Fix flake8 issues in gragrad test

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18727

Differential Revision: D14724887

Pulled By: ifedan

fbshipit-source-id: 8c1db6460303e746e4aea0142302b8d61277c067

diff --git a/test/test_autograd.py b/test/test_autograd.py
index 7bbb868..08b5530 100644 (file)
--- a/test/test_autograd.py
+++ b/test/test_autograd.py
@@ -2292,7 +2292,8 @@ class TestAutograd(TestCase):
             return output
 
         f_args_variable = torch.ones(S, S, requires_grad=True)
-        self.assertRaisesRegex(RuntimeError, 'Numerical gradient for function expected to be zero', lambda: gradcheck(autograd_fn, f_args_variable, eps=1e-6, atol=PRECISION))
+        self.assertRaisesRegex(RuntimeError, 'Numerical gradient for function expected to be zero',
+                               lambda: gradcheck(autograd_fn, f_args_variable, eps=1e-6, atol=PRECISION))
 
     def test_variable_traverse(self):
         def get_out_and_unrefed_cycle():

diff --git a/torch/autograd/gradcheck.py b/torch/autograd/gradcheck.py
index 6dd4cc7..4615464 100644 (file)
--- a/torch/autograd/gradcheck.py
+++ b/torch/autograd/gradcheck.py
@@ -203,8 +203,7 @@ def gradcheck(func, inputs, eps=1e-6, atol=1e-5, rtol=1e-3, raise_exception=True
 
     tupled_inputs = _as_tuple(inputs)
     if any(t.is_sparse for t in tupled_inputs if isinstance(t, torch.Tensor)) and not check_sparse_nnz:
-        return fail_test('gradcheck expects all tensor inputs '
-                  'are dense when check_sparse_nnz is set to False.')
+        return fail_test('gradcheck expects all tensor inputs are dense when check_sparse_nnz is set to False.')
 
     # Make sure that gradients are saved for all inputs
     any_input_requiring_grad = False

diff --git a/torch/distributed/distributed_c10d.py b/torch/distributed/distributed_c10d.py
index 775485b..1fea9d5 100644 (file)
--- a/torch/distributed/distributed_c10d.py
+++ b/torch/distributed/distributed_c10d.py
@@ -12,7 +12,6 @@ from . import BroadcastOptions, AllreduceOptions, ReduceOptions, \
 from . import ReduceOp
 from . import PrefixStore
 from . import ProcessGroupGloo
-from . import Reducer
 
 
 _MPI_AVAILABLE = True