[BE][Ez]: ISC001 Auto concatenate implicit one line strings (pytorch#146408)

Apply ruff rule about implicit string concatenation, this autofixes strings that are all the same type and on the same line. These lines are broken up likely as the result of autoformatters in the past. All fixes are automated using the autofixes in ISC001.

Pull Request resolved: pytorch#146408
Approved by: https://github.com/justinchuby, https://github.com/janeyx99

Author: Skylion007

functorch/examples/dp_cifar10/cifar10_opacus.py

@@ -119,7 +119,7 @@ def test(args, model, test_loader, device):
 
     top1_avg = np.mean(top1_acc)
 
-    print(f"\tTest set:" f"Loss: {np.mean(losses):.6f} " f"Acc@1: {top1_avg :.6f} ")
+    print(f"\tTest set:Loss: {np.mean(losses):.6f} Acc@1: {top1_avg :.6f} ")
     return np.mean(top1_acc)
 
 

functorch/examples/dp_cifar10/cifar10_transforms.py

@@ -185,7 +185,7 @@ def test(args, model, test_loader, device):
 
     top1_avg = np.mean(top1_acc)
 
-    print(f"\tTest set:" f"Loss: {np.mean(losses):.6f} " f"Acc@1: {top1_avg :.6f} ")
+    print(f"\tTest set:Loss: {np.mean(losses):.6f} Acc@1: {top1_avg :.6f} ")
     return np.mean(top1_acc)
 
 

test/distributed/fsdp/test_fsdp_grad_acc.py

@@ -61,7 +61,7 @@ class _GradAccConfig:
     def __repr__(self) -> str:
         # Override to remove any spaces in the string to appease the internal
         # build's test name parser
-        return f"(use_no_sync={self.use_no_sync}," f"num_iters={self.num_iters})"
+        return f"(use_no_sync={self.use_no_sync},num_iters={self.num_iters})"
 
 
 @dataclass

test/jit/test_exception.py

@@ -36,7 +36,7 @@ def close_match(x):
 
         with self.assertRaisesRegex(
             RuntimeError,
-            "This op may not exist or may not be currently " "supported in TorchScript",
+            "This op may not exist or may not be currently supported in TorchScript",
         ):
 
             @torch.jit.script

test/jit/test_tracer.py

@@ -1702,7 +1702,7 @@ def test_index_put(target, indices, rhs):
     def test_trace_checker_dot_data(self):
         with self.assertRaisesRegex(
             torch.jit.TracingCheckError,
-            r"Tensor-valued Constant nodes differed in value " r"across invocations",
+            r"Tensor-valued Constant nodes differed in value across invocations",
         ):
 
             @_trace(torch.rand(3, 4), check_inputs=[(torch.rand(3, 4),)])

test/jit/test_union.py

@@ -754,7 +754,7 @@ def fn():
             template,
             "Union[List[str], List[torch.Tensor]]",
             lhs["list_literal_of_mixed"],
-            "none of those types match the types of the" " given list elements",
+            "none of those types match the types of the given list elements",
         )
 
         self._assert_passes(
@@ -784,21 +784,21 @@ def fn():
             template,
             "Union[int, torch.Tensor]",
             lhs["list_literal_empty"],
-            "Expected an Union type annotation with an " "inner List type",
+            "Expected an Union type annotation with an inner List type",
         )
 
         self._assert_raises(
             template,
             "Union[int, torch.Tensor]",
             lhs["list_literal_of_tensor"],
-            "Expected an Union type annotation with an " "inner List type",
+            "Expected an Union type annotation with an inner List type",
         )
 
         self._assert_raises(
             template,
             "Union[int, torch.Tensor]",
             lhs["list_comprehension_of_tensor"],
-            "Expected an Union type annotation with an " "inner List type",
+            "Expected an Union type annotation with an inner List type",
         )
 
         """
@@ -890,7 +890,7 @@ def fn():
             template,
             "Union[List[str], List[torch.Tensor]]",
             lhs["dict_literal_empty"],
-            "Expected an Union type annotation with an " "inner Dict type",
+            "Expected an Union type annotation with an inner Dict type",
         )
 
         self._assert_passes(
@@ -974,14 +974,14 @@ def fn():
             template,
             "Union[int, torch.Tensor]",
             lhs["dict_literal_empty"],
-            "Expected an Union type annotation with " "an inner Dict type",
+            "Expected an Union type annotation with an inner Dict type",
         )
 
         self._assert_raises(
             template,
             "Union[int, torch.Tensor]",
             lhs["dict_literal_of_str_tensor"],
-            "Expected an Union type annotation with " "an inner Dict type",
+            "Expected an Union type annotation with an inner Dict type",
         )
 
         # See above--string frontend does not support tuple unpacking

test/jit/test_union_pep604.py

@@ -762,7 +762,7 @@ def fn():
             template,
             "List[str] | List[torch.Tensor]",
             lhs["list_literal_of_mixed"],
-            "none of those types match the types of the" " given list elements",
+            "none of those types match the types of the given list elements",
         )
 
         self._assert_passes(
@@ -790,21 +790,21 @@ def fn():
             template,
             "int | torch.Tensor",
             lhs["list_literal_empty"],
-            "Expected an Union type annotation with an " "inner List type",
+            "Expected an Union type annotation with an inner List type",
         )
 
         self._assert_raises(
             template,
             "int | torch.Tensor",
             lhs["list_literal_of_tensor"],
-            "Expected an Union type annotation with an " "inner List type",
+            "Expected an Union type annotation with an inner List type",
         )
 
         self._assert_raises(
             template,
             "int | torch.Tensor",
             lhs["list_comprehension_of_tensor"],
-            "Expected an Union type annotation with an " "inner List type",
+            "Expected an Union type annotation with an inner List type",
         )
 
         """
@@ -894,7 +894,7 @@ def fn():
             template,
             "List[str] | List[torch.Tensor]",
             lhs["dict_literal_empty"],
-            "Expected an Union type annotation with an " "inner Dict type",
+            "Expected an Union type annotation with an inner Dict type",
         )
 
         self._assert_passes(
@@ -978,14 +978,14 @@ def fn():
             template,
             "int | torch.Tensor",
             lhs["dict_literal_empty"],
-            "Expected an Union type annotation with " "an inner Dict type",
+            "Expected an Union type annotation with an inner Dict type",
         )
 
         self._assert_raises(
             template,
             "int | torch.Tensor",
             lhs["dict_literal_of_str_tensor"],
-            "Expected an Union type annotation with " "an inner Dict type",
+            "Expected an Union type annotation with an inner Dict type",
         )
 
         # See above--string frontend does not support tuple unpacking

test/onnx/test_verification.py

@@ -253,7 +253,7 @@ def test_preserve_mismatch_source_location(self):
                 leaf_info.pretty_print_mismatch(graph=True)
             self.assertRegex(
                 f.getvalue(),
-                r"(.|\n)*" r"aten::relu.*/torch/nn/functional.py:[0-9]+(.|\n)*",
+                r"(.|\n)*aten::relu.*/torch/nn/functional.py:[0-9]+(.|\n)*",
             )
 
     def test_find_all_mismatch_operators(self):

test/test_autograd.py

@@ -8091,7 +8091,7 @@ def backward(ctx, grad):
         view_a = a.unbind()[0]
         with self.assertRaisesRegex(
             RuntimeError,
-            "This view is the output of a function that returns " "multiple views.",
+            "This view is the output of a function that returns multiple views.",
         ):
             view_a.copy_(b)
 

test/test_xnnpack_integration.py

@@ -23,7 +23,7 @@
 
 @unittest.skipUnless(
     torch.backends.xnnpack.enabled,
-    " XNNPACK must be enabled for these tests." " Please build with USE_XNNPACK=1.",
+    " XNNPACK must be enabled for these tests. Please build with USE_XNNPACK=1.",
 )
 @unittest.skipIf(
     TEST_WITH_TSAN,
@@ -231,7 +231,7 @@ def test_conv2d_transpose(
 
 @unittest.skipUnless(
     torch.backends.xnnpack.enabled,
-    " XNNPACK must be enabled for these tests." " Please build with USE_XNNPACK=1.",
+    " XNNPACK must be enabled for these tests. Please build with USE_XNNPACK=1.",
 )
 @unittest.skipIf(
     TEST_WITH_TSAN,
@@ -753,7 +753,7 @@ def forward(self, x):
 
 @unittest.skipUnless(
     torch.backends.xnnpack.enabled,
-    " XNNPACK must be enabled for these tests." " Please build with USE_XNNPACK=1.",
+    " XNNPACK must be enabled for these tests. Please build with USE_XNNPACK=1.",
 )
 @unittest.skipIf(
     TEST_WITH_TSAN,
@@ -1241,7 +1241,7 @@ def forward(self, x):
 
 @unittest.skipUnless(
     torch.backends.xnnpack.enabled,
-    " XNNPACK must be enabled for these tests." " Please build with USE_XNNPACK=1.",
+    " XNNPACK must be enabled for these tests. Please build with USE_XNNPACK=1.",
 )
 @unittest.skipIf(
     TEST_WITH_TSAN,

test/torch_np/numpy_tests/core/test_numeric.py

@@ -3029,7 +3029,7 @@ def test_broadcast_error_kwargs(self):
     def test_shape_mismatch_error_message(self):
         with assert_raises(
             ValueError,
-            match=r"arg 0 with shape \(1, 3\) and " r"arg 2 with shape \(2,\)",
+            match=r"arg 0 with shape \(1, 3\) and arg 2 with shape \(2,\)",
         ):
             np.broadcast([[1, 2, 3]], [[4], [5]], [6, 7])
 

test/torch_np/numpy_tests/core/test_scalarmath.py

@@ -250,7 +250,7 @@ def test_mixed_types(self):
                 a = t1(3)
                 b = t2(2)
                 result = a**b
-                msg = f"error with {t1!r} and {t2!r}:" f"got {result!r}, expected {9!r}"
+                msg = f"error with {t1!r} and {t2!r}:got {result!r}, expected {9!r}"
                 if np.issubdtype(np.dtype(result), np.integer):
                     assert_(result == 9, msg)
                 else:

tools/packaging/build_wheel.py

@@ -105,7 +105,7 @@ def parse_args() -> argparse.Namespace:
         "--destination",
         default="dist/",
         type=str,
-        help=("Destination to put the compailed binaries" ""),
+        help=("Destination to put the compailed binaries"),
     )
     return parser.parse_args()
 

torch/_dynamo/variables/higher_order_ops.py

@@ -1482,7 +1482,7 @@ def non_single_tensor_return_unsupported(api, ret):
 
     if not isinstance(ret, TensorVariable):
         raise Unsupported(
-            f"{api} over function that returns something " f"other than one Tensor"
+            f"{api} over function that returns something other than one Tensor"
         )
 
 

torch/_functorch/eager_transforms.py

@@ -73,9 +73,7 @@ def create_differentiable(x):
         if isinstance(x, torch.Tensor):
             with enable_inplace_requires_grad(True):
                 return _set_tensor_requires_grad(x)
-        raise ValueError(
-            f"Thing passed to transform API must be Tensor, " f"got {type(x)}"
-        )
+        raise ValueError(f"Thing passed to transform API must be Tensor, got {type(x)}")
 
     return tree_map(create_differentiable, inps)
 
@@ -954,7 +952,7 @@ def assert_non_empty_list_of_tensors(
         if isinstance(out, torch.Tensor):
             continue
         raise RuntimeError(
-            f"{api}: Expected {argname} to only contain Tensors, got " f"{type(out)}"
+            f"{api}: Expected {argname} to only contain Tensors, got {type(out)}"
         )
 
 

torch/_functorch/partitioners.py

@@ -885,7 +885,7 @@ def is_fusible(a, b):
         import networkx as nx
     except ImportError as e:
         raise RuntimeError(
-            "Need networkx installed to perform smart recomputation " "heuristics"
+            "Need networkx installed to perform smart recomputation heuristics"
         ) from e
 
     def is_materialized_backwards(node):

torch/_library/infer_schema.py

@@ -66,9 +66,7 @@ def infer_schema(
     sig = inspect.signature(prototype_function)
 
     def error_fn(what):
-        raise ValueError(
-            f"infer_schema(func): {what} " f"Got func with signature {sig})"
-        )
+        raise ValueError(f"infer_schema(func): {what} Got func with signature {sig})")
 
     def convert_type_string(annotation_type: str):
         try:

torch/_tensor_str.py

@@ -689,9 +689,7 @@ def _functorch_wrapper_str_intern(tensor, *, tensor_contents=None):
             f")"
         )
     if torch._C._functorch.is_gradtrackingtensor(tensor):
-        return (
-            f"GradTrackingTensor(lvl={level}, value=\n" f"{indented_value_repr}\n" f")"
-        )
+        return f"GradTrackingTensor(lvl={level}, value=\n{indented_value_repr}\n)"
     if torch._C._functorch.is_functionaltensor(tensor):
         return f"FunctionalTensor(lvl={level}, value=\\\n{value_repr})"
 

torch/distributed/_shard/sharded_tensor/utils.py

@@ -69,7 +69,7 @@ def _validate_output_tensor_for_gather(
             )
     elif dst_tensor:
         raise ValueError(
-            "Argument ``dst_tensor`` must NOT be specified " "on non-destination ranks."
+            "Argument ``dst_tensor`` must NOT be specified on non-destination ranks."
         )
 
 

torch/distributed/elastic/multiprocessing/errors/error_handler.py

@@ -117,7 +117,7 @@ def dump_error_file(self, rootcause_error_file: str, error_code: int = 0):
                     rootcause_error_file, rootcause_error, error_code
                 )
             logger.debug(
-                "child error file (%s) contents:\n" "%s",
+                "child error file (%s) contents:\n%s",
                 rootcause_error_file,
                 json.dumps(rootcause_error, indent=2),
             )

torch/distributed/elastic/timer/api.py

@@ -194,7 +194,7 @@ def _run_watchdog(self):
         reaped_worker_ids = set()
         for worker_id, expired_timers in self.get_expired_timers(now).items():
             logger.info(
-                "Reaping worker_id=[%s]." " Expired timers: %s",
+                "Reaping worker_id=[%s]. Expired timers: %s",
                 worker_id,
                 self._get_scopes(expired_timers),
             )
@@ -212,7 +212,7 @@ def _get_scopes(self, timer_requests):
 
     def start(self) -> None:
         logger.info(
-            "Starting %s..." " max_interval=%s," " daemon=%s",
+            "Starting %s... max_interval=%s, daemon=%s",
             type(self).__name__,
             self._max_interval,
             self._daemon,

torch/distributed/optim/utils.py

@@ -50,7 +50,7 @@ def as_functional_optim(optim_cls: type, *args, **kwargs):
         functional_cls = functional_optim_map[optim_cls]
     except KeyError as e:
         raise ValueError(
-            f"Optimizer {optim_cls} does not have a functional " f"counterpart!"
+            f"Optimizer {optim_cls} does not have a functional counterpart!"
         ) from e
 
     return _create_functional_optim(functional_cls, *args, **kwargs)

torch/distributed/optim/zero_redundancy_optimizer.py

@@ -1517,14 +1517,14 @@ def _init_local_optimizer(self) -> None:
                     self._bucket_assignments_per_rank[self.global_rank]
                 )
                 logger.info(
-                    "rank %s with %s parameters " "across %s buckets",
+                    "rank %s with %s parameters across %s buckets",
                     self.global_rank,
                     local_numel,
                     num_assigned_buckets,
                 )
                 if self.global_rank == 0:
                     logger.info(
-                        "%s DDP " "buckets and " "%s bucket " "assignments",
+                        "%s DDP buckets and %s bucket assignments",
                         len(self._overlap_info.params_per_bucket),
                         self._overlap_info.num_bucket_assignments,
                     )

torch/distributed/rpc/backend_registry.py

@@ -188,9 +188,7 @@ def _validate_device_maps(
     for node in all_names:
         devices = all_devices[node]
         if len(set(devices)) != len(devices):
-            raise ValueError(
-                f"Node {node} has duplicated devices\n" f"devices = {devices}"
-            )
+            raise ValueError(f"Node {node} has duplicated devices\ndevices = {devices}")
         if not _tensorpipe_validate_devices(devices, all_device_counts[node]):
             raise ValueError(
                 f"Node {node} has devices with invalid indices\n"

torch/distributed/tensor/_sharding_prop.py

@@ -421,7 +421,7 @@ def spec_to_strategy(spec: object) -> object:
                 raise e
             except Exception as e:
                 raise RuntimeError(
-                    f"Sharding propagation failed on op {op_schema}.\n" f"Error: {e}"
+                    f"Sharding propagation failed on op {op_schema}.\nError: {e}"
                 ) from e
 
             # step 2. if can't get output_spec from sharding

torch/fx/experimental/symbolic_shapes.py

@@ -6397,7 +6397,7 @@ def _get_stack_summary(
             maybe_extra_debug += "\nC++ stack trace:\n" + "".join(cpp_stack.format())
         elif is_debug:
             maybe_extra_debug += (
-                "\nFor C++ stack trace, run with " "TORCHDYNAMO_EXTENDED_DEBUG_CPP=1"
+                "\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1"
             )
 
         return SLoc(floc, maybe_user_loc), maybe_extra_debug