llama.cpp/examples/export-lora/export-lora.cpp
Daniel Bevenius 930f907d3e
export-lora : use LLAMA_FILE_MAGIC_GGLA (#4894)
This commit replaces the magic number used in export-lora.cpp with
the one defined in llama.h, which is indirectly included via common.h.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-01-12 19:54:53 +02:00

474 lines
15 KiB
C++

#include "common.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include <vector>
#include <string>
#include <thread>
static const size_t tensor_alignment = 32;
struct lora_info {
std::string filename;
float scale;
};
struct export_lora_params {
std::string fn_model_base;
std::string fn_model_out;
std::vector<struct lora_info> lora;
int n_threads;
};
struct lora_data {
struct lora_info info;
std::vector<uint8_t> data;
struct ggml_context * ctx;
uint32_t lora_r;
uint32_t lora_alpha;
};
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
size = 0;
} else {
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
if (ferror(fp)) {
die_fmt("read error: %s", strerror(errno));
}
if (ret != 1) {
die("unexpectedly reached end of file");
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
die_fmt("write error: %s", strerror(errno));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
bool eof() {
return tell() >= size;
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
static struct export_lora_params get_default_export_lora_params() {
struct export_lora_params result;
result.fn_model_base = "";
result.fn_model_out = "";
result.n_threads = GGML_DEFAULT_N_THREADS;
return result;
}
static void export_lora_print_usage(int /*argc*/, char ** argv, const struct export_lora_params * params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -m FNAME, --model-base FNAME model path from which to load base model (default '%s')\n", params->fn_model_base.c_str());
fprintf(stderr, " -o FNAME, --model-out FNAME path to save exported model (default '%s')\n", params->fn_model_out.c_str());
fprintf(stderr, " -l FNAME, --lora FNAME apply LoRA adapter\n");
fprintf(stderr, " -s FNAME S, --lora-scaled FNAME S apply LoRA adapter with user defined scaling S\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params->n_threads);
}
static bool export_lora_params_parse(int argc, char ** argv, struct export_lora_params * params) {
bool invalid_param = false;
std::string arg;
struct export_lora_params default_params = get_default_export_lora_params();
const std::string arg_prefix = "--";
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg == "-m" || arg == "--model-base") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_model_base = argv[i];
} else if (arg == "-o" || arg == "--model-out") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_model_out = argv[i];
} else if (arg == "-l" || arg == "--lora") {
if (++i >= argc) {
invalid_param = true;
break;
}
struct lora_info lora;
lora.filename = argv[i];
lora.scale = 1.0f;
params->lora.push_back(lora);
} else if (arg == "-s" || arg == "--lora-scaled") {
if (++i >= argc) {
invalid_param = true;
break;
}
struct lora_info lora;
lora.filename = argv[i];
if (++i >= argc) {
invalid_param = true;
break;
}
lora.scale = std::stof(argv[i]);
params->lora.push_back(lora);
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_threads = std::stoi(argv[i]);
if (params->n_threads <= 0) {
params->n_threads = std::thread::hardware_concurrency();
}
} else {
fprintf(stderr, "error: unknown argument: '%s'\n", arg.c_str());
export_lora_print_usage(argc, argv, &default_params);
exit(1);
}
}
if (params->fn_model_base == default_params.fn_model_base) {
fprintf(stderr, "error: please specify a filename for model-base.\n");
export_lora_print_usage(argc, argv, &default_params);
exit(1);
}
if (params->fn_model_out == default_params.fn_model_out) {
fprintf(stderr, "error: please specify a filename for model-out.\n");
export_lora_print_usage(argc, argv, &default_params);
exit(1);
}
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: '%s'\n", arg.c_str());
export_lora_print_usage(argc, argv, &default_params);
exit(1);
}
return true;
}
static void free_lora(struct lora_data * lora) {
if (lora->ctx != NULL) {
ggml_free(lora->ctx);
}
delete lora;
}
static struct lora_data * load_lora(struct lora_info * info) {
struct lora_data * result = new struct lora_data;
result->info = *info;
result->ctx = NULL;
result->lora_r = 1;
result->lora_alpha = 1;
struct llama_file file(info->filename.c_str(), "rb");
if (file.fp == NULL) {
fprintf(stderr, "warning: Could not open lora adapter '%s'. Ignoring this adapter.\n",
info->filename.c_str());
free_lora(result);
return NULL;
}
struct ggml_init_params params_ggml;
params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE;
params_ggml.mem_buffer = NULL;
params_ggml.no_alloc = true;
result->ctx = ggml_init(params_ggml);
uint32_t magic = file.read_u32();
if (magic != LLAMA_FILE_MAGIC_GGLA) {
die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str());
}
uint32_t version = file.read_u32();
if (version != 1) {
die_fmt("unexpected lora file version '%u' in '%s'", (unsigned) version, info->filename.c_str());
}
result->lora_r = file.read_u32();
result->lora_alpha = file.read_u32();
// read tensor infos from file
std::vector<char> name_buf;
std::vector<struct ggml_tensor *> tensors;
std::vector<size_t> tensors_offset;
size_t total_nbytes_pad = 0;
while(!file.eof()) {
int64_t ne[4] = {1,1,1,1};
uint32_t n_dims = file.read_u32();
uint32_t namelen = file.read_u32();
uint32_t type = file.read_u32();
for (uint32_t k = 0; k < n_dims; ++k) {
ne[k] = (int64_t)file.read_u32();
}
name_buf.clear();
name_buf.resize(namelen + 1, '\0');
file.read_raw(name_buf.data(), namelen);
file.seek((0-file.tell()) & 31, SEEK_CUR);
size_t offset = file.tell();
struct ggml_tensor * tensor = ggml_new_tensor(result->ctx, (enum ggml_type) type, n_dims, ne);
ggml_set_name(tensor, name_buf.data());
size_t nbytes = ggml_nbytes(tensor);
size_t nbytes_pad = ggml_nbytes_pad(tensor);
total_nbytes_pad += nbytes_pad;
tensors.push_back(tensor);
tensors_offset.push_back(offset);
file.seek(nbytes, SEEK_CUR);
}
// read tensor data
result->data.resize(total_nbytes_pad);
size_t data_offset = 0;
for (size_t i = 0; i < tensors.size(); ++i) {
struct ggml_tensor * tensor = tensors[i];
size_t offset = tensors_offset[i];
size_t nbytes = ggml_nbytes(tensor);
size_t nbytes_pad = ggml_nbytes_pad(tensor);
file.seek(offset, SEEK_SET);
tensor->data = result->data.data() + data_offset;
file.read_raw(tensor->data, nbytes);
data_offset += nbytes_pad;
}
return result;
}
static struct ggml_cgraph * build_graph_lora(
struct ggml_context * ctx,
struct ggml_tensor * tensor,
struct ggml_tensor * lora_a,
struct ggml_tensor * lora_b,
float scaling
) {
struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b);
if (scaling != 1.0f) {
ab = ggml_scale(ctx, ab, scaling);
}
struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab);
struct ggml_cgraph * gf = ggml_new_graph(ctx);
ggml_build_forward_expand (gf, res);
return gf;
}
static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int n_threads) {
if (lora->ctx == NULL) {
return false;
}
std::string name = ggml_get_name(tensor);
std::string name_a = name + std::string(".loraA");
std::string name_b = name + std::string(".loraB");
struct ggml_tensor * lora_a = ggml_get_tensor(lora->ctx, name_a.c_str());
struct ggml_tensor * lora_b = ggml_get_tensor(lora->ctx, name_b.c_str());
if (lora_a == NULL || lora_b == NULL) {
return false;
}
float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;
struct ggml_init_params params;
params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
params.mem_buffer = NULL;
params.no_alloc = true;
struct ggml_context * ctx = NULL;
struct ggml_allocr * alloc = NULL;
struct ggml_cgraph * gf = NULL;
ctx = ggml_init(params);
alloc = ggml_allocr_new_measure(tensor_alignment);
gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
size_t alloc_size = ggml_allocr_alloc_graph(alloc, gf);
ggml_allocr_free(alloc);
ggml_free(ctx);
static std::vector<uint8_t> data_compute;
data_compute.resize(alloc_size + tensor_alignment);
ctx = ggml_init(params);
alloc = ggml_allocr_new(data_compute.data(), data_compute.size(), tensor_alignment);
gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
ggml_allocr_alloc_graph(alloc, gf);
ggml_allocr_free(alloc);
struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);
static std::vector<uint8_t> data_work;
data_work.resize(cplan.work_size);
cplan.work_data = data_work.data();
ggml_graph_compute(gf, &cplan);
ggml_free(ctx);
return true;
}
static void export_lora(struct export_lora_params * params) {
// load all loras
std::vector<struct lora_data *> loras;
for (size_t i = 0; i < params->lora.size(); ++i) {
struct lora_data * lora = load_lora(&params->lora[i]);
if (lora != NULL) {
loras.push_back(lora);
}
}
if (loras.size() == 0) {
fprintf(stderr, "warning: no lora adapters will be applied.\n");
}
// open input file
struct llama_file fin(params->fn_model_base.c_str(), "rb");
if (!fin.fp) {
die_fmt("Could not open file '%s'\n", params->fn_model_base.c_str());
}
// open base model gguf, read tensors without their data
struct ggml_context * ctx_in;
struct gguf_init_params params_gguf;
params_gguf.no_alloc = true;
params_gguf.ctx = &ctx_in;
struct gguf_context * gguf_in = gguf_init_from_file(params->fn_model_base.c_str(), params_gguf);
// create new gguf
struct gguf_context * gguf_out = gguf_init_empty();
// copy meta data from base model: kv and tensors
gguf_set_kv(gguf_out, gguf_in);
int n_tensors = gguf_get_n_tensors(gguf_in);
for (int i=0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(gguf_in, i);
struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
gguf_add_tensor(gguf_out, tensor);
}
// create output file
struct llama_file fout(params->fn_model_out.c_str(), "wb");
if (!fout.fp) {
die_fmt("Could not create file '%s'\n", params->fn_model_out.c_str());
}
// write gguf meta data
std::vector<uint8_t> meta;
meta.resize(gguf_get_meta_size(gguf_out));
gguf_get_meta_data(gguf_out, meta.data());
fout.write_raw(meta.data(), meta.size());
std::vector<uint8_t> data;
std::vector<uint8_t> padding;
for (int i=0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(gguf_in, i);
struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
// read tensor data
data.resize(ggml_nbytes(tensor));
tensor->data = data.data();
size_t offset = gguf_get_tensor_offset(gguf_in, i);
fin.seek(offset + meta.size(), SEEK_SET);
fin.read_raw(data.data(), data.size());
// apply all loras
for (size_t k = 0; k < loras.size(); ++k) {
apply_lora(tensor, loras[k], params->n_threads);
}
// write tensor data + padding
padding.clear();
padding.resize(GGML_PAD(data.size(), gguf_get_alignment(gguf_out)) - data.size(), 0);
GGML_ASSERT(fout.tell() == offset + meta.size());
// fout.seek(offset + meta.size(), SEEK_SET);
fout.write_raw(data.data(), data.size());
fout.write_raw(padding.data(), padding.size());
if (i % 2 == 0) {
printf(".");
}
}
printf("\n");
// close gguf
gguf_free(gguf_out);
gguf_free(gguf_in);
// free loras
for (size_t i = 0; i < loras.size(); ++i) {
free_lora(loras[i]);
}
}
int main(int argc, char ** argv) {
struct export_lora_params params = get_default_export_lora_params();
if (!export_lora_params_parse(argc, argv, &params)) {
return 1;
}
export_lora(&params);
return 0;
}