[trainer] parametrize default output_dir

This PR:

* fixes trainer to have the logger agree with the actual default `output_dir`, but setting it one place and passing it as an argument to both places

@sgugger

src/transformers/models/bart/modeling_bart.py

@@ -110,12 +110,13 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
 
 
 def BartLayerNorm(normalized_shape: torch.Size, eps: float = 1e-5, elementwise_affine: bool = True):
-    try:
-        from apex.normalization import FusedLayerNorm
+    if torch.cuda.is_available():
+        try:
+            from apex.normalization import FusedLayerNorm
 
-        return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
-    except ImportError:
-        pass
+            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
+        except ImportError:
+            pass
     return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
 
 

src/transformers/models/fsmt/modeling_fsmt.py

@@ -265,12 +265,14 @@ FSMT_INPUTS_DOCSTRING = r"""
 
 
 have_fused_layer_norm = False
-try:
-    from apex.normalization import FusedLayerNorm
+if torch.cuda.is_available():
+    try:
+        from apex.normalization import FusedLayerNorm
+
+        have_fused_layer_norm = True
+    except ImportError:
+        pass
 
-    have_fused_layer_norm = True
-except ImportError:
-    pass
 LayerNorm = FusedLayerNorm if have_fused_layer_norm else torch.nn.LayerNorm
 
 

src/transformers/models/prophetnet/modeling_prophetnet.py

@@ -511,12 +511,13 @@ class ProphetNetDecoderLMOutput(ModelOutput):
 
 
 def ProphetNetLayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True):
-    try:
-        from apex.normalization import FusedLayerNorm
+    if torch.cuda.is_available():
+        try:
+            from apex.normalization import FusedLayerNorm
 
-        return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
-    except ImportError:
-        pass
+            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
+        except ImportError:
+            pass
     return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
 
 

src/transformers/trainer.py

@@ -228,8 +228,9 @@ class Trainer:
         optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
     ):
         if args is None:
-            logger.info("No `TrainingArguments` passed, using the current path as `output_dir`.")
-            args = TrainingArguments("tmp_trainer")
+            output_dir = "tmp_trainer"
+            logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
+            args = TrainingArguments(output_dir=output_dir)
         self.args = args
         # Seed must be set before instantiating the model when using model
         set_seed(self.args.seed)