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[2/N] Apply py39 ruff fixes (#141938)

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Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141938
Approved by: https://github.com/ezyang
This commit is contained in:
cyy
2024-12-05 06:26:03 +00:00
committed by PyTorch MergeBot
parent 653efe14e4
commit aa95618268
16 changed files with 53 additions and 62 deletions
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7
tools/pyi/gen_pyi.py
View File

@ -1063,13 +1063,6 @@ def gen_pyi(
# NB: Keep this in sync with enum in aten/src/ATen/core/Reduction.h
hint = hint.replace("at::Reduction::Mean", "1")
hint = hint.replace(": Tensor = None", ": Optional[Tensor] = None")
# Match both:
# ": Union[Tensor, Tuple[Tensor, ...], List[Tensor]] = None"
# ": Union[Tuple[Tensor, ...], List[Tensor]] = None"
hint = hint.replace(
"Tuple[Tensor, ...], List[Tensor]] = None",
"Tuple[Tensor, ...], List[Tensor], None] = None",
)
return hint
docstrs = gather_docstrs()

14
torchgen/_autoheuristic/ah_tree.py
View File

@ -1,4 +1,4 @@
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Optional
import numpy as np
from sklearn.tree import _tree # type: ignore[import-untyped]
@ -34,10 +34,10 @@ class DecisionTree:
does not seem to be easy with sklearn.
"""
def __init__(self, sklearn_tree: Any, feature_names: List[str]) -> None:
def __init__(self, sklearn_tree: Any, feature_names: list[str]) -> None:
self.feature_names = feature_names
self.root = self._convert_sklearn_tree(sklearn_tree.tree_)
self.classes_: List[str] = sklearn_tree.classes_
self.classes_: list[str] = sklearn_tree.classes_
def _convert_sklearn_tree(
self, sklearn_tree: Any, node_id: int = 0
@ -193,9 +193,9 @@ class DecisionTree:
def codegen(
self,
dummy_col_2_col_val: Dict[str, Tuple[str, Any]],
lines: List[str],
unsafe_leaves: List[int],
dummy_col_2_col_val: dict[str, tuple[str, Any]],
lines: list[str],
unsafe_leaves: list[int],
) -> None:
# generates python code for the decision tree
def codegen_node(node: DecisionTreeNode, depth: int) -> None:
@ -223,7 +223,7 @@ class DecisionTree:
codegen_node(node.right, depth + 1)
def handle_leaf(
node: DecisionTreeNode, indent: str, unsafe_leaves: List[int]
node: DecisionTreeNode, indent: str, unsafe_leaves: list[int]
) -> str:
"""
This generates the code for a leaf node in the decision tree. If the leaf is unsafe, the learned heuristic

4
torchgen/_autoheuristic/benchmark_runner.py
View File

@ -2,7 +2,7 @@ import argparse
import random
import time
from abc import abstractmethod
from typing import Any, Tuple
from typing import Any
from tqdm import tqdm # type: ignore[import-untyped]
@ -71,7 +71,7 @@ class BenchmarkRunner:
def run_benchmark(self, *args: Any) -> None: ...
@abstractmethod
def create_input(self) -> Tuple[Any, ...]: ...
def create_input(self) -> tuple[Any, ...]: ...
def main(self, num_samples: int, num_reps: int) -> None:
for _ in tqdm(range(num_samples)):

6
torchgen/_autoheuristic/benchmark_utils.py
View File

@ -1,10 +1,10 @@
import random
from typing import Any, Tuple
from typing import Any
import torch
def transpose_tensors(p_transpose_both: float = 0.05) -> Tuple[bool, bool]:
def transpose_tensors(p_transpose_both: float = 0.05) -> tuple[bool, bool]:
transpose_both = random.choices(
[True, False], [p_transpose_both, 1 - p_transpose_both]
)[0]
@ -31,7 +31,7 @@ def get_mm_tensors(
transpose_right: bool,
dtype_left: Any,
dtype_right: Any,
) -> Tuple[Any, Any]:
) -> tuple[Any, Any]:
if transpose_left:
a = torch.randn(k, m, dtype=dtype_left).t()
else:

7
torchgen/_autoheuristic/merge_data.py
View File

@ -1,14 +1,13 @@
import sys
from typing import List
def merge_txt_files(file_list: List[str], output_file: str) -> None:
def merge_txt_files(file_list: list[str], output_file: str) -> None:
if not file_list:
print("No input files provided.")
return
metadata: List[str] = []
content: List[str] = []
metadata: list[str] = []
content: list[str] = []
# Read metadata and content from all files
for file_path in file_list:

6
torchgen/_autoheuristic/mixed_mm/gen_data_mixed_mm.py
View File

@ -6,7 +6,7 @@ import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from typing import Any, Tuple
from typing import Any
from benchmark_runner import BenchmarkRunner # type: ignore[import-not-found]
from benchmark_utils import ( # type: ignore[import-not-found]
@ -33,7 +33,7 @@ class BenchmarkRunnerMixedMM(BenchmarkRunner): # type: ignore[misc, no-any-unim
def __init__(self) -> None:
super().__init__("mixed_mm")
def create_input(self) -> Tuple[Any, ...]:
def create_input(self) -> tuple[Any, ...]:
dtype1, dtype2 = self.get_dtypes()
m, k, n = self.get_m_k_n(dtype1)
transpose_left, transpose_right = False, True
@ -109,7 +109,7 @@ class BenchmarkRunnerMixedMM(BenchmarkRunner): # type: ignore[misc, no-any-unim
else:
return get_random_between_pow2(1, 7)
def get_m_k_n(self, dtype: Any) -> Tuple[int, int, int]:
def get_m_k_n(self, dtype: Any) -> tuple[int, int, int]:
numel_max = 2**31
# repeat until tensors fit in memory

12
torchgen/_autoheuristic/mixed_mm/test_mixed_mm.py
View File

@ -24,7 +24,7 @@ class TestMixedMM(TestCase):
# fmt: off
# This file was generated by AutoHeuristic. Do not modify it manually!
# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/mixed_mm/
from typing import List, Optional, Tuple
from typing import Optional
from torch._inductor.autoheuristic.autoheuristic_utils import (
AHContext,
@ -39,7 +39,7 @@ from torch._inductor.autoheuristic.learnedheuristic_interface import (
class MixedMMA100(LearnedHeuristicDecision):
def __init__(self) -> None:
self.choices: List[Choice] = []
self.choices: list[Choice] = []
self.fill_choices()
def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
@ -84,7 +84,7 @@ class MixedMMA100(LearnedHeuristicDecision):
def get_name(self) -> str:
return 'mixed_mm'
def get_best_choices(self, context: AHContext) -> Optional[List[Tuple[float, int]]]:
def get_best_choices(self, context: AHContext) -> Optional[list[tuple[float, int]]]:
if str(context.get_value('1LEQmLEQ16')) != 'True':
if context.get_value('m') <= 32.5:
if context.get_value('n') <= 6976.0:
@ -186,7 +186,7 @@ class MixedMMA100(LearnedHeuristicDecision):
# fmt: off
# This file was generated by AutoHeuristic. Do not modify it manually!
# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/mixed_mm/
from typing import List, Optional, Tuple
from typing import Optional
from torch._inductor.autoheuristic.autoheuristic_utils import (
AHContext,
@ -201,7 +201,7 @@ from torch._inductor.autoheuristic.learnedheuristic_interface import (
class MixedMMH100(LearnedHeuristicDecision):
def __init__(self) -> None:
self.choices: List[Choice] = []
self.choices: list[Choice] = []
self.fill_choices()
def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
@ -245,7 +245,7 @@ class MixedMMH100(LearnedHeuristicDecision):
def get_name(self) -> str:
return 'mixed_mm'
def get_best_choices(self, context: AHContext) -> Optional[List[Tuple[float, int]]]:
def get_best_choices(self, context: AHContext) -> Optional[list[tuple[float, int]]]:
if context.get_value('arith_intensity') <= 15.988086223602295:
if context.get_value('n') <= 25280.0:
if context.get_value('n') <= 1344.0:

6
torchgen/_autoheuristic/mm/gen_data_mm.py
View File

@ -6,7 +6,7 @@ import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from typing import Any, Tuple
from typing import Any
from benchmark_runner import BenchmarkRunner # type: ignore[import-not-found]
from benchmark_utils import ( # type: ignore[import-not-found]
@ -28,7 +28,7 @@ class BenchmarkRunnerMM(BenchmarkRunner): # type: ignore[misc, no-any-unimporte
def __init__(self) -> None:
super().__init__("mm")
def create_input(self) -> Tuple[Any, ...]:
def create_input(self) -> tuple[Any, ...]:
dtype = random.choices([torch.float32, torch.float16, torch.bfloat16])[0]
set_precision(dtype)
m, k, n = self.get_m_k_n(dtype)
@ -100,7 +100,7 @@ class BenchmarkRunnerMM(BenchmarkRunner): # type: ignore[misc, no-any-unimporte
print(f"random_type {distr_type} not supported")
sys.exit(1)
def get_m_k_n(self, dtype: Any) -> Tuple[int, int, int]:
def get_m_k_n(self, dtype: Any) -> tuple[int, int, int]:
numel_max = 2**31
# repeat until tensors fit in memory

6
torchgen/_autoheuristic/pad_mm/gen_data_pad_mm.py
View File

@ -5,7 +5,7 @@ import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from typing import Any, Tuple
from typing import Any
from benchmark_runner import BenchmarkRunner # type: ignore[import-not-found]
from benchmark_utils import ( # type: ignore[import-not-found]
@ -30,7 +30,7 @@ class BenchmarkRunnerPadMM(BenchmarkRunner): # type: ignore[misc, no-any-unimpo
def __init__(self) -> None:
super().__init__("pad_mm")
def create_input(self) -> Tuple[Any, ...]:
def create_input(self) -> tuple[Any, ...]:
dtype = self.get_dtype()
set_precision(dtype)
m, k, n = self.get_m_k_n(dtype)
@ -113,7 +113,7 @@ class BenchmarkRunnerPadMM(BenchmarkRunner): # type: ignore[misc, no-any-unimpo
def is_aligned(self, dim: int, align_size: int) -> bool:
return dim % align_size == 0
def get_m_k_n(self, dtype: Any) -> Tuple[int, int, int]:
def get_m_k_n(self, dtype: Any) -> tuple[int, int, int]:
uniform = random.choices([True, False])[0]
align_size = get_alignment_size_dtype(dtype)

6
torchgen/_autoheuristic/train_decision.py
View File

@ -531,7 +531,7 @@ class AHTrainDecisionTree(AHTrain):
"""
Generates the definition of the predict function.
"""
return "def get_best_choices(self, context: AHContext) -> Optional[List[Tuple[float, int]]]:"
return "def get_best_choices(self, context: AHContext) -> Optional[list[tuple[float, int]]]:"
def codegen_boilerplate(
self, heuristic_name, opt_name, threshold, shared_memory, device_capa, classes
@ -545,7 +545,7 @@ class AHTrainDecisionTree(AHTrain):
# fmt: off
# This file was generated by AutoHeuristic. Do not modify it manually!
# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/{opt_name}/
from typing import List, Optional, Tuple
from typing import Optional
from torch._inductor.autoheuristic.autoheuristic_utils import (
AHContext,
@ -560,7 +560,7 @@ from torch._inductor.autoheuristic.learnedheuristic_interface import (
class {heuristic_name}(LearnedHeuristicDecision):
def __init__(self) -> None:
self.choices: List[Choice] = []
self.choices: list[Choice] = []
self.fill_choices()
{self.gen_precondition(opt_name, shared_memory, device_capa)}

2
torchgen/api/lazy.py
View File

@ -80,7 +80,7 @@ def process_ir_type(
(3) making cpp-reference types into cpp-value types (e.g. vector instead of IntArrayRef)
(1) converts at::Tensors to lazy::Values (which wrap lazy::Nodes, with which Lazy IR represents tensors.)
There is special handling for Optional[Tensor] or List[Tensor], etc- hence 'tensor-like'
There is special handling for Optional[Tensor] or list[Tensor], etc- hence 'tensor-like'
This is incomplete- there are assertions in places that it's expected to need to add
more types as the codegen is used with more operators.

21
torchgen/api/python.py
View File

@ -956,14 +956,13 @@ def argument_type_str_pyi(t: Type) -> str:
ret = "Union[_int, _size]" if t.size is not None else "_size"
elif t.is_tensor_like():
# TODO: this doesn't seem right...
# Tensor?[] currently translates to Optional[Union[Tuple[Tensor, ...], List[Tensor]]]
# It should probably translate to Union[Tuple[Optional[Tensor], ...], List[Optional[Tensor]]]
if isinstance(t.elem, OptionalType):
add_optional = True
# Tensor?[] currently translates to Optional[Union[tuple[Tensor, ...], list[Tensor]]]
# It should probably translate to Union[tuple[Optional[Tensor], ...], list[Optional[Tensor]]]
add_optional = True
ret = (
"Union[Tensor, Tuple[Tensor, ...], List[Tensor]]"
"Union[Tensor, tuple[Tensor, ...], list[Tensor]]"
if t.size is not None
else "Union[Tuple[Tensor, ...], List[Tensor]]"
else "Union[tuple[Tensor, ...], list[Tensor]]"
)
elif str(t.elem) == "float":
ret = "Sequence[_float]"
@ -1001,7 +1000,7 @@ def return_type_str_pyi(t: Type) -> str:
if isinstance(t, ListType):
inner = return_type_str_pyi(t.elem)
return f"Tuple[{inner}, ...]"
return f"tuple[{inner}, ...]"
return argument_type_str_pyi(t)
@ -1014,7 +1013,7 @@ def returns_structseq_pyi(signature: PythonSignature) -> tuple[str, str] | None:
# These types are structseq objects which act like named NamedTuples, but
# the constructor acts like the constructor of tuple. Using typing.NamedTuple
# does not allow us to override __init__.
seq_type = f"Tuple[{', '.join(python_returns)}]"
seq_type = f"tuple[{', '.join(python_returns)}]"
structseq_def_lines = [
f"class {structseq_name}({seq_type}):",
]
@ -1038,12 +1037,12 @@ def returns_structseq_pyi(signature: PythonSignature) -> tuple[str, str] | None:
structseq_def = "\n".join(structseq_def_lines)
# Example:
# structseq_def = (
# "class max(Tuple[Tensor, Tensor]):\n"
# "class max(tuple[Tensor, Tensor]):\n"
# " @property\n"
# " def values(self) -> Tensor: ...\n"
# " @property\n"
# " def indices(self) -> Tensor: ...\n"
# " def __new__(cls, sequence: Tuple[Tensor, Tensor]): ...\n"
# " def __new__(cls, sequence: tuple[Tensor, Tensor]): ...\n"
# " n_fields: _int = 2",
# " n_sequeunce_fields: _int = 2",
# " n_unnamed_fields: _int = 0",
@ -1060,7 +1059,7 @@ def returns_str_pyi(signature: PythonSignature) -> str:
python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
if len(python_returns) > 1:
return "Tuple[" + ", ".join(python_returns) + "]"
return "tuple[" + ", ".join(python_returns) + "]"
if len(python_returns) == 1:
return python_returns[0]
return "None"

4
torchgen/context.py
View File

@ -2,7 +2,7 @@ from __future__ import annotations
import contextlib
import functools
from typing import Any, Callable, List, Optional, Tuple, TYPE_CHECKING, TypeVar, Union
from typing import Any, Callable, Optional, TYPE_CHECKING, TypeVar, Union
import torchgen.local as local
from torchgen.model import (
@ -39,7 +39,7 @@ F2 = TypeVar(
str,
)
F3 = TypeVar("F3", Tuple[NativeFunction, Any], List[NativeFunction])
F3 = TypeVar("F3", tuple[NativeFunction, Any], list[NativeFunction])
@contextlib.contextmanager

2
torchgen/executorch/model.py
View File

@ -1,5 +1,5 @@
# Represents all kernels used by an Executorch model.
# It maintains a Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]] structure.
# It maintains a dict[OperatorName, dict[ETKernelKey, BackendMetadata]] structure.
from __future__ import annotations

6
torchgen/gen_schema_utils.py
View File

@ -1,4 +1,4 @@
from typing import Any, Optional, Tuple, Union
from typing import Any, Optional, Union
from torchgen.model import (
Annotation,
@ -80,8 +80,8 @@ class FunctionSchemaGen:
@staticmethod
def from_example(
op_name: str,
example_inputs: Tuple[Tuple[str, Any], ...],
example_outputs: Tuple[Any, ...],
example_inputs: tuple[tuple[str, Any], ...],
example_outputs: tuple[Any, ...],
) -> FunctionSchema:
args = []
for name, inp in example_inputs:

6
torchgen/model.py
View File

@ -5,7 +5,7 @@ import itertools
import re
from dataclasses import dataclass
from enum import auto, Enum
from typing import Callable, List, TYPE_CHECKING
from typing import Callable, TYPE_CHECKING
from torchgen.utils import assert_never, NamespaceHelper, OrderedSet
@ -249,7 +249,7 @@ class _TorchDispatchModeKey(Enum):
def codegen_per_backend_entries() -> str:
r: List[str] = []
r: list[str] = []
for fk in FUNCTIONALITY_KEYS:
r.extend(f" {fk}{bc} = auto()" for bc in BACKEND_COMPONENTS)
return "\n".join(r)
@ -1518,7 +1518,7 @@ class FunctionSchema:
and self.returns[0].annotation == self_a.argument.annotation
)
else:
# You can't method chain on non-tensor self arguments though (like a List[Tensor])
# You can't method chain on non-tensor self arguments though (like a list[Tensor])
# so in all other cases we expect the return type to be none.
assert len(self.returns) == 0