root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-09-22 21:16:20 +00:00
Code Issues Actions 16 Packages Projects Releases Wiki Activity

auto scale

Browse Source
This commit is contained in:
ngxson 2024-07-15 11:41:18 +02:00
parent 703573f608
commit 42415a4874
4 changed files with 36 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
common/common.cpp
View File

@ -684,7 +684,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
if (arg == "--lora") {
CHECK_ARG
params.lora_adapter.emplace_back(argv[i], 1.0f);
params.lora_adapter.emplace_back(argv[i], 0.0f);
return true;
}
if (arg == "--lora-scaled") {
@ -2089,6 +2089,9 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
}
if (lora_scale == 0.0f) {
lora_scale = llama_lora_adapter_get_default_scale(adapter);
}
llama_lora_adapter_set(lctx, adapter, lora_scale);
}

2
convert_lora_to_gguf.py
View File

@ -366,9 +366,11 @@ if __name__ == '__main__':
lparams: dict[str, Any] = json.load(f)
alpha = lparams["lora_alpha"]
rank = lparams["r"]
model_instance.gguf_writer.add_string("training.type", "finetune_lora")
model_instance.gguf_writer.add_float32("training.lora.alpha", float(alpha))
model_instance.gguf_writer.add_float32("training.lora.scale", float(alpha) / float(rank))
model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
logger.info("Exporting model...")

35
include/llama.h
View File

@ -513,12 +513,33 @@ extern "C" {
const char * fname_out,
const llama_model_quantize_params * params);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector.
// n_embd should be the size of a single layer's control, and data should point
// to an n_embd x n_layers buffer starting from layer 1.
// il_start and il_end are the layer range the vector should apply to (both inclusive)
// See llama_control_vector_load in common to load a control vector.
LLAMA_API int32_t llama_control_vector_apply(
struct llama_context * lctx,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end);
//
// LoRA
//
// 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
LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
struct llama_model * model,
const char * path_lora);
// Get default scale of an adapter
LLAMA_API float llama_lora_adapter_get_default_scale(struct llama_lora_adapter * adapter);
// Add a loaded LoRA adapter to given context
// This will not modify model's weight
LLAMA_API int32_t llama_lora_adapter_set(
@ -536,20 +557,6 @@ extern "C" {
// Note: loaded adapters will be free when the associated model is deleted
LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector.
// n_embd should be the size of a single layer's control, and data should point
// to an n_embd x n_layers buffer starting from layer 1.
// il_start and il_end are the layer range the vector should apply to (both inclusive)
// See llama_control_vector_load in common to load a control vector.
LLAMA_API int32_t llama_control_vector_apply(
struct llama_context * lctx,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end);
//
// KV cache
//

10
src/llama.cpp
View File

@ -380,6 +380,7 @@ enum llm_kv {
LLM_KV_TRAINING_TYPE,
LLM_KV_TRAINING_LORA_ALPHA,
LLM_KV_TRAINING_LORA_SCALE,
};
static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@ -476,6 +477,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TRAINING_TYPE, "training.type" },
{ LLM_KV_TRAINING_LORA_ALPHA, "training.lora.alpha" },
{ LLM_KV_TRAINING_LORA_SCALE, "training.lora.scale" },
};
struct LLM_KV {
@ -2851,6 +2853,7 @@ struct llama_lora_adapter {
std::vector<ggml_backend_buffer_t> bufs;
float alpha;
float scale; // default scale
llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
base_model->lora_adapters.insert(this);
@ -18578,7 +18581,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
LLAMA_LOG_INFO("%s: applying lora adapter from '%s' ...\n", __func__, path_lora);
LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
@ -18615,6 +18618,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c
}
adapter.alpha = get_kv_f32(llm_kv(LLM_KV_TRAINING_LORA_ALPHA));
adapter.scale = get_kv_f32(llm_kv(LLM_KV_TRAINING_LORA_SCALE));
}
int n_tensors = gguf_get_n_tensors(ctx_gguf);
@ -18749,6 +18753,10 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c
ggml_free(ctx);
}
float llama_lora_adapter_get_default_scale(struct llama_lora_adapter * adapter) {
return adapter->scale;
}
int32_t llama_lora_adapter_set(
struct llama_context * ctx,
struct llama_lora_adapter * adapter,