[Enh] add validation for hydra config

ppsci/data/__init__.py

@@ -160,7 +160,7 @@ def build_dataloader(_dataset, cfg):
             num_workers=cfg.get("num_workers", _DEFAULT_NUM_WORKERS),
             use_shared_memory=cfg.get("use_shared_memory", False),
             worker_init_fn=init_fn,
-            # TODO: Do not enable persistent_workers' below for
+            # TODO: Do not enable 'persistent_workers' below for
             # 'IndexError: pop from empty list ...' will be raised in certain cases
             # persistent_workers=cfg.get("num_workers", _DEFAULT_NUM_WORKERS) > 0,
         )

ppsci/solver/eval.py

@@ -128,7 +128,11 @@ def _eval_by_dataset(
             solver.eval_time_info["batch_cost"].update(batch_cost)
             batch_size = next(iter(input_dict.values())).shape[0]
             printer.update_eval_loss(solver, loss_dict, batch_size)
-            if iter_id == 1 or iter_id % log_freq == 0:
+            if (
+                iter_id == 1
+                or iter_id % log_freq == 0
+                or iter_id == len(_validator.data_loader)
+            ):
                 printer.log_eval_info(
                     solver,
                     batch_size,
@@ -247,7 +251,11 @@ def _eval_by_batch(
             solver.eval_time_info["reader_cost"].update(reader_cost)
             solver.eval_time_info["batch_cost"].update(batch_cost)
             printer.update_eval_loss(solver, loss_dict, batch_size)
-            if iter_id == 1 or iter_id % log_freq == 0:
+            if (
+                iter_id == 1
+                or iter_id % log_freq == 0
+                or iter_id == len(_validator.data_loader)
+            ):
                 printer.log_eval_info(
                     solver,
                     batch_size,

ppsci/solver/solver.py

@@ -74,7 +74,7 @@ class Solver:
         validator (Optional[Dict[str, ppsci.validate.Validator]]): Validator dict. Defaults to None.
         visualizer (Optional[Dict[str, ppsci.visualize.Visualizer]]): Visualizer dict. Defaults to None.
         use_amp (bool, optional): Whether use AMP. Defaults to False.
-        amp_level (Literal["O1", "O2", "O0"], optional): AMP level. Defaults to "O0".
+        amp_level (Literal["O0", "O1", "O2", "OD"], optional): AMP level. Defaults to "O1".
         pretrained_model_path (Optional[str]): Pretrained model path. Defaults to None.
         checkpoint_path (Optional[str]): Checkpoint path. Defaults to None.
         compute_metric_by_batch (bool, optional): Whether calculate metrics after each batch during evaluation. Defaults to False.
@@ -86,7 +86,7 @@ class Solver:
     Examples:
         >>> import ppsci
         >>> model = ppsci.arch.MLP(("x",), ("u",), 5, 20)
-        >>> opt = ppsci.optimizer.AdamW(1e-3)((model,))
+        >>> opt = ppsci.optimizer.AdamW(1e-3)(model)
         >>> geom = ppsci.geometry.Rectangle((0, 0), (1, 1))
         >>> pde_constraint = ppsci.constraint.InteriorConstraint(
         ...     {"u": lambda out: out["u"]},
@@ -134,7 +134,7 @@ def __init__(
         validator: Optional[Dict[str, ppsci.validate.Validator]] = None,
         visualizer: Optional[Dict[str, ppsci.visualize.Visualizer]] = None,
         use_amp: bool = False,
-        amp_level: Literal["O1", "O2", "O0"] = "O0",
+        amp_level: Literal["O0", "O1", "O2", "OD"] = "O1",
         pretrained_model_path: Optional[str] = None,
         checkpoint_path: Optional[str] = None,
         compute_metric_by_batch: bool = False,
@@ -152,7 +152,28 @@ def __init__(
         # set optimizer
         self.optimizer = optimizer
         # set learning rate scheduler
-        self.lr_scheduler = lr_scheduler
+        if lr_scheduler is not None:
+            logger.warning(
+                "The argument: 'lr_scheduler' now automatically retrieves from "
+                "'optimizer._learning_rate' when 'optimizer' is given, so it is "
+                "recommended to remove it from the Solver's initialization arguments."
+            )
+        self.lr_scheduler = (
+            optimizer._learning_rate
+            if (
+                isinstance(optimizer, optim.Optimizer)
+                and isinstance(optimizer._learning_rate, optim.lr.LRScheduler)
+            )
+            else None
+        )
+        if isinstance(self.optimizer, ppsci.optimizer.OptimizerList):
+            self.lr_scheduler = ppsci.optimizer.lr_scheduler.SchedulerList(
+                tuple(
+                    opt._learning_rate
+                    for opt in self.optimizer
+                    if isinstance(opt._learning_rate, optim.lr.LRScheduler)
+                )
+            )
 
         # set training hyper-parameter
         self.epochs = epochs

ppsci/utils/callbacks.py

@@ -12,20 +12,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib.util
+import inspect
+import sys
 from os import path as osp
 from typing import Any
 
 from hydra.experimental.callback import Callback
 from omegaconf import DictConfig
 
+from ppsci.utils import config as config_module
 from ppsci.utils import logger
 from ppsci.utils import misc
 
+RUNTIME_EXIT_CODE = 1  # for other errors
+VALIDATION_ERROR_EXIT_CODE = 2  # for invalid argument detected in config file
+
 
 class InitCallback(Callback):
     """Callback class for:
-    1. Fixing random seed to 'config.seed'
-    2. Initialize logger while creating output directory(if not exist).
+    1. Parse config dict from given yaml file and check its validity, complete missing items by its' default values.
+    2. Fixing random seed to 'config.seed'.
+    3. Initialize logger while creating output directory(if not exist).
 
     NOTE: This callback is mainly for reducing unnecessary duplicate code in each
     examples code when runing with hydra.
@@ -52,10 +60,40 @@ class InitCallback(Callback):
     """
 
     def on_job_start(self, config: DictConfig, **kwargs: Any) -> None:
+        # check given cfg using pre-defined pydantic schema in 'SolverConfig', error(s) will be raised
+        # if any checking failed at this step
+        if importlib.util.find_spec("pydantic") is not None:
+            from pydantic import ValidationError
+        else:
+            logger.error(
+                f"ModuleNotFoundError at {__file__}:{inspect.currentframe().f_lineno}\n"
+                "Please install pydantic with `pip install pydantic` when set callbacks"
+                " in your config yaml."
+            )
+            sys.exit(RUNTIME_EXIT_CODE)
+
+        # check given cfg using pre-defined pydantic schema in 'SolverConfig',
+        # error(s) will be printed and exit program if any checking failed at this step
+        try:
+            _model_pydantic = config_module.SolverConfig(**dict(config))
+            # complete missing items with default values pre-defined in pydantic schema in
+            # 'SolverConfig'
+            full_cfg = DictConfig(_model_pydantic.model_dump())
+        except ValidationError as e:
+            print(e)
+            sys.exit(VALIDATION_ERROR_EXIT_CODE)
+        except Exception as e:
+            print(e)
+            sys.exit(RUNTIME_EXIT_CODE)
+
         # fix random seed for reproducibility
-        misc.set_random_seed(config.seed)
+        misc.set_random_seed(full_cfg.seed)
 
-        # create output directory
+        # initialze logger while creating output directory
         logger.init_logger(
-            "ppsci", osp.join(config.output_dir, f"{config.mode}.log"), "info"
+            "ppsci",
+            osp.join(full_cfg.output_dir, f"{full_cfg.mode}.log")
+            if full_cfg.output_dir
+            else None,
+            full_cfg.log_level,
         )