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convert_lora : fix default filename

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The default filename was previously hardcoded.

* convert_hf : Model.fname_out can no longer be None
This commit is contained in:
Francis Couture-Harpin
2024-07-20 16:40:58 -04:00
parent a3d154b260
commit bf8e71b0c0
4 changed files with 35 additions and 31 deletions
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36
convert_hf_to_gguf.py
View File

@@ -48,7 +48,7 @@ class Model:
dir_model: Path
ftype: gguf.LlamaFileType
fname_out: Path | None
fname_out: Path
is_big_endian: bool
endianess: gguf.GGUFEndian
use_temp_file: bool
@@ -67,7 +67,7 @@ class Model:
# subclasses should define this!
model_arch: gguf.MODEL_ARCH
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path | None, is_big_endian: bool = False,
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
use_temp_file: bool = False, eager: bool = False,
metadata_override: Path | None = None, model_name: str | None = None,
split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
@@ -347,7 +347,7 @@ class Model:
total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model, self.model_name, self.dir_model_card, total_params)
self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)
# Fallback to model directory name if metadata name is still missing
if self.metadata.name is None:
@@ -361,27 +361,22 @@ class Model:
output_type: str = self.ftype.name.partition("_")[2]
# Filename Output
# Note: `not is_dir()` is used because `.is_file()` will not detect
# file template strings as it doesn't actually exist as a file
if self.fname_out is not None and not self.fname_out.is_dir():
# Output path is a custom defined templated filename
# Process templated file name with the output ftype, useful with the "auto" ftype
self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
else:
if self.fname_out.is_dir():
# Generate default filename based on model specification and available metadata
if not vocab_only:
fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
else:
fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")
# Check if preferred output directory path was provided
if self.fname_out is not None and self.fname_out.is_dir():
# output path is a directory
self.fname_out = self.fname_out / f"{fname_default}.gguf"
else:
# output in the same directory as the model by default
self.fname_out = self.dir_model / f"{fname_default}.gguf"
# Use the default filename
self.fname_out = self.fname_out / f"{fname_default}.gguf"
else:
# Output path is a custom defined templated filename
# Note: `not is_dir()` is used because `.is_file()` will not detect
# file template strings as it doesn't actually exist as a file
# Process templated file name with the output ftype, useful with the "auto" ftype
self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
self.set_type()
@@ -3626,10 +3621,10 @@ def main() -> None:
logger.error("Error: Cannot use temp file when splitting")
sys.exit(1)
fname_out = None
if args.outfile is not None:
fname_out = args.outfile
else:
fname_out = dir_model
logger.info(f"Loading model: {dir_model.name}")
@@ -3660,7 +3655,6 @@ def main() -> None:
else:
logger.info("Exporting model...")
model_instance.write()
assert model_instance.fname_out is not None
out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
logger.info(f"Model successfully exported to {out_path}")

2
convert_lora_to_gguf.py
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@@ -290,7 +290,7 @@ if __name__ == '__main__':
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_lora / 'ggml-lora-{ftype}.gguf'
fname_out = dir_lora
if os.path.exists(input_model):
# lazy import load_file only if lora is in safetensors format.

19
gguf-py/gguf/metadata.py
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@@ -44,7 +44,7 @@ class Metadata:
datasets: Optional[list[str]] = None
@staticmethod
def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, model_card_path: Optional[Path] = None, total_params: int = 0) -> Metadata:
def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata:
# This grabs as many contextual authorship metadata as possible from the model repository
# making any conversion as required to match the gguf kv store metadata format
# as well as giving users the ability to override any authorship metadata that may be incorrect
@@ -52,14 +52,12 @@ class Metadata:
# Create a new Metadata instance
metadata = Metadata()
if model_card_path is None:
model_card_path = model_path
model_card = Metadata.load_model_card(model_card_path)
model_card = Metadata.load_model_card(model_path)
hf_params = Metadata.load_hf_parameters(model_path)
# TODO: load adapter_config.json when possible, it usually contains the base model of the LoRA adapter
# heuristics
metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_card_path, total_params)
metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params)
# Metadata Override File Provided
# This is based on LLM_KV_NAMES mapping in llama.cpp
@@ -232,11 +230,14 @@ class Metadata:
name_parts[i] = part
# Some easy to recognize finetune names
elif i > 0 and re.fullmatch(r'chat|instruct|vision|lora', part, re.IGNORECASE):
name_types[i].add("finetune")
if part.lower() == "lora":
name_parts[i] = "LoRA"
if total_params < 0 and part.lower() == "lora":
# ignore redundant "lora" in the finetune part when the output is a lora adapter
name_types[i].add("type")
else:
name_types[i].add("finetune")
# Ignore word-based size labels when there is at least a number-based one present
# TODO: should word-based size labels always be removed instead?
if any(c.isdecimal() for n, t in zip(name_parts, name_types) if "size_label" in t for c in n):
for n, t in zip(name_parts, name_types):
if "size_label" in t:

9
gguf-py/tests/test_metadata.py
View File

@@ -159,6 +159,15 @@ class TestMetadataMethod(unittest.TestCase):
self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"),
('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None))
## LoRA ##
self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"),
('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B'))
# Negative size --> output is a LoRA adaper --> prune "LoRA" out of the name to avoid redundancy with the suffix
self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234),
('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B'))
def test_apply_metadata_heuristic_from_model_card(self):
model_card = {
'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],