v1.5.2

Author: muellerzr

setup.py

@@ -49,7 +49,7 @@
 
 setup(
     name="accelerate",
-    version="1.5.1",
+    version="1.5.2",
     description="Accelerate",
     long_description=open("README.md", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/accelerate/__init__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = "1.5.1"
+__version__ = "1.5.2"
 
 from .accelerator import Accelerator
 from .big_modeling import (

src/accelerate/data_loader.py

@@ -89,7 +89,9 @@ def __init__(self, *args, **kwargs):
 
     def __iter__(self):
         if self.generator is None:
-            self.generator = torch.Generator(device=torch.get_default_device())
+            self.generator = torch.Generator(
+                device=torch.get_default_device() if hasattr(torch, "get_default_device") else "cpu"
+            )
             self.generator.manual_seed(self.initial_seed)
 
         # Allow `self.epoch` to modify the seed of the generator
@@ -1156,13 +1158,19 @@ def prepare_data_loader(
             data_source=sampler.data_source,
             replacement=sampler.replacement,
             num_samples=sampler._num_samples,
-            generator=getattr(sampler, "generator", torch.Generator(device=torch.get_default_device())),
+            generator=getattr(
+                sampler,
+                "generator",
+                torch.Generator(device=torch.get_default_device() if hasattr(torch, "get_default_device") else "cpu"),
+            ),
             data_seed=data_seed,
         )
 
     if isinstance(dataloader.sampler, RandomSampler) and state.distributed_type == DistributedType.XLA:
         # isinstance(dataloader.sampler, RandomSampler) indicates the original dataloader has `shuffle` enabled.
-        generator = torch.Generator(device=torch.get_default_device()).manual_seed(42)
+        generator = torch.Generator(
+            device=torch.get_default_device() if hasattr(torch, "get_default_device") else "cpu"
+        ).manual_seed(42)
         dataloader.generator = generator
         dataloader.sampler.generator = generator
     # No change if no multiprocess
@@ -1181,7 +1189,9 @@ def prepare_data_loader(
         else:
             if not use_seedable_sampler and hasattr(sampler, "generator"):
                 if sampler.generator is None:
-                    sampler.generator = torch.Generator(device=torch.get_default_device())
+                    sampler.generator = torch.Generator(
+                        device=torch.get_default_device() if hasattr(torch, "get_default_device") else "cpu"
+                    )
                 synchronized_generator = sampler.generator
             batch_sampler = dataloader.sampler if sampler_is_batch_sampler else dataloader.batch_sampler
             new_batch_sampler = BatchSamplerShard(

src/accelerate/test_utils/testing.py

@@ -536,6 +536,7 @@ def run_first(test_case):
     """
     if is_pytest_available():
         import pytest
+
         return pytest.mark.order(1)(test_case)
     return test_case