Fixing FLOPS merge by checking if torch is available (#7013)

* Should check if `torch` is available

* fixed samples_count error, distributed_concat arguments

* style

* Import torch at beginning of file
Co-authored-by: TevenLeScao <teven.lescao@gmail.com>

src/transformers/trainer.py

@@ -1315,8 +1315,6 @@ class Trainer:
                 label_ids = xm.mesh_reduce("eval_label_ids", label_ids, torch.cat)
             if eval_losses is not None:
                 eval_losses = xm.mesh_reduce("eval_losses", torch.tensor(eval_losses), torch.cat).tolist()
-            if samples_count is not None:
-                samples_count = sum(xm.mesh_reduce("samples_count", torch.tensor([samples_count]), torch.cat).tolist())
 
         # Finally, turn the aggregated tensors into numpy arrays.
         if preds is not None:

src/transformers/trainer_utils.py

@@ -2,12 +2,15 @@ import random
 from typing import Any, Dict, List, NamedTuple, Optional, Union
 
 import numpy as np
-import torch
 
 from .file_utils import is_tf_available, is_torch_available
 from .tokenization_utils_base import ExplicitEnum
 
 
+if is_torch_available():
+    import torch
+
+
 def set_seed(seed: int):
     """
     Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf``
@@ -129,9 +132,9 @@ default_hp_space = {
 }
 
 
-def distributed_concat(self, tensor: torch.Tensor, num_total_examples: Optional[int] = None) -> torch.Tensor:
-    assert self.args.local_rank != -1
-
+def distributed_concat(tensor: "torch.Tensor", num_total_examples: Optional[int] = None) -> "torch.Tensor":
+    if is_torch_available():
+        try:
             output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())]
             torch.distributed.all_gather(output_tensors, tensor)
             concat = torch.cat(output_tensors, dim=0)
@@ -140,13 +143,17 @@ def distributed_concat(self, tensor: torch.Tensor, num_total_examples: Optional[
             if num_total_examples is not None:
                 concat = concat[:num_total_examples]
             return concat
+        except AssertionError:
+            raise AssertionError("Not currently using distributed training")
+    else:
+        raise ImportError("Torch must be installed to use `distributed_concat`")
 
 
 def distributed_broadcast_scalars(
-    self, scalars: List[Union[int, float]], num_total_examples: Optional[int] = None
-) -> torch.Tensor:
-    assert self.args.local_rank != -1
-
+    scalars: List[Union[int, float]], num_total_examples: Optional[int] = None
+) -> "torch.Tensor":
+    if is_torch_available():
+        try:
             tensorized_scalar = torch.Tensor(scalars).cuda()
             output_tensors = [tensorized_scalar.clone() for _ in range(torch.distributed.get_world_size())]
             torch.distributed.all_gather(output_tensors, tensorized_scalar)
@@ -156,3 +163,7 @@ def distributed_broadcast_scalars(
             if num_total_examples is not None:
                 concat = concat[:num_total_examples]
             return concat
+        except AssertionError:
+            raise AssertionError("Not currently using distributed training")
+    else:
+        raise ImportError("Torch must be installed to use `distributed_broadcast_scalars`")