removing some redundant str splits (#106089)

drop some redundant string splits, no factual changes, just cleaning the codebase

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106089
Approved by: https://github.com/albanD, https://github.com/malfet

test/distributed/launcher/bin/test_script_local_rank.py

@@ -19,7 +19,7 @@ def parse_args():
         "--local_rank",
         type=int,
         required=True,
-        help="The rank of the node for multi-node distributed " "training",
+        help="The rank of the node for multi-node distributed training",
     )
 
     return parser.parse_args()

test/onnx/model_defs/squeezenet.py

@@ -32,7 +32,7 @@ class SqueezeNet(nn.Module):
         super().__init__()
         if version not in [1.0, 1.1]:
             raise ValueError(
-                f"Unsupported SqueezeNet version {version}:" "1.0 or 1.1 expected"
+                f"Unsupported SqueezeNet version {version}:1.0 or 1.1 expected"
             )
         self.num_classes = num_classes
         if version == 1.0:

test/test_nestedtensor.py

@@ -360,19 +360,17 @@ class TestNestedTensor(TestCase):
     @torch.inference_mode()
     def test_repr_string(self):
         a = torch.nested.nested_tensor([])
-        expected = "nested_tensor([" "\n\n])"
+        expected = "nested_tensor([\n\n])"
         self.assertEqual(str(a), expected)
         self.assertEqual(repr(a), expected)
 
         a = torch.nested.nested_tensor([torch.tensor(1.0)])
-        expected = "nested_tensor([" "\n  tensor(1.)" "\n])"
+        expected = "nested_tensor([\n  tensor(1.)\n])"
         self.assertEqual(str(a), expected)
         self.assertEqual(repr(a), expected)
 
         a = torch.nested.nested_tensor([torch.tensor([[1, 2]]), torch.tensor([[4, 5]])])
-        expected = (
+        expected = "nested_tensor([\n  tensor([[1, 2]]),\n  tensor([[4, 5]])\n])"
-            "nested_tensor([" "\n  tensor([[1, 2]])" "," "\n  tensor([[4, 5]])" "\n])"
-        )
         self.assertEqual(str(a), expected)
         self.assertEqual(repr(a), expected)
 

test/test_weak.py

@@ -57,7 +57,7 @@ class WeakTest(TestCase):
         self.assertIsNot(
             value1,
             value2,
-            "invalid test" " -- value parameters must be distinct objects",
+            "invalid test -- value parameters must be distinct objects",
         )
         weakdict = klass()
         o = weakdict.setdefault(key, value1)

test/torch_np/numpy_tests/core/test_indexing.py

@@ -700,7 +700,7 @@ class TestMultiIndexingAutomated:
                 in_indices[i] = indx
             elif indx.dtype.kind != "b" and indx.dtype.kind != "i":
                 raise IndexError(
-                    "arrays used as indices must be of " "integer (or boolean) type"
+                    "arrays used as indices must be of integer (or boolean) type"
                 )
             if indx.ndim != 0:
                 no_copy = False

test/torch_np/numpy_tests/lib/index_tricks.py

@@ -390,7 +390,7 @@ class AxisConcatenator:
                         newobj = newobj.swapaxes(-1, trans1d)
             elif isinstance(item, str):
                 if k != 0:
-                    raise ValueError("special directives must be the " "first entry.")
+                    raise ValueError("special directives must be the first entry.")
                 if item in ("r", "c"):
                     matrix = True
                     col = item == "c"

test/torch_np/numpy_tests/lib/test_function_base.py

@@ -2920,7 +2920,7 @@ class TestPercentile:
         assert_equal(c1.shape, r1.shape)
 
     @pytest.mark.xfail(
-        reason="numpy: x.dtype is int, out is int; " "torch: result is float"
+        reason="numpy: x.dtype is int, out is int; torch: result is float"
     )
     def test_scalar_q_2(self):
         x = np.arange(12).reshape(3, 4)

test/torch_np/numpy_tests/lib/test_histograms.py

@@ -566,7 +566,7 @@ class TestHistogramOptimBinNums:
             assert_equal(
                 len(a),
                 numbins,
-                err_msg=f"{estimator} estimator, " "No Variance test",
+                err_msg=f"{estimator} estimator, No Variance test",
             )
 
     def test_limited_variance(self):

test/torch_np/numpy_tests/linalg/test_linalg.py

@@ -784,7 +784,7 @@ class TestCond(CondCases):
             linalg.cond(A, p)
 
     @pytest.mark.xfail(
-        True, run=False, reason="Platform/LAPACK-dependent failure, " "see gh-18914"
+        True, run=False, reason="Platform/LAPACK-dependent failure, see gh-18914"
     )
     def test_nan(self):
         # nans should be passed through, not converted to infs

tools/lite_interpreter/gen_selected_mobile_ops_header.py

@@ -149,7 +149,7 @@ def main() -> None:
         "--yaml_file_path",
         type=str,
         required=True,
-        help="Path to the yaml" " file with a list of operators used by the model.",
+        help="Path to the yaml file with a list of operators used by the model.",
     )
     parser.add_argument(
         "-o",

tools/nightly.py

@@ -343,7 +343,7 @@ def deps_install(deps: List[str], existing_env: bool, env_opts: List[str]) -> No
 
 @timed("Installing pytorch nightly binaries")
 def pytorch_install(url: str) -> "tempfile.TemporaryDirectory[str]":
-    """ "Install pytorch into a temporary directory"""
+    """Install pytorch into a temporary directory"""
     pytdir = tempfile.TemporaryDirectory()
     cmd = ["conda", "create", "--yes", "--no-deps", "--prefix", pytdir.name, url]
     p = subprocess.run(cmd, check=True)

torch/_higher_order_ops/cond.py

@@ -405,12 +405,12 @@ def cond_func(pred, true_fn, false_fn, inputs):
         for branch in [true_fn, false_fn]:
             if _has_potential_branch_input_mutation(branch, unwrapped_inputs):
                 raise UnsupportedAliasMutationException(
-                    "One of torch.cond branch " "might be modifying the input!"
+                    "One of torch.cond branch might be modifying the input!"
                 )
 
             if _has_potential_branch_input_alias(branch, unwrapped_inputs):
                 raise UnsupportedAliasMutationException(
-                    "One of torch.cond branch " "might be aliasing the input!"
+                    "One of torch.cond branch might be aliasing the input!"
                 )
 
         cond_return = cond_op(
@@ -443,12 +443,12 @@ def cond_functionalize(interpreter, pred, true_fn, false_fn, inputs):
         for branch in [functional_true_fn, functional_false_fn]:
             if _has_potential_branch_input_mutation(branch, unwrapped_inputs):
                 raise UnsupportedAliasMutationException(
-                    "One of torch.cond branch " "might be modifying the input!"
+                    "One of torch.cond branch might be modifying the input!"
                 )
         for branch in [true_fn, false_fn]:
             if _has_potential_branch_input_alias(branch, unwrapped_inputs):
                 raise UnsupportedAliasMutationException(
-                    "One of torch.cond branch " "might be aliasing the input!"
+                    "One of torch.cond branch might be aliasing the input!"
                 )
 
         cond_return = cond_op(

torch/_numpy/_funcs_impl.py

@@ -1226,7 +1226,7 @@ def cross(a: ArrayLike, b: ArrayLike, axisa=-1, axisb=-1, axisc=-1, axis=None):
     # Move working axis to the end of the shape
     a = torch.moveaxis(a, axisa, -1)
     b = torch.moveaxis(b, axisb, -1)
-    msg = "incompatible dimensions for cross product\n" "(dimension must be 2 or 3)"
+    msg = "incompatible dimensions for cross product\n(dimension must be 2 or 3)"
     if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
         raise ValueError(msg)
 

torch/_numpy/_reductions_impl.py

@@ -321,7 +321,7 @@ def average(
         if a.shape != weights.shape:
             if axis is None:
                 raise TypeError(
-                    "Axis must be specified when shapes of a and weights " "differ."
+                    "Axis must be specified when shapes of a and weights differ."
                 )
             if weights.ndim != 1:
                 raise TypeError(

torch/autograd/function.py

@@ -358,7 +358,7 @@ class _SingleLevelFunction(
         if they are intended to be used for in ``jvp``.
         """
         raise NotImplementedError(
-            "You must implement the forward function for custom" " autograd.Function."
+            "You must implement the forward function for custom autograd.Function."
         )
 
     @staticmethod

torch/distributed/fsdp/fully_sharded_data_parallel.py

@@ -2043,9 +2043,9 @@ def _get_param_to_fqn(
     """
     param_to_param_names = _get_param_to_fqns(model)
     for param_names in param_to_param_names.values():
-        assert len(param_names) > 0, (
+        assert (
-            "`_get_param_to_fqns()` " "should not construct empty lists"
+            len(param_names) > 0
-        )
+        ), "`_get_param_to_fqns()` should not construct empty lists"
         if len(param_names) > 1:
             raise RuntimeError(
                 "Each parameter should only map to one parameter name but got "

torch/jit/frontend.py

@@ -1060,7 +1060,7 @@ class ExprBuilder(Builder):
             if isinstance(index_expr.value, ast.Tuple):
                 raise NotSupportedError(
                     base.range(),
-                    "slicing multiple dimensions with " "tuples not supported yet",
+                    "slicing multiple dimensions with tuples not supported yet",
                 )
             return build_expr(ctx, index_expr.value)
 

torch/package/_importlib.py

@@ -44,9 +44,7 @@ def _sanity_check(name, package, level):
         if not isinstance(package, str):
             raise TypeError("__package__ not set to a string")
         elif not package:
-            raise ImportError(
+            raise ImportError("attempted relative import with no known parent package")
-                "attempted relative import with no known parent " "package"
-            )
     if not name and level == 0:
         raise ValueError("Empty module name")