[BE][Easy][14/19] enforce style for empty lines in import segments in torch/_[a-c]*/ and torch/_[e-h]*/ and torch/_[j-z]*/ (pytorch#129765)

See pytorch#129751 (comment). 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: pytorch#129765
Approved by: https://github.com/ezyang

Author: XuehaiPan

tools/linter/adapters/ufmt_linter.py

@@ -48,16 +48,12 @@
                     # test/[q-z]*/**
                     "test/[q-z]*/**",
                     # torch/**
-                    "torch/**",
                     # torch/_[a-c]*/**
-                    "torch/_[a-c]*/**",
                     # torch/_d*/**
                     "torch/_d*/**",
                     # torch/_[e-h]*/**
-                    "torch/_[e-h]*/**",
                     # torch/_i*/**
                     # torch/_[j-z]*/**
-                    "torch/_[j-z]*/**",
                     # torch/[a-c]*/**
                     "torch/[a-c]*/**",
                     # torch/d*/**

torch/__init__.py

@@ -36,6 +36,7 @@
 )
 from typing_extensions import ParamSpec as _ParamSpec, TypeGuard as _TypeGuard
 
+
 if TYPE_CHECKING:
     from .types import IntLikeType
 
@@ -59,6 +60,7 @@ def _running_with_deploy() -> builtins.bool:
     USE_RTLD_GLOBAL_WITH_LIBTORCH,
 )
 
+
 # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
 if _running_with_deploy():
     __version__ = "torch-deploy-1.8"
@@ -915,6 +917,7 @@ def sym_ite(b, t, f):
 # Make an explicit reference to the _C submodule to appease linters
 from torch import _C as _C
 
+
 __name, __obj = "", None
 for __name in dir(_C):
     if __name[0] != "_" and not __name.endswith("Base"):
@@ -1680,6 +1683,7 @@ def _check_tensor_all(cond, message=None):  # noqa: F811
 # NumPy consistency (https://numpy.org/devdocs/reference/constants.html)
 from math import e, inf, nan, pi
 
+
 newaxis: None = None
 
 __all__.extend(["e", "pi", "nan", "inf", "newaxis"])
@@ -1922,6 +1926,7 @@ def _dtype(self):
 from torch.random import get_rng_state, initial_seed, manual_seed, seed, set_rng_state
 from torch.serialization import load, save
 
+
 ################################################################################
 # Initialize extension
 ################################################################################
@@ -1983,6 +1988,7 @@ def _manager_path():
 
 import torch
 
+
 __all__.extend(
     name for name in dir(torch) if isinstance(getattr(torch, name), torch.dtype)
 )
@@ -2074,6 +2080,7 @@ def _assert(condition, message):
 )
 from torch.signal import windows as windows
 
+
 # Quantized, sparse, AO, etc. should be last to get imported, as nothing
 # is expected to depend on them.
 from torch import ao as ao  # usort: skip
@@ -2084,11 +2091,13 @@ def _assert(condition, message):
 import torch.nn.quantizable
 import torch.nn.quantized
 
+
 _C._init_names(list(_storage_classes))
 
 # attach docstrings to torch and tensor functions
 from torch import _size_docs, _storage_docs, _tensor_docs, _torch_docs
 
+
 del _torch_docs, _tensor_docs, _storage_docs, _size_docs
 
 
@@ -2098,9 +2107,10 @@ def compiled_with_cxx11_abi() -> builtins.bool:
 
 
 from torch import _library as _library, _ops as _ops
+from torch._classes import classes as classes
+
 
 # Import the ops "namespace"
-from torch._classes import classes as classes
 from torch._ops import ops as ops  # usort: skip
 
 # quantization depends on torch.fx and torch.ops
@@ -2118,13 +2128,15 @@ def compiled_with_cxx11_abi() -> builtins.bool:
 # Register fork handler to initialize OpenMP in child processes (see gh-28389)
 from torch.multiprocessing._atfork import register_after_fork
 
+
 register_after_fork(torch.get_num_threads)
 del register_after_fork
 
 # Import tools that require fully imported torch (for applying
 # torch.jit.script as a decorator, for instance):
 from torch._lobpcg import lobpcg as lobpcg
 
+
 # These were previously defined in native_functions.yaml and appeared on the
 # `torch` namespace, but we moved them to c10 dispatch to facilitate custom
 # class usage. We add these lines here to preserve backward compatibility.
@@ -2144,7 +2156,6 @@ def compiled_with_cxx11_abi() -> builtins.bool:
     matrix_rank,
     solve,
 )
-
 from torch.utils.dlpack import from_dlpack, to_dlpack
 
 
@@ -2466,6 +2477,7 @@ def _register_device_module(device_type, module):
 from torch._higher_order_ops import cond as cond, while_loop as while_loop
 from torch.func import vmap as vmap
 
+
 if not TYPE_CHECKING:
     from torch import _meta_registrations
 
@@ -2478,6 +2490,7 @@ def _register_device_module(device_type, module):
 # Populate magic methods on SymInt and SymFloat
 import torch.fx.experimental.sym_node
 
+
 # Register MPS specific decomps
 torch.backends.mps._init()
 
@@ -2592,6 +2605,7 @@ def _constrain_as_size(
 
 from torch import _logging
 
+
 _logging._init_logs()
 
 

torch/_appdirs.py

@@ -53,6 +53,7 @@
 import os
 import sys
 
+
 unicode = str
 
 if sys.platform.startswith("java"):

torch/_custom_ops.py

@@ -10,6 +10,7 @@
 )
 from torch.library import get_ctx
 
+
 __all__ = [
     "custom_op",
     "impl",

torch/_export/converter.py

@@ -3,13 +3,11 @@
 import logging
 import operator
 import warnings
-
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
 import torch
 import torch.export._trace
-
 from torch.export.exported_program import ExportedProgram
 from torch.export.graph_signature import (
     ConstantArgument,
@@ -22,6 +20,7 @@
 from torch.fx import subgraph_rewriter
 from torch.onnx.utils import _create_jit_graph
 
+
 log = logging.getLogger(__name__)
 
 

torch/_export/db/examples/model_attr_mutation.py

@@ -1,6 +1,5 @@
 # mypy: allow-untyped-defs
 import torch
-
 from torch._export.db.case import SupportLevel
 
 

torch/_export/db/examples/optional_input.py

@@ -1,6 +1,5 @@
 # mypy: allow-untyped-defs
 import torch
-
 from torch._export.db.case import SupportLevel
 
 

torch/_export/db/examples/torch_sym_min.py

@@ -1,6 +1,5 @@
 # mypy: allow-untyped-defs
 import torch
-
 from torch._export.db.case import SupportLevel
 
 

torch/_export/non_strict_utils.py

@@ -38,6 +38,7 @@
     tree_map_with_path,
 )
 
+
 if TYPE_CHECKING:
     from sympy import Symbol
 

torch/_export/passes/collect_tracepoints_pass.py

@@ -2,10 +2,10 @@
 import operator
 
 import torch
-
 from torch.export.exported_program import ConstantArgument, TensorArgument
 from torch.fx.passes.infra.pass_base import PassBase, PassResult
 
+
 __all__ = ["CollectTracepointsPass"]
 
 

torch/_export/passes/constant_folding.py

@@ -6,6 +6,7 @@
 import torch
 import torch.utils._pytree as pytree
 
+
 aten = torch.ops.aten
 
 # We would like to split modules into two subgraphs for runtime weight updates to work correctly.

torch/_export/passes/lift_constants_pass.py

@@ -5,7 +5,6 @@
 import torch
 from torch._export.verifier import SpecViolationError
 from torch._guards import detect_fake_mode
-
 from torch._library.fake_class_registry import FakeScriptObject
 from torch.export.exported_program import (
     ArgumentSpec,

torch/_export/tools.py

@@ -8,6 +8,7 @@
 import torch.export._trace
 from torch._utils_internal import log_export_usage
 
+
 log = logging.getLogger(__name__)
 
 __all__ = ["report_exportability"]

torch/_export/utils.py

@@ -5,13 +5,11 @@
 import math
 import operator
 import re
-
 from inspect import Parameter
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
 
 import torch
 from torch._subclasses.fake_tensor import FakeTensor
-
 from torch.export import ExportedProgram
 from torch.export.exported_program import (
     _name_hoo_subgraph_placeholders,
@@ -33,6 +31,7 @@
     UnflattenFunc,
 )
 
+
 placeholder_prefixes = {
     InputKind.USER_INPUT: "",
     InputKind.PARAMETER: "p_",

torch/_functorch/_aot_autograd/autograd_cache.py

@@ -8,15 +8,12 @@
 import os
 import pickle
 import shutil
-
 from dataclasses import dataclass
-
 from typing import Callable, List, Optional, TYPE_CHECKING, Union
 
 import torch
 from torch._dynamo.utils import counters
 from torch._functorch import config
-
 from torch._inductor.codecache import (
     _ident,
     BypassFxGraphCache,
@@ -27,7 +24,6 @@
     FxGraphHashDetails,
     write_atomic,
 )
-
 from torch._inductor.runtime.runtime_utils import cache_dir
 
 from .runtime_wrappers import (
@@ -39,9 +35,9 @@
     RuntimeWrapper,
     SubclassMeta,
 )
-
 from .schemas import AOTConfig, ViewAndMutationMeta  # noqa: F401
 
+
 if TYPE_CHECKING:
     from torch.fx.node import Node
 

torch/_functorch/_aot_autograd/collect_metadata_analysis.py

@@ -45,9 +45,9 @@
     ViewAndMutationMeta,
 )
 from .subclass_utils import create_subclass_meta
-
 from .utils import _get_autocast_states, KNOWN_TYPES, strict_zip
 
+
 zip = strict_zip
 
 log = logging.getLogger(__name__)

torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py

@@ -12,7 +12,6 @@
 import torch.utils.dlpack
 from torch import Tensor
 from torch._dispatch.python import enable_python_dispatcher
-
 from torch._dynamo.utils import lazy_format_graph_code
 from torch._logging import getArtifactLogger, trace_structured
 from torch._subclasses.functional_tensor import FunctionalTensorMode
@@ -34,6 +33,7 @@
 )
 from .utils import root_module_when_exporting_non_strict, unlift_tokens
 
+
 aot_graphs_log = getArtifactLogger(__name__, "aot_graphs")
 
 

torch/_functorch/_aot_autograd/functional_utils.py

@@ -23,6 +23,7 @@
     transform_subclass,
 )
 
+
 aot_joint_log = getArtifactLogger(__name__, "aot_joint_graph")
 
 

torch/_functorch/_aot_autograd/input_output_analysis.py

@@ -18,6 +18,7 @@
 from torch import Tensor
 from torch._subclasses.functional_tensor import FunctionalTensor
 from torch.fx.experimental.symbolic_shapes import is_concrete_int
+
 from .. import config
 from .collect_metadata_analysis import coerce_tangent
 from .schemas import (
@@ -30,6 +31,7 @@
 )
 from .utils import strict_zip
 
+
 zip = strict_zip
 
 

torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py

@@ -13,7 +13,6 @@
 import logging
 import traceback
 from contextlib import nullcontext
-
 from typing import Any, Callable, List, Optional, Sequence, Tuple
 
 import torch
@@ -27,6 +26,7 @@
 from torch.fx.experimental.proxy_tensor import is_sym_node
 from torch.fx.experimental.symbolic_shapes import fx_placeholder_vals
 from torch.multiprocessing.reductions import StorageWeakRef
+
 from .. import config
 from .autograd_cache import (
     AOTAutogradCache,
@@ -39,7 +39,6 @@
     aot_dispatch_base_graph,
 )
 from .logging_utils import track_graph_compiling
-
 from .runtime_wrappers import (
     AOTDedupeWrapper,
     AOTDispatchAutograd,
@@ -57,9 +56,9 @@
 )
 from .schemas import AOTConfig, MutationType, ViewAndMutationMeta
 from .subclass_utils import compute_inner_mutated_inp_indices_from_subclass_meta
-
 from .utils import _get_symint_hints, make_boxed_func, strict_zip, unlift_tokens
 
+
 zip = strict_zip
 
 log = logging.getLogger(__name__)