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[misc] upgrade format to py39 (#7256)

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hoshi-hiyouga
2025-03-12 00:08:41 +08:00
committed by GitHub
parent 5995800bce
commit 264538cb26
113 changed files with 984 additions and 1407 deletions
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72
src/llamafactory/webui/runner.py
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@@ -14,9 +14,10 @@
import json
import os
from collections.abc import Generator
from copy import deepcopy
from subprocess import Popen, TimeoutExpired
from typing import TYPE_CHECKING, Any, Dict, Generator, Optional
from typing import TYPE_CHECKING, Any, Optional
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.utils import is_torch_npu_available
@@ -51,17 +52,16 @@ if TYPE_CHECKING:
class Runner:
r"""
A class to manage the running status of the trainers.
"""
r"""A class to manage the running status of the trainers."""
def __init__(self, manager: "Manager", demo_mode: bool = False) -> None:
r"""Init a runner."""
self.manager = manager
self.demo_mode = demo_mode
""" Resume """
self.trainer: Optional["Popen"] = None
self.trainer: Optional[Popen] = None
self.do_train = True
self.running_data: Dict["Component", Any] = None
self.running_data: dict[Component, Any] = None
""" State """
self.aborted = False
self.running = False
@@ -71,10 +71,8 @@ class Runner:
if self.trainer is not None:
abort_process(self.trainer.pid)
def _initialize(self, data: Dict["Component", Any], do_train: bool, from_preview: bool) -> str:
r"""
Validates the configuration.
"""
def _initialize(self, data: dict["Component", Any], do_train: bool, from_preview: bool) -> str:
r"""Validate the configuration."""
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
dataset = get("train.dataset") if do_train else get("eval.dataset")
@@ -116,9 +114,7 @@ class Runner:
return ""
def _finalize(self, lang: str, finish_info: str) -> str:
r"""
Cleans the cached memory and resets the runner.
"""
r"""Clean the cached memory and resets the runner."""
finish_info = ALERTS["info_aborted"][lang] if self.aborted else finish_info
gr.Info(finish_info)
self.trainer = None
@@ -128,10 +124,8 @@ class Runner:
torch_gc()
return finish_info
def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
r"""
Builds and validates the training arguments.
"""
def _parse_train_args(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build and validate the training arguments."""
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
user_config = load_config()
@@ -291,10 +285,8 @@ class Runner:
return args
def _parse_eval_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
r"""
Builds and validates the evaluation arguments.
"""
def _parse_eval_args(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build and validate the evaluation arguments."""
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
user_config = load_config()
@@ -345,10 +337,8 @@ class Runner:
return args
def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", str], None, None]:
r"""
Previews the training commands.
"""
def _preview(self, data: dict["Component", Any], do_train: bool) -> Generator[dict["Component", str], None, None]:
r"""Preview the training commands."""
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
error = self._initialize(data, do_train, from_preview=True)
if error:
@@ -358,10 +348,8 @@ class Runner:
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
yield {output_box: gen_cmd(args)}
def _launch(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", Any], None, None]:
r"""
Starts the training process.
"""
def _launch(self, data: dict["Component", Any], do_train: bool) -> Generator[dict["Component", Any], None, None]:
r"""Start the training process."""
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
error = self._initialize(data, do_train, from_preview=False)
if error:
@@ -383,10 +371,8 @@ class Runner:
self.trainer = Popen(["llamafactory-cli", "train", save_cmd(args)], env=env)
yield from self.monitor()
def _build_config_dict(self, data: Dict["Component", Any]) -> Dict[str, Any]:
r"""
Builds a dictionary containing the current training configuration.
"""
def _build_config_dict(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build a dictionary containing the current training configuration."""
config_dict = {}
skip_ids = ["top.lang", "top.model_path", "train.output_dir", "train.config_path"]
for elem, value in data.items():
@@ -409,9 +395,7 @@ class Runner:
yield from self._launch(data, do_train=False)
def monitor(self):
r"""
Monitors the training progress and logs.
"""
r"""Monitorgit the training progress and logs."""
self.aborted = False
self.running = True
@@ -469,9 +453,7 @@ class Runner:
yield return_dict
def save_args(self, data):
r"""
Saves the training configuration to config path.
"""
r"""Save the training configuration to config path."""
output_box = self.manager.get_elem_by_id("train.output_box")
error = self._initialize(data, do_train=True, from_preview=True)
if error:
@@ -487,27 +469,23 @@ class Runner:
return {output_box: ALERTS["info_config_saved"][lang] + save_path}
def load_args(self, lang: str, config_path: str):
r"""
Loads the training configuration from config path.
"""
r"""Load the training configuration from config path."""
output_box = self.manager.get_elem_by_id("train.output_box")
config_dict = load_args(os.path.join(DEFAULT_CONFIG_DIR, config_path))
if config_dict is None:
gr.Warning(ALERTS["err_config_not_found"][lang])
return {output_box: ALERTS["err_config_not_found"][lang]}
output_dict: Dict["Component", Any] = {output_box: ALERTS["info_config_loaded"][lang]}
output_dict: dict[Component, Any] = {output_box: ALERTS["info_config_loaded"][lang]}
for elem_id, value in config_dict.items():
output_dict[self.manager.get_elem_by_id(elem_id)] = value
return output_dict
def check_output_dir(self, lang: str, model_name: str, finetuning_type: str, output_dir: str):
r"""
Restore the training status if output_dir exists.
"""
r"""Restore the training status if output_dir exists."""
output_box = self.manager.get_elem_by_id("train.output_box")
output_dict: Dict["Component", Any] = {output_box: LOCALES["output_box"][lang]["value"]}
output_dict: dict[Component, Any] = {output_box: LOCALES["output_box"][lang]["value"]}
if model_name and output_dir and os.path.isdir(get_save_dir(model_name, finetuning_type, output_dir)):
gr.Warning(ALERTS["warn_output_dir_exists"][lang])
output_dict[output_box] = ALERTS["warn_output_dir_exists"][lang]