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upgrade gradio to 4.21.0

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Former-commit-id: 63eecbeb967d849e1d03d8d03fb6421c0ee89257
This commit is contained in:
hiyouga
2024-03-30 20:37:08 +08:00
parent fbd0584391
commit b0efebf853
18 changed files with 167 additions and 159 deletions
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46
src/llmtuner/webui/runner.py
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@@ -48,8 +48,8 @@ class Runner:
def set_abort(self) -> None:
self.aborted = True
def _initialize(self, data: Dict[Component, Any], do_train: bool, from_preview: bool) -> str:
get = lambda name: data[self.manager.get_elem_by_name(name)]
def _initialize(self, data: Dict["Component", Any], do_train: bool, from_preview: bool) -> str:
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")
@@ -95,8 +95,8 @@ class Runner:
else:
return finish_info
def _parse_train_args(self, data: Dict[Component, Any]) -> Dict[str, Any]:
get = lambda name: data[self.manager.get_elem_by_name(name)]
def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
user_config = load_config()
if get("top.adapter_path"):
@@ -196,8 +196,8 @@ class Runner:
return args
def _parse_eval_args(self, data: Dict[Component, Any]) -> Dict[str, Any]:
get = lambda name: data[self.manager.get_elem_by_name(name)]
def _parse_eval_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
user_config = load_config()
if get("top.adapter_path"):
@@ -232,6 +232,7 @@ class Runner:
temperature=get("eval.temperature"),
output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("eval.output_dir")),
)
args["disable_tqdm"] = True
if get("eval.predict"):
args["do_predict"] = True
@@ -240,22 +241,20 @@ class Runner:
return args
def _preview(
self, data: Dict[Component, Any], do_train: bool
) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Tuple[str, "gr.Slider"], None, None]:
error = self._initialize(data, do_train, from_preview=True)
if error:
gr.Warning(error)
yield error, gr.update(visible=False)
yield error, gr.Slider(visible=False)
else:
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
yield gen_cmd(args), gr.update(visible=False)
yield gen_cmd(args), gr.Slider(visible=False)
def _launch(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
def _launch(self, data: Dict["Component", Any], do_train: bool) -> Generator[Tuple[str, "gr.Slider"], None, None]:
error = self._initialize(data, do_train, from_preview=False)
if error:
gr.Warning(error)
yield error, gr.update(visible=False)
yield error, gr.Slider(visible=False)
else:
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
run_kwargs = dict(args=args, callbacks=[self.trainer_callback])
@@ -264,20 +263,20 @@ class Runner:
self.thread.start()
yield from self.monitor()
def preview_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
def preview_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, gr.Slider], None, None]:
yield from self._preview(data, do_train=True)
def preview_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
def preview_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, gr.Slider], None, None]:
yield from self._preview(data, do_train=False)
def run_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
def run_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, gr.Slider], None, None]:
yield from self._launch(data, do_train=True)
def run_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
def run_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, gr.Slider], None, None]:
yield from self._launch(data, do_train=False)
def monitor(self) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
get = lambda name: self.running_data[self.manager.get_elem_by_name(name)]
def monitor(self) -> Generator[Tuple[str, "gr.Slider"], None, None]:
get = lambda elem_id: self.running_data[self.manager.get_elem_by_id(elem_id)]
self.running = True
lang = get("top.lang")
output_dir = get_save_dir(
@@ -286,13 +285,14 @@ class Runner:
get("{}.output_dir".format("train" if self.do_train else "eval")),
)
while self.thread.is_alive():
time.sleep(2)
while self.thread is not None and self.thread.is_alive():
if self.aborted:
yield ALERTS["info_aborting"][lang], gr.update(visible=False)
yield ALERTS["info_aborting"][lang], gr.Slider(visible=False)
else:
yield self.logger_handler.log, update_process_bar(self.trainer_callback)
time.sleep(2)
if self.do_train:
if os.path.exists(os.path.join(output_dir, TRAINING_ARGS_NAME)):
finish_info = ALERTS["info_finished"][lang]
@@ -304,4 +304,4 @@ class Runner:
else:
finish_info = ALERTS["err_failed"][lang]
yield self._finalize(lang, finish_info), gr.update(visible=False)
yield self._finalize(lang, finish_info), gr.Slider(visible=False)