fairseq/optim/fairseq_optimizer.py

Survived

Mutant 3217

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -14,7 +14,6 @@
         super().__init__()
         self.args = args
 
-    @staticmethod
     def add_args(parser):
         """Add optimizer-specific arguments to the parser."""
         pass

Mutant 3218

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -19,7 +19,6 @@
         """Add optimizer-specific arguments to the parser."""
         pass
 
-    @property
     def optimizer(self):
         """Return a torch.optim.optimizer.Optimizer instance."""
         if not hasattr(self, '_optimizer'):

Mutant 3219

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -28,7 +28,6 @@
             raise ValueError('_optimizer must be an instance of torch.optim.Optimizer')
         return self._optimizer
 
-    @property
     def optimizer_config(self):
         """
         Return a kwarg dictionary that will be used to override optimizer

Mutant 3220

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -38,7 +38,6 @@
         """
         raise NotImplementedError
 
-    @property
     def params(self):
         """Return an iterable of the parameters held by the optimizer."""
         for param_group in self.optimizer.param_groups:

Mutant 3221

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -90,7 +90,7 @@
         """Clips gradient norm."""
         return utils.clip_grad_norm_(self.params, max_norm, aggregate_norm_fn)
 
-    def step(self, closure=None, scale=1.):
+    def step(self, closure=None, scale=2.0):
         """Performs a single optimization step."""
         if self.supports_step_with_scale:
             self.optimizer.step(closure, scale=scale)

Mutant 3222

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -103,7 +103,6 @@
             p.grad = None
         self.optimizer.zero_grad()
 
-    @property
     def supports_memory_efficient_fp16(self):
         if hasattr(self.optimizer, 'supports_memory_efficient_fp16'):
             return self.optimizer.supports_memory_efficient_fp16

Mutant 3223

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -109,7 +109,6 @@
             return self.optimizer.supports_memory_efficient_fp16
         return False
 
-    @property
     def supports_step_with_scale(self):
         if hasattr(self.optimizer, 'supports_step_with_scale'):
             return self.optimizer.supports_step_with_scale

Mutant 3224

--- fairseq/optim/fairseq_optimizer.py
+++ fairseq/optim/fairseq_optimizer.py
@@ -115,7 +115,6 @@
             return self.optimizer.supports_step_with_scale
         return False
 
-    @property
     def supports_flat_params(self):
         """
         Whether the optimizer supports collapsing of the model