[BE][Ez]: Use interned hardcoded string FURB156 (pytorch#138330)

Uses string constants from string module.

Pull Request resolved: pytorch#138330
Approved by: https://github.com/albanD

Author: Skylion007

test/test_testing.py

@@ -30,6 +30,7 @@
 from torch.testing._internal.common_modules import modules, module_db, ModuleInfo
 from torch.testing._internal.opinfo.core import SampleInput, DecorateInfo, OpInfo
 import operator
+import string
 
 # For testing TestCase methods and torch.testing functions
 class TestTesting(TestCase):
@@ -2299,7 +2300,7 @@ def test_no_mutate_global_logging_on_import(self, path) -> None:
         # Calling logging.basicConfig, among other things, modifies the global
         # logging state. It is not OK to modify the global logging state on
         # `import torch` (or other submodules we own) because users do not expect it.
-        expected = 'abcdefghijklmnopqrstuvwxyz'
+        expected = string.ascii_lowercase
         commands = [
             'import logging',
             f'import {path}',

test/torch_np/numpy_tests/lib/test_function_base.py

@@ -33,6 +33,8 @@
 IS_WASM = False
 IS_PYPY = False
 
+import string
+
 # FIXME: make from torch._numpy
 # These are commented, as if they are imported, some of the tests pass for the wrong reasons
 # from numpy lib import digitize, piecewise, trapz, select, trim_zeros, interp
@@ -1528,7 +1530,7 @@ def test_execution_order_ticket_1487(self):
     def test_string_ticket_1892(self):
         # Test vectorization over strings: issue 1892.
         f = np.vectorize(lambda x: x)
-        s = "0123456789" * 10
+        s = string.digits * 10
         assert_equal(s, f(s))
 
     def test_cache(self):

torch/distributed/tensor/_ops/_common_rules.py

@@ -1,4 +1,5 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates
+import string
 from typing import cast, Dict, List, Optional, Tuple
 
 import torch
@@ -234,7 +235,7 @@ def pointwise_rule(op_schema: OpSchema, linearity: bool = False) -> OutputShardi
         ij,ij->ij - addition/mul
         ij,j->ij - broadcasted addition
     """
-    alphabet = "abcdefghijklmnopqrstuvwxyz"
+    alphabet = string.ascii_lowercase
     # find the max_dim first in case we need to broadcasting
     input_specs = op_schema.args_spec
     max_dim = max(input.ndim for input in input_specs)

torchgen/native_function_generation.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import string
 from collections import defaultdict
 from typing import Sequence
 
@@ -194,9 +195,7 @@ def generate_out_args_from_schema(
         lambda a: [] if a.annotation is None else a.annotation.alias_set,
         func.arguments.flat_all,
     )
-    valid_annotations = [
-        x for x in "abcdefghijklmnopqrstuvwxyz" if x not in used_annotations
-    ]
+    valid_annotations = [x for x in string.ascii_lowercase if x not in used_annotations]
 
     all_rets_are_tensors = all(r.type == BaseType(BaseTy.Tensor) for r in func.returns)