mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 02:14:35 +00:00
split: allow --split-max-size option (#6343)
* 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
GitHub
parent
ba0c7c70ab
commit
f7fc5f6c6f
@ -28,9 +28,11 @@ enum split_operation : uint8_t {
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struct split_params {
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split_operation operation = SPLIT_OP_SPLIT;
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size_t n_bytes_split = 0;
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int n_split_tensors = 128;
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std::string input;
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std::string output;
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bool dry_run = false;
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};
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static void split_print_usage(const char * executable) {
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@ -41,15 +43,36 @@ static void split_print_usage(const char * executable) {
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printf("Apply a GGUF operation on IN to OUT.");
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printf("\n");
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printf("options:\n");
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printf(" -h, --help show this help message and exit\n");
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printf(" --version show version and build info\n");
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printf(" --split split GGUF to multiple GGUF (default)\n");
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printf(" --split-max-tensors max tensors in each split: default(%d)\n", default_params.n_split_tensors);
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printf(" --merge merge multiple GGUF to a single GGUF\n");
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printf(" -h, --help show this help message and exit\n");
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printf(" --version show version and build info\n");
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printf(" --split split GGUF to multiple GGUF (enabled by default)\n");
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printf(" --merge merge multiple GGUF to a single GGUF\n");
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printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors);
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printf(" --split-max-size N(M|G) max size per split\n");
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printf(" --dry-run only print out a split plan and exit, without writing any new files\n");
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printf("\n");
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}
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static bool split_params_parse_ex(int argc, const char ** argv, split_params & params) {
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// return convert string, for example "128M" or "4G" to number of bytes
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static size_t split_str_to_n_bytes(std::string str) {
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size_t n_bytes = 0;
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int n;
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if (str.back() == 'M') {
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sscanf(str.c_str(), "%d", &n);
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n_bytes = n * 1024 * 1024; // megabytes
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} else if (str.back() == 'G') {
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sscanf(str.c_str(), "%d", &n);
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n_bytes = n * 1024 * 1024 * 1024; // gigabytes
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} else {
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throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
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}
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if (n <= 0) {
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throw std::invalid_argument("error: size must be a positive value");
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}
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return n_bytes;
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}
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static void split_params_parse_ex(int argc, const char ** argv, split_params & params) {
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std::string arg;
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const std::string arg_prefix = "--";
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bool invalid_param = false;
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@ -62,6 +85,8 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
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}
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bool arg_found = false;
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bool is_op_set = false;
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bool is_mode_set = false;
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if (arg == "-h" || arg == "--help") {
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split_print_usage(argv[0]);
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exit(0);
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@ -71,23 +96,46 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
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fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
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exit(0);
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}
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if (arg == "--dry-run") {
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arg_found = true;
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params.dry_run = true;
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}
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if (is_op_set) {
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throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
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}
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if (arg == "--merge") {
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arg_found = true;
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is_op_set = true;
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params.operation = SPLIT_OP_MERGE;
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}
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if (arg == "--split") {
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arg_found = true;
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is_op_set = true;
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params.operation = SPLIT_OP_SPLIT;
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}
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if (is_mode_set) {
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throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
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}
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if (arg == "--split-max-tensors") {
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if (++arg_idx >= argc) {
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invalid_param = true;
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break;
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}
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arg_found = true;
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is_mode_set = true;
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params.n_split_tensors = atoi(argv[arg_idx]);
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}
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if (arg == "--split-max-size") {
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if (++arg_idx >= argc) {
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invalid_param = true;
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break;
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}
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arg_found = true;
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is_mode_set = true;
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params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
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}
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if (!arg_found) {
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throw std::invalid_argument("error: unknown argument: " + arg);
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@ -99,24 +147,17 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
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}
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if (argc - arg_idx < 2) {
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printf("%s: bad arguments\n", argv[0]);
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split_print_usage(argv[0]);
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return false;
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throw std::invalid_argument("error: bad arguments");
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}
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params.input = argv[arg_idx++];
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params.output = argv[arg_idx++];
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return true;
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}
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static bool split_params_parse(int argc, const char ** argv, split_params & params) {
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bool result = true;
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try {
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if (!split_params_parse_ex(argc, argv, params)) {
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split_print_usage(argv[0]);
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exit(EXIT_FAILURE);
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}
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split_params_parse_ex(argc, argv, params);
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}
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catch (const std::invalid_argument & ex) {
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fprintf(stderr, "%s\n", ex.what());
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@ -140,15 +181,11 @@ struct split_strategy {
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struct ggml_context * ctx_meta = NULL;
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const int n_tensors;
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const int n_split;
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int i_split = 0;
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// one ctx_out per one output file
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std::vector<struct gguf_context *> ctx_outs;
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int i_tensor = 0;
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std::vector<uint8_t> read_data;
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struct gguf_context * ctx_out;
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std::ofstream fout;
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// temporary buffer for reading in tensor data
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std::vector<uint8_t> read_buf;
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split_strategy(const split_params & params,
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std::ifstream & f_input,
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@ -158,79 +195,141 @@ struct split_strategy {
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f_input(f_input),
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ctx_gguf(ctx_gguf),
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ctx_meta(ctx_meta),
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n_tensors(gguf_get_n_tensors(ctx_gguf)),
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n_split(std::ceil(1. * n_tensors / params.n_split_tensors)) {
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n_tensors(gguf_get_n_tensors(ctx_gguf)) {
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// because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
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int i_split = -1;
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struct gguf_context * ctx_out = NULL;
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auto new_ctx_out = [&]() {
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i_split++;
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if (ctx_out != NULL) {
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if (gguf_get_n_tensors(ctx_out) == 0) {
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fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
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exit(EXIT_FAILURE);
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}
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ctx_outs.push_back(ctx_out);
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}
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ctx_out = gguf_init_empty();
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// Save all metadata in first split only
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if (i_split == 0) {
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gguf_set_kv(ctx_out, ctx_gguf);
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}
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gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
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gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder
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gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
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};
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// initialize ctx_out for the first split
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new_ctx_out();
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// process tensors one by one
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size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
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for (int i = 0; i < n_tensors; ++i) {
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struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
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// calculate the "imaginary" size = the current size + next tensor size
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size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
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size_t next_tensors_size = curr_tensors_size + n_bytes;
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if (should_split(i, next_tensors_size)) {
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new_ctx_out();
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curr_tensors_size = n_bytes;
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} else {
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curr_tensors_size = next_tensors_size;
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}
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gguf_add_tensor(ctx_out, t);
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}
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bool should_split() const {
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return i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
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// push the last ctx_out
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ctx_outs.push_back(ctx_out);
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// set the correct n_split for all ctx_out
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for (auto & ctx : ctx_outs) {
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gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size());
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}
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}
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void split_start() {
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ctx_out = gguf_init_empty();
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// Save all metadata in first split only
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if (i_split == 0) {
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gguf_set_kv(ctx_out, ctx_gguf);
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~split_strategy() {
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for (auto & ctx_out : ctx_outs) {
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gguf_free(ctx_out);
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}
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gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
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gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, n_split);
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gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
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// populate the original tensors, so we get an initial metadata
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for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) {
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struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
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gguf_add_tensor(ctx_out, meta);
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}
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char split_path[PATH_MAX] = {0};
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llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
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fprintf(stderr, "%s: %s ...", __func__, split_path);
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fout = std::ofstream(split_path, std::ios::binary);
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fout.exceptions(std::ofstream::failbit); // fail fast on write errors
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auto meta_size = gguf_get_meta_size(ctx_out);
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// placeholder for the meta data
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::zeros(fout, meta_size);
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i_split++;
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}
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void next_tensor() {
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const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
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struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
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auto n_bytes = ggml_nbytes(t);
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if (read_data.size() < n_bytes) {
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read_data.resize(n_bytes);
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bool should_split(int i_tensor, size_t next_size) {
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if (params.n_bytes_split > 0) {
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// split by max size per file
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return next_size > params.n_bytes_split;
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} else {
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// split by number of tensors per file
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return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
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}
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auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
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f_input.seekg(offset);
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f_input.read((char *)read_data.data(), n_bytes);
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t->data = read_data.data();
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// write tensor data + padding
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fout.write((const char *)t->data, n_bytes);
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zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
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i_tensor++;
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}
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void split_end() {
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// go back to beginning of file and write the updated metadata
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fout.seekp(0);
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std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
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gguf_get_meta_data(ctx_out, data.data());
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fout.write((const char *)data.data(), data.size());
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void print_info() {
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printf("n_split: %ld\n", ctx_outs.size());
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int i_split = 0;
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for (auto & ctx_out : ctx_outs) {
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// re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
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size_t total_size = gguf_get_meta_size(ctx_out);
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for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
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struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
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total_size += ggml_nbytes(t);
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}
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total_size = total_size / 1024 / 1024; // convert to megabytes
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printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
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i_split++;
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}
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}
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fout.close();
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gguf_free(ctx_out);
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void write() {
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int i_split = 0;
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int n_split = ctx_outs.size();
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for (auto & ctx_out : ctx_outs) {
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// construct file path
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char split_path[PATH_MAX] = {0};
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llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
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fprintf(stderr, "\033[3Ddone\n");
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// open the output file
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printf("Writing file %s ... ", split_path);
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fflush(stdout);
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std::ofstream fout = std::ofstream(split_path, std::ios::binary);
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fout.exceptions(std::ofstream::failbit); // fail fast on write errors
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// write metadata
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std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
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gguf_get_meta_data(ctx_out, data.data());
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fout.write((const char *)data.data(), data.size());
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// write tensors
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for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
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// read tensor meta and prepare buffer
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const char * t_name = gguf_get_tensor_name(ctx_out, i);
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struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
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auto n_bytes = ggml_nbytes(t);
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read_buf.resize(n_bytes);
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// calculate offset
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auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file
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auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
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// copy tensor from input to output file
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copy_file_to_file(f_input, fout, offset, n_bytes);
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zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
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}
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printf("done\n");
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// close the file
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fout.close();
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i_split++;
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}
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}
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void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) {
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// TODO: detect OS and use copy_file_range() here for better performance
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if (read_buf.size() < len) {
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read_buf.resize(len);
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}
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f_in.seekg(in_offset);
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f_in.read((char *)read_buf.data(), len);
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f_out.write((const char *)read_buf.data(), len);
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}
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};
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@ -254,32 +353,22 @@ static void gguf_split(const split_params & split_params) {
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exit(EXIT_FAILURE);
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}
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// prepare the strategy
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split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
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int n_split = strategy.ctx_outs.size();
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strategy.print_info();
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char first_split_path[PATH_MAX] = {0};
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llama_split_path(first_split_path, sizeof(first_split_path),
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split_params.output.c_str(), strategy.i_split, strategy.n_split);
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fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n",
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__func__, split_params.input.c_str(),
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first_split_path,
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split_params.n_split_tensors);
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strategy.split_start();
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while (strategy.i_tensor < strategy.n_tensors) {
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strategy.next_tensor();
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if (strategy.should_split()) {
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strategy.split_end();
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strategy.split_start();
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}
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if (!split_params.dry_run) {
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// write all output splits
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strategy.write();
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}
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strategy.split_end();
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// done, clean up
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gguf_free(ctx_gguf);
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f_input.close();
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fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
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__func__, strategy.n_split, strategy.n_tensors);
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__func__, n_split, strategy.n_tensors);
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}
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static void gguf_merge(const split_params & split_params) {
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@ -448,10 +537,6 @@ static void gguf_merge(const split_params & split_params) {
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}
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int main(int argc, const char ** argv) {
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if (argc < 3) {
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split_print_usage(argv[0]);
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}
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split_params params;
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split_params_parse(argc, argv, params);
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