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llama : adapter

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-12-22 22:28:20 +02:00
parent 0969970a48
commit 963fb4d26f
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GPG Key ID: 449E073F9DC10735
7 changed files with 383 additions and 329 deletions
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10
common/common.h
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@ -481,7 +481,17 @@ std::string fs_get_cache_file(const std::string & filename);
struct common_init_result {
struct llama_model * model = nullptr;
struct llama_context * context = nullptr;
std::vector<common_lora_adapter_container> lora_adapters;
~common_init_result() {
llama_free(context);
llama_free_model(model);
for (auto & lora_adapter : lora_adapters) {
llama_lora_adapter_free(lora_adapter.adapter);
}
}
};
struct common_init_result common_init_from_params(common_params & params);

5
include/llama.h
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@ -417,6 +417,7 @@ extern "C" {
const char * path_model,
struct llama_model_params params);
// TODO: rename to llama_model_free
LLAMA_API void llama_free_model(struct llama_model * model);
// TODO: rename to llama_init_from_model
@ -507,7 +508,6 @@ extern "C" {
//
// Load a LoRA adapter from file
// The loaded adapter will be associated to the given model, and will be free when the model is deleted
// TODO: rename to llama_adapter_lora_init
LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
struct llama_model * model,
@ -530,8 +530,7 @@ extern "C" {
// Remove all LoRA adapters from given context
// TODO: rename to llama_clear_adapter_lora
LLAMA_API void llama_lora_adapter_clear(
struct llama_context * ctx);
LLAMA_API void llama_lora_adapter_clear(struct llama_context * ctx);
// Manually free a LoRA adapter
// Note: loaded adapters will be free when the associated model is deleted

313
src/llama-adapter.cpp
View File

@ -1,5 +1,318 @@
#include "llama-adapter.h"
#include "llama-model.h"
#include <algorithm>
#include <map>
#include <cassert>
// vec
struct ggml_tensor * llama_control_vector::tensor_for(int il) const {
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
return nullptr;
}
return tensors[il];
}
struct ggml_tensor * llama_control_vector::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
ggml_tensor * layer_dir = tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx, cur, layer_dir);
}
return cur;
}
static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
const auto & hparams = model.hparams;
GGML_ASSERT(cvec.tensors.empty());
GGML_ASSERT(cvec.ctxs.empty());
GGML_ASSERT(cvec.bufs.empty());
// create a context for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
struct ggml_init_params params = {
/*.mem_size =*/ hparams.n_layer*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * ctx = ggml_init(params);
if (!ctx) {
return nullptr;
}
ctx_map[buft] = ctx;
cvec.ctxs.emplace_back(ctx);
return ctx;
}
return it->second;
};
// make tensors
cvec.tensors.reserve(hparams.n_layer);
cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
for (size_t il = 1; il < hparams.n_layer; il++) {
ggml_backend_buffer_type_t buft = llama_model_select_buft(model, il);
ggml_context * ctx = ctx_for_buft(buft);
if (!ctx) {
LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
return false;
}
ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
cvec.tensors.push_back(tensor);
}
// allocate tensors / buffers and zero
cvec.bufs.reserve(ctx_map.size());
for (auto it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx = it.second;
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (!buf) {
LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
return false;
}
ggml_backend_buffer_clear(buf, 0);
cvec.bufs.emplace_back(buf);
}
return true;
}
int32_t llama_control_vector_apply(
struct llama_control_vector & cvec,
const llama_model & model,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end) {
const auto & hparams = model.hparams;
if (data == nullptr) {
// disable the current control vector (but leave allocated for later)
cvec.layer_start = -1;
cvec.layer_end = -1;
return 0;
}
if (n_embd != (int) hparams.n_embd) {
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
return 1;
}
if (cvec.tensors.empty()) {
if (!llama_control_vector_init(cvec, model)) {
return 1;
}
}
cvec.layer_start = il_start;
cvec.layer_end = il_end;
for (size_t il = 1; il < hparams.n_layer; il++) {
assert(cvec.tensors[il] != nullptr);
const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
if (off + n_embd <= len) {
ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
}
}
return 0;
}
// lora
llama_lora_weight * llama_lora_adapter::get_weight(struct ggml_tensor * w) {
const std::string name(w->name);
const auto pos = ab_map.find(name);
if (pos != ab_map.end()) {
return &pos->second;
}
return nullptr;
}
void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
delete adapter;
}
void llama_lora_adapter_init_impl(struct llama_model & model, const char * path_lora, struct llama_lora_adapter & adapter) {
LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
ggml_context * ctx_init;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ true,
/* .ctx = */ &ctx_init,
};
gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };
if (!ctx_gguf) {
throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
}
ggml_context_ptr ctx { ctx_init };
// check metadata
{
auto get_kv_str = [&](const std::string & key) -> std::string {
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
};
auto get_kv_f32 = [&](const std::string & key) -> float {
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
};
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
if (general_type != "adapter") {
throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
}
auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
auto general_arch = llm_arch_from_string(general_arch_str);
if (general_arch != model.arch) {
throw std::runtime_error("model arch and LoRA arch mismatch");
}
auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
if (adapter_type != "lora") {
throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
}
adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
}
int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
// contexts for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
// add a new context
struct ggml_init_params params = {
/*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * buft_ctx = ggml_init(params);
if (!buft_ctx) {
return nullptr;
}
ctx_map[buft] = buft_ctx;
adapter.ctxs.emplace_back(buft_ctx);
return buft_ctx;
};
return it->second;
};
// bundle lora_a and lora_b into pairs
std::map<std::string, llama_lora_weight> ab_map;
auto str_endswith = [](const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
};
for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {
std::string name(cur->name);
if (str_endswith(name, ".lora_a")) {
replace_all(name, ".lora_a", "");
if (ab_map.find(name) == ab_map.end()) {
ab_map[name] = llama_lora_weight(cur, nullptr);
} else {
ab_map[name].a = cur;
}
} else if (str_endswith(name, ".lora_b")) {
replace_all(name, ".lora_b", "");
if (ab_map.find(name) == ab_map.end()) {
ab_map[name] = llama_lora_weight(nullptr, cur);
} else {
ab_map[name].b = cur;
}
} else {
throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
}
}
// add tensors
for (auto & it : ab_map) {
const std::string & name = it.first;
llama_lora_weight & w = it.second;
if (!w.a || !w.b) {
throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
}
// device buft and device ctx
auto * model_tensor = llama_model_get_tensor(model, name.c_str());
if (!model_tensor) {
throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
}
struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
// validate tensor shape
if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
throw std::runtime_error("tensor '" + name + "' has incorrect shape");
}
if (w.a->ne[1] != w.b->ne[0]) {
throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
}
// save tensor to adapter
struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
ggml_set_name(tensor_a, w.a->name);
ggml_set_name(tensor_b, w.b->name);
adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
}
// allocate tensors / buffers and zero
{
adapter.ctxs.reserve(ctx_map.size());
adapter.bufs.reserve(ctx_map.size());
for (auto & it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx_dev = it.second;
ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };
if (!buf) {
throw std::runtime_error("failed to allocate buffer for lora adapter\n");
}
LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);
adapter.bufs.emplace_back(std::move(buf));
}
}
// set tensor data
{
llama_file gguf_file(path_lora, "rb");
std::vector<uint8_t> read_buf;
auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
size_t size = ggml_nbytes(orig);
read_buf.resize(size);
gguf_file.seek(offs, SEEK_SET);
gguf_file.read_raw(read_buf.data(), size);
ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
};
for (auto & it : adapter.ab_map) {
auto orig = ab_map[it.first];
auto dev = it.second;
set_tensor(orig.a, dev.a);
set_tensor(orig.b, dev.b);
}
}
LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
}

327
src/llama-adapter.h
View File

@ -1,13 +1,11 @@
#pragma once
#include "llama-impl.h"
#include "llama-hparams.h"
#include "ggml-cpp.h"
#include "llama-model.h" // TODO: need only hparams
#include <algorithm>
#include <cassert>
#include <map>
#include <unordered_map>
#include <vector>
//
@ -16,119 +14,27 @@
// TODO: rename to llama_adapter_vec
struct llama_control_vector {
std::vector<struct ggml_tensor *> tensors; // per layer
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
std::vector<struct ggml_tensor *> tensors; // per layer
int32_t layer_start = -1;
int32_t layer_end = -1;
struct ggml_tensor * tensor_for(int il) const {
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
return nullptr;
}
return tensors[il];
}
struct ggml_tensor * tensor_for(int il) const;
struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
ggml_tensor * layer_dir = tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx, cur, layer_dir);
}
return cur;
}
struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const;
};
static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
GGML_ASSERT(cvec.tensors.empty());
GGML_ASSERT(cvec.ctxs.empty());
GGML_ASSERT(cvec.bufs.empty());
// create a context for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
struct ggml_init_params params = {
/*.mem_size =*/ model.hparams.n_layer*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * ctx = ggml_init(params);
if (!ctx) {
return nullptr;
}
ctx_map[buft] = ctx;
cvec.ctxs.emplace_back(ctx);
return ctx;
}
return it->second;
};
// make tensors
cvec.tensors.reserve(model.hparams.n_layer);
cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
for (size_t il = 1; il < model.hparams.n_layer; il++) {
ggml_backend_buffer_type_t buft = llama_model_select_buft(model, il);
ggml_context * ctx = ctx_for_buft(buft);
if (!ctx) {
LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
return false;
}
ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
cvec.tensors.push_back(tensor);
}
// allocate tensors / buffers and zero
cvec.bufs.reserve(ctx_map.size());
for (auto it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx = it.second;
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (!buf) {
LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
return false;
}
ggml_backend_buffer_clear(buf, 0);
cvec.bufs.emplace_back(buf);
}
return true;
}
static int32_t llama_control_vector_apply(struct llama_control_vector & cvec, const llama_model & model, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
if (data == nullptr) {
// disable the current control vector (but leave allocated for later)
cvec.layer_start = -1;
cvec.layer_end = -1;
return 0;
}
if (n_embd != (int) model.hparams.n_embd) {
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
return 1;
}
if (cvec.tensors.empty()) {
if (!llama_control_vector_init(cvec, model)) {
return 1;
}
}
cvec.layer_start = il_start;
cvec.layer_end = il_end;
for (size_t il = 1; il < model.hparams.n_layer; il++) {
assert(cvec.tensors[il] != nullptr);
const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
if (off + n_embd <= len) {
ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
}
}
return 0;
}
int32_t llama_control_vector_apply(
struct llama_control_vector & cvec,
const llama_model & model,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end);
//
// llama_adapter_lora
@ -138,214 +44,25 @@ static int32_t llama_control_vector_apply(struct llama_control_vector & cvec, co
struct llama_lora_weight {
struct ggml_tensor * a = nullptr;
struct ggml_tensor * b = nullptr;
llama_lora_weight() = default;
llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b) : a(a), b(b) {}
};
// TODO: rename to llama_adapter_lora
struct llama_lora_adapter {
struct llama_model * base_model;
// map tensor name to lora_a_b
std::unordered_map<std::string, struct llama_lora_weight> ab_map;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
float alpha;
llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
base_model->lora_adapters.insert(this);
}
llama_lora_adapter() = default;
~llama_lora_adapter() = default;
llama_lora_weight * get_weight(struct ggml_tensor * w) {
std::string name(w->name);
auto pos = ab_map.find(name);
if (ab_map.find(name) != ab_map.end()) {
return &pos->second;
}
return nullptr;
}
~llama_lora_adapter() {
auto pos = base_model->lora_adapters.find(this);
if (pos != base_model->lora_adapters.end()) {
base_model->lora_adapters.erase(pos);
}
}
llama_lora_weight * get_weight(struct ggml_tensor * w);
};
static struct ggml_tensor * llama_get_model_tensor(const struct llama_model * model, const char * name) {
auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
[name](const std::pair<std::string, struct ggml_tensor *> & it) {
return it.first == name;
});
if (it == model->tensors_by_name.end()) {
return nullptr;
}
return it->second;
}
static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
ggml_context * ctx_init;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ true,
/* .ctx = */ &ctx_init,
};
gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };
if (!ctx_gguf) {
throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
}
ggml_context_ptr ctx { ctx_init };
// check metadata
{
auto get_kv_str = [&](const std::string & key) -> std::string {
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
};
auto get_kv_f32 = [&](const std::string & key) -> float {
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
};
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
if (general_type != "adapter") {
throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
}
auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
auto general_arch = llm_arch_from_string(general_arch_str);
if (general_arch != model->arch) {
throw std::runtime_error("model arch and LoRA arch mismatch");
}
auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
if (adapter_type != "lora") {
throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
}
adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
}
int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
// contexts for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
// add a new context
struct ggml_init_params params = {
/*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * buft_ctx = ggml_init(params);
if (!buft_ctx) {
return nullptr;
}
ctx_map[buft] = buft_ctx;
adapter.ctxs.emplace_back(buft_ctx);
return buft_ctx;
};
return it->second;
};
// bundle lora_a and lora_b into pairs
std::map<std::string, llama_lora_weight> ab_map;
auto str_endswith = [](const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
};
for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {
std::string name(cur->name);
if (str_endswith(name, ".lora_a")) {
replace_all(name, ".lora_a", "");
if (ab_map.find(name) == ab_map.end()) {
ab_map[name] = llama_lora_weight(cur, nullptr);
} else {
ab_map[name].a = cur;
}
} else if (str_endswith(name, ".lora_b")) {
replace_all(name, ".lora_b", "");
if (ab_map.find(name) == ab_map.end()) {
ab_map[name] = llama_lora_weight(nullptr, cur);
} else {
ab_map[name].b = cur;
}
} else {
throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
}
}
// add tensors
for (auto & it : ab_map) {
const std::string & name = it.first;
llama_lora_weight & w = it.second;
if (!w.a || !w.b) {
throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
}
// device buft and device ctx
auto * model_tensor = llama_get_model_tensor(model, name.c_str());
if (!model_tensor) {
throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
}
struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
// validate tensor shape
if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
throw std::runtime_error("tensor '" + name + "' has incorrect shape");
}
if (w.a->ne[1] != w.b->ne[0]) {
throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
}
// save tensor to adapter
struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
ggml_set_name(tensor_a, w.a->name);
ggml_set_name(tensor_b, w.b->name);
adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
}
// allocate tensors / buffers and zero
{
adapter.ctxs.reserve(ctx_map.size());
adapter.bufs.reserve(ctx_map.size());
for (auto & it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx_dev = it.second;
ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };
if (!buf) {
throw std::runtime_error("failed to allocate buffer for lora adapter\n");
}
LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);
adapter.bufs.emplace_back(std::move(buf));
}
}
// set tensor data
{
llama_file gguf_file(path_lora, "rb");
std::vector<uint8_t> read_buf;
auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
size_t size = ggml_nbytes(orig);
read_buf.resize(size);
gguf_file.seek(offs, SEEK_SET);
gguf_file.read_raw(read_buf.data(), size);
ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
};
for (auto & it : adapter.ab_map) {
auto orig = ab_map[it.first];
auto dev = it.second;
set_tensor(orig.a, dev.a);
set_tensor(orig.b, dev.b);
}
}
LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
}
void llama_lora_adapter_init_impl(struct llama_model & model, const char * path_lora, struct llama_lora_adapter & adapter);

13
src/llama-model.cpp
View File

@ -2,6 +2,7 @@
#include "llama-impl.h"
#include <algorithm>
#include <cassert>
const char * llm_type_name(llm_type type) {
@ -172,3 +173,15 @@ ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, in
return ggml_add(ctx, cur, layer_dir);
});
}
struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name) {
auto it = std::find_if(model.tensors_by_name.begin(), model.tensors_by_name.end(),
[name](const std::pair<std::string, struct ggml_tensor *> & it) {
return it.first == name;
});
if (it == model.tensors_by_name.end()) {
return nullptr;
}
return it->second;
}

15
src/llama-model.h
View File

@ -363,15 +363,6 @@ struct llama_model {
// total size of all the tensors in the model in bytes
size_t n_bytes = 0;
// keep track of loaded lora adapters
std::set<struct llama_lora_adapter *> lora_adapters;
~llama_model() {
while (!lora_adapters.empty()) {
llama_lora_adapter_free(*lora_adapters.begin());
}
}
};
const char * llm_type_name(llm_type type);
@ -380,5 +371,9 @@ std::string llama_model_arch_name (const llama_model & model);
std::string llama_model_type_name (const llama_model & model);
std::string llama_model_ftype_name(const llama_model & model);
// TODO: this probably belongs to llama-adapter
// used by llama_adapter_vec
ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il);
// used by llama_adapter_lora
struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name);

29
src/llama.cpp
View File

@ -15670,6 +15670,21 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
}
struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
struct llama_lora_adapter * adapter = new llama_lora_adapter();
try {
llama_lora_adapter_init_impl(*model, path_lora, *adapter);
return adapter;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
delete adapter;
}
return nullptr;
}
int32_t llama_lora_adapter_set(
struct llama_context * ctx,
struct llama_lora_adapter * adapter,
@ -15678,7 +15693,9 @@ int32_t llama_lora_adapter_set(
LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
return -1;
}
ctx->lora_adapters[adapter] = scale;
return 0;
}
@ -15690,6 +15707,7 @@ int32_t llama_lora_adapter_remove(
ctx->lora_adapters.erase(pos);
return 0;
}
return -1;
}
@ -16560,17 +16578,6 @@ uint32_t llama_model_quantize(
}
}
struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
try {
struct llama_lora_adapter * adapter = new llama_lora_adapter(model);
llama_lora_adapter_init_internal(model, path_lora, *adapter);
return adapter;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
return nullptr;
}
}
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
struct llama_kv_cache_view result = {
/*.n_cells = */ 0,