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gguf : add ftype meta info to the model (#2710)

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* llama : add ftype meta info to the model

ggml-ci

* convert.py : add ftype when converting (does not work)

* convert.py : fix Enum to IntEnum

ggml-ci
This commit is contained in:
Georgi Gerganov 2023-08-22 20:05:59 +03:00 committed by GitHub
parent bac66994cf
commit deb7dfca4b
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4 changed files with 47 additions and 9 deletions
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29
convert.py
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@ -69,7 +69,10 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
'I32': DT_I32,
}
class GGMLFileType(enum.Enum):
# TODO: match this with `llama_ftype`
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`
class GGMLFileType(enum.IntEnum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
@ -101,6 +104,8 @@ class Params:
n_head_kv: int
f_norm_eps: float
ftype: Optional[GGMLFileType] = None
@staticmethod
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
@ -738,6 +743,9 @@ class OutputFile:
self.gguf.add_head_count_kv (params.n_head_kv)
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
if params.ftype:
self.gguf.add_file_type(params.ftype)
def add_meta_vocab(self, vocab: Vocab) -> None:
tokens = []
scores = []
@ -1020,6 +1028,12 @@ def main(args_in: Optional[List[str]] = None) -> None:
" - LLaMA v2: --ctx 4096\n")
params.n_ctx = args.ctx
if args.outtype:
params.ftype = {
"f32": GGMLFileType.AllF32,
"f16": GGMLFileType.MostlyF16,
}[args.outtype]
print(f"params = {params}")
vocab: Vocab
@ -1040,11 +1054,14 @@ def main(args_in: Optional[List[str]] = None) -> None:
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(vocab_dir, args.vocabtype)
model = model_plus.model
model = convert_model_names(model, params)
output_type = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, output_type)
outfile = args.outfile or default_outfile(model_plus.paths, output_type)
model = model_plus.model
model = convert_model_names(model, params)
ftype = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, ftype)
outfile = args.outfile or default_outfile(model_plus.paths, ftype)
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
OutputFile.write_all(outfile, params, model, vocab)
print(f"Wrote {outfile}")

4
gguf.py
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@ -26,6 +26,7 @@ KEY_GENERAL_DESCRIPTION = "general.description"
KEY_GENERAL_LICENSE = "general.license"
KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
KEY_GENERAL_FILE_TYPE = "general.file_type"
# LLM
KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
@ -595,6 +596,9 @@ class GGUFWriter:
def add_source_hf_repo(self, repo: str):
self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
def add_file_type(self, ftype: int):
self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype)
def add_name(self, name: str):
self.add_string(KEY_GENERAL_NAME, name)

21
llama.cpp
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@ -995,6 +995,16 @@ struct llama_model_loader {
} break;
}
// this is a way to mark that we have "guessed" the file type
ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
{
const int kid = gguf_find_key(ctx_gguf, "general.file_type");
if (kid >= 0) {
ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
}
}
for (int i = 0; i < n_kv; i++) {
const char * name = gguf_get_key(ctx_gguf, i);
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
@ -1197,7 +1207,11 @@ struct llama_model_loader {
// load LLaMA models
//
const char * llama_model_ftype_name(enum llama_ftype ftype) {
std::string llama_model_ftype_name(enum llama_ftype ftype) {
if (ftype & LLAMA_FTYPE_GUESSED) {
return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
}
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
@ -1426,7 +1440,7 @@ static void llama_model_load_internal(
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str());
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_elements*1e-9);
// general kv
@ -3450,6 +3464,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// copy the KV pairs from the input file
gguf_set_kv (ctx_out, model_loader->ctx_gguf);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
#ifdef GGML_USE_K_QUANTS
int n_attention_wv = 0;
@ -4310,7 +4325,7 @@ int llama_model_n_embd(const struct llama_model * model) {
}
int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "LLaMA %s %s", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype));
return snprintf(buf, buf_size, "LLaMA %s %s", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str());
}
int llama_model_quantize(

2
llama.h
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@ -103,6 +103,8 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
typedef struct llama_token_data {