llama.cpp/examples/gguf-split/gguf-split.cpp
slaren e6deac31f7
gguf-split : add basic checks (#9499)
* gguf-split : do not overwrite existing files when merging

* gguf-split : error when too many arguments are passed
2024-09-15 19:02:27 +02:00

572 lines
20 KiB
C++

#include "llama.h"
#include "common.h"
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <fstream>
#include <string>
#include <vector>
#include <stdio.h>
#include <string.h>
#include <climits>
#include <stdexcept>
#if defined(_WIN32)
#include <windows.h>
#ifndef PATH_MAX
#define PATH_MAX MAX_PATH
#endif
#include <io.h>
#endif
enum split_operation : uint8_t {
SPLIT_OP_SPLIT,
SPLIT_OP_MERGE,
};
struct split_params {
split_operation operation = SPLIT_OP_SPLIT;
size_t n_bytes_split = 0;
int n_split_tensors = 128;
std::string input;
std::string output;
bool no_tensor_first_split = false;
bool dry_run = false;
};
static void split_print_usage(const char * executable) {
const split_params default_params;
printf("\n");
printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable);
printf("\n");
printf("Apply a GGUF operation on IN to OUT.");
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\n");
printf(" --split split GGUF to multiple GGUF (enabled by default)\n");
printf(" --merge merge multiple GGUF to a single GGUF\n");
printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors);
printf(" --split-max-size N(M|G) max size per split\n");
printf(" --no-tensor-first-split do not add tensors to the first split (disabled by default)\n");
printf(" --dry-run only print out a split plan and exit, without writing any new files\n");
printf("\n");
}
// return convert string, for example "128M" or "4G" to number of bytes
static size_t split_str_to_n_bytes(std::string str) {
size_t n_bytes = 0;
int n;
if (str.back() == 'M') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1000 * 1000; // megabytes
} else if (str.back() == 'G') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes
} else {
throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
}
if (n <= 0) {
throw std::invalid_argument("error: size must be a positive value");
}
return n_bytes;
}
static void split_params_parse_ex(int argc, const char ** argv, split_params & params) {
std::string arg;
const std::string arg_prefix = "--";
bool invalid_param = false;
int arg_idx = 1;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
arg = argv[arg_idx];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
bool arg_found = false;
bool is_op_set = false;
bool is_mode_set = false;
if (arg == "-h" || arg == "--help") {
split_print_usage(argv[0]);
exit(0);
}
if (arg == "--version") {
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
}
if (arg == "--dry-run") {
arg_found = true;
params.dry_run = true;
}
if (arg == "--no-tensor-first-split") {
arg_found = true;
params.no_tensor_first_split = true;
}
if (is_op_set) {
throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
}
if (arg == "--merge") {
arg_found = true;
is_op_set = true;
params.operation = SPLIT_OP_MERGE;
}
if (arg == "--split") {
arg_found = true;
is_op_set = true;
params.operation = SPLIT_OP_SPLIT;
}
if (is_mode_set) {
throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
}
if (arg == "--split-max-tensors") {
if (++arg_idx >= argc) {
invalid_param = true;
break;
}
arg_found = true;
is_mode_set = true;
params.n_split_tensors = atoi(argv[arg_idx]);
}
if (arg == "--split-max-size") {
if (++arg_idx >= argc) {
invalid_param = true;
break;
}
arg_found = true;
is_mode_set = true;
params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
}
if (!arg_found) {
throw std::invalid_argument("error: unknown argument: " + arg);
}
}
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
}
if (argc - arg_idx != 2) {
throw std::invalid_argument("error: bad arguments");
}
params.input = argv[arg_idx++];
params.output = argv[arg_idx++];
}
static bool split_params_parse(int argc, const char ** argv, split_params & params) {
bool result = true;
try {
split_params_parse_ex(argc, argv, params);
}
catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
return result;
}
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
file.write(&zero, 1);
}
}
struct split_strategy {
const split_params params;
std::ifstream & f_input;
struct gguf_context * ctx_gguf;
struct ggml_context * ctx_meta = NULL;
const int n_tensors;
// one ctx_out per one output file
std::vector<struct gguf_context *> ctx_outs;
// temporary buffer for reading in tensor data
std::vector<uint8_t> read_buf;
split_strategy(const split_params & params,
std::ifstream & f_input,
struct gguf_context * ctx_gguf,
struct ggml_context * ctx_meta) :
params(params),
f_input(f_input),
ctx_gguf(ctx_gguf),
ctx_meta(ctx_meta),
n_tensors(gguf_get_n_tensors(ctx_gguf)) {
// because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
int i_split = -1;
struct gguf_context * ctx_out = NULL;
auto new_ctx_out = [&](bool allow_no_tensors) {
i_split++;
if (ctx_out != NULL) {
if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) {
fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
exit(EXIT_FAILURE);
}
ctx_outs.push_back(ctx_out);
}
ctx_out = gguf_init_empty();
// Save all metadata in first split only
if (i_split == 0) {
gguf_set_kv(ctx_out, ctx_gguf);
}
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder
gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
};
// initialize ctx_out for the first split
new_ctx_out(false);
// skip first split if no_tensor_first_split is set
if (params.no_tensor_first_split) {
new_ctx_out(true);
}
// process tensors one by one
size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
for (int i = 0; i < n_tensors; ++i) {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
// calculate the "imaginary" size = the current size + next tensor size
size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
size_t next_tensors_size = curr_tensors_size + n_bytes;
if (should_split(i, next_tensors_size)) {
new_ctx_out(false);
curr_tensors_size = n_bytes;
} else {
curr_tensors_size = next_tensors_size;
}
gguf_add_tensor(ctx_out, t);
}
// push the last ctx_out
ctx_outs.push_back(ctx_out);
// set the correct n_split for all ctx_out
for (auto & ctx : ctx_outs) {
gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size());
}
}
~split_strategy() {
for (auto & ctx_out : ctx_outs) {
gguf_free(ctx_out);
}
}
bool should_split(int i_tensor, size_t next_size) {
if (params.n_bytes_split > 0) {
// split by max size per file
return next_size > params.n_bytes_split;
} else {
// split by number of tensors per file
return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
}
}
void print_info() {
printf("n_split: %ld\n", ctx_outs.size());
int i_split = 0;
for (auto & ctx_out : ctx_outs) {
// re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
size_t total_size = gguf_get_meta_size(ctx_out);
for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
total_size += ggml_nbytes(t);
}
total_size = total_size / 1000 / 1000; // convert to megabytes
printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
i_split++;
}
}
void write() {
int i_split = 0;
int n_split = ctx_outs.size();
for (auto & ctx_out : ctx_outs) {
// construct file path
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
// open the output file
printf("Writing file %s ... ", split_path);
fflush(stdout);
std::ofstream fout = std::ofstream(split_path, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
// write metadata
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *)data.data(), data.size());
// write tensors
for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
// read tensor meta and prepare buffer
const char * t_name = gguf_get_tensor_name(ctx_out, i);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
auto n_bytes = ggml_nbytes(t);
read_buf.resize(n_bytes);
// calculate offset
auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
// copy tensor from input to output file
copy_file_to_file(f_input, fout, offset, n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
}
printf("done\n");
// close the file
fout.close();
i_split++;
}
}
void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) {
// TODO: detect OS and use copy_file_range() here for better performance
if (read_buf.size() < len) {
read_buf.resize(len);
}
f_in.seekg(in_offset);
f_in.read((char *)read_buf.data(), len);
f_out.write((const char *)read_buf.data(), len);
}
};
static void gguf_split(const split_params & split_params) {
struct ggml_context * ctx_meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
if (!f_input.is_open()) {
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
exit(EXIT_FAILURE);
}
auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
if (!ctx_gguf) {
fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
exit(EXIT_FAILURE);
}
// prepare the strategy
split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
int n_split = strategy.ctx_outs.size();
strategy.print_info();
if (!split_params.dry_run) {
// write all output splits
strategy.write();
}
// done, clean up
gguf_free(ctx_gguf);
f_input.close();
fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
__func__, n_split, strategy.n_tensors);
}
static void gguf_merge(const split_params & split_params) {
fprintf(stderr, "%s: %s -> %s\n",
__func__, split_params.input.c_str(),
split_params.output.c_str());
int n_split = 1;
int total_tensors = 0;
// avoid overwriting existing output file
if (std::ifstream(split_params.output.c_str())) {
fprintf(stderr, "%s: output file %s already exists\n", __func__, split_params.output.c_str());
exit(EXIT_FAILURE);
}
std::ofstream fout(split_params.output.c_str(), std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
auto * ctx_out = gguf_init_empty();
std::vector<uint8_t> read_data;
std::vector<ggml_context *> ctx_metas;
std::vector<gguf_context *> ctx_ggufs;
char split_path[PATH_MAX] = {0};
strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1);
char split_prefix[PATH_MAX] = {0};
// First pass to find KV and tensors metadata
for (int i_split = 0; i_split < n_split; i_split++) {
struct ggml_context * ctx_meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
if (i_split > 0) {
llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
}
fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path);
auto * ctx_gguf = gguf_init_from_file(split_path, params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
exit(EXIT_FAILURE);
}
ctx_ggufs.push_back(ctx_gguf);
ctx_metas.push_back(ctx_meta);
if (i_split == 0) {
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
if (key_n_split < 0) {
fprintf(stderr,
"\n%s: input file does not contain %s metadata\n",
__func__,
LLM_KV_SPLIT_COUNT);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
fout.close();
exit(EXIT_FAILURE);
}
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
if (n_split < 1) {
fprintf(stderr,
"\n%s: input file does not contain a valid split count %d\n",
__func__,
n_split);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
fout.close();
exit(EXIT_FAILURE);
}
// Verify the file naming and extract split_prefix
if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) {
fprintf(stderr, "\n%s: unexpected input file name: %s"
" i_split=%d"
" n_split=%d\n", __func__,
split_path, i_split, n_split);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
fout.close();
exit(EXIT_FAILURE);
}
// Do not trigger merge if we try to merge again the output
gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0);
// Set metadata from the first split
gguf_set_kv(ctx_out, ctx_gguf);
}
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
gguf_add_tensor(ctx_out, t);
}
total_tensors += n_tensors;
fprintf(stderr, "\033[3Ddone\n");
}
// placeholder for the meta data
{
auto meta_size = gguf_get_meta_size(ctx_out);
::zeros(fout, meta_size);
}
// Write tensors data
for (int i_split = 0; i_split < n_split; i_split++) {
llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
std::ifstream f_input(split_path, std::ios::binary);
if (!f_input.is_open()) {
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_path);
for (uint32_t i = 0; i < ctx_ggufs.size(); i++) {
gguf_free(ctx_ggufs[i]);
ggml_free(ctx_metas[i]);
}
gguf_free(ctx_out);
fout.close();
exit(EXIT_FAILURE);
}
fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path);
auto * ctx_gguf = ctx_ggufs[i_split];
auto * ctx_meta = ctx_metas[i_split];
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
auto n_bytes = ggml_nbytes(t);
if (read_data.size() < n_bytes) {
read_data.resize(n_bytes);
}
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
f_input.seekg(offset);
f_input.read((char *)read_data.data(), n_bytes);
// write tensor data + padding
fout.write((const char *)read_data.data(), n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
}
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
f_input.close();
fprintf(stderr, "\033[3Ddone\n");
}
{
// go back to beginning of file and write the updated metadata
fout.seekp(0);
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *)data.data(), data.size());
fout.close();
gguf_free(ctx_out);
}
fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
__func__, split_params.output.c_str(), n_split, total_tensors);
}
int main(int argc, const char ** argv) {
split_params params;
split_params_parse(argc, argv, params);
switch (params.operation) {
case SPLIT_OP_SPLIT: gguf_split(params);
break;
case SPLIT_OP_MERGE: gguf_merge(params);
break;
default: split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
return 0;
}