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imatrix : use GGUF to store imatrix data

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This commit is contained in:
Francis Couture-Harpin 2024-09-06 17:17:25 -04:00
parent 347247a24e
commit 3de9300c37
4 changed files with 352 additions and 149 deletions
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247
examples/imatrix/imatrix.cpp
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@ -5,11 +5,9 @@
#include <cstdio>
#include <cstring>
#include <ctime>
#include <sstream>
#include <thread>
#include <mutex>
#include <vector>
#include <fstream>
#include <unordered_map>
#include <algorithm>
@ -22,16 +20,19 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) {
LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] [--verbosity 1] \\\n"
" [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
" [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
" [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
LOG_TEE("\n");
}
static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset";
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<int> counts;
int ncall = 0;
std::vector<double> values;
std::vector<int64_t> counts;
};
class IMatrixCollector {
@ -39,13 +40,13 @@ public:
IMatrixCollector() = default;
void set_params(gpt_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix(int ncall = -1) const;
void save_imatrix(int32_t n_chunk = -1) const;
bool load_imatrix(const char * file_name);
private:
std::unordered_map<std::string, Stats> m_stats;
gpt_params m_params;
std::mutex m_mutex;
int m_last_call = 0;
int32_t m_last_chunk = 0;
std::vector<float> m_src1_data;
std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
};
@ -119,18 +120,24 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto & e = m_stats[wname];
++e.ncall;
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(src1->ne[0]*n_as, 0);
e.counts.resize(n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", 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) {
fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)n_as);
exit(1); //GGML_ABORT("fatal error");
}
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
printf("%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 (int ex = 0; ex < n_as; ++ex) {
@ -148,23 +155,26 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
const int64_t i12 = row;
const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
e.counts[ex]++;
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[e_start + j] += x[j]*x[j];
e.counts[e_start + j]++;
if (!std::isfinite(e.values[e_start + j])) {
fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
if (!std::isfinite((float)e.values[e_start + j])) {
fprintf(stderr, "%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
exit(1);
}
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_out_freq == 0) {
const int32_t n_chunk = e.counts[ex] / (m_params.n_ctx / m_params.n_parallel);
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_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
save_imatrix(m_last_chunk);
}
}
}
@ -172,34 +182,40 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto & e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.counts.resize(src1->ne[0], 0);
e.counts.resize(1, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ABORT("fatal error");
}
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
else if (e.counts.size() != 1) {
fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), 1);
exit(1); //GGML_ABORT("fatal error");
}
if (m_params.verbosity > 1) {
printf("%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[1], (int)src1->type);
}
// TODO: higher dimensions
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const float * x = data + row * src1->ne[0];
e.counts[0]++;
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.counts[j]++;
if (!std::isfinite(e.values[j])) {
fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str());
if (!std::isfinite((float)e.values[j])) {
fprintf(stderr, "%f detected in %s\n", (float)e.values[j], wname.c_str());
exit(1);
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_out_freq == 0) {
const int32_t n_chunk = e.counts[0] / (m_params.n_ctx / m_params.n_parallel);
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_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
save_imatrix(m_last_chunk);
}
}
}
@ -207,15 +223,15 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
return true;
}
void IMatrixCollector::save_imatrix(int ncall) const {
void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
auto fname = m_params.out_file;
if (fname.empty()) {
fname = "imatrix.dat";
fname = "imatrix.gguf";
}
if (ncall > 0) {
if (n_chunk > 0) {
fname += ".at_";
fname += std::to_string(ncall);
fname += std::to_string(n_chunk);
}
// avoid writing imatrix entries that do not have full data
@ -223,6 +239,7 @@ void IMatrixCollector::save_imatrix(int ncall) const {
int n_entries = 0;
std::vector<std::string> to_store;
size_t data_size = 0;
bool is_first = true; // for printing
for (const auto & kv : m_stats) {
@ -256,100 +273,132 @@ void IMatrixCollector::save_imatrix(int ncall) const {
n_entries++;
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);
}
if (to_store.size() < m_stats.size()) {
fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
}
std::ofstream out(fname, std::ios::binary);
out.write((const char *) &n_entries, sizeof(n_entries));
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();
gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
// Write the input filename to later on specify it in quantize
gguf_set_val_str(ctx_gguf, LLM_KV_IMATRIX_DATASET, m_params.prompt_file.c_str());
// 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);
int len = name.size();
out.write((const char *) &len, sizeof(len));
out.write(name.c_str(), len);
out.write((const char *) &stat.ncall, sizeof(stat.ncall));
int nval = stat.values.size();
out.write((const char *) &nval, sizeof(nval));
const int32_t nval = (int32_t) stat.values.size();
const int32_t nmat = (int32_t) stat.counts.size();
if (nval > 0) {
std::vector<float> tmp(nval);
for (int i = 0; i < nval; i++) {
tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
struct ggml_tensor * sums = 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_set_name(sums, (name + ".sums").c_str());
ggml_set_name(counts, (name + ".counts").c_str());
for (int32_t j = 0; j < nval; ++j) {
((float *) sums->data)[j] = (float) stat.values[j];
}
out.write((const char*)tmp.data(), nval*sizeof(float));
for (int32_t j = 0; j < nmat; ++j) {
((float *) counts->data)[j] = (float) stat.counts[j];
}
gguf_add_tensor(ctx_gguf, sums);
gguf_add_tensor(ctx_gguf, counts);
}
}
// Write the number of call the matrix was computed with
out.write((const char *) &m_last_call, sizeof(m_last_call));
// Write the input filename at the end of the file to later on specify it in quantize
{
int len = m_params.prompt_file.size();
out.write((const char *) &len, sizeof(len));
out.write(m_params.prompt_file.c_str(), len);
}
gguf_write_to_file(ctx_gguf, fname.c_str(), false);
if (m_params.verbosity > 0) {
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
fprintf(stderr, "\n%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(const char * fname) {
std::ifstream in(fname, std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__, fname);
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 false;
}
int n_entries;
in.read((char*)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, fname);
const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
if (n_entries < 2) {
fprintf(stderr, "%s: no data in file %s\n", __func__, file_name);
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
for (int i = 0; i < n_entries; ++i) {
int len; 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()) {
printf("%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)];
int ncall;
in.read((char*)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n",__func__,i);
m_stats = {};
const std::string sums_suffix{".sums"};
const std::string counts_suffix{".counts"};
// TODO: allow loading from mis-ordered imatrix files
for (int32_t i = 0; i < n_entries - 1; i += 2) {
std::string sums_name{gguf_get_tensor_name(ctx_gguf, i + 0)};
std::string counts_name{gguf_get_tensor_name(ctx_gguf, i + 1)};
if (sums_name.size() < sums_suffix.size() ||
counts_name.size() < counts_suffix.size() ||
!std::equal(sums_name.begin(), sums_name.end() - sums_suffix.size(), counts_name.begin()) ||
!std::equal(sums_suffix.rbegin(), sums_suffix.rend(), sums_name.rbegin()) ||
!std::equal(counts_suffix.rbegin(), counts_suffix.rend(), counts_name.rbegin())) {
fprintf(stderr, "%s: mismatched sums and counts for entry %d\n", __func__, i / 2);
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
struct ggml_tensor * sums = ggml_get_tensor(ctx, sums_name.c_str());
struct ggml_tensor * counts = ggml_get_tensor(ctx, counts_name.c_str());
if (!sums || !counts) {
fprintf(stderr, "%s: failed reading data for entry %d\n", __func__, i / 2);
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
std::string name = sums_name.substr(0, sums_name.size() - sums_suffix.size());
auto & e = m_stats[name];
int32_t nval = ggml_nelements(sums);
if (e.values.empty()) {
e.values.resize(nval, 0);
e.counts.resize(nval, 0);
}
int32_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]);
}
std::vector<float> tmp(nval);
in.read((char*)tmp.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n",__func__,i);
m_stats = {};
return false;
// Recreate the state as expected by save_imatrix()
for (int32_t j = 0; j < nval; j++) {
e.values[j] += ((const float *) sums->data)[j];
}
// Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
for (int i = 0; i < nval; i++) {
e.values[i] += tmp[i];
e.counts[i] += ncall;
for (int32_t j = 0; j < ncounts; j++) {
e.counts[j] += std::lround(((const float *) counts->data)[j]);
}
e.ncall += ncall;
}
gguf_free(ctx_gguf);
ggml_free(ctx);
return true;
}