PEP585 update - test (#145176)

See #145101 for details.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145176
Approved by: https://github.com/bobrenjc93

test/benchmark_utils/test_benchmark_utils.py

@@ -7,7 +7,7 @@ import re
 import textwrap
 import timeit
 import unittest
-from typing import Any, List, Tuple
+from typing import Any
 
 import expecttest
 import numpy as np
@@ -67,7 +67,7 @@ def generate_callgrind_artifacts() -> None:
 
 
 def load_callgrind_artifacts() -> (
-    Tuple[benchmark_utils.CallgrindStats, benchmark_utils.CallgrindStats]
+    tuple[benchmark_utils.CallgrindStats, benchmark_utils.CallgrindStats]
 ):
     """Hermetic artifact to unit test Callgrind wrapper.
 
@@ -85,9 +85,9 @@ def load_callgrind_artifacts() -> (
     pattern = re.compile(r"^\s*([0-9]+)\s(.+)$")
 
     def to_function_counts(
-        count_strings: List[str], inclusive: bool
+        count_strings: list[str], inclusive: bool
     ) -> benchmark_utils.FunctionCounts:
-        data: List[benchmark_utils.FunctionCount] = []
+        data: list[benchmark_utils.FunctionCount] = []
         for cs in count_strings:
             # Storing entries as f"{c} {fn}" rather than [c, fn] adds some work
             # reviving the artifact, but it makes the json much easier to read.

test/conftest.py

@@ -7,7 +7,7 @@ import sys
 import xml.etree.ElementTree as ET
 from collections import defaultdict
 from types import MethodType
-from typing import Any, List, Optional, TYPE_CHECKING, Union
+from typing import Any, Optional, TYPE_CHECKING, Union
 
 import pytest
 from _pytest.config import Config, filename_arg
@@ -241,7 +241,7 @@ def pytest_report_teststatus(report, config):
 
 
 @pytest.hookimpl(trylast=True)
-def pytest_collection_modifyitems(items: List[Any]) -> None:
+def pytest_collection_modifyitems(items: list[Any]) -> None:
     """
     This hook is used when rerunning disabled tests to get rid of all skipped tests
     instead of running and skipping them N times. This avoids flooding the console
@@ -304,7 +304,7 @@ class StepcurrentPlugin:
         self.skip: bool = config.getoption("stepcurrent_skip")
         self.run_single: bool = config.getoption("run_single")
 
-    def pytest_collection_modifyitems(self, config: Config, items: List[Any]) -> None:
+    def pytest_collection_modifyitems(self, config: Config, items: list[Any]) -> None:
         if not self.lastrun:
             self.report_status = "Cannot find last run test, not skipping"
             return

test/cpp_extensions/python_agnostic_extension/python_agnostic/ops.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import torch
 from torch import Tensor
 
@@ -8,7 +6,7 @@ lib = torch.library._scoped_library("python_agnostic", "FRAGMENT")
 lib.define("ultra_norm(Tensor[] inputs) -> Tensor")
 
 
-def ultra_norm(inputs: List[Tensor]) -> Tensor:
+def ultra_norm(inputs: list[Tensor]) -> Tensor:
     """
     Computes the ultra-L2-norm of a list of tensors via computing the norm of norms.
 

test/custom_operator/test_infer_schema_annotation.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import typing
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 import torch
 from torch import Tensor, types
@@ -87,7 +87,7 @@ class TestInferSchemaWithAnnotation(TestCase):
         result = torch.library.infer_schema(foo_op_2, mutates_args=mutates_args)
         self.assertEqual(result, "(SymInt[] x) -> SymInt")
 
-        def foo_op_3(x: typing.List[int]) -> int:
+        def foo_op_3(x: list[int]) -> int:
             return 1
 
         result = torch.library.infer_schema(foo_op_3, mutates_args=mutates_args)
@@ -99,7 +99,7 @@ class TestInferSchemaWithAnnotation(TestCase):
         result = torch.library.infer_schema(foo_op_4, mutates_args=mutates_args)
         self.assertEqual(result, "(SymInt[]? x) -> SymInt")
 
-        def foo_op_5(x: typing.Optional[typing.List[int]]) -> int:
+        def foo_op_5(x: typing.Optional[list[int]]) -> int:
             return 1
 
         result = torch.library.infer_schema(foo_op_5, mutates_args=mutates_args)
@@ -136,7 +136,7 @@ class TestInferSchemaWithAnnotation(TestCase):
         result = torch.library.infer_schema(foo_op_3, mutates_args=mutates_args)
         self.assertEqual(result, "(Tensor x) -> Tensor")
 
-        def foo_op_4(x: List[int]) -> types.Number:
+        def foo_op_4(x: list[int]) -> types.Number:
             return x[0]
 
         result = torch.library.infer_schema(foo_op_4, mutates_args=mutates_args)
@@ -154,7 +154,7 @@ class TestInferSchemaWithAnnotation(TestCase):
         result = torch.library.infer_schema(foo_op_6, mutates_args=mutates_args)
         self.assertEqual(result, "(SymInt[] x) -> SymInt")
 
-        def foo_op_7(x: List[int]) -> int:
+        def foo_op_7(x: list[int]) -> int:
             return 1
 
         result = torch.library.infer_schema(foo_op_7, mutates_args=mutates_args)
@@ -166,7 +166,7 @@ class TestInferSchemaWithAnnotation(TestCase):
         result = torch.library.infer_schema(foo_op_8, mutates_args=mutates_args)
         self.assertEqual(result, "(SymInt[]? x) -> SymInt")
 
-        def foo_op_9(x: Optional[List[int]]) -> int:
+        def foo_op_9(x: Optional[list[int]]) -> int:
             return 1
 
         result = torch.library.infer_schema(foo_op_9, mutates_args=mutates_args)

test/distributed/_composable/fsdp/test_fully_shard_autograd.py

@@ -5,7 +5,7 @@ import copy
 import functools
 import itertools
 import unittest
-from typing import Any, List, Optional, Type, Union
+from typing import Any, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -117,7 +117,7 @@ class TestFullyShardAutograd(FSDPTest):
             local_inp = global_inp[
                 self.rank * local_batch_size : (self.rank + 1) * local_batch_size
             ].detach()
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, inp in ((ref_model, global_inp), (model, local_inp)):
                 losses.append(_model(inp).sum())
                 losses[-1].backward()
@@ -141,7 +141,7 @@ class TestFullyShardAutograd(FSDPTest):
             self._test_nontensor_activations,
         )
 
-    def _test_nontensor_activations(self, container_type: Type):
+    def _test_nontensor_activations(self, container_type: type):
         class Module(nn.Module):
             def __init__(self, dim: int):
                 super().__init__()
@@ -170,7 +170,7 @@ class TestFullyShardAutograd(FSDPTest):
                 return self.relu(self.lin2(self.relu(self.lin1(x))))
 
         class ToContainerType(nn.Module):
-            def __init__(self, container_type: Type):
+            def __init__(self, container_type: type):
                 super().__init__()
                 self.container_type = container_type
 
@@ -190,7 +190,7 @@ class TestFullyShardAutograd(FSDPTest):
                     )
 
         class FromContainerType(nn.Module):
-            def __init__(self, container_type: Type):
+            def __init__(self, container_type: type):
                 super().__init__()
                 self.container_type = container_type
 
@@ -227,7 +227,7 @@ class TestFullyShardAutograd(FSDPTest):
             local_inp = global_inp[
                 self.rank * local_batch_size : (self.rank + 1) * local_batch_size
             ].detach()
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, inp in ((ref_model, global_inp), (model, local_inp)):
                 losses.append(_model(inp).sum())
                 losses[-1].backward()

test/distributed/_composable/fsdp/test_fully_shard_comm.py

@@ -4,7 +4,7 @@ import copy
 import functools
 import itertools
 import unittest
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -58,7 +58,7 @@ from torch.testing._internal.distributed._tensor.common_dtensor import (
 c10d_ops = torch.ops.c10d
 
 # For recording FSDP events like unshard or post-backward
-EventType = Tuple[str, str, TrainingState]
+EventType = tuple[str, str, TrainingState]
 
 
 class TestFullyShardCollectiveOps(FSDPTestMultiThread):
@@ -70,7 +70,7 @@ class TestFullyShardCollectiveOps(FSDPTestMultiThread):
     def device(self) -> torch.device:
         return torch.device("cuda:0")
 
-    def _get_param_sizes(self) -> List[torch.Size]:
+    def _get_param_sizes(self) -> list[torch.Size]:
         # For world size 128, the fp32 all-gather and reduce-scatter testing
         # requires ~0.22 GB
         return [
@@ -84,7 +84,7 @@ class TestFullyShardCollectiveOps(FSDPTestMultiThread):
             torch.Size([64, 297]),
         ]
 
-    def _init_params(self, param_sizes: List[torch.Size]) -> List[nn.Parameter]:
+    def _init_params(self, param_sizes: list[torch.Size]) -> list[nn.Parameter]:
         torch.manual_seed(42)
         orig_params = [
             nn.Parameter(torch.randn(size, device=self.device)) for size in param_sizes
@@ -96,7 +96,7 @@ class TestFullyShardCollectiveOps(FSDPTestMultiThread):
         return orig_params
 
     def _init_fsdp_param_group(
-        self, params: List[nn.Parameter], reshard_after_forward: Union[bool, int]
+        self, params: list[nn.Parameter], reshard_after_forward: Union[bool, int]
     ):
         module = nn.ParameterList([param.detach().clone() for param in params])
         mesh_info = FSDPMeshInfo(_init_default_fully_shard_mesh(), shard_mesh_dim=0)
@@ -143,7 +143,7 @@ class TestFullyShardCollectiveOps(FSDPTestMultiThread):
 
     def _test_all_gather(
         self,
-        param_sizes: List[torch.Size],
+        param_sizes: list[torch.Size],
         reshard_after_forward: Union[bool, int],
         async_op: bool,
         all_gather_copy_in_stream: torch.cuda.Stream,
@@ -165,7 +165,7 @@ class TestFullyShardCollectiveOps(FSDPTestMultiThread):
             fsdp_param_group._to_unsharded()
 
         def check_all_gathered_params(
-            orig_params: List[nn.Parameter], module: nn.Module
+            orig_params: list[nn.Parameter], module: nn.Module
         ):
             for orig_param, param in zip(orig_params, module.parameters()):
                 self.assertIsInstance(param, torch.Tensor)
@@ -228,7 +228,7 @@ class TestFullyShardCollectiveOps(FSDPTestMultiThread):
 
     def _test_reduce_scatter(
         self,
-        param_sizes: List[torch.Size],
+        param_sizes: list[torch.Size],
         reduce_scatter_stream: torch.cuda.Stream,
         reduce_scatter_dtype: torch.dtype,
     ):
@@ -453,7 +453,7 @@ class TestFullyShardPrefetch(FSDPTest):
         model, optim, inp = self._init_transformer(
             n_layers, reshard_after_forward, checkpoint_impl
         )
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -504,7 +504,7 @@ class TestFullyShardPrefetch(FSDPTest):
         model, _, inp = self._init_transformer(
             n_layers, reshard_after_forward, checkpoint_impl
         )
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -582,7 +582,7 @@ class TestFullyShardPrefetch(FSDPTest):
         fully_shard(model[1].lin2, reshard_after_forward=reshard_after_forward)
         fully_shard(model, reshard_after_forward=reshard_after_forward)
         inp = torch.randn((4, dim), device="cuda")
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -652,7 +652,7 @@ class TestFullyShardPrefetch(FSDPTest):
                 ]
                 layer.set_modules_to_forward_prefetch(layers_to_prefetch)
 
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -742,7 +742,7 @@ class TestFullyShardPrefetch(FSDPTest):
                 ]
                 layer.set_modules_to_backward_prefetch(layers_to_prefetch)
 
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -834,7 +834,7 @@ class TestFullyShardPrefetch(FSDPTest):
         fully_shard(model)
         optim = torch.optim.AdamW(model.parameters(), lr=1e-2)
 
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -915,7 +915,7 @@ class TestFullyShardPrefetch(FSDPTest):
         fully_shard(model)
         optim = torch.optim.AdamW(model.parameters(), lr=1e-2)
 
-        events: List[EventType] = []
+        events: list[EventType] = []
         unshard_with_record = self._get_unshard_with_record(
             FSDPParamGroup.unshard, events
         )
@@ -1011,7 +1011,7 @@ class TestFullyShardPrefetch(FSDPTest):
         return model, optim, inp
 
     def _get_unshard_with_record(
-        self, orig_unshard: Callable, events: List[EventType]
+        self, orig_unshard: Callable, events: list[EventType]
     ) -> Callable:
         def unshard_with_record(self, *args, **kwargs):
             nonlocal events
@@ -1025,7 +1025,7 @@ class TestFullyShardPrefetch(FSDPTest):
         return unshard_with_record
 
     def _get_reshard_with_record(
-        self, orig_reshard: Callable, events: List[EventType]
+        self, orig_reshard: Callable, events: list[EventType]
     ) -> Callable:
         def reshard_with_record(self, *args, **kwargs):
             nonlocal events
@@ -1040,7 +1040,7 @@ class TestFullyShardPrefetch(FSDPTest):
         return reshard_with_record
 
     def _get_post_backward_with_record(
-        self, orig_post_backward: Callable, events: List[EventType]
+        self, orig_post_backward: Callable, events: list[EventType]
     ) -> Callable:
         def post_backward_with_record(self, *args, **kwargs):
             nonlocal events
@@ -1080,7 +1080,7 @@ class TestFullyShardUnshardMultiProcess(FSDPTest):
                 self.mlp2 = MLP(dim)
                 self.mlp3 = MLP(dim)
 
-            def forward(self, ys: List[torch.Tensor], works: List[dist.Work]):
+            def forward(self, ys: list[torch.Tensor], works: list[dist.Work]):
                 (y1, y2, y3), (work1, work2, work3) = ys, works
                 work1.wait()
                 z1 = self.mlp1(y1)
@@ -1126,7 +1126,7 @@ class TestFullyShardUnshardMultiProcess(FSDPTest):
         torch.manual_seed(42 + self.rank + 1)
         inp = torch.randn((batch_size, dim), device="cuda")
         for _ in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 losses.append(_model(inp).sum())
                 losses[-1].backward()

test/distributed/_composable/fsdp/test_fully_shard_extensions.py

@@ -6,7 +6,7 @@ import functools
 import math
 import threading
 import unittest
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -29,7 +29,7 @@ from torch.testing._internal.two_tensor import TwoTensor
 
 def two_tensor_fsdp_pre_all_gather_v1(
     self, mesh: DeviceMesh
-) -> Tuple[Tuple[torch.Tensor, ...], Any]:
+) -> tuple[tuple[torch.Tensor, ...], Any]:
     all_gather_inputs = (self.a, self.b)
     metadata = None
     return all_gather_inputs, metadata
@@ -39,10 +39,10 @@ def two_tensor_fsdp_pre_all_gather_v2(
     self,
     mesh: DeviceMesh,
     outer_size: torch.Size,
-    outer_stride: Tuple[int, ...],
+    outer_stride: tuple[int, ...],
     module: nn.Module,
     mp_policy: MixedPrecisionPolicy,
-) -> Tuple[Tuple[torch.Tensor, ...], Any]:
+) -> tuple[tuple[torch.Tensor, ...], Any]:
     all_gather_inputs = (self.a, self.b)
     metadata = None
     return all_gather_inputs, metadata
@@ -50,12 +50,12 @@ def two_tensor_fsdp_pre_all_gather_v2(
 
 def two_tensor_fsdp_post_all_gather(
     self,
-    all_gather_outputs: Tuple[torch.Tensor, ...],
+    all_gather_outputs: tuple[torch.Tensor, ...],
     metadata: Any,
     param_dtype: torch.dtype,
     *,
     out: Optional[torch.Tensor] = None,
-) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor, ...]], None]:
+) -> Union[tuple[torch.Tensor, tuple[torch.Tensor, ...]], None]:
     assert metadata is None, f"{metadata}"
     a, b = all_gather_outputs
     if out is not None:
@@ -96,10 +96,10 @@ class BFloat16AllGatherTensor(torch.Tensor):
         self,
         mesh: DeviceMesh,
         outer_size: torch.Size,
-        outer_stride: Tuple[int, ...],
+        outer_stride: tuple[int, ...],
         module: nn.Module,
         mp_policy: MixedPrecisionPolicy,
-    ) -> Tuple[Tuple[torch.Tensor, ...], Any]:
+    ) -> tuple[tuple[torch.Tensor, ...], Any]:
         assert mesh.ndim == 1, f"{mesh.ndim}"
         mesh_size = mesh.size()
         requires_padding = outer_size[0] % mesh_size != 0
@@ -116,12 +116,12 @@ class BFloat16AllGatherTensor(torch.Tensor):
 
     def fsdp_post_all_gather(
         self,
-        all_gather_outputs: Tuple[torch.Tensor, ...],
+        all_gather_outputs: tuple[torch.Tensor, ...],
         metadata: Any,
         param_dtype: torch.dtype,
         *,
         out: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor, ...]], None]:
+    ) -> Union[tuple[torch.Tensor, tuple[torch.Tensor, ...]], None]:
         assert metadata is None, f"{metadata}"
         (tensor,) = all_gather_outputs
         assert tensor.dtype == torch.bfloat16, f"{tensor.dtype}"
@@ -157,7 +157,7 @@ class BFloat16AllGatherTensor(torch.Tensor):
 
     @staticmethod
     def __tensor_unflatten__(
-        inner_tensors, outer_size: torch.Size, outer_stride: Tuple[int, ...]
+        inner_tensors, outer_size: torch.Size, outer_stride: tuple[int, ...]
     ):
         return inner_tensors["_data"]
 
@@ -236,7 +236,7 @@ class TestFullyShardAllGatherExtensionsMultiProcess(
         torch.manual_seed(42 + self.rank + 1)
         inp = torch.randn((2, 8), device="cuda")
         for iter_idx in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model in (ref_model, model):
                 losses.append(_model(inp).sum())
                 losses[-1].backward()
@@ -314,10 +314,10 @@ class TestFullyShardAllGatherExtensionsMultiThread(
             self,
             mesh: DeviceMesh,
             outer_size: torch.Size,
-            outer_stride: Tuple[int, ...],
+            outer_stride: tuple[int, ...],
             module: nn.Module,
             mp_policy: MixedPrecisionPolicy,
-        ) -> Tuple[Tuple[torch.Tensor, ...], Any]:
+        ) -> tuple[tuple[torch.Tensor, ...], Any]:
             nonlocal tls
             tls.ran_pre_all_gather = True
             return (self.to(torch.bfloat16),), None
@@ -325,12 +325,12 @@ class TestFullyShardAllGatherExtensionsMultiThread(
         @torch.no_grad()
         def fsdp_post_all_gather(
             self,
-            all_gather_outputs: Tuple[torch.Tensor, ...],
+            all_gather_outputs: tuple[torch.Tensor, ...],
             metadata: Any,
             param_dtype: torch.dtype,
             *,
             out: Optional[torch.Tensor] = None,
-        ) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor, ...]], None]:
+        ) -> Union[tuple[torch.Tensor, tuple[torch.Tensor, ...]], None]:
             (tensor,) = all_gather_outputs
             assert metadata is None, f"{metadata}"
             assert tensor.dtype == torch.bfloat16, f"{tensor.dtype}"
@@ -416,10 +416,10 @@ class TestFullyShardAllGatherExtensionsMultiThread(
             self,
             mesh: DeviceMesh,
             outer_size: torch.Size,
-            outer_stride: Tuple[int, ...],
+            outer_stride: tuple[int, ...],
             module: nn.Module,
             mp_policy: MixedPrecisionPolicy,
-        ) -> Tuple[Tuple[torch.Tensor, ...], Any]:
+        ) -> tuple[tuple[torch.Tensor, ...], Any]:
             nonlocal tls
             tls.mesh = mesh
             return (self,), None
@@ -427,12 +427,12 @@ class TestFullyShardAllGatherExtensionsMultiThread(
         @torch.no_grad()
         def fsdp_post_all_gather(
             self,
-            all_gather_outputs: Tuple[torch.Tensor, ...],
+            all_gather_outputs: tuple[torch.Tensor, ...],
             metadata: Any,
             param_dtype: torch.dtype,
             *,
             out: Optional[torch.Tensor] = None,
-        ) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor, ...]], None]:
+        ) -> Union[tuple[torch.Tensor, tuple[torch.Tensor, ...]], None]:
             (tensor,) = all_gather_outputs
             if out is not None:
                 return

test/distributed/_composable/fsdp/test_fully_shard_frozen.py

@@ -3,7 +3,7 @@
 import copy
 import functools
 import itertools
-from typing import List, Union
+from typing import Union
 
 import torch
 import torch.distributed as dist
@@ -116,7 +116,7 @@ class TestFullyShardFrozen(FSDPTest):
         ), patch_register_post_backward_hook_backward(backward_with_count):
             for iter_idx in range(10):
                 inp = torch.randn((8, lin_dim), device=device)
-                losses: List[torch.Tensor] = []
+                losses: list[torch.Tensor] = []
                 for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                     _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                     losses.append(_model(inp).sum())
@@ -151,7 +151,7 @@ class TestFullyShardFrozen(FSDPTest):
     ):
         torch.manual_seed(42)
         num_linears, lin_dim = (6, 32)
-        modules: List[nn.Module] = []
+        modules: list[nn.Module] = []
         for _ in range(num_linears):
             modules += [nn.Linear(lin_dim, lin_dim), nn.ReLU()]
         model = nn.Sequential(*modules)
@@ -187,7 +187,7 @@ class TestFullyShardFrozen(FSDPTest):
         inp = torch.randn((8, lin_dim), device="cuda")
         with patch_register_post_backward_hook_backward(backward_with_count):
             for iter_idx in range(num_iters):
-                losses: List[torch.Tensor] = []
+                losses: list[torch.Tensor] = []
                 for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                     # Unfreeze the parameters on the last step to emulate some
                     # kinds of fine-tuning
@@ -251,7 +251,7 @@ class TestFullyShardFrozen(FSDPTest):
         optim = torch.optim.Adam(model.parameters(), lr=1e-2)
         for iter_idx in range(10):
             inp = torch.randn((8, 5), device="cuda")
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(inp).sum())

test/distributed/_composable/fsdp/test_fully_shard_init.py

@@ -3,7 +3,7 @@
 import copy
 import itertools
 import unittest
-from typing import List, Optional
+from typing import Optional
 
 import torch
 import torch.distributed as dist
@@ -211,8 +211,8 @@ class TestFullyShardManagedModulesAndStates(FSDPTestMultiThread):
 
     def _check_managed_modules(
         self,
-        managed_modules: List[nn.Module],
-        expected_managed_modules: List[nn.Module],
+        managed_modules: list[nn.Module],
+        expected_managed_modules: list[nn.Module],
     ):
         self.assertEqual(len(managed_modules), len(expected_managed_modules))
         # Check set comparison since we do not require anything about the order
@@ -262,10 +262,10 @@ class TestFullyShardManagedModulesAndStates(FSDPTestMultiThread):
 
     def _check_managed_states(
         self,
-        managed_params: List[nn.Parameter],
-        managed_buffers: List[torch.Tensor],
-        expected_managed_params: List[nn.Parameter],
-        expected_managed_buffers: List[torch.Tensor],
+        managed_params: list[nn.Parameter],
+        managed_buffers: list[torch.Tensor],
+        expected_managed_params: list[nn.Parameter],
+        expected_managed_buffers: list[torch.Tensor],
     ):
         self.assertEqual(len(managed_params), len(expected_managed_params))
         self.assertEqual(len(managed_buffers), len(expected_managed_buffers))
@@ -370,7 +370,7 @@ class TestFullyShardShardedParameterTensor(FSDPTestMultiThread):
         self._check_1d_sharded_parameters(orig_params, sharded_params)
 
     def _check_1d_sharded_parameters(
-        self, orig_params: List[nn.Parameter], sharded_params: List[nn.Parameter]
+        self, orig_params: list[nn.Parameter], sharded_params: list[nn.Parameter]
     ):
         self.assertEqual(len(orig_params), len(sharded_params))
         global_mesh = init_device_mesh("cuda", (self.world_size,))

test/distributed/_composable/fsdp/test_fully_shard_mixed_precision.py

@@ -2,7 +2,7 @@
 
 import copy
 import functools
-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -339,7 +339,7 @@ class TestFullyShardMixedPrecisionTraining(FSDPTest):
                 model.set_reshard_after_backward(
                     is_last_microbatch or reshard_after_forward
                 )
-                losses: List[torch.Tensor] = []
+                losses: list[torch.Tensor] = []
                 for _model in (ref_model_compute, model):
                     losses.append(
                         _model(microbatch_inps[microbatch_idx].detach()).sum()
@@ -391,7 +391,7 @@ class TestFullyShardMixedPrecisionCasts(FSDPTestMultiThread):
 
         # Subtest 1: use fp16 on the second child submodule -- does not require
         # any additional casting logic
-        forward_inputs: Dict[str, nn.Module] = {}
+        forward_inputs: dict[str, nn.Module] = {}
         model = SaveForwardInputsModel(
             forward_inputs,
             cast_forward_inputs=False,
@@ -405,7 +405,7 @@ class TestFullyShardMixedPrecisionCasts(FSDPTestMultiThread):
 
         # Subtest 2: use fp16 on the second child module, where the user module
         # owns the cast
-        forward_inputs: Dict[nn.Module, torch.Tensor] = {}
+        forward_inputs: dict[nn.Module, torch.Tensor] = {}
         model = SaveForwardInputsModel(
             forward_inputs=forward_inputs, cast_forward_inputs=True
         ).cuda()
@@ -423,7 +423,7 @@ class TestFullyShardMixedPrecisionCasts(FSDPTestMultiThread):
 
         # Subtest 3: use fp16 on the first child module and specify its output
         # dtype so that the second child module does not need to cast
-        forward_inputs: Dict[nn.Module, torch.Tensor] = {}
+        forward_inputs: dict[nn.Module, torch.Tensor] = {}
         model = SaveForwardInputsModel(
             forward_inputs=forward_inputs, cast_forward_inputs=False
         ).cuda()
@@ -448,7 +448,7 @@ class TestFullyShardMixedPrecisionCasts(FSDPTestMultiThread):
 
     def _test_submodules_with_external_inputs(self, enable_submodule_cast: bool):
         class ToyModule(nn.Module):
-            def __init__(self, forward_inputs: Dict[str, torch.Tensor]) -> None:
+            def __init__(self, forward_inputs: dict[str, torch.Tensor]) -> None:
                 super().__init__()
                 self.l = nn.Linear(100, 100)
                 self.forward_inputs = forward_inputs
@@ -459,7 +459,7 @@ class TestFullyShardMixedPrecisionCasts(FSDPTestMultiThread):
                 return self.l(x)
 
         class ToyModel(nn.Module):
-            def __init__(self, forward_inputs: Dict[str, torch.Tensor]) -> None:
+            def __init__(self, forward_inputs: dict[str, torch.Tensor]) -> None:
                 super().__init__()
                 self.l1 = nn.Linear(100, 100)
                 self.l2 = ToyModule(forward_inputs)
@@ -472,7 +472,7 @@ class TestFullyShardMixedPrecisionCasts(FSDPTestMultiThread):
                 )  # external input
                 return self.l2(self.l1(x), y)
 
-        forward_inputs: Dict[str, torch.Tensor] = {}
+        forward_inputs: dict[str, torch.Tensor] = {}
         model = ToyModel(forward_inputs).cuda()
         x = torch.zeros(2, 100, device="cuda", dtype=torch.float32)
         fully_shard(

test/distributed/_composable/fsdp/test_fully_shard_state_dict.py

@@ -4,7 +4,7 @@ import copy
 import functools
 import unittest
 from contextlib import nullcontext
-from typing import Dict, Optional
+from typing import Optional
 
 import torch
 import torch.nn as nn
@@ -308,7 +308,7 @@ class TestFullyShardStateDictMultiProcess(FSDPTest):
 
         # Verify that we can load a new state dict that contains DTensors with
         # storages different from the current model parameters
-        new_state_dict: Dict[str, DTensor] = {}
+        new_state_dict: dict[str, DTensor] = {}
         for param_name, dtensor in state_dict.items():
             # Construct new DTensors to exercise load state dict writeback
             new_state_dict[param_name] = dtensor.detach().clone().fill_(new_fill_value)

test/distributed/_composable/fsdp/test_fully_shard_training.py

@@ -7,7 +7,7 @@ import itertools
 import unittest
 from collections import defaultdict
 from collections.abc import Iterable
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -65,7 +65,7 @@ class TestFullyShardForwardInputs(FSDPTestMultiThread):
         device = torch.device("cuda", 0)
 
         class ParamlessModule(nn.Module):
-            def forward(self, x: torch.Tensor, ys: Tuple[torch.Tensor, ...]):
+            def forward(self, x: torch.Tensor, ys: tuple[torch.Tensor, ...]):
                 # Check that FSDP moved the inputs to GPU, including recursing
                 # into the tuple data structure
                 assert x.device == device, f"Expects {device} but got {x.device}"
@@ -224,7 +224,7 @@ class TestFullyShardCastAfterInit(FSDPTestMultiThread):
         torch.manual_seed(42 + self.rank + 1)
         inp = torch.randn((2, mlp_dim), device="cuda", dtype=dtype)
         for iter_idx in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model in (ref_model, model):
                 losses.append(_model(inp).sum())
                 losses[-1].backward()
@@ -281,7 +281,7 @@ class TestFullyShard1DTrainingCore(FSDPTest):
         )
 
     def _test_train_parity_single_group(
-        self, lin_shapes: List[Tuple[int, int]], use_shard_placement_fn: bool
+        self, lin_shapes: list[tuple[int, int]], use_shard_placement_fn: bool
     ):
         torch.manual_seed(42)
         model = nn.Sequential(
@@ -308,7 +308,7 @@ class TestFullyShard1DTrainingCore(FSDPTest):
         torch.manual_seed(42 + self.rank + 1)
         inp = (torch.randn((4, lin_shapes[0][0]), device="cuda"),)
         for iter_idx in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(*inp).sum())
@@ -461,7 +461,7 @@ class TestFullyShard1DTrainingCore(FSDPTest):
         with patch_all_gather_ctx, patch_reduce_scatter_ctx:
             for iter_idx in range(10):
                 inp = torch.randint(0, vocab_size, (3, 64), device=device_type)
-                losses: List[torch.Tensor] = []
+                losses: list[torch.Tensor] = []
                 for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                     _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                     losses.append(_model(inp).sum())
@@ -554,7 +554,7 @@ class TestFullyShard1DTrainingCore(FSDPTest):
         torch.manual_seed(42 + self.rank)
         inp = torch.randn((32, 4), device="cuda")
         for iter_idx in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(inp).sum())
@@ -592,7 +592,7 @@ class TestFullyShard1DTrainingCore(FSDPTest):
         torch.manual_seed(42 + self.rank)
         inp = torch.randint(0, model_args.vocab_size, (2, 8), device="cuda")
         for _ in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad()
                 losses.append(_model(inp).sum())
@@ -623,8 +623,8 @@ class TestFullyShard1DTrainingCore(FSDPTest):
         inp = torch.randint(0, model_args.vocab_size, (2, 8), device="cuda")
         # Track all losses and check for equality at the end to avoid a CPU
         # sync point after each iteration
-        ref_losses: List[torch.Tensor] = []
-        losses: List[torch.Tensor] = []
+        ref_losses: list[torch.Tensor] = []
+        losses: list[torch.Tensor] = []
         for _ in range(10):
             ref_optim.zero_grad()
             ref_losses.append(ref_model(inp).sum())
@@ -736,7 +736,7 @@ class TestFullyShard1DTrainingCompose(FSDPTest):
             self, ref_model, model, prefixes_to_ignore=prefixes_to_ignore
         )
         for iter_idx in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model in (ref_model, model):
                 torch.manual_seed(iter_idx + 1)  # for dropout determinism
                 losses.append(_model(inp).sum())
@@ -886,7 +886,7 @@ class TestFullyShardSharedParams(FSDPTest):
         torch.manual_seed(42 + self.rank + 1)
         for iter_idx in range(10):
             inp = torch.randint(0, model_args.vocab_size, (2, 16), device="cuda")
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(inp).sum())
@@ -1009,7 +1009,7 @@ class TestFullyShardGradientAccumulation(FSDPTest):
                 is_last_microbatch = microbatch_idx == num_microbatches - 1
                 set_backward_flags(model, is_last_microbatch)
                 inp = torch.randn(batch_size, lin_dim, device="cuda")
-                losses: List[torch.Tensor] = []
+                losses: list[torch.Tensor] = []
                 for _model in (ref_model, model):
                     with CommDebugMode() as comm_mode:
                         losses.append(_model(inp).sum())
@@ -1125,8 +1125,8 @@ class TestFullyShardGradientAccumulation(FSDPTest):
 
         # Emulate the 1f1b pipeline schedule and only reduce gradients on the
         # last microbatch
-        losses: List[torch.Tensor] = []
-        ref_losses: List[torch.Tensor] = []
+        losses: list[torch.Tensor] = []
+        ref_losses: list[torch.Tensor] = []
         for inp_idx, inp in enumerate(inps):
             is_last_microbatch = inp_idx == num_microbatches - 1
             model.set_requires_gradient_sync(is_last_microbatch)
@@ -1210,7 +1210,7 @@ class TestFullyShardNDTraining(FSDPTest):
         device = torch.device("cuda")
         for iter_idx in range(10):
             inp = torch.randn((8, mlp_dim), device=device)
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(inp).sum())
@@ -1281,7 +1281,7 @@ class TestFullyShardHSDP3DTraining(FSDPTest):
         device = torch.device("cuda")
         for iter_idx in range(10):
             inp = torch.randn((8, mlp_dim), device=device)
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(inp).sum())
@@ -1360,7 +1360,7 @@ class TestFullyShardHSDPTraining(FSDPTest):
                 if sync_gradients_at_last_batch:
                     model.set_requires_gradient_sync(is_last_microbatch)
                 inp = torch.randn((8, mlp_dim), device=device)
-                losses: List[torch.Tensor] = []
+                losses: list[torch.Tensor] = []
                 for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                     losses.append(_model(inp).sum())
                     losses[-1].backward()

test/distributed/_composable/test_checkpoint.py

@@ -5,7 +5,6 @@ from collections import deque, OrderedDict
 from contextlib import ContextDecorator, contextmanager, nullcontext
 from copy import deepcopy
 from functools import partial
-from typing import Tuple
 
 import torch
 import torch.nn as nn
@@ -70,7 +69,7 @@ class MultiOutputModel(nn.Module):
         self.w1 = nn.Parameter(torch.randn((100, 100), device=device))
         self.w2 = nn.Parameter(torch.randn((100, 100), device=device))
 
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         z = x @ self.w1
         z = nn.functional.relu(z)
         z = z @ self.w2
@@ -82,7 +81,7 @@ class MultiInputModel(nn.Module):
         super().__init__()
         self.w = nn.Parameter(torch.randn((100, 100), device=device))
 
-    def forward(self, xs: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
+    def forward(self, xs: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
         assert len(xs) == 2, f"Expects 2 args but got {len(xs)}"
         x, y = xs
         z = x + y

test/distributed/_composable/test_composability/test_2d_composability.py

@@ -4,7 +4,7 @@ import copy
 import functools
 import io
 from copy import deepcopy
-from typing import List, Optional, Type
+from typing import Optional
 
 import torch
 import torch.distributed as dist
@@ -166,7 +166,7 @@ class TestFullyShard2DTraining(FSDPTest):
         device = torch.device("cuda")
         for iter_idx in range(10):
             inp = torch.randn((8, mlp_dim), device=device)
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 _optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
                 losses.append(_model(inp).sum())
@@ -335,7 +335,7 @@ class TestFullyShard2DTraining(FSDPTest):
         self,
         use_seq_parallel: bool,
         reuse_model_optim: bool,
-        optimizer_class: Type[torch.optim.Optimizer],
+        optimizer_class: type[torch.optim.Optimizer],
         foreach: bool,
     ):
         def train_step(

test/distributed/_composable/test_contract.py

@@ -1,7 +1,6 @@
 # Owner(s): ["oncall: distributed"]
 
 from copy import deepcopy
-from typing import List, Tuple
 
 import torch
 import torch.nn as nn
@@ -28,12 +27,12 @@ class TestContract(TestCase):
     @skipIfTorchDynamo("Dynamo does not support the state key")
     def test_add_hooks(self):
         def forward_pre_hook(
-            module: nn.Module, inp: Tuple[torch.Tensor]
-        ) -> Tuple[torch.Tensor]:
+            module: nn.Module, inp: tuple[torch.Tensor]
+        ) -> tuple[torch.Tensor]:
             return inp
 
         def forward_hook(
-            module: nn.Module, inp: Tuple[torch.Tensor], out: torch.Tensor
+            module: nn.Module, inp: tuple[torch.Tensor], out: torch.Tensor
         ) -> torch.Tensor:
             return out
 
@@ -44,9 +43,9 @@ class TestContract(TestCase):
 
         def backward_hook(
             module: nn.Module,
-            grad_input: Tuple[torch.Tensor],
+            grad_input: tuple[torch.Tensor],
             grad_output: torch.Tensor,
-        ) -> Tuple[torch.Tensor]:
+        ) -> tuple[torch.Tensor]:
             return grad_input
 
         @contract()
@@ -92,8 +91,8 @@ class TestContract(TestCase):
     @skipIfTorchDynamo("Dynamo does not support the state key")
     def test_state(self):
         def check_and_update_state_hook(
-            module: nn.Module, inp: Tuple[torch.Tensor]
-        ) -> Tuple[torch.Tensor]:
+            module: nn.Module, inp: tuple[torch.Tensor]
+        ) -> tuple[torch.Tensor]:
             self.assertEqual(api.state(module).dummy_state, 7)
             api.state(module).dummy_state = 8
             return inp
@@ -139,7 +138,7 @@ class TestContract(TestCase):
     @skipIfTorchDynamo("Dynamo does not support the state key")
     def test_multi_module_api(self):
         @contract()
-        def multi_module_api(modules: List[nn.Module]) -> nn.Module:
+        def multi_module_api(modules: list[nn.Module]) -> nn.Module:
             return modules
 
         model = nn.Sequential(*[nn.Linear(3, 3) for _ in range(5)])

test/distributed/_shard/sharded_tensor/test_sharded_tensor.py

@@ -6,7 +6,6 @@ import itertools
 import math
 import pickle
 import sys
-from typing import List
 
 import torch
 import torch.distributed as dist
@@ -3186,7 +3185,7 @@ class TestCreateTensorNoProcessGroupMode(TestCase):
             ],
             size=torch.Size([4, 2]),
         )
-        st_local_shards: List[Shard] = []
+        st_local_shards: list[Shard] = []
         for shard_metadata in st_metadata.shards_metadata:
             st_local_shards.append(
                 Shard(
@@ -3215,7 +3214,7 @@ class TestCreateTensorNoProcessGroupMode(TestCase):
             ],
             size=torch.Size([4, 2]),
         )
-        st_local_shards: List[Shard] = []
+        st_local_shards: list[Shard] = []
         src = torch.randn(4, 2)
         for shard_metadata in st_metadata.shards_metadata:
             offsets = shard_metadata.shard_offsets

test/distributed/_shard/sharding_spec/test_sharding_spec.py

@@ -1,7 +1,7 @@
 # Owner(s): ["oncall: distributed"]
 import copy
 from dataclasses import dataclass
-from typing import List, Union
+from typing import Union
 
 import torch
 from torch.distributed._shard import _shard_tensor, sharded_tensor
@@ -495,7 +495,7 @@ class TestShardingSpec(TestCase):
 @dataclass
 class GridShardingSpec(ShardingSpec):
     grid_size: int
-    placements: List[Union[torch.distributed._remote_device, str]]
+    placements: list[Union[torch.distributed._remote_device, str]]
 
     def __post_init__(self):
         for i, remote_device in enumerate(self.placements):

test/distributed/_tools/test_mem_tracker.py

@@ -1,7 +1,6 @@
 # Owner(s): ["module: unknown"]
 import gc
 import unittest
-from typing import Tuple
 
 import torch
 import torch.nn as nn
@@ -161,7 +160,7 @@ class TestMemTracker(TestCase):
 
         def get_param_grad_optstate_actual_bytes(
             model: nn.Module, opt: torch.optim.Optimizer
-        ) -> Tuple[int, int, int]:
+        ) -> tuple[int, int, int]:
             param_bytes = 0
             grad_bytes = 0
             opt_state_bytes = 0
@@ -179,7 +178,7 @@ class TestMemTracker(TestCase):
 
         def get_param_grad_optstate_bytes_from_tracker(
             tracker: MemTracker,
-        ) -> Tuple[int, int, int]:
+        ) -> tuple[int, int, int]:
             snapshot = tracker.get_tracker_snapshot()
             param_bytes = snapshot[dev]["Parameter"]
             grad_bytes = snapshot[dev]["Gradient"]

test/distributed/_tools/test_runtime_estimator.py

@@ -1,7 +1,7 @@
 # Owner(s): ["module: unknown"]
 import unittest
 from dataclasses import dataclass
-from typing import Any, Callable, cast, Tuple, Union
+from typing import Any, Callable, cast, Union
 
 import torch
 from torch import nn, optim
@@ -73,7 +73,7 @@ class TestRuntimeEstimator(TestCase):
     def _measure_actual_cuda_time(
         self,
         func: Callable,
-        args: Tuple[Any, ...],
+        args: tuple[Any, ...],
     ) -> float:
         warmup_iters, actual_iters = 2, 5
         start_event = torch.cuda.Event(enable_timing=True)
@@ -92,7 +92,7 @@ class TestRuntimeEstimator(TestCase):
         self,
         estimate_mode: str,
         func: Callable,
-        args: Tuple[Any, ...],
+        args: tuple[Any, ...],
     ) -> float:
         # Optimizer init step
         func(*args)
@@ -106,7 +106,7 @@ class TestRuntimeEstimator(TestCase):
         model_type: str,
         model_args: Union[ConvArgs, ModelArgs],
         bsz: int,
-    ) -> Tuple[nn.Module, optim.Optimizer, torch.Tensor]:
+    ) -> tuple[nn.Module, optim.Optimizer, torch.Tensor]:
         dev = torch.cuda.current_device()
         if model_type == "Transformer":
             model_args = cast(ModelArgs, model_args)

test/distributed/_tools/test_sac_ilp.py

@@ -1,7 +1,6 @@
 # Owner(s): ["module: unknown"]
 import copy
 import unittest
-from typing import Tuple
 
 import torch
 from torch._subclasses.fake_tensor import FakeTensorMode
@@ -40,7 +39,7 @@ class TestSACILP(TestCase):
 
     def _init_model_input_optimizer(
         self,
-    ) -> Tuple[torch.nn.Module, torch.optim.Optimizer, torch.Tensor]:
+    ) -> tuple[torch.nn.Module, torch.optim.Optimizer, torch.Tensor]:
         bsz = 8
         model_args = ModelArgs(
             n_layers=4,

test/distributed/checkpoint/e2e/test_e2e_save_and_load.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass, field
 from enum import auto, Enum
 from functools import partial
 from io import BytesIO
-from typing import Any, Dict, List
+from typing import Any
 
 import torch
 import torch.distributed as dist
@@ -95,9 +95,9 @@ class ModelType(Enum):
 class TestTrainState:
     step: int = 0
     current_loss: float = -1
-    losses: List[float] = field(default_factory=list)
+    losses: list[float] = field(default_factory=list)
 
-    def state_dict(self) -> Dict[str, Any]:
+    def state_dict(self) -> dict[str, Any]:
         loss_bytes = BytesIO()
         torch.save(self.losses, loss_bytes)
         return {
@@ -284,7 +284,7 @@ class TestE2ESaveAndLoad(DTensorTestBase, VerifyStateDictMixin):
 
     @with_temp_dir
     def test_stateful_and_non_stateful_loads(self) -> None:
-        class StateDict(Dict):
+        class StateDict(dict):
             def __init__(self):
                 self.set_sd_item_called = False
 

test/distributed/checkpoint/test_checkpoint.py

@@ -2,7 +2,7 @@
 
 import os
 import sys
-from typing import cast, List, Optional, Union
+from typing import cast, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -177,17 +177,17 @@ class FaultyStorageWriter(TestStorageBase, StorageWriter):
         self._fail_rank("fail_prepare_local_plan")
         return plan
 
-    def prepare_global_plan(self, plans: List[SavePlan]) -> List[SavePlan]:
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
         self._fail_rank("fail_prepare_global_plan")
         return plans
 
     def write_data(
         self, plan: SavePlan, planner: SavePlanner
-    ) -> Future[List[WriteResult]]:
+    ) -> Future[list[WriteResult]]:
         self._fail_rank("fail_write_data")
         return self._fail_rank_async("fail_write_data_async", [])
 
-    def finish(self, metadata: Metadata, results: List[List[WriteResult]]) -> None:
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
         self._fail_rank("fail_finish")
 
     @classmethod
@@ -210,7 +210,7 @@ class FaultyStorageReader(TestStorageBase, StorageReader):
         self._fail_rank("fail_prepare_local_plan")
         return plan
 
-    def prepare_global_plan(self, plans: List[LoadPlan]) -> List[LoadPlan]:
+    def prepare_global_plan(self, plans: list[LoadPlan]) -> list[LoadPlan]:
         self._fail_rank("fail_prepare_global_plan")
         return plans
 

test/distributed/checkpoint/test_dtensor_checkpoint.py

@@ -1,5 +1,5 @@
 # Owner(s): ["oncall: distributed"]
-from typing import Dict, Union
+from typing import Union
 
 import torch
 import torch.distributed as dist
@@ -58,14 +58,14 @@ class MyTestModule(torch.nn.Module):
     def extra_state_tensor(self, new_extra_state_tensor: torch.Tensor) -> None:
         self._extra_state_tensor = new_extra_state_tensor
 
-    def get_extra_state(self) -> Dict[str, Union[int, torch._tensor.Tensor]]:
+    def get_extra_state(self) -> dict[str, Union[int, torch._tensor.Tensor]]:
         return {
             "extra_state": self._extra_state,
             "extra_state_tensor": self._extra_state_tensor,
         }
 
     def set_extra_state(
-        self, state: Dict[str, Union[int, torch._tensor.Tensor]]
+        self, state: dict[str, Union[int, torch._tensor.Tensor]]
     ) -> None:
         self._extra_state = state["extra_state"]  # pyre-ignore[8]
         self._extra_state_tensor = state["extra_state_tensor"]  # pyre-ignore[8]

test/distributed/checkpoint/test_file_system_checkpoint.py

@@ -4,7 +4,6 @@ import os
 import shutil
 import sys
 import tempfile
-from typing import Dict
 
 import torch
 import torch.distributed as dist
@@ -53,8 +52,8 @@ if TEST_WITH_DEV_DBG_ASAN:
 
 def assert_state_dict_equal(
     self: TestCase,
-    state_dict_1: Dict[str, torch.Tensor],
-    state_dict_2: Dict[str, torch.Tensor],
+    state_dict_1: dict[str, torch.Tensor],
+    state_dict_2: dict[str, torch.Tensor],
 ) -> bool:
     self.assertEqual(
         len(state_dict_1), len(state_dict_2), "state_dict must be the same size"

test/distributed/checkpoint/test_file_system_checkpoint_cpu.py

@@ -2,7 +2,7 @@
 
 import sys
 import tempfile
-from typing import Any, Dict, IO
+from typing import Any, IO
 
 import torch
 import torch.distributed as dist
@@ -56,8 +56,8 @@ _THREAD_COUNTS = {1, 2}
 
 def assert_state_dict_equal(
     self: TestCase,
-    state_dict_1: Dict[str, torch.Tensor],
-    state_dict_2: Dict[str, torch.Tensor],
+    state_dict_1: dict[str, torch.Tensor],
+    state_dict_2: dict[str, torch.Tensor],
 ) -> bool:
     self.assertEqual(
         len(state_dict_1), len(state_dict_2), "state_dict must be the same size"
@@ -113,10 +113,10 @@ class BlobState:
     def __init__(self, value: IO[bytes]) -> Any:
         self.state = {"blob": value}
 
-    def state_dict(self) -> Dict[str, Any]:
+    def state_dict(self) -> dict[str, Any]:
         return self.state
 
-    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
         self.state = state_dict
 
     def __eq__(self, other: object) -> bool:

test/distributed/checkpoint/test_fsspec.py

@@ -3,7 +3,7 @@
 import shutil
 import tempfile
 from functools import wraps
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Any, Callable, Optional
 
 import torch
 import torch.distributed as dist
@@ -35,7 +35,7 @@ def with_temp_dir(
     assert func is not None
 
     @wraps(func)
-    def wrapper(self, *args: Tuple[object], **kwargs: Dict[str, Any]) -> None:
+    def wrapper(self, *args: tuple[object], **kwargs: dict[str, Any]) -> None:
         # Only create temp_dir when rank is 0 (or no pg)
         if not dist.is_initialized() or dist.get_rank() == 0:
             temp_dir = tempfile.mkdtemp()

test/distributed/checkpoint/test_state_dict.py

@@ -4,7 +4,7 @@ import copy
 import functools
 import sys
 from itertools import chain
-from typing import Callable, Tuple, Type, Union
+from typing import Callable, Union
 
 import torch
 import torch.distributed as dist
@@ -154,9 +154,9 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
         *,
         use_orig_params: bool,
         use_dtensor: bool,
-        wrapping: Tuple[nn.Module] = (),
+        wrapping: tuple[nn.Module] = (),
         compile_model: bool = False,
-        optimizer_class: Type[Optimizer],
+        optimizer_class: type[Optimizer],
     ) -> None:
         if not use_orig_params:
             return
@@ -232,7 +232,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
         self,
         *,
         reshard_after_forward: Union[bool, int],
-        optimizer_class: Type[Optimizer],
+        optimizer_class: type[Optimizer],
         compile_model: bool,
         foreach: bool = True,
     ):
@@ -272,7 +272,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
             self._test_fsdp2,
         )
 
-    def _test_ddp(self, use_composable: bool, optimizer_class: Type[Optimizer]) -> None:
+    def _test_ddp(self, use_composable: bool, optimizer_class: type[Optimizer]) -> None:
         def init_model_optim():
             orig_model = CompositeParamModel(device=torch.device("cuda"))
             orig_optim = optimizer_class(orig_model.parameters(), lr=1e-4)
@@ -303,7 +303,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
 
     def _test_fsdp_ddp(
         self,
-        optimizer_class: Type[Optimizer],
+        optimizer_class: type[Optimizer],
         optim_in_backward: bool = False,
         test_frozen: bool = False,
     ) -> None:
@@ -347,7 +347,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
             self._test_fsdp_ddp,
         )
 
-    def _test_single_gpu(self, optimizer_class: Type[Optimizer]) -> None:
+    def _test_single_gpu(self, optimizer_class: type[Optimizer]) -> None:
         def init_model_optim():
             orig_model = CompositeParamModel(device=torch.device("cuda"))
             orig_optim = optimizer_class(orig_model.parameters(), lr=1e-4)
@@ -399,7 +399,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
         )
 
     def _test_cpu_offload_full_state_dict(
-        self, optimizer_class: Type[Optimizer]
+        self, optimizer_class: type[Optimizer]
     ) -> None:
         orig_model = CompositeParamModel(device=torch.device("cuda"))
         device_mesh = init_device_mesh("cuda", (self.world_size,))

test/distributed/elastic/agent/server/test/api_test.py

@@ -14,7 +14,7 @@ import signal
 import unittest
 import uuid
 from multiprocessing.pool import ThreadPool
-from typing import Any, Dict, List
+from typing import Any
 from unittest.mock import call, patch
 
 import torch.distributed as dist
@@ -135,7 +135,7 @@ class TestAgent(SimpleElasticAgent):
         worker_group.group_world_size = None
         self.stop_workers_call_count += 1
 
-    def _start_workers(self, worker_group: WorkerGroup) -> Dict[int, Any]:
+    def _start_workers(self, worker_group: WorkerGroup) -> dict[int, Any]:
         # crate fake workers; make worker id equal to global rank
         ids = {}
         for worker in worker_group.workers:
@@ -477,7 +477,7 @@ class SimpleElasticAgentTest(unittest.TestCase):
         self.assertEqual(1, mock_monitor_workers.call_count)
         self.assertEqual(spec.max_restarts, agent._remaining_restarts)
 
-    def get_worker_assigned(self, store, role_infos_len, info) -> List[Worker]:
+    def get_worker_assigned(self, store, role_infos_len, info) -> list[Worker]:
         i, role_info = info
         spec = self._get_worker_spec(
             max_restarts=3,

test/distributed/elastic/agent/server/test/local_elastic_agent_test.py

@@ -17,7 +17,7 @@ import time
 import unittest
 import uuid
 from dataclasses import dataclass
-from typing import Callable, Dict, List, Optional, Tuple
+from typing import Callable, Optional
 from unittest import mock
 from unittest.mock import Mock, patch
 
@@ -256,7 +256,7 @@ class Conf:
 
     entrypoint: Callable
     local_world_size: int
-    args: Tuple = ()
+    args: tuple = ()
     role: str = "default"
     redirects: Std = Std.NONE
     tee: Std = Std.NONE
@@ -394,10 +394,10 @@ class LocalElasticAgentTest(unittest.TestCase):
 
     def run_job(
         self,
-        node_configs: List[Conf],
+        node_configs: list[Conf],
         exit_barrier_timeout: int = 5,
         log_line_prefix_template: Optional[str] = None,
-    ) -> Dict[str, List[RunResult]]:
+    ) -> dict[str, list[RunResult]]:
         """
         Simulates running a distributed job by running multiple agents
         (one on each process). Agent 0 is run on the main process for
@@ -431,7 +431,7 @@ class LocalElasticAgentTest(unittest.TestCase):
         for p in procs:
             p.join()
 
-        results: Dict[str, List[RunResult]] = {}
+        results: dict[str, list[RunResult]] = {}
         while not agent_results.empty():
             role, run_result = agent_results.get()
             results.setdefault(role, []).append(run_result)
@@ -1032,8 +1032,8 @@ class LocalElasticAgentTest(unittest.TestCase):
 
     def assert_rank_consistency(
         self,
-        run_results: Dict[str, List[RunResult]],
-        expected_role_world_sizes: Dict[str, int],
+        run_results: dict[str, list[RunResult]],
+        expected_role_world_sizes: dict[str, int],
     ):
         """
         Asserts that ranks are consecutive w.r.t role_rank. If local world sizes are 4:
@@ -1042,11 +1042,11 @@ class LocalElasticAgentTest(unittest.TestCase):
         ... etc ...
         """
 
-        global_ranks: List[int] = []
+        global_ranks: list[int] = []
         # role -> [role_rank,...]
-        role_ranks: Dict[str, List[int]] = {}
+        role_ranks: dict[str, list[int]] = {}
         # group rank -> [(rank, role_rank),...]
-        grouped_ranks: Dict[int, List[Tuple[int, int]]] = {}
+        grouped_ranks: dict[int, list[tuple[int, int]]] = {}
 
         # global world size == sum of all the role world sizes
         expected_world_size = sum(expected_role_world_sizes.values())

test/distributed/elastic/multiprocessing/api_test.py

@@ -16,7 +16,7 @@ import sys
 import tempfile
 import time
 from itertools import product
-from typing import Callable, Dict, List, Union
+from typing import Callable, Union
 from unittest import mock
 
 import torch
@@ -141,7 +141,7 @@ def echo2(msg: str, fail: bool = False) -> str:
     return msg
 
 
-def echo_large(size: int) -> Dict[int, str]:
+def echo_large(size: int) -> dict[int, str]:
     """
     returns a large output ({0: test0", 1: "test1", ..., (size-1):f"test{size-1}"})
     """
@@ -167,13 +167,13 @@ def dummy_compute() -> torch.Tensor:
     return torch.rand(100, 100)
 
 
-def redirects_oss_test() -> List[Std]:
+def redirects_oss_test() -> list[Std]:
     return [
         Std.NONE,
     ]
 
 
-def redirects_all() -> List[Std]:
+def redirects_all() -> list[Std]:
     return [
         Std.NONE,
         Std.OUT,
@@ -240,14 +240,14 @@ class _StartProcessesTest(TestCase):
     def log_dir(self):
         return tempfile.mkdtemp(dir=self.test_dir)
 
-    def assert_in_file(self, expected: List[str], filename: str) -> None:
+    def assert_in_file(self, expected: list[str], filename: str) -> None:
         expected = [f"{line.rstrip()}\n" for line in expected]
         with open(filename) as fp:
             actual = fp.readlines()
             for line in expected:
                 self.assertIn(line, actual)
 
-    def assert_pids_noexist(self, pids: Dict[int, int]):
+    def assert_pids_noexist(self, pids: dict[int, int]):
         for local_rank, pid in pids.items():
             with self.assertRaises(
                 OSError, msg=f"local_rank: {local_rank} pid: {pid} should not exist"

test/distributed/elastic/multiprocessing/tail_log_test.py

@@ -16,7 +16,6 @@ import unittest
 from concurrent.futures import wait
 from concurrent.futures._base import ALL_COMPLETED
 from concurrent.futures.thread import ThreadPoolExecutor
-from typing import Dict, Set
 from unittest import mock
 
 from torch.distributed.elastic.multiprocessing.tail_log import TailLog
@@ -72,7 +71,7 @@ class TailLogTest(unittest.TestCase):
         tail.stop()
 
         dst.seek(0)
-        actual: Dict[int, Set[int]] = {}
+        actual: dict[int, set[int]] = {}
 
         for line in dst.readlines():
             header, num = line.split(":")
@@ -123,7 +122,7 @@ class TailLogTest(unittest.TestCase):
         tail.stop()
         dst.seek(0)
 
-        headers: Set[str] = set()
+        headers: set[str] = set()
         for line in dst.readlines():
             header, _ = line.split(":")
             headers.add(header)

test/distributed/elastic/rendezvous/api_test.py

@@ -6,7 +6,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, cast, Dict, SupportsInt
+from typing import Any, cast, SupportsInt
 from unittest import TestCase
 
 from torch.distributed.elastic.rendezvous import (
@@ -24,7 +24,7 @@ class RendezvousParametersTest(TestCase):
         self._run_id = "dummy_run_id"
         self._min_nodes = 3
         self._max_nodes = 6
-        self._kwargs: Dict[str, Any] = {}
+        self._kwargs: dict[str, Any] = {}
 
     def _create_params(self) -> RendezvousParameters:
         return RendezvousParameters(

test/distributed/elastic/rendezvous/dynamic_rendezvous_test.py

@@ -15,7 +15,7 @@ import time
 from abc import ABC, abstractmethod
 from base64 import b64encode
 from datetime import datetime, timedelta, timezone
-from typing import Callable, cast, Optional, Tuple
+from typing import Callable, cast, Optional
 from unittest import TestCase
 from unittest.mock import call, MagicMock, Mock, patch, PropertyMock
 
@@ -186,7 +186,7 @@ class FakeRendezvousBackend(RendezvousBackend):
     def name(self) -> str:
         return "fake_backend"
 
-    def get_state(self) -> Optional[Tuple[bytes, Token]]:
+    def get_state(self) -> Optional[tuple[bytes, Token]]:
         if self._token == 0:
             return None
 
@@ -194,7 +194,7 @@ class FakeRendezvousBackend(RendezvousBackend):
 
     def set_state(
         self, state: bytes, token: Optional[Token] = None
-    ) -> Optional[Tuple[bytes, Token, bool]]:
+    ) -> Optional[tuple[bytes, Token, bool]]:
         if token is None:
             token = 0
 

test/distributed/elastic/rendezvous/rendezvous_backend_test.py

@@ -7,7 +7,7 @@
 # LICENSE file in the root directory of this source tree.
 
 from abc import ABC, abstractmethod
-from typing import Any, Callable, cast, Optional, Tuple
+from typing import Any, Callable, cast, Optional
 
 from torch.distributed.elastic.rendezvous import RendezvousStateError
 from torch.distributed.elastic.rendezvous.dynamic_rendezvous import (
@@ -32,12 +32,12 @@ class RendezvousBackendTestMixin(ABC):
 
     def _set_state(
         self, state: bytes, token: Optional[Any] = None
-    ) -> Tuple[bytes, Token, bool]:
+    ) -> tuple[bytes, Token, bool]:
         result = self._backend.set_state(state, token)
 
         self.assertIsNotNone(result)
 
-        return cast(Tuple[bytes, Token, bool], result)
+        return cast(tuple[bytes, Token, bool], result)
 
     def test_get_state_returns_backend_state(self) -> None:
         self._backend.set_state(b"x")
@@ -46,7 +46,7 @@ class RendezvousBackendTestMixin(ABC):
 
         self.assertIsNotNone(result)
 
-        state, token = cast(Tuple[bytes, Token], result)
+        state, token = cast(tuple[bytes, Token], result)
 
         self.assertEqual(b"x", state)
         self.assertIsNotNone(token)

test/distributed/elastic/rendezvous/utils_test.py

@@ -10,7 +10,6 @@ import socket
 import threading
 import time
 from datetime import timedelta
-from typing import List
 from unittest import TestCase
 from unittest.mock import patch
 
@@ -350,7 +349,7 @@ class PeriodicTimerTest(TestCase):
         call_interval = 0.2
 
         # Keep the log of intervals between each consecutive call.
-        actual_call_intervals: List[float] = []
+        actual_call_intervals: list[float] = []
 
         # Keep the number of times the function was called.
         call_count = 0

test/distributed/elastic/test_control_plane.py

@@ -7,7 +7,6 @@ import pickle
 import socket
 import tempfile
 from contextlib import contextmanager
-from typing import Dict
 
 from urllib3.connection import HTTPConnection
 from urllib3.connectionpool import HTTPConnectionPool
@@ -181,7 +180,7 @@ class WorkerServerTest(TestCase):
             def body(self) -> bytes:
                 return b"dummy"
 
-            def params(self) -> Dict[str, str]:
+            def params(self) -> dict[str, str]:
                 return {}
 
         class Response(_Response):

test/distributed/elastic/utils/util_test.py

@@ -9,7 +9,6 @@
 
 import datetime
 from multiprocessing.pool import ThreadPool
-from typing import List
 from unittest import mock
 
 import torch.distributed as dist
@@ -40,7 +39,7 @@ class MockStore:
         self.ops.append(("get", key))
         return "value"
 
-    def multi_get(self, keys: List[str]) -> List[str]:
+    def multi_get(self, keys: list[str]) -> list[str]:
         self.ops.append(("multi_get", keys))
         return ["value"] * len(keys)
 
@@ -48,7 +47,7 @@ class MockStore:
         self.ops.append(("add", key, val))
         return 3
 
-    def wait(self, keys: List[str]) -> None:
+    def wait(self, keys: list[str]) -> None:
         self.ops.append(("wait", keys))
 
 
@@ -157,7 +156,7 @@ class StoreUtilTest(TestCase):
             return ""
 
         with ThreadPool(N - 1) as pool:
-            outputs: List[str] = pool.map(run_barrier_for_rank, range(N - 1))
+            outputs: list[str] = pool.map(run_barrier_for_rank, range(N - 1))
 
         self.assertTrue(any("missing_ranks=[Rank 2 host]" in msg for msg in outputs))
 

test/distributed/fsdp/test_fsdp_backward_prefetch.py

@@ -1,7 +1,6 @@
 # Owner(s): ["oncall: distributed"]
 
 import sys
-from typing import List
 from unittest.mock import patch
 
 import torch
@@ -102,7 +101,7 @@ class TestBackwardPrefetch(FSDPTest):
         tgt = torch.randn((20, 1, 1024), device=device_type)
 
         # monkey patch
-        all_handle_fqns: List[List[str]] = []
+        all_handle_fqns: list[list[str]] = []
 
         def patched_get_handle_to_prefetch(*args, **kwargs):
             handle = orig_get_handle_to_prefetch(*args, **kwargs)

test/distributed/fsdp/test_fsdp_comm.py

@@ -2,7 +2,7 @@
 import sys
 from contextlib import nullcontext
 from enum import auto, Enum
-from typing import List, Optional
+from typing import Optional
 from unittest.mock import patch
 
 import torch
@@ -319,7 +319,7 @@ class TestExplicitUnshard(FSDPTest):
                 self.mlp2 = MLP(dim)
                 self.mlp3 = MLP(dim)
 
-            def forward(self, ys: List[torch.Tensor], works: List[dist.Work]):
+            def forward(self, ys: list[torch.Tensor], works: list[dist.Work]):
                 (y1, y2, y3), (work1, work2, work3) = ys, works
                 work1.wait()
                 z1 = self.mlp1(y1)
@@ -372,7 +372,7 @@ class TestExplicitUnshard(FSDPTest):
         torch.manual_seed(42 + self.rank + 1)
         inp = torch.randn((batch_size, dim), device=device_type)
         for _ in range(10):
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for _model, _optim in ((ref_model, ref_optim), (model, optim)):
                 losses.append(_model(inp).sum())
                 losses[-1].backward()

test/distributed/fsdp/test_fsdp_core.py

@@ -4,7 +4,7 @@ import functools
 import itertools
 import sys
 import unittest
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Optional
 from unittest import mock
 
 import torch
@@ -76,7 +76,7 @@ class TestParityWithDDP(FSDPTest):
     PyTorch DDP vs. FullyShardedDataParallel.
     """
 
-    def _get_device_init_modes(self, cpu_offload: CPUOffload) -> List[DEVICEInitMode]:
+    def _get_device_init_modes(self, cpu_offload: CPUOffload) -> list[DEVICEInitMode]:
         modes = [
             DEVICEInitMode.DEVICE_AFTER,
             DEVICEInitMode.DEVICE_BEFORE,
@@ -89,7 +89,7 @@ class TestParityWithDDP(FSDPTest):
             modes.append(DEVICEInitMode.DEVICE_NEVER)
         return modes
 
-    def _get_subtest_config(self, cpu_offload: CPUOffload) -> Dict[str, List[Any]]:
+    def _get_subtest_config(self, cpu_offload: CPUOffload) -> dict[str, list[Any]]:
         """Returns a subtest configuration that subtests CUDA initialization
         modes and prefetching settings together."""
         return {

test/distributed/fsdp/test_fsdp_grad_acc.py

@@ -4,7 +4,7 @@ import contextlib
 import itertools
 import sys
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Optional
 
 import torch
 from torch import distributed as dist
@@ -71,7 +71,7 @@ class _GradAccConfigs:
     sole purpose of overriding :meth:`__repr__` to remove spaces.
     """
 
-    configs: List[_GradAccConfig]
+    configs: list[_GradAccConfig]
 
     def __repr__(self) -> str:
         # Override to remove any spaces in the string to appease the internal
@@ -90,7 +90,7 @@ class TestGradAcc(FSDPTest):
     def _test_grad_acc(
         self,
         batch_dim: int,
-        configs: List[_GradAccConfig],
+        configs: list[_GradAccConfig],
         cpu_offload: CPUOffload,
         backward_prefetch: Optional[BackwardPrefetch],
         sharding_strategy: ShardingStrategy,
@@ -146,8 +146,8 @@ class TestGradAcc(FSDPTest):
         def permute_tensor(x: torch.Tensor):
             return x.view(-1)[torch.randperm(x.numel())].view_as(x)
 
-        batch: Tuple[torch.Tensor, ...] = fsdp_model.module.get_input(device)
-        batches: List[Tuple[torch.Tensor, ...]] = [batch]
+        batch: tuple[torch.Tensor, ...] = fsdp_model.module.get_input(device)
+        batches: list[tuple[torch.Tensor, ...]] = [batch]
         num_iters_to_acc = sum(config.num_iters for config in configs)
         for _ in range(num_iters_to_acc - 1):
             batches.append(tuple(permute_tensor(t) for t in batch))
@@ -158,7 +158,7 @@ class TestGradAcc(FSDPTest):
                 ), "Check the test to make sure that batches are distinct"
 
         # Concatenate the batches along the given batch dimension
-        concat_batch: Tuple[torch.Tensor, ...] = tuple(
+        concat_batch: tuple[torch.Tensor, ...] = tuple(
             torch.cat(ts, dim=batch_dim) for ts in zip(*batches)
         )
 
@@ -214,7 +214,7 @@ class TestGradAcc(FSDPTest):
         # Check that the optimizer step does not error
         optim.step()
 
-    def _get_subtest_config(self) -> Dict[str, List[Any]]:
+    def _get_subtest_config(self) -> dict[str, list[Any]]:
         """Returns a subtest configuration that subtests prefetching."""
         return {
             "backward_prefetch": [

test/distributed/fsdp/test_fsdp_hybrid_shard.py

@@ -5,7 +5,7 @@ import sys
 from collections import Counter
 from enum import auto, Enum
 from functools import partial
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import torch
 import torch.distributed as dist
@@ -363,7 +363,7 @@ class TestFSDPHybridShard(FSDPTest):
         torch.manual_seed(global_pg.rank() + 1)
         for _ in range(5):
             inp = fsdp_model.module.get_input(torch.device("cuda"))
-            losses: List[torch.Tensor] = []
+            losses: list[torch.Tensor] = []
             for model, optim in ((fsdp_model, fsdp_optim), (hsdp_model, hsdp_optim)):
                 optim.zero_grad()
                 loss = model(*inp).sum()
@@ -396,7 +396,7 @@ class TestFSDPHybridShard(FSDPTest):
         sharding_strategy_mode: str,
         use_orig_params: bool,
         hsdp_process_groups: Optional[
-            Tuple[dist.ProcessGroup, dist.ProcessGroup]
+            tuple[dist.ProcessGroup, dist.ProcessGroup]
         ] = None,
         hsdp_device_mesh: Optional = None,
     ):

test/distributed/fsdp/test_fsdp_misc.py

@@ -8,7 +8,7 @@ from collections import namedtuple
 from contextlib import nullcontext
 from copy import deepcopy
 from itertools import chain
-from typing import Any, Tuple
+from typing import Any
 
 import torch
 import torch.distributed as dist
@@ -945,7 +945,7 @@ class TestFSDPMiscMultiThread(FSDPTestMultiThread):
             self._test_homogeneous_attributes,
         )
 
-    def _test_homogeneous_attributes(self, attr_name_and_values: Tuple[str, Any, Any]):
+    def _test_homogeneous_attributes(self, attr_name_and_values: tuple[str, Any, Any]):
         model = NestedWrappedModule.init(
             self.process_group,
             FSDPInitMode.NO_FSDP,

test/distributed/fsdp/test_fsdp_mixed_precision.py

@@ -6,7 +6,7 @@ import os
 import sys
 from functools import partial
 from itertools import product
-from typing import Any, Dict, List
+from typing import Any
 
 import torch
 import torch.cuda.nccl as nccl
@@ -521,7 +521,7 @@ class TestFSDPMixedPrecisionSharded(TestFSDPMixedPrecision):
     def world_size(self):
         return 2
 
-    def _get_subtest_config(self) -> Dict[str, List[Any]]:
+    def _get_subtest_config(self) -> dict[str, list[Any]]:
         """Returns a subtest configuration that subtests prefetching settings
         together."""
         return {
@@ -1136,7 +1136,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
 
     @skip_if_lt_x_gpu(2)
     def test_float16_on_one_submodule(self):
-        forward_inputs: Dict[str, nn.Module] = {}
+        forward_inputs: dict[str, nn.Module] = {}
         float16 = MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True)
 
         model = SaveForwardInputsModel(
@@ -1158,7 +1158,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
 
     @skip_if_lt_x_gpu(2)
     def test_float16_on_one_submodule_skip_inputs(self):
-        forward_inputs: Dict[nn.Module, torch.Tensor] = {}
+        forward_inputs: dict[nn.Module, torch.Tensor] = {}
         float16 = MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=False)
 
         model = SaveForwardInputsModel(
@@ -1179,7 +1179,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
 
     @skip_if_lt_x_gpu(2)
     def test_float16_on_one_submodule_skip_inputs_error(self):
-        forward_inputs: Dict[nn.Module, torch.Tensor] = {}
+        forward_inputs: dict[nn.Module, torch.Tensor] = {}
         float16 = MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=False)
 
         model = SaveForwardInputsModel(
@@ -1198,7 +1198,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
 
     @skip_if_lt_x_gpu(2)
     def test_submodules_with_different_precisions_error(self):
-        forward_inputs: Dict[nn.Module, torch.Tensor] = {}
+        forward_inputs: dict[nn.Module, torch.Tensor] = {}
         float16 = MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True)
         float32 = MixedPrecision(param_dtype=torch.float32, cast_forward_inputs=True)
 
@@ -1222,7 +1222,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
 
     @skip_if_lt_x_gpu(2)
     def test_submodules_with_different_precisions(self):
-        forward_inputs: Dict[nn.Module, torch.Tensor] = {}
+        forward_inputs: dict[nn.Module, torch.Tensor] = {}
         float16 = MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True)
         float32 = MixedPrecision(param_dtype=torch.float32, cast_forward_inputs=True)
 
@@ -1244,7 +1244,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
     @skip_if_lt_x_gpu(2)
     def test_submodules_with_external_inputs(self):
         class ToyModule(nn.Module):
-            def __init__(self, forward_inputs: Dict[str, torch.Tensor]) -> None:
+            def __init__(self, forward_inputs: dict[str, torch.Tensor]) -> None:
                 super().__init__()
                 self.l = nn.Linear(100, 100)
                 self.forward_inputs = forward_inputs
@@ -1255,7 +1255,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
                 return self.l(x)
 
         class ToyModel(nn.Module):
-            def __init__(self, forward_inputs: Dict[str, torch.Tensor]) -> None:
+            def __init__(self, forward_inputs: dict[str, torch.Tensor]) -> None:
                 super().__init__()
                 self.l1 = nn.Linear(100, 100)
                 self.l2 = ToyModule(forward_inputs)
@@ -1266,7 +1266,7 @@ class TestFSDPDifferentSubmodulePrecision(FSDPTest):
                 y = torch.ones(2, 100, device="cuda", dtype=torch.float32)
                 return self.l2(self.l1(x), y)
 
-        forward_inputs: Dict[str, torch.Tensor] = {}
+        forward_inputs: dict[str, torch.Tensor] = {}
 
         float16 = MixedPrecision(param_dtype=torch.float16)
         model = ToyModel(forward_inputs).cuda()
@@ -1343,7 +1343,7 @@ class TestFSDPTrainEval(FSDPTest):
         torch.manual_seed(1 + self.rank)
         eval_src = torch.randn((8, 1, 512), device=device)
         eval_tgt = torch.randn((16, 1, 512), device=device)
-        eval_out_sums: List[torch.Tensor] = []
+        eval_out_sums: list[torch.Tensor] = []
         # An iteration consists of training forward/backward/optimizer,
         # updating the EMA copy with the main copy, and eval forward
         for _ in range(3):

test/distributed/fsdp/test_fsdp_optim_state.py

@@ -4,7 +4,7 @@ import bisect
 import sys
 from copy import deepcopy
 from enum import auto, Enum
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type
+from typing import Any, Callable, Optional
 
 import torch
 import torch.nn as nn
@@ -177,7 +177,7 @@ class NestedModel(torch.nn.Module):
         model: torch.nn.Module,
         group: Optional[dist.ProcessGroup] = None,
         ignore_modules: bool = False,
-        fsdp_kwargs: Optional[Dict[str, Any]] = None,
+        fsdp_kwargs: Optional[dict[str, Any]] = None,
     ) -> torch.nn.Module:
         if fsdp_kwargs is None:
             fsdp_kwargs = {}
@@ -214,7 +214,7 @@ class NestedModel(torch.nn.Module):
     def wrap_alt(
         model: torch.nn.Module,
         group: Optional[dist.ProcessGroup] = None,
-        fsdp_kwargs: Optional[Dict[str, Any]] = None,
+        fsdp_kwargs: Optional[dict[str, Any]] = None,
     ) -> torch.nn.Module:
         if fsdp_kwargs is None:
             fsdp_kwargs = {}
@@ -231,7 +231,7 @@ class NestedModel(torch.nn.Module):
         model,
         add_to_fsdp_module: bool,
         group=None,
-    ) -> Tuple[torch.nn.Module, List[torch.nn.Parameter]]:
+    ) -> tuple[torch.nn.Module, list[torch.nn.Parameter]]:
         """Registers unmanaged parameters before wrapping with :meth:`wrap`."""
         device = next(model.parameters()).device
         unmanaged_param = torch.nn.Parameter(torch.randn(5, 5, device=device))
@@ -277,12 +277,12 @@ class NestedModel(torch.nn.Module):
 
     # NOTE: We exclude `self.bias` from either parameter group to test the
     # case where the optimizer input does not include all model parameters
-    def param_group0(self) -> List[torch.nn.Parameter]:
+    def param_group0(self) -> list[torch.nn.Parameter]:
         # Use `block1`'s parameters for the first parameter group to deviate
         # from the `model.parameters()` order
         return list(self.block1.parameters())
 
-    def param_group1(self) -> List[torch.nn.Parameter]:
+    def param_group1(self) -> list[torch.nn.Parameter]:
         # Deviate from the `model.parameters()` order further by rearranging
         # `block2`'s parameters to be before `block0`'s parameters
         return list(self.block2.parameters()) + list(self.block0.parameters())
@@ -322,10 +322,10 @@ class TestFSDPOptimState(FSDPTest):
         wrap_alt: bool = False,  # ignored if `wrap=False`
         device: torch.device = torch.device("cuda"),
         group=None,
-        optim_class: Type[torch.optim.Optimizer] = torch.optim.Adam,
+        optim_class: type[torch.optim.Optimizer] = torch.optim.Adam,
         use_multiple_param_groups: bool = False,
         use_diff_optim_inputs: bool = False,
-        fsdp_kwargs: Optional[Dict[str, Any]] = None,
+        fsdp_kwargs: Optional[dict[str, Any]] = None,
     ):
         model = NestedModel().to(device)
         if wrap:
@@ -356,7 +356,7 @@ class TestFSDPOptimState(FSDPTest):
         wrap: bool,
         device: torch.device = torch.device("cuda"),
         group=None,
-        optim_class: Type[torch.optim.Optimizer] = torch.optim.Adam,
+        optim_class: type[torch.optim.Optimizer] = torch.optim.Adam,
         use_multiple_param_groups: bool = False,
         use_diff_optim_inputs: bool = False,
     ):
@@ -383,7 +383,7 @@ class TestFSDPOptimState(FSDPTest):
         optim: torch.optim.Optimizer,
         device: torch.device = torch.device("cuda"),
         num_iters: int = 1,
-    ) -> List[float]:
+    ) -> list[float]:
         """Performs a forward pass, backward pass, and optimizer step
         ``num_iters``-many times, and returns the per-iteration losses."""
         torch.manual_seed(0)  # set seed for determinism
@@ -399,7 +399,7 @@ class TestFSDPOptimState(FSDPTest):
             optim.step()
         return losses
 
-    def _broadcast_full_osd(self, full_osd: Dict[str, Any], group=None):
+    def _broadcast_full_osd(self, full_osd: dict[str, Any], group=None):
         """Broadcasts the full optimizer state dict in place of using
         ``torch.save()`` and ``torch.load()`` so that all ranks can have it."""
         obj_list = [full_osd]
@@ -413,8 +413,8 @@ class TestFSDPOptimState(FSDPTest):
 
     def _are_equal_states(
         self,
-        state1: Dict[str, Any],
-        state2: Dict[str, Any],
+        state1: dict[str, Any],
+        state2: dict[str, Any],
     ) -> bool:
         """Checks if ``state1`` and ``state2`` contain the same mappings."""
         if set(state1.keys()) != set(state2.keys()):
@@ -1450,7 +1450,7 @@ class TestFSDPOptimState(FSDPTest):
         self,
         should_check_method_fn: Callable[[str], bool],
         context_fn: Callable,
-        fsdp_kwargs: Optional[Dict[str, Any]],
+        fsdp_kwargs: Optional[dict[str, Any]],
     ):
         """
         Runs through all optimizer state checkpointing APIs with a context

test/distributed/fsdp/test_fsdp_sharded_grad_scaler.py

@@ -5,7 +5,7 @@ import functools
 import itertools
 import sys
 import unittest
-from typing import List, Optional
+from typing import Optional
 
 import torch
 from torch import distributed as dist
@@ -259,7 +259,7 @@ class TestShardedGradScalerParityWithDDP(FSDPTest):
         )
         grad_scaler = ShardedGradScaler(init_scale=2.0)
         ref_grad_scaler = torch.amp.GradScaler(device="cuda", init_scale=2.0)
-        scaled_losses: List[torch.Tensor] = []
+        scaled_losses: list[torch.Tensor] = []
         device = torch.device("cuda")
         torch.manual_seed(42 + self.rank + 1)
 

test/distributed/fsdp/test_fsdp_state_dict.py

@@ -6,7 +6,7 @@ import sys
 from contextlib import nullcontext
 from copy import deepcopy
 from functools import partial
-from typing import Any, Dict
+from typing import Any
 
 import torch
 import torch.nn as nn
@@ -787,7 +787,7 @@ class TestFSDPStateDict(FSDPTest):
 
     @staticmethod
     def _load_state_dict(
-        model: Module, state_dict_type: str, state_dict: Dict[str, Any]
+        model: Module, state_dict_type: str, state_dict: dict[str, Any]
     ):
         try:
             enum_val = STATE_DICT_MAPPING[state_dict_type]

test/distributed/fsdp/test_fsdp_tp_integration.py

@@ -2,7 +2,7 @@
 import copy
 import sys
 from collections import OrderedDict
-from typing import Dict, List, Optional, Tuple
+from typing import Optional
 
 import torch
 from torch import distributed as dist
@@ -62,11 +62,11 @@ class SimpleModel(torch.nn.Module):
         return self.net3(self.net2(self.relu(self.net1(x))))
 
     @staticmethod
-    def get_sharded_param_names() -> List[str]:
+    def get_sharded_param_names() -> list[str]:
         return ["net1.weight", "net1.bias", "net2.weight"]
 
     @staticmethod
-    def get_non_sharded_param_names() -> List[str]:
+    def get_non_sharded_param_names() -> list[str]:
         return ["net3.weight", "net3.bias"]
 
 
@@ -87,9 +87,9 @@ class TestTPFSDPIntegration(FSDPTest):
     def _get_params_and_sharding_info(
         self,
         model: SimpleModel,
-        sharded_param_names: List[str],
+        sharded_param_names: list[str],
         tensor_parallel_size: int,
-    ) -> Tuple[Dict[str, int], Dict[str, Tuple[torch.Size, int]]]:
+    ) -> tuple[dict[str, int], dict[str, tuple[torch.Size, int]]]:
         """ """
         assert (
             type(model) is SimpleModel
@@ -131,8 +131,8 @@ class TestTPFSDPIntegration(FSDPTest):
         self,
         tp_fsdp_model: FSDP,
         tp_pg: dist.ProcessGroup,
-        param_name_to_numel: Dict[str, int],
-        non_sharded_param_names: List[str],
+        param_name_to_numel: dict[str, int],
+        non_sharded_param_names: list[str],
     ) -> None:
         """
         Syncs the tensor parallel parameters' gradients following the data
@@ -177,11 +177,11 @@ class TestTPFSDPIntegration(FSDPTest):
         self,
         model: FSDP,
         uses_tp: bool,
-        param_name_to_numel: Dict[str, int],
-        param_name_to_sharding_info: Dict[str, Tuple[torch.Size, int]],
+        param_name_to_numel: dict[str, int],
+        param_name_to_sharding_info: dict[str, tuple[torch.Size, int]],
         tp_pg: Optional[dist.ProcessGroup],
         fsdp_pg: Optional[dist.ProcessGroup],
-        sharded_param_names: Optional[List[str]],
+        sharded_param_names: Optional[list[str]],
     ) -> torch.Tensor:
         """
         Returns all unsharded gradients as a single flattened tensor. This

test/distributed/fsdp/test_fsdp_unshard_params.py

@@ -3,7 +3,7 @@ import contextlib
 import itertools
 import math
 import sys
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 import torch
 import torch.distributed.fsdp._traversal_utils as traversal_utils
@@ -55,7 +55,7 @@ class TestUnshardParamsBase(FSDPTest):
         self,
         writeback: bool,
         check_outer: bool,
-        **fsdp_kwargs: Dict[str, Any],
+        **fsdp_kwargs: dict[str, Any],
     ):
         model = nn.Sequential(
             nn.Linear(5, 5, bias=False, device=device_type.type),
@@ -101,7 +101,7 @@ class TestUnshardParamsBase(FSDPTest):
             for param in model.parameters():
                 self.assertEqual(param.device, cpu_device)
 
-    def _get_test_unshard_params_writeback_config(self) -> Dict[str, List[Any]]:
+    def _get_test_unshard_params_writeback_config(self) -> dict[str, list[Any]]:
         return {
             "writeback": [True, False],
             "check_outer": [True, False],
@@ -193,7 +193,7 @@ class TestUnshardParamsBase(FSDPTest):
             num_fsdp_roots += fsdp_state._is_root
         self.assertGreater(num_fsdp_roots, 1)
 
-    def _get_test_unshard_params_param_data_config(self) -> Dict[str, List[Any]]:
+    def _get_test_unshard_params_param_data_config(self) -> dict[str, list[Any]]:
         return {
             "rank0_only": [False, True],
             "offload_to_cpu": [False, True],
@@ -493,7 +493,7 @@ class TestUnshardParams(TestUnshardParamsBase):
         def _check_grads(
             ddp_model: DDP,
             fsdp_model: FSDP,
-            old_fsdp_grads: Optional[List[torch.Tensor]],
+            old_fsdp_grads: Optional[list[torch.Tensor]],
         ):
             """
             Checks that writes to the FSDP parameters' gradients persist or do

test/distributed/fsdp/test_fsdp_use_orig_params.py

@@ -6,7 +6,7 @@ import itertools
 import os
 import sys
 import unittest
-from typing import Any, Dict, List, Optional, Tuple, Type
+from typing import Any, Optional
 
 import torch
 import torch.nn as nn
@@ -65,7 +65,7 @@ class TestFSDPUseOrigParamsMultipleParamGroups(FSDPTest):
     def world_size(self) -> int:
         return 2
 
-    def _get_param_groups(self, model: nn.Module) -> List[Dict[str, Any]]:
+    def _get_param_groups(self, model: nn.Module) -> list[dict[str, Any]]:
         """
         Constructs separate parameter groups for weights, biases, and other
         parameters.
@@ -87,7 +87,7 @@ class TestFSDPUseOrigParamsMultipleParamGroups(FSDPTest):
     def _get_optim(
         self,
         model: nn.Module,
-        optim_class: Type[torch.optim.Optimizer],
+        optim_class: type[torch.optim.Optimizer],
         multi_tensor: bool,
     ) -> torch.optim.Optimizer:
         """
@@ -117,12 +117,12 @@ class TestFSDPUseOrigParamsMultipleParamGroups(FSDPTest):
         self,
         device_init_mode: DEVICEInitMode,
         init_optim_before_wrap: bool,
-        optim_class: Type[torch.optim.Optimizer],
+        optim_class: type[torch.optim.Optimizer],
         multi_tensor: bool,
         sharding_strategy: ShardingStrategy,
         backward_prefetch: Optional[BackwardPrefetch],
         cpu_offload: CPUOffload,
-    ) -> Tuple[FSDP, torch.optim.Optimizer]:
+    ) -> tuple[FSDP, torch.optim.Optimizer]:
         """
         Returns a transformer with shared parameters wrapped with FSDP and a
         corresponding optimizer.
@@ -335,7 +335,7 @@ class TestFSDPUseOrigParamsMultipleParamGroups(FSDPTest):
         self,
         device_init_mode: DEVICEInitMode,
         init_optim_before_wrap: bool,
-        optim_class: Type[torch.optim.Optimizer],
+        optim_class: type[torch.optim.Optimizer],
         multi_tensor: bool,
         set_to_none: bool,
         backward_prefetch: Optional[BackwardPrefetch],
@@ -566,7 +566,7 @@ class TestFSDPUseOrigParamsUnshardReshard(FSDPTest):
         self,
         sharding_strategy: ShardingStrategy,
         cpu_offload: CPUOffload,
-    ) -> Tuple[FSDP, torch.optim.Optimizer, FSDP, torch.optim.Optimizer]:
+    ) -> tuple[FSDP, torch.optim.Optimizer, FSDP, torch.optim.Optimizer]:
         """
         Returns a pair of (FSDP model, optimizer) for ``use_orig_params=False``
         and ``True``, respectively.
@@ -778,7 +778,7 @@ class TestFSDPUseOrigParamsParamAccess(FSDPTest):
                 z = self.lin2(z)
                 return z
 
-            def get_input(self, device: torch.device) -> Tuple[torch.Tensor, ...]:
+            def get_input(self, device: torch.device) -> tuple[torch.Tensor, ...]:
                 return (torch.randn((2, 5)).to(device),)
 
             def get_loss(self, inp, out):
@@ -872,7 +872,7 @@ class TestFSDPUseOrigParamsWriteback(FSDPTest):
             z = self.lin2(z)
             return z
 
-        def get_input(self, device: torch.device) -> Tuple[torch.Tensor, ...]:
+        def get_input(self, device: torch.device) -> tuple[torch.Tensor, ...]:
             return (torch.randn((2, 5)).to(device),)
 
         def get_loss(self, inp, out):

test/distributed/fsdp/test_utils.py

@@ -4,7 +4,6 @@ import random
 import sys
 from collections import OrderedDict
 from dataclasses import dataclass
-from typing import List
 
 import torch
 import torch.nn as nn
@@ -62,13 +61,13 @@ class TestUtils(TestCase):
         class NonFrozenDataClass:
             some_key: str
             some_float: float
-            some_tensor: List[torch.Tensor]
+            some_tensor: list[torch.Tensor]
 
         @dataclass(frozen=True)
         class FrozenDataClass:
             some_key: str
             some_float: float
-            some_tensor: List[torch.Tensor]
+            some_tensor: list[torch.Tensor]
 
         # create a mixed bag of data.
         data = [1, "str"]

test/distributed/launcher/api_test.py

@@ -16,7 +16,7 @@ import time
 import unittest
 import uuid
 from contextlib import closing
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 from unittest import mock
 from unittest.mock import MagicMock, Mock, patch
 
@@ -59,7 +59,7 @@ def get_test_launch_config(
     nproc_per_node: int,
     run_id: str = "",
     rdzv_backend: str = "etcd",
-    config: Optional[Dict[str, Any]] = None,
+    config: Optional[dict[str, Any]] = None,
 ) -> LaunchConfig:
     rdzv_configs = {}
     if config:

test/distributed/nn/jit/test_instantiator.py

@@ -3,7 +3,6 @@
 
 import sys
 from pathlib import Path
-from typing import Tuple
 
 import torch
 import torch.distributed as dist
@@ -22,7 +21,7 @@ from torch.testing._internal.common_utils import run_tests, TestCase
 class MyModuleInterface:
     def forward(
         self, tensor: Tensor, number: int, word: str = "default"
-    ) -> Tuple[Tensor, int, str]:
+    ) -> tuple[Tensor, int, str]:
         pass
 
 

test/distributed/optim/test_zero_redundancy_optimizer.py

@@ -10,7 +10,7 @@ import os
 import sys
 import unittest
 from contextlib import nullcontext
-from typing import Any, cast, List
+from typing import Any, cast
 
 import numpy as np
 
@@ -207,7 +207,7 @@ class TestZeroRedundancyOptimizerSingleRank(TestZeroRedundancyOptimizer):
                 super().step()
                 kwarg.append(5)
 
-        kwarg: List[Any] = []
+        kwarg: list[Any] = []
         x = torch.tensor([1.0], device=self.device, requires_grad=True)
         o = ZeroRedundancyOptimizer(
             [x],

test/distributed/pipelining/schedule_registry.py

@@ -2,7 +2,7 @@
 # Owner(s): ["oncall: distributed"]
 # This file is a Schedule zoo for testing torch.distributed.pipelining.
 # It includes schedules designed purely for testing purposes
-from typing import Callable, Dict, List, Optional
+from typing import Callable, Optional
 
 from torch.distributed.pipelining.schedules import (
     _Action,
@@ -32,9 +32,9 @@ class ScheduleVShaped(PipelineScheduleMulti):
 
     def __init__(
         self,
-        stages: List[_PipelineStageBase],
+        stages: list[_PipelineStageBase],
         n_microbatches: int,
-        stage_index_to_group_rank: Dict[int, int],
+        stage_index_to_group_rank: dict[int, int],
         loss_fn: Optional[Callable] = None,
         scale_grads: bool = True,
     ):
@@ -82,9 +82,9 @@ class ScheduleUnbalanced(PipelineScheduleMulti):
 
     def __init__(
         self,
-        stages: List[_PipelineStageBase],
+        stages: list[_PipelineStageBase],
         n_microbatches: int,
-        stage_index_to_group_rank: Dict[int, int],
+        stage_index_to_group_rank: dict[int, int],
         loss_fn: Optional[Callable] = None,
         scale_grads: bool = True,
     ):
@@ -134,7 +134,7 @@ class ScheduleWithW(PipelineScheduleMulti):
 
     def __init__(
         self,
-        stages: List[_PipelineStageBase],
+        stages: list[_PipelineStageBase],
         n_microbatches: int,
         loss_fn: Optional[Callable] = None,
         enable_zero_bubble: bool = True,
@@ -195,7 +195,7 @@ class ScheduleWithReorderedB(_PipelineScheduleRuntime):
 
     def __init__(
         self,
-        stages: List[_PipelineStageBase],
+        stages: list[_PipelineStageBase],
         n_microbatches: int,
         loss_fn: Optional[Callable] = None,
         scale_grads: bool = True,

test/distributed/pipelining/test_schedule.py

@@ -4,7 +4,6 @@ import copy
 import csv
 import logging
 import os
-from typing import List
 
 from model_registry import MultiMLP
 
@@ -356,7 +355,7 @@ instantiate_parametrized_tests(TestSchedulePlan)
 class TestScheduleLowering(TestCase):
     """Tests lowering passes that convert simple compute-only (FBW) schedules into compute+comms schedules"""
 
-    def _parse_actions(self, actions: List[str]) -> List[_Action]:
+    def _parse_actions(self, actions: list[str]) -> list[_Action]:
         return [_Action.from_str(s) for s in actions]
 
     @parametrize(

test/distributed/tensor/debug/test_comm_mode_features.py

@@ -1,7 +1,7 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates
 # Owner(s): ["oncall: distributed"]
 
-from typing import Any, Dict
+from typing import Any
 
 import torch
 from torch.distributed._tensor import DeviceMesh
@@ -57,8 +57,8 @@ class TestCommModeFeatures(DTensorTestBase):
         Used to generate the ground-truth parameter and sharding info for a given distributed model to
         verify comm_mode correctness
         """
-        module_parameters_dict: Dict[str, Any] = {}
-        module_sharding_dict: Dict[str, Any] = {}
+        module_parameters_dict: dict[str, Any] = {}
+        module_sharding_dict: dict[str, Any] = {}
 
         for name, parameters in model.named_parameters():
             # splits name into module name to create FQN and parameter name

test/distributed/tensor/experimental/test_tp_transform.py

@@ -1,6 +1,5 @@
 # Owner(s): ["oncall: distributed"]
 from collections import defaultdict
-from typing import Dict
 
 import torch
 from torch.distributed._tensor.experimental._tp_transform import (
@@ -57,9 +56,9 @@ class TensorParallelTest(DTensorTestBase):
         super().setUp()
 
     def assert_has_c10d_ops(
-        self, gm: torch.fx.GraphModule, expected_ops_count: Dict[str, int]
+        self, gm: torch.fx.GraphModule, expected_ops_count: dict[str, int]
     ) -> None:
-        actual_ops_count: Dict[str, int] = defaultdict(int)
+        actual_ops_count: dict[str, int] = defaultdict(int)
         for node in gm.graph.nodes:
             if node.op == "call_function":
                 if "c10d_functional" in str(node.target):
@@ -100,7 +99,7 @@ class TensorParallelTest(DTensorTestBase):
         torch.manual_seed(0)
         model = MLPListModule(2).to(device=self.device_type)
         inputs = (torch.randn((10, 12)).to(device=self.device_type),)
-        parallel_strategies: Dict[str, ParallelStyle] = {
+        parallel_strategies: dict[str, ParallelStyle] = {
             "mlps.0.0": ColwiseParallel,
             "mlps.0.2": RowwiseParallel,
             "mlps.1.0": ColwiseParallel,
@@ -137,7 +136,7 @@ class TensorParallelTest(DTensorTestBase):
         torch.manual_seed(0)
         model = MLPListModule(1, bias=False).to(device=self.device_type)
         inputs = (torch.randn((10, 12)).to(device=self.device_type),)
-        parallel_strategies: Dict[str, ParallelStyle] = {
+        parallel_strategies: dict[str, ParallelStyle] = {
             "mlps.0.0": ColwiseParallel,
             "mlps.0.2": RowwiseParallel,
         }

test/distributed/tensor/parallel/test_tp_examples.py

@@ -3,7 +3,7 @@
 
 import itertools
 from copy import deepcopy
-from typing import Dict, NamedTuple, Optional
+from typing import NamedTuple, Optional
 
 import torch
 import torch.distributed as dist
@@ -52,9 +52,9 @@ reduce_scatter, all_gather, all_reduce = (
 
 
 class ExpCommCounts(NamedTuple):
-    fwd: Optional[Dict] = None
-    bwd: Optional[Dict] = None
-    optim: Optional[Dict] = None
+    fwd: Optional[dict] = None
+    bwd: Optional[dict] = None
+    optim: Optional[dict] = None
 
 
 class DistTensorParallelExampleTest(DTensorTestBase):

test/distributed/tensor/test_matrix_ops.py

@@ -3,7 +3,7 @@
 
 import itertools
 import unittest
-from typing import cast, List, Optional, Tuple
+from typing import cast, Optional
 
 import torch
 import torch.nn.functional as F
@@ -27,8 +27,8 @@ from torch.testing._internal.distributed._tensor.common_dtensor import (
 
 
 def scale_for_fp8(
-    t: torch.Tensor, scale_shape: Tuple[int]
-) -> Tuple[torch.Tensor, torch.Tensor]:
+    t: torch.Tensor, scale_shape: tuple[int]
+) -> tuple[torch.Tensor, torch.Tensor]:
     if all(d == 1 for d in scale_shape):
         t = t.unsqueeze(0).unsqueeze(-2)
     else:
@@ -116,7 +116,7 @@ class DistMatrixOpsTest(DTensorTestBase):
         local_res = torch.mm(t1, t2)
 
         def test_placement_comb(
-            placements1: List[Placement], placements2: List[Placement]
+            placements1: list[Placement], placements2: list[Placement]
         ) -> None:
             dt1 = distribute_tensor(t1, device_mesh, placements1)
             dt2 = distribute_tensor(t2, device_mesh, placements2)
@@ -272,9 +272,9 @@ class DistMatrixOpsTest(DTensorTestBase):
         batch_2 = torch.rand(4, 8, 8, device=self.device_type, requires_grad=True)
 
         def test_placement_comb(
-            tensor_placements: List[Placement],
-            batch_1_placements: List[Placement],
-            batch_2_placements: List[Placement],
+            tensor_placements: list[Placement],
+            batch_1_placements: list[Placement],
+            batch_2_placements: list[Placement],
             beta: int,
             alpha: int,
             batch_1_grad: Optional[torch.Tensor],
@@ -338,8 +338,8 @@ class DistMatrixOpsTest(DTensorTestBase):
         local_result.backward(grad_local_res)
 
         def test_placement_comb(
-            placements1: List[Placement],
-            placements2: List[Placement],
+            placements1: list[Placement],
+            placements2: list[Placement],
         ) -> None:
             mat1_dt = distribute_tensor(mat1, device_mesh, placements1)
             mat2_dt = distribute_tensor(mat2, device_mesh, placements2)

test/distributed/tensor/test_pointwise_ops.py

@@ -2,7 +2,7 @@
 # Owner(s): ["oncall: distributed"]
 
 from collections.abc import Sequence
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Optional
 from unittest import skip
 
 import torch
@@ -76,7 +76,7 @@ class DistElementwiseOpsTest(DTensorOpTestBase):
         op: Callable,
         pre_op_fn: Optional[Callable] = None,
         args: Sequence[Any] = (),
-        kwargs: Optional[Dict[str, Any]] = None,
+        kwargs: Optional[dict[str, Any]] = None,
     ):
         if pre_op_fn is None:
             pre_op_fn = no_op

test/distributed/tensor/test_view_ops.py

@@ -2,7 +2,7 @@
 # Owner(s): ["oncall: distributed"]
 
 import itertools
-from typing import cast, List
+from typing import cast
 
 import torch
 import torch.distributed as dist
@@ -160,7 +160,7 @@ class TestViewOps(DTensorTestBase):
         if op == torch.unbind:
             no_shard_dims.add(kwargs.get("dim", 0))
 
-        sharding_choices = cast(List[Placement], [Replicate()]) + [
+        sharding_choices = cast(list[Placement], [Replicate()]) + [
             Shard(i) for i, s in enumerate(in_shape) if s > 1 and i not in no_shard_dims
         ]
 
@@ -513,7 +513,7 @@ class TestViewOps(DTensorTestBase):
         # test sharded computation correctness
         # NOTE: For the input to torch.view_as_complex, sharding
         #       on the last two dimensions is not supported.
-        sharding_choices: List[Placement] = [Replicate(), Shard(0)]
+        sharding_choices: list[Placement] = [Replicate(), Shard(0)]
         all_sharding_choices = itertools.product(
             *(self.device_mesh.ndim * [sharding_choices])
         )

test/distributed/tensor/test_xla_integration.py

@@ -4,7 +4,7 @@
 import os
 import unittest
 from functools import wraps
-from typing import Any, Callable, Dict, Tuple
+from typing import Any, Callable
 
 import numpy as np
 
@@ -26,7 +26,7 @@ def with_xla(func: Callable) -> Callable:
 
     @wraps(func)  # pyre-ignore[6]
     def wrapper(
-        self, *args: Tuple[object], **kwargs: Dict[str, Any]  # type: ignore[misc]
+        self, *args: tuple[object], **kwargs: dict[str, Any]  # type: ignore[misc]
     ) -> None:
         # TODO(yeounoh) replace this with xr.use_spmd() when we deprecate the flag.
         os.environ["XLA_USE_SPMD"] = "1"

test/distributed/test_c10d_common.py

@@ -12,7 +12,7 @@ from dataclasses import dataclass
 from datetime import timedelta
 from itertools import product
 from sys import platform
-from typing import Dict, Optional
+from typing import Optional
 
 import torch
 import torch.distributed as dist
@@ -972,7 +972,7 @@ class CommonDistributedDataParallelTest:
     @dataclass
     class CustomOutput:
         o1: Optional[torch.Tensor]
-        o2: Dict[str, torch.Tensor]
+        o2: dict[str, torch.Tensor]
 
     class DataclassOutputModule(nn.Module):
         def __init__(self, skip_o1):

test/distributed/test_c10d_functional_native.py

@@ -1,7 +1,6 @@
 # Owner(s): ["module: c10d"]
 import threading
 import unittest
-from typing import List
 
 import torch
 import torch.distributed as dist
@@ -65,7 +64,7 @@ class TestWithNCCL(MultiProcessTestCase):
         return 2
 
     @property
-    def ranks(self) -> List[int]:
+    def ranks(self) -> list[int]:
         return list(range(self.world_size))
 
     @property
@@ -556,7 +555,7 @@ class CompileTest(TestCase):
     @unittest.skipIf(not HAS_GPU, "Inductor+gpu needs triton and recent GPU arch")
     @fresh_inductor_cache()
     def test_inductor_all_reduce_coalesced(self):
-        def func(args: List[torch.Tensor]) -> torch.Tensor:
+        def func(args: list[torch.Tensor]) -> torch.Tensor:
             bufs = [arg + 42 for arg in args]
             # Expect in-place with inductor allocated buf
             ar0 = funcol.all_reduce_coalesced(bufs, "avg", "0")
@@ -714,7 +713,7 @@ class CompileTest(TestCase):
     @unittest.skipIf(not HAS_GPU, "Inductor+gpu needs triton and recent GPU arch")
     @fresh_inductor_cache()
     def test_inductor_all_gather_into_tensor_coalesced(self):
-        def func(args: List[torch.Tensor]) -> torch.Tensor:
+        def func(args: list[torch.Tensor]) -> torch.Tensor:
             ag0 = funcol.all_gather_into_tensor_coalesced(args, "0")
             ag0 = [funcol.wait_tensor(out) for out in ag0]
             return ag0
@@ -796,7 +795,7 @@ class CompileTest(TestCase):
     @unittest.skipIf(not HAS_GPU, "Inductor+gpu needs triton and recent GPU arch")
     @fresh_inductor_cache()
     def test_inductor_reduce_scatter_tensor_coalesced(self):
-        def func(args: List[torch.Tensor]) -> torch.Tensor:
+        def func(args: list[torch.Tensor]) -> torch.Tensor:
             rs0 = funcol.reduce_scatter_tensor_coalesced(
                 args, "avg", [0] * len(args), "0"
             )

test/distributed/test_c10d_nccl.py

@@ -27,7 +27,6 @@ if not c10d.is_available() or not c10d.is_nccl_available():
     print("c10d NCCL not available, skipping tests", file=sys.stderr)
     sys.exit(0)
 
-from typing import Dict, List
 
 import test_c10d_common
 from test_c10d_common import ConvNet, DoubleGpuNet, gpus_for_rank, ModuleForDdpCommHook
@@ -2546,7 +2545,7 @@ class WorkHookTest(MultiProcessTestCase):
     def test_on_completion_hook_broadcast(self):
         pg = self._get_process_group()
         num_hook_fired = 0
-        durations: List[float] = []
+        durations: list[float] = []
 
         def hook(work_info: torch._C._distributed_c10d.WorkInfo):
             nonlocal num_hook_fired, durations
@@ -2574,7 +2573,7 @@ class WorkHookTest(MultiProcessTestCase):
     def test_on_completion_hook_mixed_ops(self):
         pg = self._get_process_group()
         num_hook_fired = 0
-        durations: List[float] = []
+        durations: list[float] = []
 
         def hook(work_info: torch._C._distributed_c10d.WorkInfo):
             nonlocal num_hook_fired, durations
@@ -2615,8 +2614,8 @@ class WorkHookTest(MultiProcessTestCase):
     @skip_if_lt_x_gpu(2)
     def test_on_completion_hook_with_ddp(self):
         pg = self._get_process_group()
-        num_hook_fired: Dict[int, int] = {}
-        durations: Dict[OpType, List[float]] = {}
+        num_hook_fired: dict[int, int] = {}
+        durations: dict[OpType, list[float]] = {}
 
         def hook(work_info: torch._C._distributed_c10d.WorkInfo):
             nonlocal num_hook_fired, durations
@@ -2673,8 +2672,8 @@ class WorkHookTest(MultiProcessTestCase):
         torch.cuda.set_device(self.rank)
 
         pg = self._get_process_group()
-        num_hook_fired: Dict[int, int] = {}
-        durations: Dict[OpType, List[float]] = {}
+        num_hook_fired: dict[int, int] = {}
+        durations: dict[OpType, list[float]] = {}
 
         def hook(work_info: torch._C._distributed_c10d.WorkInfo):
             nonlocal num_hook_fired, durations

test/distributed/test_c10d_spawn_gloo.py

@@ -2,21 +2,15 @@
 
 import copy
 import os
-import sys
 import tempfile
 
-import test_c10d_spawn
 from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
 
 import torch
 import torch.distributed as c10d
 import torch.nn as nn
-from torch.testing._internal.common_cuda import TEST_CUDA, TEST_MULTIGPU
-from torch.testing._internal.common_distributed import (
-    create_device,
-    requires_gloo,
-    skip_if_lt_x_gpu,
-)
+from torch.testing._internal.common_cuda import TEST_CUDA
+from torch.testing._internal.common_distributed import requires_gloo, skip_if_lt_x_gpu
 from torch.testing._internal.common_utils import (
     run_tests,
     skip_but_pass_in_sandcastle_if,
@@ -26,95 +20,6 @@ from torch.testing._internal.common_utils import (
 
 
 # Fails on Python-3.9, see https://github.com/pytorch/pytorch/issues/51619
-if sys.version_info < (3, 9):
-
-    class ProcessGroupShareTensorTest(
-        test_c10d_spawn.AbstractProcessGroupShareTensorTest, TestCase
-    ):
-        @classmethod
-        def opts(cls, threads=2):
-            opts = c10d.ProcessGroupGloo._Options()
-            opts._timeout = 5.0
-            opts._devices = [create_device(interface="lo")]
-            opts._threads = threads
-            return opts
-
-        @classmethod
-        def _init_pg_gloo(cls, rank, filename, world_size):
-            store = c10d.FileStore(filename, world_size)
-            backend = c10d.ProcessGroupGloo(
-                store, rank, world_size, ProcessGroupShareTensorTest.opts()
-            )
-            # set process group backends manually
-            c10d.init_process_group(
-                backend="gloo", store=store, rank=rank, world_size=world_size
-            )
-            pg = c10d.distributed_c10d._get_default_group()
-            pg._register_backend(
-                torch.device("cpu"), c10d.ProcessGroup.BackendType.GLOO, backend
-            )
-            pg._register_backend(
-                torch.device("cuda"), c10d.ProcessGroup.BackendType.GLOO, backend
-            )
-
-            return pg
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        def test_shared_broadcast_gloo(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_broadcast_process,
-                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_gloo,
-                1,
-            )
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        def test_shared_allreduce_gloo(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allreduce_process,
-                [torch.ones(2, 2).to(i) for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_gloo,
-                1,
-            )
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        def test_shared_allgather_gloo(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allgather_process,
-                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_gloo,
-                self.world_size,
-            )
-
-        @classmethod
-        def _test_allgather_chunk_process(
-            cls, rank, filename, shared_tensor, world_size, init_pg, c2p, p2c
-        ):
-            pg = init_pg(rank, filename, world_size)
-            chunks = torch.chunk(shared_tensor, world_size, dim=0)
-            x = chunks[rank]
-            ys = [torch.zeros_like(x) for _ in range(world_size)]
-            pg.allgather(ys, x).wait()
-            c2p.put((rank, chunks[0].to("cpu"), ys[0].to("cpu")))
-            c2p.put((rank, chunks[1].to("cpu"), ys[1].to("cpu")))
-            p2c.get()
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        def test_shared_allgather_chunk_gloo(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allgather_chunk_process,
-                torch.tensor(range(4)).reshape(2, 2),
-                ProcessGroupShareTensorTest._init_pg_gloo,
-                self.world_size,
-            )
 
 
 class DistributedDataParallelSingleProcessTest(TestCase):

test/distributed/test_c10d_spawn_nccl.py

@@ -1,95 +1,21 @@
 # Owner(s): ["oncall: distributed"]
 
-import sys
 
-import test_c10d_spawn
 from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
 
 import torch
 import torch.distributed as c10d
-from torch.testing._internal.common_cuda import TEST_MULTIGPU
 from torch.testing._internal.common_distributed import requires_nccl, skip_if_lt_x_gpu
 from torch.testing._internal.common_utils import (
     run_tests,
     skip_but_pass_in_sandcastle_if,
     TEST_WITH_DEV_DBG_ASAN,
-    TestCase,
 )
 
 
 NO_NCCL = not hasattr(c10d, "ProcessGroupNCCL")
 
 # Fails on Python-3.9, see https://github.com/pytorch/pytorch/issues/51619
-if sys.version_info < (3, 9):
-
-    class ProcessGroupShareTensorTest(
-        test_c10d_spawn.AbstractProcessGroupShareTensorTest, TestCase
-    ):
-        @classmethod
-        def _init_pg_nccl(cls, rank, filename, world_size):
-            store = c10d.FileStore(filename, world_size)
-            return c10d.ProcessGroupNCCL(store, rank, world_size)
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_NCCL, "NCCL needed")
-        def test_shared_broadcast_nccl(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_broadcast_process,
-                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_nccl,
-                1,
-            )
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_NCCL, "NCCL needed")
-        def test_shared_allreduce_nccl(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allreduce_process,
-                [torch.ones(2, 2).to(i) for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_nccl,
-                1,
-            )
-
-        @classmethod
-        def _test_reduce_process(
-            cls, rank, filename, shared_tensors, world_size, init_pg, c2p, p2c
-        ):
-            pg = init_pg(rank, filename, world_size)
-            x = shared_tensors[rank]
-            pg.reduce(x, root=0, op=c10d.ReduceOp.SUM).wait()
-            if rank == 0:
-                c2p.put((rank, torch.ones(2, 2) * 2, x.to("cpu")))
-            else:
-                c2p.put((rank, torch.ones(2, 2), x.to("cpu")))
-            p2c.get()
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_NCCL, "NCCL needed")
-        def test_shared_reduce_nccl(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_reduce_process,
-                [torch.ones(2, 2).to(i) for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_nccl,
-                1,
-            )
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_NCCL, "NCCL needed")
-        def test_shared_allgather_nccl(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allgather_process,
-                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_nccl,
-                self.world_size,
-            )
 
 
 # Skip dev-asan as torch + multiprocessing spawn have known issues

test/distributed/test_c10d_spawn_ucc.py

@@ -1,74 +1,21 @@
 # Owner(s): ["oncall: distributed"]
 
-import sys
 
-import test_c10d_spawn
 from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
 
-import torch
 import torch.distributed as c10d
-from torch.testing._internal.common_cuda import TEST_MULTIGPU
 from torch.testing._internal.common_distributed import requires_ucc, skip_if_lt_x_gpu
 from torch.testing._internal.common_utils import (
     run_tests,
     skip_but_pass_in_sandcastle,
     skip_but_pass_in_sandcastle_if,
     TEST_WITH_DEV_DBG_ASAN,
-    TestCase,
 )
 
 
 NO_UCC = not hasattr(c10d, "ProcessGroupUCC")
 
 # Fails on Python-3.9, see https://github.com/pytorch/pytorch/issues/51619
-if sys.version_info < (3, 9):
-
-    class ProcessGroupShareTensorTest(
-        test_c10d_spawn.AbstractProcessGroupShareTensorTest, TestCase
-    ):
-        @classmethod
-        def _init_pg_ucc(cls, rank, filename, world_size):
-            store = c10d.FileStore(filename, world_size)
-            c10d.init_process_group(
-                backend="ucc", store=store, rank=rank, world_size=world_size
-            )
-            return c10d.distributed_c10d._get_default_group()
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_UCC, "UCC needed")
-        def test_shared_broadcast_ucc(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_broadcast_process,
-                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_ucc,
-                1,
-            )
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_UCC, "UCC needed")
-        def test_shared_allreduce_ucc(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allreduce_process,
-                [torch.ones(2, 2).to(i) for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_ucc,
-                1,
-            )
-
-        @skip_but_pass_in_sandcastle_if(
-            not TEST_MULTIGPU, "At least 2 CUDA GPUS needed"
-        )
-        @skip_but_pass_in_sandcastle_if(NO_UCC, "UCC needed")
-        def test_shared_allgather_ucc(self):
-            self._test_multiprocess(
-                ProcessGroupShareTensorTest._test_allgather_process,
-                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
-                ProcessGroupShareTensorTest._init_pg_ucc,
-                self.world_size,
-            )
 
 
 # Skip dev-asan as torch + multiprocessing spawn have known issues

test/distributed/test_dynamo_distributed.py

@@ -7,7 +7,6 @@ import unittest
 from contextlib import contextmanager
 from datetime import timedelta
 from io import StringIO
-from typing import List
 from unittest.mock import patch
 
 import numpy as np
@@ -1959,7 +1958,7 @@ class TestSingleProc(DynamoDistributedSingleProcTestCase):
         model = ModuleWithStaticMethod(False)
         x = torch.randn((2, 3), device="cuda")
         ref_out = model(x)
-        test_outs: List[torch.Tensor] = []
+        test_outs: list[torch.Tensor] = []
 
         for use_self in (False, True):
             model = ModuleWithStaticMethod(use_self)

test/dynamo/test_activation_checkpointing.py

@@ -5,7 +5,6 @@ import functools
 import math
 import unittest  # noqa: F811
 from importlib import import_module
-from typing import Set
 
 import torch
 import torch._dynamo.config
@@ -86,7 +85,7 @@ def count_ops(
     return gm
 
 
-def collect_fwd_graph_outputs(graph: torch.fx.Graph, *, fwd_outputs: Set[str]):
+def collect_fwd_graph_outputs(graph: torch.fx.Graph, *, fwd_outputs: set[str]):
     if not torch._dynamo.compiled_autograd.in_compiled_autograd_region:  # fwd graph
         return_node = list(graph.nodes)[-1]
         assert return_node.target == "output"

test/dynamo/test_export.py

@@ -12,7 +12,6 @@ import operator
 import unittest
 from collections.abc import Sequence
 from enum import Enum
-from typing import Dict, List
 from unittest.mock import patch
 
 import torch
@@ -1999,7 +1998,7 @@ def forward(self, l_x_):
                 self.assertIn("val", node.meta)
 
     def test_input_container_type(self):
-        def f(x: torch.Tensor, y: List[torch.Tensor]) -> Dict[str, torch.Tensor]:
+        def f(x: torch.Tensor, y: list[torch.Tensor]) -> dict[str, torch.Tensor]:
             return {"a": x.sum() + sum(y).sum()}
 
         inp = (torch.randn(6, 5), [torch.randn(6, 5), torch.randn(6, 5)])

test/dynamo/test_functions.py

@@ -11,7 +11,7 @@ import random
 import sys
 import unittest
 from dataclasses import dataclass, field
-from typing import Any, Dict, Generic, List, TypeVar
+from typing import Any, Generic, TypeVar
 from typing_extensions import NamedTuple
 from unittest.mock import patch
 
@@ -3896,8 +3896,8 @@ class DefaultsTests(torch._dynamo.test_case.TestCase):
         @dataclass
         class Output:
             scalar: int = 2
-            named_tensors: Dict[str, torch.Tensor] = field(default_factory=dict)
-            lists: List[torch.Tensor] = field(default_factory=list)
+            named_tensors: dict[str, torch.Tensor] = field(default_factory=dict)
+            lists: list[torch.Tensor] = field(default_factory=list)
 
             def scale(self):
                 return self.scalar * 2

test/dynamo/test_modules.py

@@ -12,7 +12,7 @@ import types
 import unittest
 from copy import deepcopy
 from functools import partial
-from typing import Dict, NamedTuple, Tuple
+from typing import NamedTuple
 from unittest.mock import patch
 
 import torch
@@ -602,7 +602,7 @@ class LazyMLP(torch.nn.Module):
 
 
 class MyInput(NamedTuple):
-    x: Dict[str, Dict[str, torch.Tensor]]
+    x: dict[str, dict[str, torch.Tensor]]
     y: torch.Tensor
 
 
@@ -2311,7 +2311,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
         m = TestModule()
 
         def forward_hook(
-            module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor
+            module: torch.nn.Module, inputs: tuple[torch.Tensor], output: torch.Tensor
         ) -> torch.Tensor:
             return 2 * output + 1
 
@@ -2358,7 +2358,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
         m = TestModule()
 
         def forward_hook(
-            module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor
+            module: torch.nn.Module, inputs: tuple[torch.Tensor], output: torch.Tensor
         ) -> torch.Tensor:
             return 2 * output + 1
 
@@ -2407,7 +2407,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
         self.assertEqual(compiled_func(inp).item(), 15)
 
         def new_forward_hook(
-            module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor
+            module: torch.nn.Module, inputs: tuple[torch.Tensor], output: torch.Tensor
         ) -> torch.Tensor:
             return 2 * output + 2
 
@@ -2426,7 +2426,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
         m = TestModule()
 
         def forward_hook(
-            module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor
+            module: torch.nn.Module, inputs: tuple[torch.Tensor], output: torch.Tensor
         ) -> torch.Tensor:
             return 2 * output + 1
 

test/dynamo/test_python_autograd.py

@@ -1,5 +1,5 @@
 # Owner(s): ["module: dynamo"]
-from typing import Callable, Dict, List, NamedTuple, Optional
+from typing import Callable, List, NamedTuple, Optional
 
 import torch
 import torch._dynamo
@@ -50,20 +50,20 @@ class Variable:
     def sum(self, name: Optional[str] = None) -> "Variable":
         return operator_sum(self, name)
 
-    def expand(self, sizes: List[int]) -> "Variable":
+    def expand(self, sizes: list[int]) -> "Variable":
         return operator_expand(self, sizes)
 
 
 class TapeEntry(NamedTuple):
     # names of the inputs to the original computation
-    inputs: List[str]
+    inputs: list[str]
     # names of the outputs of the original computation
-    outputs: List[str]
+    outputs: list[str]
     # apply chain rule
-    propagate: "Callable[List[Variable], List[Variable]]"
+    propagate: "Callable[list[Variable], list[Variable]]"
 
 
-gradient_tape: List[TapeEntry] = []
+gradient_tape: list[TapeEntry] = []
 
 
 def reset_tape():
@@ -72,9 +72,9 @@ def reset_tape():
     _name = 0
 
 
-def grad(L, desired_results: List[Variable]) -> List[Variable]:
+def grad(L, desired_results: list[Variable]) -> list[Variable]:
     # this map holds dL/dX for all values X
-    dL_d: Dict[str, Variable] = {}
+    dL_d: dict[str, Variable] = {}
     # It starts by initializing the 'seed' dL/dL, which is 1
     dL_d[L.name] = Variable(torch.ones(()))
     # print(f'd{L.name} ------------------------')

test/dynamo/test_reconstruct.py

@@ -3,7 +3,6 @@
 import contextlib
 import dis
 import unittest
-from typing import List
 
 import torch
 import torch._dynamo.test_case
@@ -33,7 +32,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         Emit code to reconstruct only the key that changed
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             # reconstruct only d[40]
@@ -57,7 +56,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         If something is pop'ed from the dict, we reconstruct everything
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             # reconstruct everything
@@ -84,7 +83,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         If something is pop'ed from the dict, we reconstruct everything
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             # reconstruct everything
@@ -128,7 +127,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         If something is deleted from the dict, we reconstruct everything
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             # reconstruct everything
@@ -154,7 +153,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         dict.get shouldn't affect anything
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             self.assertEqual(build_map[0].argval, 1)
@@ -180,7 +179,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         If dict.clear() is used, we reconstruct everything
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             # reconstruct everything
@@ -206,7 +205,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         If dict is created inside a function, everything needs to be reconstructed
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             self.assertEqual(len(build_map), 1)
             # reconstruct everything
@@ -231,7 +230,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         PyTorch shouldn't codegen any key/value when functional_call is used
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             # don't reconstruct anything
             self.assertEqual(len(build_map), 0)
@@ -260,7 +259,7 @@ class ReconstructTest(torch._dynamo.test_case.TestCase):
         PyTorch shouldn't codegen any key/value when functional_call is used
         """
 
-        def hook(instructions: List[dis.Instruction]):
+        def hook(instructions: list[dis.Instruction]):
             build_map = _filter_instructions(instructions, "BUILD_MAP")
             # don't reconstruct anything
             self.assertEqual(len(build_map), 0)

test/dynamo/test_repros.py

@@ -25,7 +25,7 @@ from collections.abc import Iterator
 from copy import deepcopy
 from enum import Enum, IntEnum
 from functools import wraps
-from typing import Any, Dict, List, Literal, Tuple, TypedDict
+from typing import Any, Literal, TypedDict
 from unittest import mock
 
 import numpy as np
@@ -712,7 +712,7 @@ def create_rand_mask_from_inputs(
 class SequentialAppendList(torch.nn.Sequential):
     """from timm/models/vovnet.py"""
 
-    def forward(self, x: torch.Tensor, concat_list: List[torch.Tensor]) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, concat_list: list[torch.Tensor]) -> torch.Tensor:
         for i, module in enumerate(self):
             if i == 0:
                 concat_list.append(module(x))
@@ -4108,7 +4108,7 @@ class ReproTests(torch._dynamo.test_case.TestCase):
     def test_graph_break_on_jit_isinstance(self):
         @torch.compile(backend="eager")
         def fn(x):
-            if torch.jit.isinstance(x, List[str]):
+            if torch.jit.isinstance(x, list[str]):
                 return x * 2
             return x
 
@@ -4819,14 +4819,14 @@ def forward(self, s0 : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
 
     def test_detectron2_instances_cat(self):
         class Instances:
-            def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
+            def __init__(self, image_size: tuple[int, int], **kwargs: Any):
                 self._image_size = image_size
-                self._fields: Dict[str, Any] = {}
+                self._fields: dict[str, Any] = {}
                 for k, v in kwargs.items():
                     self.set(k, v)
 
             @property
-            def image_size(self) -> Tuple[int, int]:
+            def image_size(self) -> tuple[int, int]:
                 return self._image_size
 
             def __setattr__(self, name: str, val: Any) -> None:
@@ -4861,7 +4861,7 @@ def forward(self, s0 : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
                 return self._fields[name]
 
             @staticmethod
-            def cat(instance_lists: List["Instances"]) -> "Instances":
+            def cat(instance_lists: list["Instances"]) -> "Instances":
                 assert all(isinstance(i, Instances) for i in instance_lists)
                 assert len(instance_lists) > 0
                 if len(instance_lists) == 1:
@@ -5997,7 +5997,7 @@ def forward(self, s0 : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
 
     # https://github.com/pytorch/pytorch/issues/88813
     def test_return_value_duplication_tensor(self) -> None:
-        def fn(val: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        def fn(val: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
             return val * 2, val * 2
 
         x = torch.randn(2, requires_grad=True)
@@ -6016,7 +6016,7 @@ def forward(self, s0 : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
 
     # https://github.com/pytorch/pytorch/issues/114344
     def test_return_value_duplication_mixed_grad(self) -> None:
-        def fn(val: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        def fn(val: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
             with torch.no_grad():
                 out0 = val + 1
             out1 = val + 1
@@ -6033,7 +6033,7 @@ def forward(self, s0 : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
 
     # https://github.com/pytorch/pytorch/pull/134726#discussion_r1738774371
     def test_return_value_duplication_scalar(self) -> None:
-        def fn(val: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        def fn(val: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
             x, y = val * 2, val * 2
             return x[0], y[0]
 

test/dynamo/test_subclasses.py

@@ -1822,7 +1822,7 @@ class GraphModule(torch.nn.Module):
     @torch._dynamo.config.patch("inline_inbuilt_nn_modules", True)
     @parametrize("dynamic", [True, False])
     def test_mark_static_with_subclass_desugaring(self, dynamic):
-        from typing import Any, Callable, List, Optional
+        from typing import Any, Callable, Optional
 
         from torch._dynamo.decorators import mark_static_address
         from torch._inductor.compile_fx import compile_fx
@@ -1835,9 +1835,9 @@ class GraphModule(torch.nn.Module):
 
         def inner_compile(
             gm: torch.fx.GraphModule,
-            example_inputs: List[torch.Tensor],
+            example_inputs: list[torch.Tensor],
             cudagraphs: Optional[BoxedBool] = None,
-            static_input_idxs: Optional[List[int]] = None,
+            static_input_idxs: Optional[list[int]] = None,
             is_backward: bool = False,
             graph_id: Optional[int] = None,
             cpp_wrapper: bool = False,
@@ -1845,7 +1845,7 @@ class GraphModule(torch.nn.Module):
             is_inference: bool = False,
             boxed_forward_device_index: Optional[BoxedDeviceIndex] = None,
             layout_opt: Optional[bool] = None,
-            extern_node_serializer: Optional[Callable[[List[Any]], Any]] = None,
+            extern_node_serializer: Optional[Callable[[list[Any]], Any]] = None,
         ):
             if dynamic:
                 self.assertEqual(static_input_idxs, [2, 3, 4])

test/dynamo/test_torchrec.py

@@ -1,7 +1,6 @@
 # Owner(s): ["module: dynamo"]
 import sys
 import unittest
-from typing import Dict, List
 
 import torch
 import torch._dynamo.config
@@ -23,7 +22,7 @@ except ImportError:
 
 @torch._dynamo.config.patch(force_unspec_int_unbacked_size_like_on_torchrec_kjt=True)
 class BucketizeMod(torch.nn.Module):
-    def __init__(self, feature_boundaries: Dict[str, List[float]]):
+    def __init__(self, feature_boundaries: dict[str, list[float]]):
         super().__init__()
         self.bucket_w = torch.nn.ParameterDict()
         self.boundaries_dict = {}
@@ -84,7 +83,7 @@ class TorchRecTests(TestCase):
 
         @torch.compile(backend=counter, fullgraph=True, dynamic=True)
         def f(id_list_features: KeyedJaggedTensor):
-            id_list_jt_dict: Dict[str, JaggedTensor] = id_list_features.to_dict()
+            id_list_jt_dict: dict[str, JaggedTensor] = id_list_features.to_dict()
             pooled_embeddings = {}
             # TODO: run feature processor
             for emb_module, feature_names in tables:

test/dynamo/test_trace_rules.py

@@ -6,7 +6,7 @@ import math
 import types
 import unittest
 import warnings
-from typing import Any, Dict, Set
+from typing import Any
 
 import torch
 import torch._dynamo.config as config
@@ -103,10 +103,10 @@ class AllowedObjects:
     from the heuristic defined in `gen_allowed_objs_and_ids`.
     """
 
-    object_ids: Dict[int, str]
-    c_binding_in_graph_functions: Set[Any]
-    non_c_binding_in_graph_functions: Set[Any]
-    name_rule_map: Dict[str, Any]
+    object_ids: dict[int, str]
+    c_binding_in_graph_functions: set[Any]
+    non_c_binding_in_graph_functions: set[Any]
+    name_rule_map: dict[str, Any]
 
 
 def gen_allowed_objs_and_ids(record=False, c_binding_only=True) -> AllowedObjects:

test/export/test_converter.py

@@ -2,7 +2,7 @@
 
 import unittest
 from collections import OrderedDict
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Optional
 
 import torch
 import torch.utils._pytree as pytree
@@ -65,11 +65,11 @@ class TestConverter(TestCase):
         self,
         M,
         tracing_inputs,
-        option: Optional[List[str]] = None,
+        option: Optional[list[str]] = None,
         check_persistent=False,
         lifted_tensor_constants=None,
-        runtime_inputs: Optional[List[Any]] = None,
-    ) -> List[ExportedProgram]:
+        runtime_inputs: Optional[list[Any]] = None,
+    ) -> list[ExportedProgram]:
         # By default, it tests both jit.trace and jit.script.
         if option is None:
             option = ["trace", "script"]
@@ -130,7 +130,7 @@ class TestConverter(TestCase):
                     self._check_tensor_list_equal(ep_out, orig_out)
         return ep_list
 
-    def _check_tensor_list_equal(self, xs: List[torch.Tensor], ys: List[torch.Tensor]):
+    def _check_tensor_list_equal(self, xs: list[torch.Tensor], ys: list[torch.Tensor]):
         self.assertEqual(len(xs), len(ys))
         for x, y in zip(xs, ys):
             if isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor):
@@ -219,7 +219,7 @@ class TestConverter(TestCase):
         self._check_equal_ts_ep_converter(Module(), inp)
 
         class Module(torch.nn.Module):
-            def forward(self, x: List[int]):
+            def forward(self, x: list[int]):
                 length = len(x)
                 return torch.ones(length)
 
@@ -228,7 +228,7 @@ class TestConverter(TestCase):
         self._check_equal_ts_ep_converter(Module(), inp, ["script"])
 
         class Module(torch.nn.Module):
-            def forward(self, x: Dict[int, str]):
+            def forward(self, x: dict[int, str]):
                 length = len(x)
                 return torch.ones(length)
 
@@ -237,7 +237,7 @@ class TestConverter(TestCase):
         self._check_equal_ts_ep_converter(Module(), inp, ["script"])
 
         class Module(torch.nn.Module):
-            def forward(self, x: Dict[bool, str]):
+            def forward(self, x: dict[bool, str]):
                 length = len(x)
                 return torch.ones(length)
 
@@ -246,7 +246,7 @@ class TestConverter(TestCase):
         self._check_equal_ts_ep_converter(Module(), inp, ["script"])
 
         class Module(torch.nn.Module):
-            def forward(self, x: Dict[float, str]):
+            def forward(self, x: dict[float, str]):
                 length = len(x)
                 return torch.ones(length)
 
@@ -255,7 +255,7 @@ class TestConverter(TestCase):
         self._check_equal_ts_ep_converter(Module(), inp, ["script"])
 
         class Module(torch.nn.Module):
-            def forward(self, x: Dict[torch.Tensor, str]):
+            def forward(self, x: dict[torch.Tensor, str]):
                 length = len(x)
                 return torch.ones(length)
 
@@ -273,7 +273,7 @@ class TestConverter(TestCase):
     def test_aten_add_t(self):
         # python list append
         class Module(torch.nn.Module):
-            def forward(self, x: List[torch.Tensor]):
+            def forward(self, x: list[torch.Tensor]):
                 out = []
                 out = out + x
                 a = torch.cat(out)
@@ -531,7 +531,7 @@ class TestConverter(TestCase):
         class Module(torch.nn.Module):
             def forward(
                 self, x: torch.Tensor, y: torch.Tensor
-            ) -> Tuple[bool, torch.Tensor]:
+            ) -> tuple[bool, torch.Tensor]:
                 z = x + 1
                 return x is y, z
 
@@ -546,7 +546,7 @@ class TestConverter(TestCase):
         class Module(torch.nn.Module):
             def forward(
                 self, x: torch.Tensor, y: torch.Tensor
-            ) -> Tuple[bool, torch.Tensor]:
+            ) -> tuple[bool, torch.Tensor]:
                 z = x + 1
                 return x is not y, z
 
@@ -558,7 +558,7 @@ class TestConverter(TestCase):
         class Module(torch.nn.Module):
             def forward(
                 self, x: torch.Tensor, y: torch.Tensor
-            ) -> Tuple[bool, torch.Tensor]:
+            ) -> tuple[bool, torch.Tensor]:
                 z = x + 1
                 return not (x is not y), z
 
@@ -573,7 +573,7 @@ class TestConverter(TestCase):
                 return x + y
 
         class MUnpackTuple(torch.nn.Module):
-            def forward(self, x_tuple: Tuple[torch.Tensor, torch.Tensor]):
+            def forward(self, x_tuple: tuple[torch.Tensor, torch.Tensor]):
                 x, y = x_tuple
                 x = x.cos()
                 return x + y
@@ -904,7 +904,7 @@ class TestConverter(TestCase):
                 return x.dtype in [torch.int8]
 
         class MTensorIn(torch.nn.Module):
-            def forward(self, x: torch.Tensor, x_dict: Dict[torch.Tensor, str]):
+            def forward(self, x: torch.Tensor, x_dict: dict[torch.Tensor, str]):
                 return x in x_dict
 
         # Traced function must return output that has tensors.
@@ -1118,14 +1118,14 @@ class TestConverter(TestCase):
 
     def test_prim_tolist(self):
         class Module(torch.nn.Module):
-            def forward(self, x: torch.Tensor) -> List[int]:
+            def forward(self, x: torch.Tensor) -> list[int]:
                 return x.tolist()
 
         inp = (torch.tensor([1, 2, 3]),)
         self._check_equal_ts_ep_converter(Module(), inp, ["script"])
 
         class Module(torch.nn.Module):
-            def forward(self, x: torch.Tensor) -> List[List[int]]:
+            def forward(self, x: torch.Tensor) -> list[list[int]]:
                 return x.tolist()
 
         inp = (torch.tensor([[1, 2, 3], [4, 5, 6]]),)
@@ -1353,7 +1353,7 @@ class TestConverter(TestCase):
 
     def test_aten_append_t(self):
         class M(torch.nn.Module):
-            def forward(self, x: List[torch.Tensor]):
+            def forward(self, x: list[torch.Tensor]):
                 out = []
                 out.append(x[0] + x[1])
                 out.append(x[0] - x[1])
@@ -1381,7 +1381,7 @@ class TestConverter(TestCase):
         self._check_equal_ts_ep_converter(M1(), inp, ["script"])
 
     def test_ts2ep_with_loop(self):
-        def func1(x, x_list: List[torch.Tensor]):
+        def func1(x, x_list: list[torch.Tensor]):
             a, b, c = x, x, x
             for _ in range(1, 5, 2):
                 for k in range(5):

test/export/test_draft_export.py

@@ -2,7 +2,6 @@
 import copy
 import tempfile
 import unittest
-from typing import List, Tuple
 
 import torch
 from torch.export import Dim, export
@@ -325,7 +324,7 @@ class TestDraftExport(TestCase):
                 return torch.ops.mylib.foo(a)
 
         @torch.library.custom_op("mylib::foo", mutates_args={})
-        def foo(a: torch.Tensor) -> List[torch.Tensor]:
+        def foo(a: torch.Tensor) -> list[torch.Tensor]:
             x = a * 2
             y = a.repeat(2, 2)
             z = a.to(torch.bfloat16)
@@ -370,7 +369,7 @@ class TestDraftExport(TestCase):
                 return torch.ops.mylib.foo(a)
 
         @torch.library.custom_op("mylib::foo", mutates_args={})
-        def foo(a: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        def foo(a: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
             return a * 2, a + 2
 
         @foo.register_fake

test/export/test_lift_unlift.py

@@ -1,7 +1,7 @@
 # Owner(s): ["oncall: export"]
 import unittest
 from collections import OrderedDict
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Optional
 
 import torch
 from torch._export.passes.lift_constants_pass import (
@@ -34,9 +34,9 @@ class GraphBuilder:
         self.graph = torch.fx.Graph()
         self.nodes = {}
         self.values = {}
-        self.nn_module_stack_key: Dict[str, int] = {}
+        self.nn_module_stack_key: dict[str, int] = {}
         self.latest_id = 0
-        self.input_to_kind: Dict[torch.fx.Node, InputKind] = {}
+        self.input_to_kind: dict[torch.fx.Node, InputKind] = {}
 
     def input(self, name: str, value: torch.Tensor, kind: InputKind):
         node = self.graph.placeholder(name)
@@ -87,7 +87,7 @@ class GraphBuilder:
 
     def create_nn_module_stack(
         self, module_fqn: str
-    ) -> OrderedDict[int, Tuple[str, type]]:
+    ) -> OrderedDict[int, tuple[str, type]]:
         cur_name = ""
         nn_module_stack = OrderedDict()
         for atom in module_fqn.split("."):

test/export/test_passes.py

@@ -8,7 +8,6 @@ import math
 import operator
 import unittest
 from re import escape
-from typing import List, Set
 
 import torch
 from functorch.experimental.control_flow import cond
@@ -75,11 +74,11 @@ class _AtenAddOperatorSupport(OperatorSupport):
         return node.op == "call_function" and node.target in {torch.ops.aten.add.Tensor}
 
 
-def _to_partition_names(partitions: List[Partition]) -> List[Set[str]]:
+def _to_partition_names(partitions: list[Partition]) -> list[set[str]]:
     return [{n.name for n in p.nodes} for p in partitions]
 
 
-def _get_output_names(gm: torch.fx.GraphModule) -> List[str]:
+def _get_output_names(gm: torch.fx.GraphModule) -> list[str]:
     output_node = next(n for n in gm.graph.nodes if n.op == "output")
     args = pytree.tree_leaves(output_node.args)
     # if isinstance(args, tuple) and len(args) == 1:

test/functorch/test_aotdispatch.py

@@ -12,7 +12,7 @@ import unittest
 import warnings
 from contextlib import ContextDecorator, nullcontext
 from functools import partial, wraps
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Optional, Union
 from unittest.mock import patch
 
 from common_utils import decorate, decorateForModules, skip, skipOps, xfail
@@ -319,8 +319,8 @@ class TestAOTAutograd(AOTTestCase):
     def run_autograd(
         self,
         f: Callable,
-        fw_graph_cell: List[Optional[Callable]],
-        decompositions: Optional[Dict],
+        fw_graph_cell: list[Optional[Callable]],
+        decompositions: Optional[dict],
         keep_input_mutations: bool,
         dynamic: bool,
     ):
@@ -358,11 +358,11 @@ class TestAOTAutograd(AOTTestCase):
     def verify_aot_autograd(
         self,
         f,
-        inp_: Union[Callable, List[Any]],
+        inp_: Union[Callable, list[Any]],
         *,
         test_mutation: bool = False,
         keep_inp_mutations: bool = False,
-        decompositions: Optional[Dict] = None,
+        decompositions: Optional[dict] = None,
         dynamic: bool = False,
         # Only active when inp_ is Callable.
         # TODO: probably consolidate all tests to make inp a Callable.
@@ -6748,8 +6748,8 @@ class TestAOTAutogradWithDynamo(TestAOTAutograd):
     def run_autograd(
         self,
         f: Callable,
-        fw_graph_cell: List[Optional[Callable]],
-        decompositions: Optional[Dict],
+        fw_graph_cell: list[Optional[Callable]],
+        decompositions: Optional[dict],
         keep_input_mutations: bool,
         dynamic: bool,
     ):
@@ -6880,8 +6880,8 @@ class TestAOTAutogradWithCache(TestAOTAutogradWithDynamo):
     def run_autograd(
         self,
         f: Callable,
-        fw_graph_cell: List[Optional[Callable]],
-        decompositions: Optional[Dict],
+        fw_graph_cell: list[Optional[Callable]],
+        decompositions: Optional[dict],
         keep_input_mutations: bool,
         dynamic: bool,
     ):
@@ -6904,11 +6904,11 @@ class TestAOTAutogradWithCache(TestAOTAutogradWithDynamo):
     def verify_aot_autograd(
         self,
         f,
-        inp_: Union[Callable, List[Any]],
+        inp_: Union[Callable, list[Any]],
         *,
         test_mutation: bool = False,
         keep_inp_mutations: bool = False,
-        decompositions: Optional[Dict] = None,
+        decompositions: Optional[dict] = None,
         dynamic: bool = False,
         # Only active when inp_ is Callable.
         # TODO: probably consolidate all tests to make inp a Callable.

test/functorch/test_parsing.py

@@ -24,7 +24,7 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
-from typing import Any, Callable, Dict
+from typing import Any, Callable
 from unittest import mock
 
 from functorch.einops._parsing import (
@@ -206,7 +206,7 @@ class TestParsedExpression(TestCase):
 
 class TestParsingUtils(TestCase):
     def test_parse_pattern_number_of_arrows(self) -> None:
-        axes_lengths: Dict[str, int] = {}
+        axes_lengths: dict[str, int] = {}
 
         too_many_arrows_pattern = "a -> b -> c -> d"
         with self.assertRaises(ValueError):
@@ -220,13 +220,13 @@ class TestParsingUtils(TestCase):
         parse_pattern(just_right_arrows, axes_lengths)
 
     def test_ellipsis_invalid_identifier(self) -> None:
-        axes_lengths: Dict[str, int] = {"a": 1, _ellipsis: 2}
+        axes_lengths: dict[str, int] = {"a": 1, _ellipsis: 2}
         pattern = f"a {_ellipsis} -> {_ellipsis} a"
         with self.assertRaises(ValueError):
             parse_pattern(pattern, axes_lengths)
 
     def test_ellipsis_matching(self) -> None:
-        axes_lengths: Dict[str, int] = {}
+        axes_lengths: dict[str, int] = {}
 
         pattern = "a -> a ..."
         with self.assertRaises(ValueError):
@@ -240,7 +240,7 @@ class TestParsingUtils(TestCase):
         parse_pattern(pattern, axes_lengths)
 
     def test_left_parenthesized_ellipsis(self) -> None:
-        axes_lengths: Dict[str, int] = {}
+        axes_lengths: dict[str, int] = {}
 
         pattern = "(...) -> ..."
         with self.assertRaises(ValueError):
@@ -254,7 +254,7 @@ class MaliciousRepr:
 
 class TestValidateRearrangeExpressions(TestCase):
     def test_validate_axes_lengths_are_integers(self) -> None:
-        axes_lengths: Dict[str, Any] = {"a": 1, "b": 2, "c": 3}
+        axes_lengths: dict[str, Any] = {"a": 1, "b": 2, "c": 3}
         pattern = "a b c -> c b a"
         left, right = parse_pattern(pattern, axes_lengths)
         validate_rearrange_expressions(left, right, axes_lengths)
@@ -265,7 +265,7 @@ class TestValidateRearrangeExpressions(TestCase):
             validate_rearrange_expressions(left, right, axes_lengths)
 
     def test_non_unitary_anonymous_axes_raises_error(self) -> None:
-        axes_lengths: Dict[str, int] = {}
+        axes_lengths: dict[str, int] = {}
 
         left_non_unitary_axis = "a 2 -> 1 1 a"
         left, right = parse_pattern(left_non_unitary_axis, axes_lengths)
@@ -278,7 +278,7 @@ class TestValidateRearrangeExpressions(TestCase):
             validate_rearrange_expressions(left, right, axes_lengths)
 
     def test_identifier_mismatch(self) -> None:
-        axes_lengths: Dict[str, int] = {}
+        axes_lengths: dict[str, int] = {}
 
         mismatched_identifiers = "a -> a b"
         left, right = parse_pattern(mismatched_identifiers, axes_lengths)
@@ -291,7 +291,7 @@ class TestValidateRearrangeExpressions(TestCase):
             validate_rearrange_expressions(left, right, axes_lengths)
 
     def test_unexpected_axes_lengths(self) -> None:
-        axes_lengths: Dict[str, int] = {"c": 2}
+        axes_lengths: dict[str, int] = {"c": 2}
 
         pattern = "a b -> b a"
         left, right = parse_pattern(pattern, axes_lengths)

test/functorch/test_rearrange.py

@@ -25,7 +25,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
 
-from typing import List, Tuple
 
 import numpy as np
 
@@ -34,7 +33,7 @@ from functorch.einops import rearrange
 from torch.testing._internal.common_utils import run_tests, TestCase
 
 
-identity_patterns: List[str] = [
+identity_patterns: list[str] = [
     "...->...",
     "a b c d e-> a b c d e",
     "a b c d e ...-> ... a b c d e",
@@ -45,7 +44,7 @@ identity_patterns: List[str] = [
     "a ... c d e -> a (...) c d e",
 ]
 
-equivalent_rearrange_patterns: List[Tuple[str, str]] = [
+equivalent_rearrange_patterns: list[tuple[str, str]] = [
     ("a b c d e -> (a b) c d e", "a b ... -> (a b) ... "),
     ("a b c d e -> a b (c d) e", "... c d e -> ... (c d) e"),
     ("a b c d e -> a b c d e", "... -> ... "),
@@ -149,7 +148,7 @@ class TestRearrange(TestCase):
 
     def test_concatenations_and_stacking(self) -> None:
         for n_arrays in [1, 2, 5]:
-            shapes: List[List[int]] = [[], [1], [1, 1], [2, 3, 5, 7], [1] * 6]
+            shapes: list[list[int]] = [[], [1], [1, 1], [2, 3, 5, 7], [1] * 6]
             for shape in shapes:
                 arrays1 = [
                     torch.arange(i, i + np.prod(shape, dtype=int)).reshape(shape)

test/fx/test_dce_pass.py

@@ -1,7 +1,7 @@
 # Owner(s): ["module: fx"]
 import copy
 import unittest
-from typing import Optional, Set, Type
+from typing import Optional
 
 import torch
 import torch.fx
@@ -38,7 +38,7 @@ class TestDCE(TestCase):
         self,
         m: torch.nn.Module,
         expect_dce_changes: bool,
-        modules_to_be_leafs: Optional[Set[Type]] = None,
+        modules_to_be_leafs: Optional[set[type]] = None,
         custom: bool = False,
     ):
         class TestTracer(torch.fx.Tracer):

test/fx/test_future.py

@@ -2,8 +2,6 @@
 
 from __future__ import annotations  # type: ignore[attr-defined]
 
-import typing
-
 import torch
 from torch.fx import symbolic_trace
 
@@ -27,13 +25,13 @@ class M2(torch.nn.Module):
 
 # Non-torch annotation with no internal forward references
 class M3(torch.nn.Module):
-    def forward(self, x: typing.List[torch.Tensor], a: A) -> torch.Tensor:
+    def forward(self, x: list[torch.Tensor], a: A) -> torch.Tensor:
         return a(x[0])
 
 
 # Non-torch annotation with internal forward references
 class M4(torch.nn.Module):
-    def forward(self, x: typing.List[torch.Tensor], a: A) -> torch.Tensor:
+    def forward(self, x: list[torch.Tensor], a: A) -> torch.Tensor:
         return a(x[0])
 
 

test/fx/test_fx_split.py

@@ -1,7 +1,6 @@
 # Owner(s): ["module: fx"]
 
 from collections import defaultdict
-from typing import Dict, List, Tuple
 
 import torch
 from torch.fx.passes.split_utils import split_by_tags
@@ -63,8 +62,8 @@ class TestSplitByTags(TestCase):
 
     @staticmethod
     def trace_and_tag(
-        module: torch.nn.Module, tags: List[str]
-    ) -> Tuple[torch.fx.GraphModule, Dict[str, List[str]]]:
+        module: torch.nn.Module, tags: list[str]
+    ) -> tuple[torch.fx.GraphModule, dict[str, list[str]]]:
         """
         Test simple gm consists of nodes with tag (only show call_module nodes here):
             linear1 - tag: "red"
@@ -167,8 +166,8 @@ class TestSplitOutputType(TestCase):
 
     @staticmethod
     def trace_and_tag(
-        module: torch.nn.Module, inputs: torch.Tensor, tags: List[str]
-    ) -> Tuple[torch.fx.GraphModule, Dict[str, List[str]]]:
+        module: torch.nn.Module, inputs: torch.Tensor, tags: list[str]
+    ) -> tuple[torch.fx.GraphModule, dict[str, list[str]]]:
         """
         Test simple gm consists of nodes with tag (only show call_module nodes here):
             conv - tag: "red"

test/higher_order_ops/test_with_effects.py

@@ -4,7 +4,7 @@
 import unittest
 from collections import deque
 from functools import partial
-from typing import List, TYPE_CHECKING
+from typing import TYPE_CHECKING
 
 import torch
 import torch._dynamo
@@ -390,7 +390,7 @@ def forward(self, arg0_1, arg1_1, arg2_1):
                 return output
 
             def add_hooks(module, config):
-                handles: List[RemovableHandle] = []
+                handles: list[RemovableHandle] = []
                 q = deque([(module.__class__.__name__, module)])
                 while q:
                     name, m = q.pop()

test/inductor/test_aot_inductor.py

@@ -5,7 +5,6 @@ import os
 import sys
 import tempfile
 import unittest
-from typing import Dict, Tuple
 from unittest import skip
 
 import torch
@@ -1744,7 +1743,7 @@ class AOTInductorTestsTemplate:
             def __init__(self) -> None:
                 super().__init__()
 
-            def forward(self, x: Dict[str, torch.Tensor]):
+            def forward(self, x: dict[str, torch.Tensor]):
                 device = next(iter(x.values())).device
                 add_ = torch.zeros(5, device=device)
                 mul_ = torch.ones(5, device=device)
@@ -2660,7 +2659,7 @@ class AOTInductorTestsTemplate:
                 def forward(
                     self,
                     self_tensor: torch.Tensor,
-                    indices: Tuple[torch.Tensor],
+                    indices: tuple[torch.Tensor],
                     values: torch.Tensor,
                 ):
                     return torch.index_put(
@@ -4285,7 +4284,7 @@ def fail_cpu(is_skip=False):
     )
 
 
-def fail_gpu(suffixes: Tuple[str, ...], is_skip=False):
+def fail_gpu(suffixes: tuple[str, ...], is_skip=False):
     return TestFailure(
         suffixes,
         is_skip=is_skip,

test/inductor/test_codecache.py

@@ -4,7 +4,7 @@ import pickle
 import shutil
 import tempfile
 import unittest
-from typing import List, Optional, Union
+from typing import Optional, Union
 from unittest import mock
 
 import torch
@@ -1434,7 +1434,7 @@ class TestCudaCompileCommand(TestCase):
         with mock.patch("subprocess.check_output") as check_output_mock:
             CUDACodeCache.compile("test123.cu", "so", ["-Wsomething"])
             check_output_mock.assert_called()
-            cmd_parts: List[str] = check_output_mock.call_args[0][0]
+            cmd_parts: list[str] = check_output_mock.call_args[0][0]
             assert cmd_parts[0] == "nvcc", cmd_parts
             assert "-Wsomething" in cmd_parts, cmd_parts
             assert "-DNDEBUG" in cmd_parts, cmd_parts

test/inductor/test_cooperative_reductions.py

@@ -1,6 +1,6 @@
 # Owner(s): ["module: inductor"]
 import unittest
-from typing import Any, Dict, List, Type
+from typing import Any
 
 import sympy
 
@@ -160,11 +160,11 @@ class TestFixedConfigs(TestCase):
         class MyHeuristics(InductorChoices):
             def triton_kernel_kwargs(
                 self,
-                kernel_cls: Type[TritonKernel],
+                kernel_cls: type[TritonKernel],
                 features: SIMDKernelFeatures,
-                groups: List[sympy.Expr],
-                kernel_kwargs: Dict[str, Any],
-            ) -> Dict[str, Any]:
+                groups: list[sympy.Expr],
+                kernel_kwargs: dict[str, Any],
+            ) -> dict[str, Any]:
                 return {
                     **kernel_kwargs,
                     "override_cooperative_reduction": cooperative,

test/inductor/test_cutlass_backend.py

@@ -3,7 +3,7 @@ import logging
 import math
 import os
 import unittest
-from typing import Callable, List, Optional
+from typing import Callable, Optional
 from unittest import mock
 
 from torch.export import Dim
@@ -114,7 +114,7 @@ class TestCutlassBackend(TestCase):
             ) as mocked_select_algorithm:
                 Y_compiled = torch.compile(mm, dynamic=False)(a, b)
                 Y = mm(a, b)
-                passed_choice_callers: List[ChoiceCaller] = mocked_select_algorithm[0][
+                passed_choice_callers: list[ChoiceCaller] = mocked_select_algorithm[0][
                     1
                 ]
                 assert all(
@@ -573,7 +573,7 @@ class TestCutlassBackend(TestCase):
             return torch.addmm(x, a, b, alpha=alpha, beta=beta)
 
         def compare_results(
-            m: int, k: int, n: int, alpha: float, beta: float, x_shape: List[int]
+            m: int, k: int, n: int, alpha: float, beta: float, x_shape: list[int]
         ) -> None:
             x = torch.randn(x_shape).cuda().half()
             a = torch.randn(m, k).cuda().half()

test/inductor/test_flex_attention.py

@@ -9,7 +9,7 @@ from collections import namedtuple
 from contextlib import contextmanager
 from dataclasses import dataclass
 from itertools import product
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 from unittest import expectedFailure, skip, skipUnless
 from unittest.mock import patch
 
@@ -511,7 +511,7 @@ class TestFlexAttention(InductorTestCase):
         block_mask,
         dtype: torch.dtype = torch.float16,
         page_size: int = 128,
-    ) -> Tuple[Tensor, Tensor, BlockMask, _score_mod_signature]:
+    ) -> tuple[Tensor, Tensor, BlockMask, _score_mod_signature]:
         assert block_mask is not None, "Must provide block_mask"
         Q_B, Q_H, Q_S, _ = q.shape
         KV_B, KV_H, KV_S, QK_D = k.shape
@@ -596,7 +596,7 @@ class TestFlexAttention(InductorTestCase):
         v: Tensor,
         dtype: torch.dtype = torch.float16,
         block_mask: Optional[BlockMask] = None,
-    ) -> Tuple[Tensor, Tensor]:
+    ) -> tuple[Tensor, Tensor]:
         B, Q_H, Q_S, KV_H, KV_S = (
             q.shape[0],
             q.shape[1],
@@ -797,7 +797,7 @@ class TestFlexAttention(InductorTestCase):
 
     def run_dynamic_test(
         self,
-        score_mask_mod: Tuple[Callable, Callable],
+        score_mask_mod: tuple[Callable, Callable],
         dtype: torch.dtype = torch.float16,
         B: int = B,
         H: int = H,
@@ -1089,7 +1089,7 @@ class TestFlexAttention(InductorTestCase):
     @common_utils.parametrize("dtype", test_dtypes_fast)
     @common_utils.parametrize("score_mask_mod", test_score_mask_mod_map.items())
     def test_builtin_score_mods_dynamic(
-        self, dtype: torch.dtype, score_mask_mod: Tuple[Callable, Callable]
+        self, dtype: torch.dtype, score_mask_mod: tuple[Callable, Callable]
     ):
         self.run_dynamic_test(score_mask_mod, dtype)
 
@@ -1127,7 +1127,7 @@ class TestFlexAttention(InductorTestCase):
         self,
         dtype: torch.dtype,
         score_mod: Callable,
-        BLOCK_SIZE: Union[int, Tuple[int, int]],
+        BLOCK_SIZE: Union[int, tuple[int, int]],
     ):
         block_mask = create_block_mask(
             noop_mask, B, H, S, S, BLOCK_SIZE=BLOCK_SIZE, device=self.device
@@ -1142,8 +1142,8 @@ class TestFlexAttention(InductorTestCase):
     def test_kv_batch_broadcast(
         self,
         dtype: torch.dtype,
-        batch_dims: Tuple[int, int],
-        head_dims: Tuple[int, int],
+        batch_dims: tuple[int, int],
+        head_dims: tuple[int, int],
         score_mod: Callable,
     ):
         Hq, Hkv = head_dims
@@ -1175,8 +1175,8 @@ class TestFlexAttention(InductorTestCase):
     def test_kv_batch_broadcast_causal_mask(
         self,
         dtype: torch.dtype,
-        batch_dims: Tuple[int, int],
-        head_dims: Tuple[int, int],
+        batch_dims: tuple[int, int],
+        head_dims: tuple[int, int],
         score_mod: Callable,
     ):
         Hq, Hkv = head_dims
@@ -3616,7 +3616,7 @@ class TestBlockMask(InductorTestCase):
 
     @supported_platform
     @common_utils.parametrize("BLOCK_SIZE", [32, 64, 128, 256, (32, 64), (64, 32)])
-    def test_block_size_changes(self, BLOCK_SIZE: Union[int, Tuple[int, int]]):
+    def test_block_size_changes(self, BLOCK_SIZE: Union[int, tuple[int, int]]):
         B, H, Q_LEN, KV_LEN = 4, 2, 2048, 2048
 
         if isinstance(BLOCK_SIZE, int):
@@ -3990,7 +3990,7 @@ BlockMask(shape=(1,s1,s2048,s2048),ssparsity=46.88%,s
             )
 
         def length_to_offsets(
-            lengths: List[int], device: Union[str, torch.device]
+            lengths: list[int], device: Union[str, torch.device]
         ) -> Tensor:
             offsets = [0]
             offsets.extend(lengths)
@@ -4561,7 +4561,7 @@ class Params:
         return f"batch:{self.batch_size}_head:{self.num_heads}_seq_len:{self.seq_length}_headdim:{self.head_dim}_dtype:{str(self.dtype).split('.')[-1]}"
 
 
-def get_params(dtypes: List[torch.dtype]) -> List[Params]:
+def get_params(dtypes: list[torch.dtype]) -> list[Params]:
     params = []
     seq_lengths = [37, 256, 277]
     for seq_len, dtype in product(seq_lengths, dtypes):

test/inductor/test_flex_decoding.py

@@ -3,7 +3,7 @@
 
 import functools
 from collections import namedtuple
-from typing import Callable, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 from unittest import expectedFailure, skipUnless
 from unittest.mock import patch
 
@@ -645,7 +645,7 @@ class TestFlexDecoding(InductorTestCase):
         self,
         dtype: torch.dtype,
         score_mod: Callable,
-        head_dims: Tuple[int, int],
+        head_dims: tuple[int, int],
         page_size: int,
     ):
         Hq, Hkv = head_dims
@@ -681,7 +681,7 @@ class TestFlexDecoding(InductorTestCase):
         self,
         dtype: torch.dtype,
         score_mod: Callable,
-        BLOCK_SIZE: Union[int, Tuple[int, int]],
+        BLOCK_SIZE: Union[int, tuple[int, int]],
     ):
         block_mask = create_block_mask(noop_mask, B, 1, 1, S, BLOCK_SIZE=BLOCK_SIZE)
         self.run_test(score_mod, dtype, block_mask=block_mask)
@@ -763,8 +763,8 @@ class TestFlexDecoding(InductorTestCase):
     def test_kv_batch_broadcast(
         self,
         dtype: torch.dtype,
-        head_dims: Tuple[int, int],
-        batch_dims: Tuple[int, int],
+        head_dims: tuple[int, int],
+        batch_dims: tuple[int, int],
         score_mod: Callable,
     ):
         Hq, Hkv = head_dims

test/inductor/test_fp8.py

@@ -2,7 +2,7 @@
 
 import functools
 import unittest
-from typing import List, Tuple, Union
+from typing import Union
 
 import torch
 from torch import Tensor
@@ -89,8 +89,8 @@ def _quantize_rowwise(x: Tensor, float8_dtype: torch.dtype):
 
 
 def _fix_fp8_dtype_for_rocm(
-    dtype: Union[torch.dtype, List[torch.dtype], Tuple[torch.dtype]], device
-) -> Union[torch.dtype, List[torch.dtype], Tuple[torch.dtype]]:
+    dtype: Union[torch.dtype, list[torch.dtype], tuple[torch.dtype]], device
+) -> Union[torch.dtype, list[torch.dtype], tuple[torch.dtype]]:
     # This function is used to change FP8 data types
     # with MI300 supported FP8 types if device is GPU:
     #    e4m3fn -> e4m3fnuz

test/inductor/test_fuzzer.py

@@ -2,7 +2,7 @@
 
 import sys
 import unittest
-from typing import List, Literal
+from typing import Literal
 from unittest.mock import MagicMock, patch
 
 import torch
@@ -50,8 +50,8 @@ class TestConfigFuzzer(TestCase):
         self.assertEqual(toggle("", bool, True), False)
         self.assertEqual(toggle("", Literal["foo", "bar"], "foo"), "bar")
         self.assertEqual(toggle("", Literal["foo", "bar"], "bar"), "foo")
-        self.assertTrue("bar" in toggle("", List[Literal["foo", "bar"]], ["foo"]))
-        self.assertTrue("foo" in toggle("", List[Literal["foo", "bar"]], ["bar"]))
+        self.assertTrue("bar" in toggle("", list[Literal["foo", "bar"]], ["foo"]))
+        self.assertTrue("foo" in toggle("", list[Literal["foo", "bar"]], ["bar"]))
 
     @unittest.skipIf(sys.version_info < (3, 10), "python < 3.10 not supported")
     def test_sampling_method_random(self):

test/inductor/test_group_batch_fusion.py

@@ -2,7 +2,6 @@
 
 import collections
 import unittest
-from typing import List
 
 import torch
 import torch._inductor
@@ -39,7 +38,7 @@ class TestHighwaySelfGating(torch.nn.Module):
 
     def forward(
         self,
-        inputs: List[torch.Tensor],
+        inputs: list[torch.Tensor],
     ) -> torch.Tensor:
         results = []
         for i in range(self.size):