include <ctime>
|
||
#include <thread>
|
||
#include <mutex>
|
||
#include <vector>
|
||
#include <fstream>
|
||
#include <unordered_map>
|
||
#include <map>
|
||
#include <regex>
|
||
#include <numeric>
|
||
|
||
#if defined(_MSC_VER)
|
||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||
#endif
|
||
|
||
static void print_usage(int, char ** argv) {
|
||
LOG("\nexample usage:\n");
|
||
LOG("\n %s \\\n"
|
||
" -m model.gguf -f some-text.txt [-o imatrix.gguf] [--no-ppl] \\\n"
|
||
" [--process-output] [--chunk 123] [--save-frequency 0] [--output-frequency 10] \\\n"
|
||
" [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...] [--parse-special] \\\n"
|
||
" [--show-statistics] [...]\n" , argv[0]);
|
||
LOG("\n");
|
||
}
|
||
|
||
static const char * const LLM_KV_IMATRIX_DATASETS = "imatrix.datasets";
|
||
static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
|
||
static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
|
||
|
||
struct Stats {
|
||
std::vector<float> values;
|
||
std::vector<int64_t> counts;
|
||
};
|
||
|
||
struct tensor_statistics {
|
||
std::string tensor;
|
||
Stats stats;
|
||
float total_sqract = 0.0f;
|
||
float mean_sqract = 0.0f;
|
||
float max_sqract = 0.0f;
|
||
float min_sqract = 0.0f;
|
||
int elements = 0;
|
||
float stddev = 0.0f;
|
||
float active = 0.0f;
|
||
float entropy = 0.0f;
|
||
float zd = 0.0f;
|
||
float cossim = 0.0f;
|
||
};
|
||
|
||
class IMatrixCollector {
|
||
public:
|
||
IMatrixCollector() = default;
|
||
void set_params(common_params params) { m_params = std::move(params); }
|
||
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
|
||
void save_imatrix_legacy(int32_t ncall = -1) const;
|
||
void save_imatrix(int32_t n_chunk = -1) const;
|
||
bool load_imatrix_legacy(const char * fname);
|
||
bool load_imatrix(const char * file_name);
|
||
const std::unordered_map<std::string, Stats> & get_mstats() const { return m_stats; }
|
||
private:
|
||
std::unordered_map<std::string, Stats> m_stats;
|
||
common_params m_params;
|
||
std::mutex m_mutex;
|
||
std::vector<std::string> m_datasets;
|
||
int32_t m_last_chunk = 0;
|
||
std::vector<char> m_src1_data;
|
||
std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
|
||
};
|
||
|
||
// remove any prefix and suffixes from the name
|
||
// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
|
||
static std::string filter_tensor_name(const char * name) {
|
||
std::string wname;
|
||
const char * p = strchr(name, '#');
|
||
if (p != NULL) {
|
||
p = p + 1;
|
||
const char * q = strchr(p, '#');
|
||
if (q != NULL) {
|
||
wname = std::string(p, q - p);
|
||
} else {
|
||
wname = p;
|
||
}
|
||
} else {
|
||
wname = name;
|
||
}
|
||
return wname;
|
||
}
|
||
|
||
static void process_tensor_name(const std::string & input, std::string & layer, std::string & tensor) {
|
||
std::vector<std::string> name;
|
||
std::istringstream stream(input);
|
||
std::string item;
|
||
|
||
while (std::getline(stream, item, '.')) {
|
||
name.push_back(item);
|
||
}
|
||
for (size_t i = 0; i < name.size(); ++i) {
|
||
if (name[i] == "blk" && i + 1 < name.size()) {
|
||
layer = name[i + 1];
|
||
break;
|
||
}
|
||
}
|
||
for (size_t i = 0; i < name.size(); ++i) {
|
||
if (name[i] == "weight" && i > 0) {
|
||
tensor = name[i - 1];
|
||
break;
|
||
}
|
||
}
|
||
|
||
if (tensor.empty()) {
|
||
tensor = input;
|
||
}
|
||
if (layer.empty()) {
|
||
layer = "-";
|
||
}
|
||
}
|
||
|
||
static void compute_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) {
|
||
if (e.values.size() % e.counts.size() != 0) {
|
||
LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.values.size());
|
||
return;
|
||
}
|
||
if (e.counts.empty()) {
|
||
LOG_ERR("%s: there are no activations for tensor %s. The imatrix may be suboptimal\n", __func__, name.c_str());
|
||
return;
|
||
}
|
||
|
||
const int n_mat = e.counts.size();
|
||
const int row_size = e.values.size() / n_mat;
|
||
|
||
std::vector<float> activations;
|
||
activations.reserve(e.values.size());
|
||
|
||
for (int i = 0; i < n_mat; ++i) {
|
||
for (int j = 0; j < row_size; ++j) {
|
||
activations.push_back(e.values[i*row_size + j] / e.counts[i]);
|
||
}
|
||
}
|
||
|
||
const float act_total = std::accumulate(activations.begin(), activations.end(), 0.0f);
|
||
const float act_max = *std::max_element(activations.begin(), activations.end());
|
||
const float act_min = *std::min_element(activations.begin(), activations.end());
|
||
const float act_mean = act_total / activations.size();
|
||
const float act_sqr_total = std::inner_product(activations.begin(), activations.end(), activations.begin(), 0.0f);
|
||
const float act_var = (act_sqr_total / activations.size()) - (act_mean * act_mean);
|
||
const float act_dev = std::sqrt(std::max(0.0f, act_var));
|
||
float threshold = 1e-5f;
|
||
const int inactive_count = std::count_if(activations.begin(), activations.end(),
|
||
[threshold](const float v) { return fabsf(v) <= threshold; });
|
||
const float active_ratio = 1 - static_cast<float>(inactive_count) / activations.size();
|
||
|
||
float entropy = 0;
|
||
if (act_total > 0) {
|
||
for (const auto act : activations) {
|
||
if (const float p = act / act_total; p > 0) {
|
||
entropy -= p * std::log2(p);
|
||
}
|
||
}
|
||
}
|
||
|
||
int z_score = 0;
|
||
if (act_dev > 0.0f) {
|
||
for (const auto act : activations) {
|
||
if (const float p = (act - act_mean) / act_dev; p > 1) {
|
||
z_score++;
|
||
}
|
||
}
|
||
}
|
||
|
||
auto & ts = tstats.emplace_back();
|
||
ts.tensor = name;
|
||
ts.stats = e;
|
||
ts.total_sqract = act_total;
|
||
ts.mean_sqract = act_mean;
|
||
ts.max_sqract = act_max;
|
||
ts.min_sqract = act_min;
|
||
ts.elements = static_cast<int>(activations.size());
|
||
ts.stddev = act_dev;
|
||
ts.active = active_ratio;
|
||
ts.entropy = entropy;
|
||
ts.zd = static_cast<float>(z_score) / ts.elements;
|
||
}
|
||
|
||
static void compute_cossim(std::vector<tensor_statistics> & tstats) {
|
||
static const std::regex pattern(R"(blk\.(\d+)\.)");
|
||
for (auto & ts : tstats) {
|
||
if (std::smatch match; std::regex_search(ts.tensor, match, pattern)) {
|
||
const int blk = std::stoi(match[1]);
|
||
std::string tname(ts.tensor);
|
||
tname.replace(match.position(1), match.length(1), std::to_string(blk-1));
|
||
auto prev = std::find_if(tstats.begin(), tstats.end(),
|
||
[tname](const tensor_statistics & t) { return t.tensor == tname; });
|
||
if (prev != tstats.end()) {
|
||
const float dp = std::inner_product(ts.stats.values.begin(), ts.stats.values.end(),
|
||
prev->stats.values.begin(), 0.0f);
|
||
const float curr_mag = std::sqrt(std::inner_product(ts.stats.values.begin(), ts.stats.values.end(),
|
||
ts.stats.values.begin(), 0.0f));
|
||
const float prev_mag = std::sqrt(std::inner_product(prev->stats.values.begin(), prev->stats.values.end(),
|
||
prev->stats.values.begin(), 0.0f));
|
||
const float cs = dp / (curr_mag * prev_mag);
|
||
ts.cossim = cs;
|
||
}
|
||
} else {
|
||
ts.cossim = 0;
|
||
}
|
||
}
|
||
}
|
||
|
||
bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
|
||
GGML_UNUSED(user_data);
|
||
|
||
const struct ggml_tensor * src0 = t->src[0];
|
||
const struct ggml_tensor * src1 = t->src[1];
|
||
std::string wname = filter_tensor_name(src0->name);
|
||
|
||
const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
|
||
|
||
// when ask is true, the scheduler wants to know if we are interested in data from this tensor
|
||
// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
|
||
if (ask) {
|
||
if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
|
||
if (t->op != GGML_OP_MUL_MAT) return false;
|
||
// why are small batches ignored (<16 tokens)?
|
||
if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
|
||
if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false;
|
||
return true;
|
||
}
|
||
|
||
std::lock_guard<std::mutex> lock(m_mutex);
|
||
|
||
// copy the data from the GPU memory if needed
|
||
const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
|
||
|
||
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));
|
||
|
||
// TODO: 4d? (is that even used in practice?)
|
||
// 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);
|
||
}
|
||
|
||
// 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]);
|
||
|
||
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.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.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 = src1->ne[2] * src1->ne[3];
|
||
|
||
if (e.values.empty()) {
|
||
e.values.resize(src1->ne[0] * n_mat, 0);
|
||
e.counts.resize(n_mat, 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");
|
||
}
|
||
else if (e.counts.size() != (size_t)n_mat) {
|
||
LOG_ERR("%s: inconsistent expert count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), (int)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) {
|
||
const int64_t mat_id = i3 * src1->ne[2] + i2;
|
||
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->ne[3]);
|
||
e.counts[mat_id]++;
|
||
for (int64_t j = 0; j < src1->ne[0]; ++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);
|
||
}
|
||
}
|
||
}
|
||
const int32_t n_chunk = e.counts[mat_id] / 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;
|
||
|
||
// TODO: use the new format in more cases
|
||
if (!string_ends_with(fname, ".gguf")) {
|
||
LOG_WRN("\n%s: saving to legacy imatrix format because output suffix is not .gguf\n", __func__);
|
||
this->save_imatrix_legacy(n_chunk);
|
||
return;
|
||
}
|
||
|
||
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.values.size(), GGML_MEM_ALIGN);
|
||
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
|
||
}
|
||
|
||
// deterministic tensor name order
|
||
std::sort(to_store.begin(), to_store.end());
|
||
|
||
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);
|
||
}
|
||
}
|
||
|
||
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_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::pair<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
|
||
sums_counts_for[std::move(name)].first = cur;
|
||
} else if (string_remove_suffix(name, counts_suffix)) {
|
||
// counts
|
||
sums_counts_for[std::move(name)].second = cur;
|
||
} else {
|
||
// ignore other tensors
|
||
}
|
||
}
|
||
|
||
for (const auto & sc : sums_counts_for) {
|
||
const std::string & name = sc.first;
|
||
const struct ggml_tensor * in_sum2 = sc.second.first;
|
||
const struct ggml_tensor * counts = sc.second.second;
|
||
|
||
if (!in_sum2 || !counts) {
|
||
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;
|
||
}
|
||
|
||
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++) {
|
||
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) {
|
||
std::vector<tensor_statistics> ts;
|
||
if (params.in_files.empty() || params.in_files.size() > 1) {
|
||
LOG_ERR("\nError: a single imatrix file is required to compute tensor statistics\n\n");
|
||
return false;
|
||
}
|
||
if (g_collector.load_imatrix(params.in_files[0].c_str())) {
|
||
for (const auto & [name, stats] :g_collector.get_mstats()) {
|
||
compute_statistics(ts, name, stats);
|
||
}
|
||
} else {
|
||
LOG_ERR("\nError: %s is not a valid imatrix file\n\n", params.in_files[0].c_str());
|
||
return false;
|
||
}
|
||
if (!ts.empty()) {
|
||
compute_cossim(ts);
|
||
} else {
|
||
LOG_ERR("Error: cannot compute statistics for %s\n\n", params.in_files[0].c_str());
|
||
return false;
|
||
}
|
||
|
||
struct tensor_comparer {
|
||
bool operator()(const tensor_statistics & a, const tensor_statistics & b) const {
|
||
std::string layer, name_a, name_b;
|
||
;
|
||
process_tensor_name(a.tensor, layer, name_a);
|
||
process_tensor_name(b.tensor, layer, name_b);
|
||
return name_a < name_b || (name_a == name_b && a.total_sqract > b.total_sqract);
|
||
}
|
||
};
|
||
std::sort(ts.begin(), ts.end(), tensor_comparer());
|
||
|
||
struct weighted_stats {
|
||
float weighted_bias = 0.0f;
|
||
float weighted_zd = 0.0f;
|
||
float weighted_cossim = 0.0f;
|
||
int total_elements = 0;
|
||
};
|
||
std::map<int, weighted_stats> ws;
|
||
|
||
LOG_INF("\nComputing statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast<int>(ts.size()));
|
||
LOG_INF("\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n", " Layer", " Tensor", " Σ(Act²)",
|
||
" Min", " Max", " μ", " σ", " % Active", "N", " Entropy", "E (norm)", "ZD",
|
||
" CosSim");
|
||
LOG_INF(
|
||
"=============================================================================================================="
|
||
"===========================================================\n");
|
||
for (const auto & tstat : ts) {
|
||
std::string layer, name;
|
||
process_tensor_name(tstat.tensor, layer, name);
|
||
|
||
int blk;
|
||
try {
|
||
blk = std::stoi(layer);
|
||
} catch (const std::exception & e) {
|
||
blk = -1; // not a block layer
|
||
}
|
||
|
||
LOG_INF("%5s\t%-20s\t%10.2f\t%8.4f\t%11.4f\t%6.2f\t%6.2f\t%8.2f%%\t%6d\t%10.4f\t%6.2f%%\t%10.2f%%\t%8.4f\n",
|
||
layer.c_str(), name.c_str(), tstat.total_sqract, tstat.min_sqract, tstat.max_sqract, tstat.mean_sqract,
|
||
tstat.stddev, tstat.active * 100.0f, tstat.elements, tstat.entropy,
|
||
100.0f * (tstat.entropy / std::log2(tstat.elements)), 100.0f * tstat.zd, tstat.cossim);
|
||
|
||
const float weighted_bias = tstat.elements * tstat.total_sqract;
|
||
const float weighted_zd = tstat.elements * tstat.zd;
|
||
const float weighted_cossim = tstat.elements * tstat.cossim;
|
||
|
||
if (ws.find(blk) != ws.end()) {
|
||
ws[blk].weighted_bias += weighted_bias;
|
||
ws[blk].weighted_zd += weighted_zd;
|
||
ws[blk].weighted_cossim += weighted_cossim;
|
||
ws[blk].total_elements += tstat.elements;
|
||
} else {
|
||
weighted_stats temp_ws;
|
||
temp_ws.weighted_bias = weighted_bias;
|
||
temp_ws.weighted_zd = weighted_zd;
|
||
temp_ws.weighted_cossim = weighted_cossim;
|
||
temp_ws.total_elements = tstat.elements;
|
||
ws[blk] = temp_ws;
|
||
}
|
||
}
|
||
|
||
const int layers = std::count_if(ws.begin(), ws.end(), [](const auto & kv) { return kv.first >= 0; });
|
||
LOG_INF("\nComputing weighted average statistics per layer (%d layers)\n", layers);
|
||
LOG_INF("\n%s\t%s\t%s\t%s\n", " Layer", " μΣ(Act²)", " μZD", "μCosSim");
|
||
LOG_INF("================================================\n");
|
||
for (const auto & [first, second] : ws) {
|
||
const auto & layer = first;
|
||
const auto & stats = second;
|
||
|
||
if (stats.total_elements == 0) {
|
||
continue;
|
||
}
|
||
|
||
if (layer >= 0) {
|
||
const float bias = stats.weighted_bias / stats.total_elements;
|
||
const float zd = stats.weighted_zd / stats.total_elements;
|
||
const float cossim = stats.weighted_cossim / stats.total_elements;
|
||
|
||
LOG_INF("%5d\t%14.2f\t%10.4f%%\t%6.4f\n", layer, bias, 100.0f * zd, cossim);
|
||
}
|
||
}
|
||
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
|
||
common_init_result llama_init = common_init_from_params(params);
|
||
|
||
llama_model * model = llama_init.model.get();
|
||
llama_context * ctx = llama_init.context.get();
|
||
|
||
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;
|
||
}
|