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split: allow --split-max-size option (#6343)

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* split by max size

* clean up arg parse

* split: ok

* add dry run option

* error on 0 tensors

* be positive

* remove next_metadata_size
This commit is contained in:
Xuan Son Nguyen 2024-03-29 22:34:44 +01:00 committed by GitHub
parent ba0c7c70ab
commit f7fc5f6c6f
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1 changed files with 191 additions and 106 deletions
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271
examples/gguf-split/gguf-split.cpp
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@ -28,9 +28,11 @@ enum split_operation : uint8_t {
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 dry_run = false;
};
static void split_print_usage(const char * executable) {
@ -43,13 +45,34 @@ static void split_print_usage(const char * executable) {
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 (default)\n");
printf(" --split-max-tensors max tensors in each split: default(%d)\n", default_params.n_split_tensors);
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(" --dry-run only print out a split plan and exit, without writing any new files\n");
printf("\n");
}
static bool split_params_parse_ex(int argc, const char ** argv, split_params & params) {
// 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 = n * 1024 * 1024; // megabytes
} else if (str.back() == 'G') {
sscanf(str.c_str(), "%d", &n);
n_bytes = n * 1024 * 1024 * 1024; // 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;
@ -62,6 +85,8 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
}
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);
@ -71,23 +96,46 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
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 (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);
@ -99,24 +147,17 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
}
if (argc - arg_idx < 2) {
printf("%s: bad arguments\n", argv[0]);
split_print_usage(argv[0]);
return false;
throw std::invalid_argument("error: bad arguments");
}
params.input = argv[arg_idx++];
params.output = argv[arg_idx++];
return true;
}
static bool split_params_parse(int argc, const char ** argv, split_params & params) {
bool result = true;
try {
if (!split_params_parse_ex(argc, argv, params)) {
split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
split_params_parse_ex(argc, argv, params);
}
catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
@ -140,15 +181,11 @@ struct split_strategy {
struct ggml_context * ctx_meta = NULL;
const int n_tensors;
const int n_split;
int i_split = 0;
// one ctx_out per one output file
std::vector<struct gguf_context *> ctx_outs;
int i_tensor = 0;
std::vector<uint8_t> read_data;
struct gguf_context * ctx_out;
std::ofstream fout;
// temporary buffer for reading in tensor data
std::vector<uint8_t> read_buf;
split_strategy(const split_params & params,
std::ifstream & f_input,
@ -158,79 +195,141 @@ struct split_strategy {
f_input(f_input),
ctx_gguf(ctx_gguf),
ctx_meta(ctx_meta),
n_tensors(gguf_get_n_tensors(ctx_gguf)),
n_split(std::ceil(1. * n_tensors / params.n_split_tensors)) {
}
n_tensors(gguf_get_n_tensors(ctx_gguf)) {
bool should_split() const {
return i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
// 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 = [&]() {
i_split++;
if (ctx_out != NULL) {
if (gguf_get_n_tensors(ctx_out) == 0) {
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);
}
void split_start() {
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, n_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);
};
// populate the original tensors, so we get an initial metadata
for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) {
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
gguf_add_tensor(ctx_out, meta);
// initialize ctx_out for the first split
new_ctx_out();
// 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();
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 / 1024 / 1024; // 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);
fprintf(stderr, "%s: %s ...", __func__, split_path);
fout = std::ofstream(split_path, std::ios::binary);
// 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
auto meta_size = gguf_get_meta_size(ctx_out);
// placeholder for the meta data
::zeros(fout, meta_size);
i_split++;
}
void next_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);
t->data = read_data.data();
// write tensor data + padding
fout.write((const char *)t->data, n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
i_tensor++;
}
void split_end() {
// go back to beginning of file and write the updated metadata
fout.seekp(0);
// 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());
fout.close();
gguf_free(ctx_out);
// 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);
fprintf(stderr, "\033[3Ddone\n");
// 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);
}
};
@ -254,32 +353,22 @@ static void gguf_split(const split_params & split_params) {
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();
char first_split_path[PATH_MAX] = {0};
llama_split_path(first_split_path, sizeof(first_split_path),
split_params.output.c_str(), strategy.i_split, strategy.n_split);
fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n",
__func__, split_params.input.c_str(),
first_split_path,
split_params.n_split_tensors);
strategy.split_start();
while (strategy.i_tensor < strategy.n_tensors) {
strategy.next_tensor();
if (strategy.should_split()) {
strategy.split_end();
strategy.split_start();
if (!split_params.dry_run) {
// write all output splits
strategy.write();
}
}
strategy.split_end();
// 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__, strategy.n_split, strategy.n_tensors);
__func__, n_split, strategy.n_tensors);
}
static void gguf_merge(const split_params & split_params) {
@ -448,10 +537,6 @@ static void gguf_merge(const split_params & split_params) {
}
int main(int argc, const char ** argv) {
if (argc < 3) {
split_print_usage(argv[0]);
}
split_params params;
split_params_parse(argc, argv, params);