[BE][Easy][18/19] enforce style for empty lines in import segments in torch/d*/ (#129770)

See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter.

You can review these PRs via:

```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129770
Approved by: https://github.com/wconstab

tools/linter/adapters/ufmt_linter.py

@@ -54,7 +54,6 @@ ISORT_SKIPLIST = re.compile(
                     # torch/[a-c]*/**
                     "torch/[a-c]*/**",
                     # torch/d*/**
-                    "torch/d*/**",
                     # torch/[e-n]*/**
                     "torch/[e-n]*/**",
                     # torch/[o-z]*/**

torch/distributed/_composable/fsdp/_fsdp_state.py

@@ -2,7 +2,6 @@
 # mypy: allow-untyped-defs
 import functools
 import logging
-
 from typing import (
     Any,
     Callable,

torch/distributed/_state_dict_utils.py

@@ -23,6 +23,7 @@ import torch.distributed as dist
 import torch.nn.functional as F
 from torch.distributed._functional_collectives import AsyncCollectiveTensor
 
+
 if dist.is_available() or TYPE_CHECKING:
     from torch.distributed import distributed_c10d
     from torch.distributed._shard.sharded_tensor import ShardedTensor

torch/distributed/_symmetric_memory/__init__.py

@@ -1,7 +1,6 @@
 # mypy: allow-untyped-decorators
 import socket
 import uuid
-
 from contextlib import contextmanager
 from datetime import timedelta
 from functools import partial
@@ -12,6 +11,7 @@ import torch.distributed._functional_collectives as funcol
 import torch.distributed.distributed_c10d as c10d
 from torch._C._distributed_c10d import _SymmetricMemory, Work as _Work
 
+
 _group_name_to_store: Dict[str, c10d.Store] = {}
 
 

torch/distributed/_tensor/_shards_wrapper.py

@@ -20,6 +20,7 @@ from torch.distributed.checkpoint.planner import (
     WriteItemType,
 )
 
+
 aten = (
     torch.ops.aten
 )  # pyre-ignore[5]: Globally accessible variable `aten` has no type specified.

torch/distributed/_tensor/debug/comm_mode.py

@@ -3,18 +3,15 @@ import copy
 import json
 import re
 import weakref
-
 from collections import defaultdict
 from typing import Any, Dict
 
 import torch
-
 import torch.nn
-
+from torch._guards import detect_fake_mode
 from torch.autograd.graph import register_multi_grad_hook
 from torch.distributed._tensor.api import DTensor
 from torch.distributed._tools.mod_tracker import ModTracker
-
 from torch.nn.modules.module import (
     register_module_forward_hook,
     register_module_forward_pre_hook,
@@ -23,10 +20,9 @@ from torch.nn.modules.module import (
 from torch.utils._python_dispatch import TorchDispatchMode
 from torch.utils._pytree import tree_flatten
 
+
 funcol_native = torch.ops._c10d_functional
 funcol_py = torch.ops.c10d_functional
-from torch._guards import detect_fake_mode
-
 funcol_autograd = torch.ops._c10d_functional_autograd
 c10d_ops = torch.ops.c10d
 

torch/distributed/_tensor/examples/comm_mode_features_example.py

@@ -1,14 +1,11 @@
 import os
-
 from typing import Callable, Dict, Union
 
 import torch
 import torch.nn as nn
-
 from torch.distributed._tensor import DeviceMesh
 from torch.distributed._tensor.debug import CommDebugMode
 from torch.distributed._tensor.examples.comm_mode_features_example_argparser import args
-
 from torch.distributed.tensor.parallel import (
     ColwiseParallel,
     parallelize_module,
@@ -21,7 +18,6 @@ from torch.testing._internal.distributed._tensor.common_dtensor import (
     NUM_DEVICES,
     Transformer,
 )
-
 from torch.utils.checkpoint import checkpoint
 
 

torch/distributed/_tensor/examples/comm_mode_features_example_argparser.py

@@ -1,5 +1,6 @@
 import argparse
 
+
 parser = argparse.ArgumentParser(
     description="comm_mode_feature examples",
     formatter_class=argparse.RawTextHelpFormatter,

torch/distributed/_tools/fsdp2_mem_tracker.py

@@ -21,6 +21,7 @@ from torch.utils._python_dispatch import TorchDispatchMode
 from torch.utils._pytree import tree_map_only
 from torch.utils.weak import WeakIdKeyDictionary, weakref
 
+
 _TOTAL_KEY = "Total"
 
 __all__ = ["FSDPMemTracker"]

torch/distributed/_tools/mem_tracker.py

@@ -17,7 +17,6 @@ from typing import (
     TYPE_CHECKING,
     Union,
 )
-
 from typing_extensions import Self
 
 import torch
@@ -32,9 +31,9 @@ from torch.utils._python_dispatch import (
     TorchDispatchMode,
 )
 from torch.utils._pytree import tree_flatten, tree_map_only
-
 from torch.utils.weak import WeakIdKeyDictionary, weakref
 
+
 if TYPE_CHECKING:
     from torch.utils.hooks import RemovableHandle
 

torch/distributed/checkpoint/logger.py

@@ -2,13 +2,13 @@
 import functools
 import time
 from typing import Any, Callable, Dict, List, TypeVar
-
 from typing_extensions import ParamSpec
 from uuid import uuid4
 
 import torch.distributed.c10d_logger as c10d_logger
 from torch.distributed.checkpoint.logging_handlers import DCP_LOGGER_NAME
 
+
 __all__: List[str] = []
 
 global _dcp_logger

torch/distributed/fsdp/_shard_utils.py

@@ -6,7 +6,6 @@ from typing import Optional
 
 import torch
 import torch.distributed as dist
-
 from torch._utils import _get_device_module
 from torch.distributed import distributed_c10d
 from torch.distributed._shard.sharded_tensor import (

torch/distributed/pipelining/_utils.py

@@ -7,6 +7,7 @@ from typing import List, Tuple, Union
 import torch
 from torch import fx
 
+
 logger = logging.getLogger(__name__)
 
 

torch/distributions/__init__.py

@@ -71,6 +71,7 @@ derivative would be as follows::
     loss.backward()
 """
 
+from . import transforms
 from .bernoulli import Bernoulli
 from .beta import Beta
 from .binomial import Binomial
@@ -111,12 +112,12 @@ from .relaxed_categorical import RelaxedOneHotCategorical
 from .studentT import StudentT
 from .transformed_distribution import TransformedDistribution
 from .transforms import *  # noqa: F403
-from . import transforms
 from .uniform import Uniform
 from .von_mises import VonMises
 from .weibull import Weibull
 from .wishart import Wishart
 
+
 _add_kl_info()
 del _add_kl_info
 

torch/distributions/bernoulli.py

@@ -13,6 +13,7 @@ from torch.distributions.utils import (
 )
 from torch.nn.functional import binary_cross_entropy_with_logits
 
+
 __all__ = ["Bernoulli"]
 
 

torch/distributions/beta.py

@@ -7,6 +7,7 @@ from torch.distributions.dirichlet import Dirichlet
 from torch.distributions.exp_family import ExponentialFamily
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Beta"]
 
 

torch/distributions/binomial.py

@@ -9,6 +9,7 @@ from torch.distributions.utils import (
     probs_to_logits,
 )
 
+
 __all__ = ["Binomial"]
 
 

torch/distributions/categorical.py

@@ -5,6 +5,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import lazy_property, logits_to_probs, probs_to_logits
 
+
 __all__ = ["Categorical"]
 
 

torch/distributions/cauchy.py

@@ -8,6 +8,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Cauchy"]
 
 

torch/distributions/chi2.py

@@ -2,6 +2,7 @@
 from torch.distributions import constraints
 from torch.distributions.gamma import Gamma
 
+
 __all__ = ["Chi2"]
 
 

torch/distributions/constraint_registry.py

@@ -70,6 +70,7 @@ import numbers
 
 from torch.distributions import constraints, transforms
 
+
 __all__ = [
     "ConstraintRegistry",
     "biject_to",

torch/distributions/constraints.py

@@ -34,6 +34,7 @@ The following constraints are implemented:
 
 import torch
 
+
 __all__ = [
     "Constraint",
     "boolean",

torch/distributions/continuous_bernoulli.py

@@ -14,6 +14,7 @@ from torch.distributions.utils import (
 )
 from torch.nn.functional import binary_cross_entropy_with_logits
 
+
 __all__ = ["ContinuousBernoulli"]
 
 

torch/distributions/dirichlet.py

@@ -5,6 +5,7 @@ from torch.autograd.function import once_differentiable
 from torch.distributions import constraints
 from torch.distributions.exp_family import ExponentialFamily
 
+
 __all__ = ["Dirichlet"]
 
 

torch/distributions/distribution.py

@@ -8,6 +8,7 @@ from torch.distributions import constraints
 from torch.distributions.utils import lazy_property
 from torch.types import _size
 
+
 __all__ = ["Distribution"]
 
 

torch/distributions/exp_family.py

@@ -2,6 +2,7 @@
 import torch
 from torch.distributions.distribution import Distribution
 
+
 __all__ = ["ExponentialFamily"]
 
 

torch/distributions/exponential.py

@@ -6,6 +6,7 @@ from torch.distributions import constraints
 from torch.distributions.exp_family import ExponentialFamily
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Exponential"]
 
 

torch/distributions/fishersnedecor.py

@@ -8,6 +8,7 @@ from torch.distributions.distribution import Distribution
 from torch.distributions.gamma import Gamma
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["FisherSnedecor"]
 
 

torch/distributions/gamma.py

@@ -6,6 +6,7 @@ from torch.distributions import constraints
 from torch.distributions.exp_family import ExponentialFamily
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Gamma"]
 
 

torch/distributions/geometric.py

@@ -12,6 +12,7 @@ from torch.distributions.utils import (
 )
 from torch.nn.functional import binary_cross_entropy_with_logits
 
+
 __all__ = ["Geometric"]
 
 

torch/distributions/gumbel.py

@@ -9,6 +9,7 @@ from torch.distributions.transforms import AffineTransform, ExpTransform
 from torch.distributions.uniform import Uniform
 from torch.distributions.utils import broadcast_all, euler_constant
 
+
 __all__ = ["Gumbel"]
 
 

torch/distributions/half_cauchy.py

@@ -8,6 +8,7 @@ from torch.distributions.cauchy import Cauchy
 from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import AbsTransform
 
+
 __all__ = ["HalfCauchy"]
 
 

torch/distributions/half_normal.py

@@ -8,6 +8,7 @@ from torch.distributions.normal import Normal
 from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import AbsTransform
 
+
 __all__ = ["HalfNormal"]
 
 

torch/distributions/independent.py

@@ -6,6 +6,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import _sum_rightmost
 
+
 __all__ = ["Independent"]
 
 

torch/distributions/kl.py

@@ -37,6 +37,7 @@ from .transformed_distribution import TransformedDistribution
 from .uniform import Uniform
 from .utils import _sum_rightmost, euler_constant as _euler_gamma
 
+
 _KL_REGISTRY: Dict[
     Tuple[Type, Type], Callable
 ] = {}  # Source of truth mapping a few general (type, type) pairs to functions.

torch/distributions/kumaraswamy.py

@@ -7,6 +7,7 @@ from torch.distributions.transforms import AffineTransform, PowerTransform
 from torch.distributions.uniform import Uniform
 from torch.distributions.utils import broadcast_all, euler_constant
 
+
 __all__ = ["Kumaraswamy"]
 
 

torch/distributions/laplace.py

@@ -6,6 +6,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Laplace"]
 
 

torch/distributions/lkj_cholesky.py

@@ -15,6 +15,7 @@ from torch.distributions import Beta, constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["LKJCholesky"]
 
 

torch/distributions/log_normal.py

@@ -4,6 +4,7 @@ from torch.distributions.normal import Normal
 from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import ExpTransform
 
+
 __all__ = ["LogNormal"]
 
 

torch/distributions/logistic_normal.py

@@ -4,6 +4,7 @@ from torch.distributions.normal import Normal
 from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import StickBreakingTransform
 
+
 __all__ = ["LogisticNormal"]
 
 

torch/distributions/lowrank_multivariate_normal.py

@@ -7,6 +7,7 @@ from torch.distributions.distribution import Distribution
 from torch.distributions.multivariate_normal import _batch_mahalanobis, _batch_mv
 from torch.distributions.utils import _standard_normal, lazy_property
 
+
 __all__ = ["LowRankMultivariateNormal"]
 
 

torch/distributions/mixture_same_family.py

@@ -5,6 +5,7 @@ import torch
 from torch.distributions import Categorical, constraints
 from torch.distributions.distribution import Distribution
 
+
 __all__ = ["MixtureSameFamily"]
 
 

torch/distributions/multinomial.py

@@ -6,6 +6,7 @@ from torch.distributions.binomial import Binomial
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Multinomial"]
 
 

torch/distributions/multivariate_normal.py

@@ -6,6 +6,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import _standard_normal, lazy_property
 
+
 __all__ = ["MultivariateNormal"]
 
 

torch/distributions/negative_binomial.py

@@ -10,6 +10,7 @@ from torch.distributions.utils import (
     probs_to_logits,
 )
 
+
 __all__ = ["NegativeBinomial"]
 
 

torch/distributions/normal.py

@@ -7,6 +7,7 @@ from torch.distributions import constraints
 from torch.distributions.exp_family import ExponentialFamily
 from torch.distributions.utils import _standard_normal, broadcast_all
 
+
 __all__ = ["Normal"]
 
 

torch/distributions/one_hot_categorical.py

@@ -4,6 +4,7 @@ from torch.distributions import constraints
 from torch.distributions.categorical import Categorical
 from torch.distributions.distribution import Distribution
 
+
 __all__ = ["OneHotCategorical", "OneHotCategoricalStraightThrough"]
 
 

torch/distributions/pareto.py

@@ -5,6 +5,7 @@ from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import AffineTransform, ExpTransform
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Pareto"]
 
 

torch/distributions/poisson.py

@@ -6,6 +6,7 @@ from torch.distributions import constraints
 from torch.distributions.exp_family import ExponentialFamily
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Poisson"]
 
 

torch/distributions/relaxed_bernoulli.py

@@ -14,6 +14,7 @@ from torch.distributions.utils import (
     probs_to_logits,
 )
 
+
 __all__ = ["LogitRelaxedBernoulli", "RelaxedBernoulli"]
 
 

torch/distributions/relaxed_categorical.py

@@ -7,6 +7,7 @@ from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import ExpTransform
 from torch.distributions.utils import broadcast_all, clamp_probs
 
+
 __all__ = ["ExpRelaxedCategorical", "RelaxedOneHotCategorical"]
 
 

torch/distributions/studentT.py

@@ -7,6 +7,7 @@ from torch.distributions import Chi2, constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import _standard_normal, broadcast_all
 
+
 __all__ = ["StudentT"]
 
 

torch/distributions/transformed_distribution.py

@@ -8,6 +8,7 @@ from torch.distributions.independent import Independent
 from torch.distributions.transforms import ComposeTransform, Transform
 from torch.distributions.utils import _sum_rightmost
 
+
 __all__ = ["TransformedDistribution"]
 
 

torch/distributions/transforms.py

@@ -18,6 +18,7 @@ from torch.distributions.utils import (
 )
 from torch.nn.functional import pad, softplus
 
+
 __all__ = [
     "AbsTransform",
     "AffineTransform",

torch/distributions/uniform.py

@@ -7,6 +7,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Uniform"]
 
 

torch/distributions/utils.py

@@ -7,6 +7,7 @@ import torch
 import torch.nn.functional as F
 from torch.overrides import is_tensor_like
 
+
 euler_constant = 0.57721566490153286060  # Euler Mascheroni Constant
 
 __all__ = [

torch/distributions/von_mises.py

@@ -7,6 +7,7 @@ from torch.distributions import constraints
 from torch.distributions.distribution import Distribution
 from torch.distributions.utils import broadcast_all, lazy_property
 
+
 __all__ = ["VonMises"]
 
 

torch/distributions/weibull.py

@@ -7,6 +7,7 @@ from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import AffineTransform, PowerTransform
 from torch.distributions.utils import broadcast_all
 
+
 __all__ = ["Weibull"]