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fix llamaboard with ray

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Former-commit-id: c46675d5e56d175c27d705ef0068fb47dc89a872
This commit is contained in:
hiyouga 2025-01-07 09:59:24 +00:00
parent b4174021d6
commit 0c1ad5f3fb
3 changed files with 11 additions and 12 deletions
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6
src/llamafactory/train/callbacks.py
View File

@ -35,7 +35,7 @@ from typing_extensions import override
from ..extras import logging
from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.misc import get_peak_memory
from ..extras.misc import get_peak_memory, use_ray
if is_safetensors_available():
@ -194,7 +194,7 @@ class LogCallback(TrainerCallback):
self.do_train = False
# Web UI
self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
if self.webui_mode:
if self.webui_mode and not use_ray():
signal.signal(signal.SIGABRT, self._set_abort)
self.logger_handler = logging.LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
logging.add_handler(self.logger_handler)
@ -383,7 +383,7 @@ class ReporterCallback(TrainerCallback):
)
if self.finetuning_args.use_swanlab:
import swanlab
import swanlab # type: ignore
swanlab.config.update(
{

11
src/llamafactory/train/tuner.py
View File

@ -46,11 +46,12 @@ if TYPE_CHECKING:
logger = logging.get_logger(__name__)
def training_function(config: Dict[str, Any]) -> None:
def _training_function(config: Dict[str, Any]) -> None:
args = config.get("args")
callbacks: List[Any] = config.get("callbacks")
model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
callbacks.append(LogCallback())
if finetuning_args.pissa_convert:
callbacks.append(PissaConvertCallback())
@ -76,21 +77,19 @@ def training_function(config: Dict[str, Any]) -> None:
def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None) -> None:
callbacks = callbacks or []
callbacks.append(LogCallback())
args = read_args(args)
ray_args = get_ray_args(args)
callbacks = callbacks or []
if ray_args.use_ray:
callbacks.append(RayTrainReportCallback())
trainer = get_ray_trainer(
training_function=training_function,
training_function=_training_function,
train_loop_config={"args": args, "callbacks": callbacks},
ray_args=ray_args,
)
trainer.fit()
else:
training_function(config={"args": args, "callbacks": callbacks})
_training_function(config={"args": args, "callbacks": callbacks})
def export_model(args: Optional[Dict[str, Any]] = None) -> None:

6
src/llamafactory/webui/runner.py
View File

@ -21,7 +21,7 @@ from typing import TYPE_CHECKING, Any, Dict, Generator, Optional
from transformers.trainer import TRAINING_ARGS_NAME
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_gpu_or_npu_available, torch_gc
from ..extras.misc import is_gpu_or_npu_available, torch_gc, use_ray
from ..extras.packages import is_gradio_available, is_transformers_version_equal_to_4_46
from .common import DEFAULT_CACHE_DIR, DEFAULT_CONFIG_DIR, QUANTIZATION_BITS, get_save_dir, load_config
from .locales import ALERTS, LOCALES
@ -394,12 +394,12 @@ class Runner:
continue
if self.do_train:
if os.path.exists(os.path.join(output_path, TRAINING_ARGS_NAME)):
if os.path.exists(os.path.join(output_path, TRAINING_ARGS_NAME)) or use_ray():
finish_info = ALERTS["info_finished"][lang]
else:
finish_info = ALERTS["err_failed"][lang]
else:
if os.path.exists(os.path.join(output_path, "all_results.json")):
if os.path.exists(os.path.join(output_path, "all_results.json")) or use_ray():
finish_info = get_eval_results(os.path.join(output_path, "all_results.json"))
else:
finish_info = ALERTS["err_failed"][lang]