resolve flake8

pytorch_lightning/utilities/apply_func.py

@@ -45,9 +45,10 @@ def from_numpy(value, device: torch.device = None):
         )
     return torch.from_numpy(value).to(device)
 
+
 CONVERSION_DTYPES = [
-    # bool -> int as torch.bool: RuntimeError: Unsupported data type for NCCL process group
-    (bool, partial(to_dtype_tensor, dtype=torch.int)),
+    # bool -> uint8 as bool -> torch.bool triggers RuntimeError: Unsupported data type for NCCL process group
+    (bool, partial(to_dtype_tensor, dtype=torch.uint8)),
     (int, partial(to_dtype_tensor, dtype=torch.int)),
     (float, partial(to_dtype_tensor, dtype=torch.float)),
     (np.ndarray, from_numpy),
@@ -147,6 +148,8 @@ def batch_to(data):
 
     dtype = (TransferableDataType, Batch) if _TORCHTEXT_AVAILABLE else TransferableDataType
     return apply_to_collection(batch, dtype=dtype, function=batch_to)
+
+
 def convert_to_tensors(data, device: torch.device = None):
     if device is None:
         raise MisconfigurationException(

tests/utilities/test_all_gather_grad.py

@@ -1,11 +1,11 @@
 import os
 import sys
 
+import numpy as np
 import pytest
 import torch
-import numpy as np
 
-from pytorch_lightning import Trainer, seed_everything
+from pytorch_lightning import seed_everything, Trainer
 from pytorch_lightning.utilities import AllGatherGrad
 from tests.base.boring_model import BoringModel
 
@@ -70,7 +70,7 @@ def training_epoch_end(self, outputs) -> None:
             })
             assert gathered_loss["losses_np_ndarray"][0].dtype == torch.int64
             # torch.bool can't be all_gathered
-            assert gathered_loss["losses_bool"][0].dtype == torch.int32
+            assert gathered_loss["losses_bool"][0].dtype == torch.uint8
             assert gathered_loss["losses_float"][0].dtype == torch.float
             assert gathered_loss["losses_int"][0].dtype == torch.int
             assert gathered_loss["losses_list"][0].numel() == 2 * len(losses)