#include "llama-adapter.h"
#include "llama-impl.h"
#include "llama-mmap.h"
#include "llama-model.h"
#include <map>
#include <cassert>
#include <sstream>
#include <stdexcept>
// vec
ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
return nullptr;
}
return tensors[il];
}
ggml_tensor * llama_adapter_cvec::apply_to(ggml_context * ctx, 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;
}
bool llama_adapter_cvec::init(const llama_model & model) {
const auto & hparams = model.hparams;
GGML_ASSERT(tensors.empty());
GGML_ASSERT(ctxs.empty());
GGML_ASSERT(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()) {
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;
ctxs.emplace_back(ctx);
return ctx;
}
return it->second;
};
// make tensors
tensors.reserve(hparams.n_layer());
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 = model.select_buft(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);
tensors.push_back(tensor);
}
// allocate tensors / buffers and zero
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);
bufs.emplace_back(buf);
}
return true;
}
bool llama_adapter_cvec::apply(
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)
layer_start = -1;
layer_end = -1;
return true;
}
if (n_embd != (int) hparams.n_embd) {
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
return false;
}
if (tensors.empty()) {
if (!init(model)) {
return false;
}
}
layer_start = il_start;
layer_end = il_end;
for (size_t il = 1; il < hparams.n_layer(); il++) {
assert(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(tensors[il], data + off, 0, n_embd * ggml_element_size(tensors[il]));
}
}
return true;
}
// lora
llama_adapter_lora_weight * llama_adapter_lora::get_weight(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;
}
static void llama_adapter_lora_init_impl(llama_model & model, const char * path_lora, llama_adapter_lora & adapter) {
LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
ggml_context * ctx_init;
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
{
const gguf_context * gguf_ctx = ctx_gguf.get();
LLAMA_LOG_INFO("%s: Dumping metadata keys/values.\n", __func__);
// get metadata as string
for (int i = 0; i < gguf_get_n_kv(gguf_ctx); i++) {
gguf_type type = gguf_get_kv_type(gguf_ctx, i);
const std::string type_name =
type == GGUF_TYPE_ARRAY
? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(gguf_ctx, i)), gguf_get_arr_n(gguf_ctx, i))
: gguf_type_name(type);
const char * name = gguf_get_key(gguf_ctx, i);
const std::string value = gguf_kv_to_str(gguf_ctx, i);
if (type != GGUF_TYPE_ARRAY) {
adapter.gguf_kv.emplace(name, value);
}
const size_t MAX_VALUE_LEN = 40;
std::string print_value = value.size() > MAX_VALUE_LEN ? format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()) : value;
replace_all(print_value, "\n", "\\n");
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), print_value.c_str());
}
auto get_kv_str = [&](const std::string & key) -> std::string {
int id = gguf_find_key(gguf_ctx, key.c_str());
return id < 0 ? "" : std::string(gguf_get_val_str(gguf_ctx, id));
};
auto get_kv_f32 = [&](const std::string & key) -> float {
int id = gguf_find_key(gguf_ctx, key.c_str());
return id < 0 ? 0.0f : gguf_get_val_f32(gguf_ctx, 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));
// parse alora invocation sequence vector
const auto & key = llm_kv(LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS);
const int kid = gguf_find_key(ctx_gguf.get(), key.c_str());
if (kid >= 0) {
if (gguf_get_kv_type(ctx_gguf.get(), kid) != GGUF_TYPE_ARRAY) {
throw std::runtime_error("invalid gguf type for " + key);
}
const auto arr_type = gguf_get_arr_type(ctx_gguf.get(), kid);
if (arr_type != GGUF_TYPE_UINT32) {
throw std::runtime_error("invalid gguf element type for " + key);
}
const size_t seq_len = gguf_get_arr_n(ctx_gguf.get(), kid);
const void * data = gguf_get_arr_data(ctx_gguf.get(), kid);
adapter.alora_invocation_tokens.resize(seq_len);
std::copy(
(const llama_token *)data,
(const llama_token *)data + seq_len,
adapter.alora_invocation_tokens.begin());
}
}
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
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_adapter_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_adapter_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_adapter_lora_weight(nullptr, cur);
} else {
ab_map[name].b = cur;
}
} else if (str_endswith(name, "_norm.weight")) {
// TODO: add support for norm vector
// for now, we don't really care because most adapters still work fine without it
continue;
} else {
throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
}
}
// get extra buffer types of the CPU
// TODO: a more general solution for non-CPU extra buft should be implemented in the future
// ref: https://github.com/ggml-org/llama.cpp/pull/12593#pullrequestreview-2718659948
std::vector<ggml_backend_buffer_type_t> buft_extra;
{
auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
if (!cpu_dev) {
throw std::runtime_error(format("%s: no CPU backend found", __func__));
}
auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
if (ggml_backend_dev_get_extra_bufts_fn) {
ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
while (extra_bufts && *extra_bufts) {
buft_extra.emplace_back(*extra_bufts);
++extra_bufts;
}
}
}
// add tensors
for (auto & it : ab_map) {
const std::string & name = it.first;
llama_adapter_lora_weight & w = it.second;
bool is_token_embd = str_endswith(name, "token_embd.weight");
if (!w.a || !w.b) {
throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
}
// device buft and device ctx
const auto * model_tensor = model.get_tensor(name.c_str());
if (!model_tensor) {
throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");
}
auto * buft = ggml_backend_buffer_get_type(model_tensor->buffer);
// do not load loras to extra buffer types (i.e. bufts for repacking) -> use the CPU in that case
for (auto & ex : buft_extra) {
if (ex == buft) {
LLAMA_LOG_WARN("%s: lora for '%s' cannot use buft '%s', fallback to CPU\n", __func__, model_tensor->name, ggml_backend_buft_name(buft));
auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
if (!cpu_dev) {
throw std::runtime_error(format("%s: no CPU backend found", __func__));
}
buft = ggml_backend_dev_buffer_type(cpu_dev);
break;
}
}
LLAMA_LOG_DEBUG("%s: lora for '%s' -> '%s'\n", __func__, model_tensor->name, ggml_backend_buft_name(buft));
ggml_context * dev_ctx = ctx_for_buft(buft);
// validate tensor shape
if (is_token_embd) {
// expect B to be non-transposed, A and B are flipped; see llm_build_inp_embd()
if (model_tensor->ne[0] != w.b->ne[1] || model_tensor->ne[1] != w.a->ne[1]) {
throw std::runtime_error("tensor '" + name + "' has incorrect shape (hint: maybe wrong base model?)");
}
} else {
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 (hint: maybe wrong base model?)");
}
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
ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
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_adapter_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 = [&](ggml_tensor * orig, ggml_tensor * dev) {
const 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));
const size_t size = ggml_nbytes(orig);
if (offs + size < offs || offs + size > gguf_file.size()) {
throw std::runtime_error(format("LoRA tensor '%s' data is not within the file bounds, file is corrupted or incomplete", orig->name));
}
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);
}
}
// register adapter with model
model.loras.insert(&adapter);
LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
}
llama_adapter_lora * llama_adapter_lora_init(llama_model * model, const char * path_lora) {
llama_adapter_lora * adapter = new llama_adapter_lora(model);
try {
llama_adapter_lora_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_adapter_meta_val_str(const llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size) {
const auto & it = adapter->gguf_kv.find(key);
if (it == adapter->gguf_kv.end()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_adapter_meta_count(const llama_adapter_lora * adapter) {
return (int)adapter->gguf_kv.size();
}
int32_t llama_adapter_meta_key_by_index(const llama_adapter_lora * adapter, int i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)adapter->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = adapter->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->first.c_str());
}
int32_t llama_adapter_meta_val_str_by_index(const llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)adapter->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = adapter->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
void llama_adapter_lora_free(llama_adapter_lora * adapter) {
if (adapter == nullptr) {
return;
}
if (adapter->model != nullptr) {
adapter->model->loras.erase(adapter);
adapter->model = nullptr;
}
delete adapter;
}
uint64_t llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter) {
if (!adapter) {
return 0;
}
return adapter->alora_invocation_tokens.size();
}
const llama_token * llama_adapter_get_alora_invocation_tokens(const llama_adapter_lora * adapter) {
GGML_ASSERT(adapter);
return adapter->alora_invocation_tokens.data();
}