update deprecated validator for field_validator

src/refiners/training_utils/config.py

@@ -6,7 +6,7 @@ from typing import Any, Callable, Iterable, Literal, Type, TypeVar
 import tomli
 from bitsandbytes.optim import AdamW8bit, Lion8bit  # type: ignore
 from prodigyopt import Prodigy  # type: ignore
-from pydantic import BaseModel, ConfigDict, validator
+from pydantic import BaseModel, ConfigDict, field_validator
 from torch import Tensor
 from torch.optim import SGD, Adam, AdamW, Optimizer
 
@@ -32,7 +32,7 @@ class TrainingConfig(BaseModel):
 
     model_config = ConfigDict(extra="forbid")
 
-    @validator("duration", "gradient_accumulation", "evaluation_interval", pre=True)
+    @field_validator("duration", "gradient_accumulation", "evaluation_interval", mode="before")
     def parse_field(cls, value: Any) -> TimeValue:
         return parse_number_unit_field(value)
 
@@ -80,7 +80,7 @@ class SchedulerConfig(BaseModel):
 
     model_config = ConfigDict(extra="forbid")
 
-    @validator("update_interval", "warmup", pre=True)
+    @field_validator("update_interval", "warmup", mode="before")
     def parse_field(cls, value: Any) -> TimeValue:
         return parse_number_unit_field(value)
 

src/refiners/training_utils/wandb.py

@@ -1,14 +1,19 @@
+import warnings
 from abc import ABC
 from pathlib import Path
 from typing import Any, Literal
 
-import wandb
 from PIL import Image
 
 from refiners.training_utils.callback import Callback, CallbackConfig
 from refiners.training_utils.config import BaseConfig
 from refiners.training_utils.trainer import Trainer, register_callback
 
+with warnings.catch_warnings():
+    # TODO: remove when https://github.com/wandb/wandb/issues/6711 gets solved
+    warnings.filterwarnings("ignore", category=DeprecationWarning, message="pkg_resources is deprecated as an API")
+    import wandb
+
 number = float | int
 WandbLoggable = number | Image.Image | list[number] | dict[str, list[number]]
 

tests/training_utils/test_trainer.py

@@ -1,3 +1,4 @@
+import warnings
 from dataclasses import dataclass
 from pathlib import Path
 from typing import cast
@@ -222,8 +223,14 @@ def test_initial_lr(warmup_scheduler: WarmupScheduler) -> None:
 
 
 def test_warmup_lr(warmup_scheduler: WarmupScheduler) -> None:
-    for _ in range(102):
-        warmup_scheduler.step()
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            category=UserWarning,
+            message=r"Detected call of `lr_scheduler.step\(\)` before `optimizer.step\(\)`",
+        )
+        for _ in range(102):
+            warmup_scheduler.step()
     optimizer = warmup_scheduler.optimizer
     for group in optimizer.param_groups:
         assert group["lr"] == 0.1