Force conversion of LR variables to float due to type coercion from manager serialization

src/sparseml/optim/learning_rate.py

@@ -156,6 +156,9 @@ def validate_lr_info(self):
         else:
             raise ValueError("unknown lr_class given of {}".format(self._lr_class))
 
+        if isinstance(self._init_lr, str):
+            self._init_lr = float(self._init_lr)
+
         if self._init_lr <= 0.0:
             raise ValueError("init_lr must be greater than 0")
 

src/sparseml/pytorch/optim/modifier_lr.py

@@ -414,6 +414,9 @@ def validate(self):
         if self.lr_func not in lr_funcs:
             raise ValueError(f"lr_func must be one of {lr_funcs}")
 
+        if isinstance(self.init_lr, str):
+            self.init_lr = float(self.init_lr)
+
         if (
             (not self.init_lr and self.init_lr != 0)
             or self.init_lr < 0.0
@@ -423,6 +426,9 @@ def validate(self):
                 f"init_lr must be within range [0.0, 1.0], given {self.init_lr}"
             )
 
+        if isinstance(self.final_lr, str):
+            self.final_lr = float(self.final_lr)
+
         if (
             (not self.final_lr and self.final_lr != 0)
             or self.final_lr < 0.0