mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
d283d02bf2
Warning types fixed (observed under MSYS2 GCC 14.2.0): * format '%ld' expects argument of type 'long int', but argument has type 'size_t' * llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp:81:46: warning: missing initializer for member '_STARTUPINFOA::lpDesktop' [-Wmissing-field-initializers] (emitted for all struct field except first)
422 lines
16 KiB
C++
422 lines
16 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include <map>
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#include <vector>
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#include <string>
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#include <thread>
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#include <fstream>
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static bool g_verbose = false;
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struct tensor_transformation {
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struct ggml_tensor * in;
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struct ggml_tensor * out;
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bool is_copy;
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};
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static std::string get_kv_str(struct gguf_context * ctx_gguf, const std::string & key){
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int id = gguf_find_key(ctx_gguf, key.c_str());
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return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
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}
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static float get_kv_f32(struct gguf_context * ctx_gguf, const std::string & key) {
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int id = gguf_find_key(ctx_gguf, key.c_str());
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return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
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}
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static void zeros(std::ofstream & file, size_t n) {
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char zero = 0;
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for (size_t i = 0; i < n; ++i) {
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file.write(&zero, 1);
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}
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}
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static std::string ggml_ne_string(const ggml_tensor * t) {
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std::string str;
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for (int i = 0; i < GGML_MAX_DIMS; ++i) {
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str += std::to_string(t->ne[i]);
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if (i + 1 < GGML_MAX_DIMS) {
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str += ", ";
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}
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}
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return str;
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}
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static struct gguf_context * load_gguf(std::string & fname, struct ggml_context ** ctx_ggml) {
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struct gguf_init_params params = {
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/*.no_alloc = */ true,
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/*.ctx = */ ctx_ggml,
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};
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struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), params);
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if (!ctx_gguf) {
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throw std::runtime_error("failed to load input GGUF from " + fname);
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}
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return ctx_gguf;
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}
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struct file_input {
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struct ggml_context * ctx_meta = nullptr;
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struct gguf_context * ctx_gguf = nullptr;
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std::ifstream f_in;
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std::map<std::string, ggml_tensor *> tensors;
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float alpha;
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float scale;
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file_input(std::string & fname, float scale): f_in(fname, std::ios::binary), scale(scale) {
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if (!f_in.is_open()) {
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throw std::runtime_error("failed to open input gguf from " + fname);
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}
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ctx_gguf = load_gguf(fname, &ctx_meta);
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alpha = get_kv_f32(ctx_gguf, "adapter.lora.alpha");
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printf("%s: loaded gguf from %s\n", __func__, fname.c_str());
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for (ggml_tensor * cur = ggml_get_first_tensor(ctx_meta); cur; cur = ggml_get_next_tensor(ctx_meta, cur)) {
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std::string name(cur->name);
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tensors[name] = cur;
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if (g_verbose) {
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printf("%s: %s\n", __func__, cur->name);
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}
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}
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}
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ggml_tensor * get_tensor(std::string name) {
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if (tensors.find(name) == tensors.end()) {
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return nullptr;
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}
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return tensors[name];
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}
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void read_tensor_data(std::string name, std::vector<uint8_t> & buf) {
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if (tensors.find(name) == tensors.end()) {
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throw std::runtime_error("cannot find tensor with name: " + name);
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}
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auto len = ggml_nbytes(tensors[name]);
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if (buf.size() < len) {
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buf.resize(len);
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}
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auto i_tensor_in = gguf_find_tensor(ctx_gguf, name.c_str()); // 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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f_in.seekg(offset);
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f_in.read((char* )buf.data(), len);
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}
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~file_input() {
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gguf_free(ctx_gguf);
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ggml_free(ctx_meta);
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}
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};
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struct lora_merge_ctx {
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// input base model + adapters
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file_input base_model;
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std::vector<std::unique_ptr<file_input>> adapters;
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// for computing merged tensor
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int n_threads;
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ggml_backend_t backend = nullptr;
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ggml_gallocr_t allocr = nullptr;
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std::vector<uint8_t> read_buf;
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// output file
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struct gguf_context * ctx_out;
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struct ggml_context * ctx_out_ggml;
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std::ofstream fout;
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lora_merge_ctx(
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std::string & base_fname,
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std::vector<common_lora_adapter_info> & lora_files,
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std::string & outfile,
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int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
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fout.exceptions(std::ofstream::failbit); // fail fast on write errors
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if (gguf_find_key(base_model.ctx_gguf, LLM_KV_SPLIT_COUNT) >= 0) {
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throw std::runtime_error("split model is not yet supported");
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}
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for (auto & lora_inp : lora_files) {
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auto fname = lora_inp.path;
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auto scale = lora_inp.scale;
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std::unique_ptr<file_input> adapter(new file_input(fname, scale));
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check_metadata_lora(adapter.get());
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adapters.push_back(std::move(adapter));
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}
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ctx_out = gguf_init_empty();
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struct ggml_init_params params = {
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/*.mem_size =*/ gguf_get_n_tensors(base_model.ctx_gguf)*ggml_tensor_overhead(),
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ctx_out_ggml = ggml_init(params);
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backend = ggml_backend_cpu_init();
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allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
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}
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void check_metadata_lora(file_input * adapter) {
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auto general_type = get_kv_str(adapter->ctx_gguf, "general.type");
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if (general_type != "adapter") {
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throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
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}
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auto adapter_type = get_kv_str(adapter->ctx_gguf, "adapter.type");
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if (adapter_type != "lora") {
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throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
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}
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auto general_arch_base = get_kv_str(base_model.ctx_gguf, "general.architecture");
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auto general_arch_lora = get_kv_str(adapter->ctx_gguf, "general.architecture");
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if (general_arch_base != general_arch_lora) {
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throw std::runtime_error("model arch and LoRA arch mismatch");
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}
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}
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ggml_type get_out_tensor_type(struct ggml_tensor * t) {
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if (t->type == GGML_TYPE_F32) {
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return GGML_TYPE_F32;
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} else {
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return GGML_TYPE_F16;
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}
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}
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void run_merge() {
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// prepare metadata
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gguf_set_kv(ctx_out, base_model.ctx_gguf);
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// output is forced to f16 for now
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gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16);
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// check if all lora adapters have the same tensors
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// TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777
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static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once.";
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if (adapters.size() > 1) {
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for (size_t i = 1; i < adapters.size(); ++i) {
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if (adapters[0]->tensors.size() != adapters[i]->tensors.size()) {
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throw std::runtime_error(err_no_subset_adapter);
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}
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for (auto & it : adapters[i]->tensors) {
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if (adapters[0]->get_tensor(it.first) == nullptr) {
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throw std::runtime_error(err_no_subset_adapter);
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}
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}
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}
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}
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// mapping base tensor to out tensor (same shape with base, but different type)
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std::vector<tensor_transformation> trans;
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for (auto & it : base_model.tensors) {
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bool t_a = true;
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bool t_b = true;
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for (auto & adapter : adapters) {
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t_a &= nullptr != adapter->get_tensor(it.first + ".lora_a");
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t_b &= nullptr != adapter->get_tensor(it.first + ".lora_b");
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}
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auto base_tensor = it.second;
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if (!t_a && !t_b) {
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// only copy
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struct ggml_tensor * cpy_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
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ggml_set_name(cpy_tensor, base_tensor->name);
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trans.push_back({
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cpy_tensor,
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cpy_tensor,
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true,
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});
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gguf_add_tensor(ctx_out, cpy_tensor);
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} else if (t_a && t_b) {
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// need merging
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struct ggml_tensor * out_tensor = ggml_new_tensor(
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ctx_out_ggml, get_out_tensor_type(base_tensor), GGML_MAX_DIMS, base_tensor->ne);
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ggml_set_name(out_tensor, base_tensor->name);
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trans.push_back({
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base_tensor,
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out_tensor,
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false,
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});
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gguf_add_tensor(ctx_out, out_tensor);
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} else {
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throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b");
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}
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}
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// placeholder for the meta data
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{
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size_t meta_size = gguf_get_meta_size(ctx_out);
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zeros(fout, meta_size);
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}
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// process base model tensors
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size_t n_merged = 0;
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for (auto & it : trans) {
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if (!it.is_copy) {
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merge_tensor(it.in, it.out);
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n_merged++;
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} else {
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copy_tensor(it.in);
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}
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}
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// write output metadata
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{
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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.seekp(0);
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fout.write((const char *)data.data(), data.size());
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}
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printf("%s : merged %zu tensors with lora adapters\n", __func__, n_merged);
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printf("%s : wrote %zu tensors to output file\n", __func__, trans.size());
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}
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void copy_tensor(struct ggml_tensor * base) {
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printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
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size_t len = ggml_nbytes(base);
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base_model.read_tensor_data(base->name, read_buf);
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fout.write((char* )read_buf.data(), len);
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zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
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}
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void merge_tensor(struct ggml_tensor * base, struct ggml_tensor * out) {
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std::string name_base(base->name);
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std::string name_lora_a = name_base + ".lora_a";
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std::string name_lora_b = name_base + ".lora_b";
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printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
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// context for input tensor
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std::vector<struct ggml_tensor *> inp_a(adapters.size());
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std::vector<struct ggml_tensor *> inp_b(adapters.size());
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struct ggml_init_params params {
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/*.mem_size =*/ ggml_tensor_overhead()*(2+adapters.size()*2),
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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struct ggml_context * ctx = ggml_init(params);
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// alloc tensors
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struct ggml_tensor * inp_base = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, base->ne);
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for (size_t i = 0; i < adapters.size(); ++i) {
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auto t_a = adapters[i]->get_tensor(name_lora_a);
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auto t_b = adapters[i]->get_tensor(name_lora_b);
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// TODO: add support for quantized lora
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if (ggml_is_quantized(t_a->type) || ggml_is_quantized(t_b->type)) {
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throw std::runtime_error("quantized LoRA adapters is not supported, please retry with f16 or f32");
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}
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inp_a[i] = ggml_dup_tensor(ctx, t_a);
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inp_b[i] = ggml_dup_tensor(ctx, t_b);
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}
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ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
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// load base tensor to backend buffer
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base_model.read_tensor_data(name_base, read_buf);
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if (base->type != GGML_TYPE_F32) {
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// optionally dequantize it
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printf("%s : + dequantize base tensor from %s to F32\n", __func__, ggml_type_name(base->type));
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auto nels = ggml_nelements(inp_base);
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const auto * qtype = ggml_get_type_traits(base->type);
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std::vector<uint8_t> dequant_buf(nels * sizeof(float));
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qtype->to_float(read_buf.data(), (float *)dequant_buf.data(), nels);
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ggml_backend_tensor_set(inp_base, dequant_buf.data(), 0, dequant_buf.size());
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} else {
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ggml_backend_tensor_set(inp_base, read_buf.data(), 0, ggml_nbytes(inp_base));
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}
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// load lora tensors to backend buffer
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for (size_t i = 0; i < adapters.size(); ++i) {
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adapters[i]->read_tensor_data(name_lora_a, read_buf);
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ggml_backend_tensor_set(inp_a[i], read_buf.data(), 0, ggml_nbytes(inp_a[i]));
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adapters[i]->read_tensor_data(name_lora_b, read_buf);
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ggml_backend_tensor_set(inp_b[i], read_buf.data(), 0, ggml_nbytes(inp_b[i]));
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}
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// build graph
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struct ggml_cgraph * gf;
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{
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static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
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static std::vector<uint8_t> buf(buf_size);
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struct ggml_init_params params0 = {
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/*.mem_size =*/ buf_size,
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/*.mem_buffer =*/ buf.data(),
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/*.no_alloc =*/ true,
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};
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struct ggml_context * ctx0 = ggml_init(params0);
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gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur = inp_base;
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for (size_t i = 0; i < adapters.size(); ++i) {
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struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)));
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struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
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// scale
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const float alpha = adapters[i]->alpha;
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const float rank = (float) inp_b[i]->ne[0];
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const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale;
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delta = ggml_scale(ctx0, delta, scale);
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cur = ggml_add(ctx0, delta, cur);
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printf("%s : + merging from adapter[%zu] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type));
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printf("%s : input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]);
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}
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cur = ggml_cast(ctx0, cur, out->type);
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printf("%s : + output type is %s\n", __func__, ggml_type_name(out->type));
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ggml_build_forward_expand(gf, cur);
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ggml_free(ctx0);
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}
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// compute
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{
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ggml_gallocr_alloc_graph(allocr, gf);
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ggml_backend_cpu_set_n_threads(backend, n_threads);
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ggml_backend_graph_compute(backend, gf);
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}
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// write data to output file
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{
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auto * result = ggml_graph_node(gf, -1);
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size_t len = ggml_nbytes(result);
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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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ggml_backend_tensor_get(result, read_buf.data(), 0, len);
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fout.write((char* )read_buf.data(), len);
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zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
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}
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ggml_free(ctx);
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ggml_backend_buffer_free(buffer);
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}
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~lora_merge_ctx() {
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ggml_gallocr_free(allocr);
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ggml_backend_free(backend);
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gguf_free(ctx_out);
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ggml_free(ctx_out_ggml);
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}
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};
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static void print_usage(int, char ** argv) {
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printf("\nexample usage:\n");
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printf("\n %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]);
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printf("\nNOTE: output model is F16\n");
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printf("\n");
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}
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int main(int argc, char ** argv) {
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common_params params;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
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return 1;
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}
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g_verbose = (params.verbosity > 1);
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try {
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lora_merge_ctx ctx(params.model, params.lora_adapters, params.lora_outfile, params.cpuparams.n_threads);
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ctx.run_merge();
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} catch (const std::exception & err) {
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fprintf(stderr, "%s\n", err.what());
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exit(EXIT_FAILURE);
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}
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printf("done, output file is %s\n", params.lora_outfile.c_str());
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return 0;
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}
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