Fix all is_torch_tpu_available issues (#17936)

* Fix all is_torch_tpu_available 

examples/pytorch/question-answering/trainer_qa.py

@@ -20,7 +20,7 @@ from transformers import Trainer, is_torch_tpu_available
 from transformers.trainer_utils import PredictionOutput
 
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
 

examples/pytorch/question-answering/trainer_seq2seq_qa.py

@@ -23,7 +23,7 @@ from transformers import Seq2SeqTrainer, is_torch_tpu_available
 from transformers.trainer_utils import PredictionOutput
 
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
 

examples/research_projects/quantization-qdqbert/trainer_quant_qa.py

@@ -30,7 +30,7 @@ from transformers.trainer_utils import PredictionOutput
 
 logger = logging.getLogger(__name__)
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
 

src/transformers/benchmark/benchmark_args.py

@@ -24,7 +24,7 @@ from .benchmark_args_utils import BenchmarkArguments
 if is_torch_available():
     import torch
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
 
 

src/transformers/testing_utils.py

@@ -467,7 +467,7 @@ def require_torch_tpu(test_case):
     """
     Decorator marking a test that requires a TPU (in PyTorch).
     """
-    return unittest.skipUnless(is_torch_tpu_available(), "test requires PyTorch TPU")(test_case)
+    return unittest.skipUnless(is_torch_tpu_available(check_device=False), "test requires PyTorch TPU")(test_case)
 
 
 if is_torch_available():

src/transformers/trainer.py

@@ -171,7 +171,7 @@ if version.parse(torch.__version__) >= version.parse("1.10"):
 if is_datasets_available():
     import datasets
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
     import torch_xla.distributed.parallel_loader as pl

src/transformers/trainer_pt_utils.py

@@ -43,7 +43,7 @@ from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_
 if is_training_run_on_sagemaker():
     logging.add_handler(StreamHandler(sys.stdout))
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
 
 # this is used to suppress an undesired warning emitted by pytorch versions 1.4.2-1.7.0

src/transformers/trainer_utils.py

@@ -307,7 +307,7 @@ def is_main_process(local_rank):
     Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
     `local_rank`.
     """
-    if is_torch_tpu_available():
+    if is_torch_tpu_available(check_device=True):
         import torch_xla.core.xla_model as xm
 
         return xm.get_ordinal() == 0
@@ -318,7 +318,7 @@ def total_processes_number(local_rank):
     """
     Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
     """
-    if is_torch_tpu_available():
+    if is_torch_tpu_available(check_device=True):
         import torch_xla.core.xla_model as xm
 
         return xm.xrt_world_size()

src/transformers/training_args.py

@@ -52,7 +52,7 @@ if is_torch_available():
     import torch
     import torch.distributed as dist
 
-if is_torch_tpu_available():
+if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
 
 

src/transformers/utils/import_utils.py

@@ -396,19 +396,22 @@ def is_ftfy_available():
     return _ftfy_available
 
 
-def is_torch_tpu_available():
+def is_torch_tpu_available(check_device=True):
+    "Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
     if not _torch_available:
         return False
-    if importlib.util.find_spec("torch_xla") is None:
-        return False
-    import torch_xla.core.xla_model as xm
-
+    if importlib.util.find_spec("torch_xla") is not None:
+        if check_device:
             # We need to check if `xla_device` can be found, will raise a RuntimeError if not
             try:
-        xm.xla_device()
+                import torch_xla.core.xla_model as xm
+
+                _ = xm.xla_device()
                 return True
             except RuntimeError:
                 return False
+        return True
+    return False
 
 
 def is_torchdynamo_available():