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

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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: https://github.com/pytorch/pytorch/pull/146408
Approved by: https://github.com/justinchuby, https://github.com/janeyx99
This commit is contained in:
Aaron Gokaslan
2025-02-04 19:07:02 +00:00
committed by PyTorch MergeBot
parent f38a2ea0d4
commit 292af3cc89
36 changed files with 56 additions and 64 deletions
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2
functorch/examples/dp_cifar10/cifar10_opacus.py
View File

@ -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)

2
functorch/examples/dp_cifar10/cifar10_transforms.py
View File

@ -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)

2
test/distributed/fsdp/test_fsdp_grad_acc.py
View File

@ -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

2
test/jit/test_exception.py
View File

@ -36,7 +36,7 @@ class TestException(TestCase):
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

2
test/jit/test_tracer.py
View File

@ -1702,7 +1702,7 @@ class TestTracer(JitTestCase):
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),)])

14
test/jit/test_union.py
View File

@ -754,7 +754,7 @@ class TestUnion(JitTestCase):
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 @@ class TestUnion(JitTestCase):
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 @@ class TestUnion(JitTestCase):
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 @@ class TestUnion(JitTestCase):
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

14
test/jit/test_union_pep604.py
View File

@ -762,7 +762,7 @@ class TestUnion(JitTestCase):
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 @@ class TestUnion(JitTestCase):
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 @@ class TestUnion(JitTestCase):
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 @@ class TestUnion(JitTestCase):
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

2
test/onnx/test_verification.py
View File

@ -253,7 +253,7 @@ class TestFindMismatch(pytorch_test_common.ExportTestCase):
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):

2
test/test_autograd.py
View File

@ -8091,7 +8091,7 @@ for shape in [(1,), ()]:
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)

8
test/test_xnnpack_integration.py
View File

@ -23,7 +23,7 @@ from torch.utils.mobile_optimizer import optimize_for_mobile
@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 @@ class TestXNNPACKOps(TestCase):
@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 @@ class TestXNNPACKSerDes(TestCase):
@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 @@ class TestXNNPACKRewritePass(TestCase):
@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,

2
test/torch_np/numpy_tests/core/test_numeric.py
View File

@ -3029,7 +3029,7 @@ class TestBroadcast(TestCase):
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])

2
test/torch_np/numpy_tests/core/test_scalarmath.py
View File

@ -250,7 +250,7 @@ class TestPower(TestCase):
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:

2
tools/packaging/build_wheel.py
View File

@ -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()

2
torch/_dynamo/variables/higher_order_ops.py
View File

@ -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"
)

6
torch/_functorch/eager_transforms.py
View File

@ -73,9 +73,7 @@ def _create_differentiable(inps, level=None):
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)}"
)

2
torch/_functorch/partitioners.py
View File

@ -885,7 +885,7 @@ def solve_min_cut(
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):

4
torch/_library/infer_schema.py
View File

@ -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:

4
torch/_tensor_str.py
View File

@ -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})"

2
torch/distributed/_shard/sharded_tensor/utils.py
View File

@ -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."
)

2
torch/distributed/elastic/multiprocessing/errors/error_handler.py
View File

@ -117,7 +117,7 @@ class ErrorHandler:
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),
)

4
torch/distributed/elastic/timer/api.py
View File

@ -194,7 +194,7 @@ class TimerServer(abc.ABC):
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 @@ class TimerServer(abc.ABC):
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,

2
torch/distributed/optim/utils.py
View File

@ -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)

4
torch/distributed/optim/zero_redundancy_optimizer.py
View File

@ -1517,14 +1517,14 @@ class ZeroRedundancyOptimizer(Optimizer, Joinable):
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,
)

4
torch/distributed/rpc/backend_registry.py
View File

@ -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"

2
torch/distributed/tensor/_sharding_prop.py
View File

@ -421,7 +421,7 @@ class ShardingPropagator:
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

2
torch/fx/experimental/symbolic_shapes.py
View File

@ -6397,7 +6397,7 @@ class ShapeEnv:
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

2
torch/fx/passes/shape_prop.py
View File

@ -176,7 +176,7 @@ class ShapeProp(torch.fx.Interpreter):
except Exception as e:
traceback.print_exc()
raise RuntimeError(
f"ShapeProp error for: node={n.format_node()} with " f"meta={n.meta}"
f"ShapeProp error for: node={n.format_node()} with meta={n.meta}"
) from e
found_tensor = False

2
torch/nn/modules/adaptive.py
View File

@ -200,7 +200,7 @@ class AdaptiveLogSoftmaxWithLoss(Module):
)
else:
raise RuntimeError(
"0D or 1D target tensor expected, " "multi-target not supported"
"0D or 1D target tensor expected, multi-target not supported"
)
is_batched = targ_dim > 0

6
torch/nn/modules/module.py
View File

@ -706,13 +706,13 @@ class Module:
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(
mod._get_name() + " has no " "attribute `" + item + "`"
mod._get_name() + " has no attribute `" + item + "`"
)
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not " "an nn.Module")
raise AttributeError("`" + item + "` is not an nn.Module")
return mod
@ -829,7 +829,7 @@ class Module:
param: torch.nn.Parameter = getattr(mod, param_name)
if not isinstance(param, torch.nn.Parameter):
raise AttributeError("`" + param_name + "` is not an " "nn.Parameter")
raise AttributeError("`" + param_name + "` is not an nn.Parameter")
return param

2
torch/nn/modules/normalization.py
View File

@ -313,7 +313,7 @@ class GroupNorm(Module):
return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)
def extra_repr(self) -> str:
return "{num_groups}, {num_channels}, eps={eps}, " "affine={affine}".format(
return "{num_groups}, {num_channels}, eps={eps}, affine={affine}".format(
**self.__dict__
)

2
torch/onnx/_internal/fx/onnxfunction_dispatcher.py
View File

@ -42,7 +42,7 @@ def _find_opschema_matched_symbolic_function_disagnostic_message_formatter(
for symbolic_func in default_and_custom_functions:
overload_func = symbolic_func.onnx_function
all_function_overload_names += f"ONNX Node: {overload_func.name}[opset={overload_func.opset};is_custom={symbolic_func.is_custom}]. \n" # noqa: B950
return f"FX Node: {node.target}. \n" f"{all_function_overload_names}"
return f"FX Node: {node.target}. \n{all_function_overload_names}"
def _find_operator_overloads_in_onnx_registry_disagnostic_message_formatter(

2
torch/optim/lbfgs.py
View File

@ -244,7 +244,7 @@ class LBFGS(Optimizer):
if len(self.param_groups) != 1:
raise ValueError(
"LBFGS doesn't support per-parameter options " "(parameter groups)"
"LBFGS doesn't support per-parameter options (parameter groups)"
)
self._params = self.param_groups[0]["params"]

2
torch/optim/optimizer.py
View File

@ -864,7 +864,7 @@ class Optimizer:
if len(groups) != len(saved_groups):
raise ValueError(
"loaded state dict has a different number of " "parameter groups"
"loaded state dict has a different number of parameter groups"
)
param_lens = (len(g["params"]) for g in groups)
saved_lens = (len(g["params"]) for g in saved_groups)

2
torch/package/package_importer.py
View File

@ -562,7 +562,7 @@ class PackageImporter(Importer):
else:
where = "``from list''"
raise TypeError(
f"Item in {where} must be str, " f"not {type(x).__name__}"
f"Item in {where} must be str, not {type(x).__name__}"
)
elif x == "*":
if not recursive and hasattr(module, "__all__"):

2
torch/utils/data/dataloader.py
View File

@ -356,7 +356,7 @@ class DataLoader(Generic[_T_co]):
self._dataset_kind = _DatasetKind.Map
if sampler is not None and shuffle:
raise ValueError("sampler option is mutually exclusive with " "shuffle")
raise ValueError("sampler option is mutually exclusive with shuffle")
if batch_sampler is not None:
# auto_collation with custom batch_sampler

2
torch/utils/data/datapipes/iter/combining.py
View File

@ -680,7 +680,7 @@ class ZipperIterDataPipe(IterDataPipe[tuple[_T_co]]):
def __init__(self, *datapipes: IterDataPipe):
if not all(isinstance(dp, IterDataPipe) for dp in datapipes):
raise TypeError(
"All inputs are required to be `IterDataPipe` " "for `ZipIterDataPipe`."
"All inputs are required to be `IterDataPipe` for `ZipIterDataPipe`."
)
super().__init__()
self.datapipes = datapipes # type: ignore[assignment]