Remove mutable defaults in mlp_model (apache#12546)

python/tvm/meta_schedule/cost_model/mlp_model.py

@@ -26,7 +26,7 @@
 import tempfile
 from collections import OrderedDict
 from itertools import chain as itertools_chain
-from typing import Dict, List, NamedTuple, Tuple
+from typing import Dict, List, NamedTuple, Optional, Tuple
 
 import numpy as np  # type: ignore
 import torch  # type: ignore
@@ -418,8 +418,8 @@ def forward(  # pylint: disable=missing-function-docstring
 def extract_features(
     context: TuneContext,
     candidates: List[MeasureCandidate],
-    results: List[RunnerResult] = None,
-    extractor: FeatureExtractor = PerStoreFeature(extract_workload=True),
+    results: Optional[List[RunnerResult]] = None,
+    extractor: Optional[FeatureExtractor] = None,
 ):
     """Extract feature vectors and compute mean costs.
 
@@ -429,9 +429,9 @@ def extract_features(
         The tuning context.
     candidates: List[MeasureCandidate]
         The measure candidates.
-    results: List[RunnerResult]
+    results: Optional[List[RunnerResult]]
         The measured results, can be None if used in prediction.
-    extractor: FeatureExtractor
+    extractor: Optional[FeatureExtractor]
         The feature extractor.
 
     Returns
@@ -441,6 +441,7 @@ def extract_features(
     new_mean_costs: np.ndarray
         The mean costs.
     """
+    extractor = extractor or PerStoreFeature(extract_workload=True)
 
     def _feature(feature: NDArray) -> np.ndarray:
         return feature.numpy().astype("float32")
@@ -481,9 +482,12 @@ class State:
 
     def __init__(
         self,
-        model_config: SegmentSumMLPConfig = SegmentSumMLPConfig(),
-        extractor: FeatureExtractor = PerStoreFeature(extract_workload=True),
+        model_config: Optional[SegmentSumMLPConfig] = None,
+        extractor: Optional[FeatureExtractor] = None,
     ):
+        model_config = model_config or SegmentSumMLPConfig()
+        extractor = extractor or PerStoreFeature(extract_workload=True)
+
         self.model = SegmentSumMLP(**model_config.to_dict())
         self.data = OrderedDict()
         self.data_size = 0
@@ -662,9 +666,12 @@ class SegmentSumMLPTrainer:
 
     def __init__(
         self,
-        train_config: TrainerConfig = TrainerConfig(),
-        state: State = State(),
+        train_config: Optional[TrainerConfig] = None,
+        state: Optional[State] = None,
     ):
+        train_config = train_config or TrainerConfig()
+        state = state or State()
+
         config = train_config.to_dict()
         for attr in config:
             setattr(self, attr, config[attr])
@@ -676,7 +683,7 @@ def train_step(
         self,
         data: Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"],
         batch: int = 0,
-        train_loss: float = None,
+        train_loss: Optional[float] = None,
     ) -> float:
         """Helper function for training on a single batch.
 
@@ -686,7 +693,7 @@ def train_step(
             A batch of data, should be a tuple of (segment_sizes, features, gt_results).
         batch: int = 0
             The current batch number.
-        train_loss: float = None
+        train_loss: Optional[float] = None
             The previous averaged training loss, None if it is the first batch.
 
         Returns
@@ -863,15 +870,15 @@ def train_incremental(
     def predict_incremental(
         self,
         features: List[np.ndarray],
-        results: np.ndarray = None,
+        results: Optional[np.ndarray] = None,
     ) -> np.ndarray:
         """Predicting (validating) on incremental data.
 
         Parameters
         ----------
         features: List[np.ndarray]
             The extracted features.
-        results: np.ndarray
+        results: Optional[np.ndarray]
             The measured results, can be None if used for predicting.
 
         Returns
@@ -943,10 +950,10 @@ class MLPModel(PyCostModel):
     def __init__(
         self,
         *,
-        trainer: SegmentSumMLPTrainer = SegmentSumMLPTrainer(),
+        trainer: Optional[SegmentSumMLPTrainer] = None,
     ):
         super().__init__()
-        self.trainer = trainer
+        self.trainer = trainer or SegmentSumMLPTrainer()
 
     def load(self, path: str) -> None:
         """Load the cost model, cached data or raw data from given file location.