fix llamaboard with ray

Former-commit-id: c46675d5e56d175c27d705ef0068fb47dc89a872

src/llamafactory/train/callbacks.py

@@ -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(
                 {

src/llamafactory/train/tuner.py

@@ -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:

src/llamafactory/webui/runner.py

@@ -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]