Add None return type to init

ghstack-source-id: 8aa50782aa1bb0b413fdb5d54fd0bdf377647727 Pull Request resolved: #132335
pytorch · Jul 31, 2024 · d733b03 · d733b03
1 parent f0da167
commit d733b03
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Showing 128 changed files with 290 additions and 290 deletions.
diff --git a/torch/_classes.py b/torch/_classes.py
@@ -19,7 +19,7 @@ def __getattr__(self, attr):
 class _Classes(types.ModuleType):
     __file__ = "_classes.py"
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("torch.classes")
 
     def __getattr__(self, name):

diff --git a/torch/_decomp/decompositions_for_rng.py b/torch/_decomp/decompositions_for_rng.py
@@ -71,7 +71,7 @@ class PhiloxState:
     trace time.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.reset()
 
     def reset(self):

diff --git a/torch/_dynamo/backends/distributed.py b/torch/_dynamo/backends/distributed.py
@@ -247,7 +247,7 @@ def run_node(self, n: Node) -> Any:
             # This gives us the appropriately strided outputs here which will reflect runtime strides.
 
             class FakeifyFirstAOTInvocationGuard:
-                def __init__(self):
+                def __init__(self) -> None:
                     self.tc = torch._guards.TracingContext.try_get()
                     assert self.tc
                     torch._guards.TracingContext.try_get().fakify_first_call = True

diff --git a/torch/_dynamo/code_context.py b/torch/_dynamo/code_context.py
@@ -5,7 +5,7 @@
 
 
 class CodeContextDict:
-    def __init__(self):
+    def __init__(self) -> None:
         self.code_context = ExactWeakKeyDictionary()
 
     def has_context(self, code: types.CodeType):

diff --git a/torch/_dynamo/debug_utils.py b/torch/_dynamo/debug_utils.py
@@ -170,7 +170,7 @@ def convert(gm):
             """
             from torch.nn import *
             class Repro(torch.nn.Module):
-                def __init__(self):
+                def __init__(self) -> None:
                     super().__init__()
             """
         )
@@ -491,7 +491,7 @@ def _mk_defaulter(d: T) -> Callable[[Optional[T]], T]:
 
 
 class NopInputReader:
-    def __init__(self):
+    def __init__(self) -> None:
         self.total = 0
 
     def storage(self, storage_hash, nbytes, *, device=None, dtype_hint=None):

diff --git a/torch/_dynamo/eval_frame.py b/torch/_dynamo/eval_frame.py
@@ -496,7 +496,7 @@ def _fn(*args, **kwargs):
                         wrapper function.
 
                         >> class CallableClass:
-                        >>     def __init__(self):
+                        >>     def __init__(self) -> None:
                         >>         super().__init__()
                         >>         self.relu = torch.nn.ReLU()
                         >>
@@ -577,7 +577,7 @@ def __reduce__(self):
 
 
 class RunOnlyContext(_TorchDynamoContext):
-    def __init__(self):
+    def __init__(self) -> None:
         # cudagraph trees relies on generation increment
         def on_enter():
             torch._dynamo.mutation_guard.GenerationTracker.generation += 1
@@ -589,7 +589,7 @@ def __reduce__(self):
 
 
 class DisableContext(_TorchDynamoContext):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__(callback=None)
 
     def __call__(self, fn):

diff --git a/torch/_dynamo/exc.py b/torch/_dynamo/exc.py
@@ -74,7 +74,7 @@ def __init__(self, name):
 
 
 class ResetRequired(TorchDynamoException):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__(
             textwrap.dedent(
                 """

diff --git a/torch/_dynamo/profiler.py b/torch/_dynamo/profiler.py
@@ -92,7 +92,7 @@ def print_missing(stack):
 class Profiler:
     unique_graphs = 0
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.prof = torch.profiler.profile(
             activities=[torch.profiler.ProfilerActivity.CPU],
             with_stack=should_print_missing(),

diff --git a/torch/_dynamo/variables/base.py b/torch/_dynamo/variables/base.py
@@ -70,7 +70,7 @@ class MutableLocal(MutableLocalBase):
     state.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__(MutableLocalSource.Local)
 
     def __hash__(self):

diff --git a/torch/_dynamo/variables/builder.py b/torch/_dynamo/variables/builder.py
@@ -270,7 +270,7 @@ def __eq__(self, other):
 
 
 class BackwardStateGraphArg(GraphArg):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__(
             source=None,
             _example=BackwardState(),
@@ -2638,7 +2638,7 @@ class SourcelessBuilder:
     if/else type->VariableTracker trees that were cropping up all over dynamo.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         raise AssertionError("Use SourcelessBuilder.create()")
 
     @staticmethod

diff --git a/torch/_export/db/examples/class_method.py b/torch/_export/db/examples/class_method.py
@@ -10,7 +10,7 @@ class ClassMethod(torch.nn.Module):
     def method(cls, x):
         return x + 1
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.linear = torch.nn.Linear(4, 2)
 

diff --git a/torch/_export/db/examples/cond_branch_class_method.py b/torch/_export/db/examples/cond_branch_class_method.py
@@ -26,7 +26,7 @@ class CondBranchClassMethod(torch.nn.Module):
     NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.subm = MySubModule()
 

diff --git a/torch/_export/db/examples/model_attr_mutation.py b/torch/_export/db/examples/model_attr_mutation.py
@@ -8,7 +8,7 @@ class ModelAttrMutation(torch.nn.Module):
     Attribute mutation is not supported.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.attr_list = [torch.randn(3, 2), torch.randn(3, 2)]
 

diff --git a/torch/_export/db/examples/scalar_output.py b/torch/_export/db/examples/scalar_output.py
@@ -11,7 +11,7 @@ class ScalarOutput(torch.nn.Module):
     Returning scalar values from the graph is supported, in addition to Tensor
     outputs. Symbolic shapes are captured and rank is specialized.
     """
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
 
     def forward(self, x):

diff --git a/torch/_export/db/examples/specialized_attribute.py b/torch/_export/db/examples/specialized_attribute.py
@@ -11,7 +11,7 @@ class SpecializedAttribute(torch.nn.Module):
     Model attributes are specialized.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.a = "moo"
         self.b = 4

diff --git a/torch/_export/passes/lift_constants_pass.py b/torch/_export/passes/lift_constants_pass.py
@@ -24,7 +24,7 @@ class ConstantAttrMap(collections.abc.MutableMapping):
     if that's the case).
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         # Underlying dict that we use to implement this mapping.
         self._constant_attrs: Dict[
             Union[int, torch.Tensor, FakeScriptObject], List[Any]

diff --git a/torch/_export/serde/serialize.py b/torch/_export/serde/serialize.py
@@ -1413,7 +1413,7 @@ class Result:
         constants: Dict[str, Union[torch.Tensor, FakeScriptObject, torch.ScriptObject]]
         example_inputs: Optional[Tuple[Tuple[torch.Tensor, ...], Dict[str, Any]]]
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.serialized_name_to_node: Dict[str, torch.fx.Node] = {}
         self.serialized_name_to_meta: Dict[str, MetaType] = {}
         self.graph = torch.fx.Graph()

diff --git a/torch/_functorch/_aot_autograd/schemas.py b/torch/_functorch/_aot_autograd/schemas.py
@@ -602,7 +602,7 @@ class SubclassMeta:
     # Optional field because we don't compute for inference graphs
     grad_input_metas: Optional[List[Union[int, SubclassCreationMeta]]] = None
 
-    def __init__(self):
+    def __init__(self) -> None:
         # The fields in this class get set after its construction.
         pass
 

diff --git a/torch/_functorch/aot_autograd.py b/torch/_functorch/aot_autograd.py
@@ -878,7 +878,7 @@ def functional_call(named_params, named_buffers, *args, **kwargs):
     )
 
     class AOTModule(nn.Module):
-        def __init__(self):
+        def __init__(self) -> None:
             super().__init__()
             self.orig_module = mod
 

diff --git a/torch/_functorch/autograd_function.py b/torch/_functorch/autograd_function.py
@@ -30,7 +30,7 @@
 # We do this by using creating a custom HigherOrderOperator that only functorch
 # dispatches specially.
 class CustomFunctionHigherOrderOperator(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("custom_function_call")
 
     def __call__(self, autograd_function, *args, **kwargs):
@@ -713,7 +713,7 @@ def new_forward(ctx, *args, **kwargs):
 
 
 class AutogradFunctionApply(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("autograd_function_apply")
 
     def __call__(self, fwd, bwd, *fwd_args, **fwd_kwargs):

diff --git a/torch/_guards.py b/torch/_guards.py
@@ -407,7 +407,7 @@ def __eq__(self, other):
 
 
 class ModuleContext(Checkpointable[ModuleContextCheckpointState]):
-    def __init__(self):
+    def __init__(self) -> None:
         self.nn_modules: Dict[str, Any] = {}
 
     def copy_graphstate(self):
@@ -456,7 +456,7 @@ class GlobalContext(Checkpointable[GlobalContextCheckpointState]):
         "autocast_cache_enabled",
     }
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.global_state: Dict[str, Tuple[Callable, ...]] = {}
 
     def copy_graphstate(self):
@@ -524,7 +524,7 @@ def remove_guards_with_source(self, source):
 
 
 class GuardsContext(Checkpointable[GuardsCheckpointState]):
-    def __init__(self):
+    def __init__(self) -> None:
         self.dynamo_guards: GuardsSet = GuardsSet()
         self.aotautograd_guards: List[GuardEnvExpr] = []
 

diff --git a/torch/_higher_order_ops/auto_functionalize.py b/torch/_higher_order_ops/auto_functionalize.py
@@ -54,7 +54,7 @@ class AutoFunctionalized(HigherOrderOperator):
     underscore is to prevent collisions with kwarg names in **kwargs.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("auto_functionalized")
 
     def __call__(

diff --git a/torch/_higher_order_ops/effects.py b/torch/_higher_order_ops/effects.py
@@ -55,7 +55,7 @@ class WithEffects(HigherOrderOperator):
     per "effect type", which are enumerated in the _EffectType enum.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("with_effects")
 
     def __call__(

diff --git a/torch/_higher_order_ops/flex_attention.py b/torch/_higher_order_ops/flex_attention.py
@@ -38,7 +38,7 @@ def __torch_function__(self, func, types, args, kwargs=None):
 
 
 class FlexAttentionHOP(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("flex_attention")
 
     def __call__(
@@ -74,7 +74,7 @@ def __call__(
 
 
 class FlexAttentionBackwardHOP(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("flex_attention_backward")
 
     def __call__(

diff --git a/torch/_higher_order_ops/out_dtype.py b/torch/_higher_order_ops/out_dtype.py
@@ -45,7 +45,7 @@ class OutDtypeOperator(HigherOrderOperator):
         3. Cast the output to `out_dtype`
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("out_dtype")
         # TODO(ydwu4): Subclassing HigherOrderOperator causes __module__ to
         # become different (torch._higher_order_ops.out_dtype) which will result

diff --git a/torch/_higher_order_ops/triton_kernel_wrap.py b/torch/_higher_order_ops/triton_kernel_wrap.py
@@ -519,7 +519,7 @@ def identify_mutated_tensors(kernel, kwargs):
 
 # Used for wrapping a Triton Kernel
 class TritonKernelWrapperMutation(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("triton_kernel_wrapper_mutation")
 
 
@@ -528,7 +528,7 @@ def __init__(self):
 
 # Used for wrapping a Triton Kernel in a functional manner
 class TritonKernelWrapperFunctional(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("triton_kernel_wrapper_functional")
 
 

diff --git a/torch/_higher_order_ops/while_loop.py b/torch/_higher_order_ops/while_loop.py
@@ -18,7 +18,7 @@
 
 
 class WhileLoopOp(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("while_loop")
 
     def __call__(

diff --git a/torch/_higher_order_ops/wrap.py b/torch/_higher_order_ops/wrap.py
@@ -15,7 +15,7 @@
 
 # Used for testing the HigherOrderOperator mechanism
 class Wrap(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("wrap")
 
     def __call__(self, func, *args, **kwargs):
@@ -36,7 +36,7 @@ def wrapper():
 
 
 class WrapWithSetGradEnabled(HigherOrderOperator):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("wrap_with_set_grad_enabled")
 
     def __call__(self, enable_grad, wrapped_func, *args, **kwargs):
@@ -74,7 +74,7 @@ class WrapActivationCheckpoint(HigherOrderOperator):
     partitioners. See TagActivationCheckpoint for more information.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("wrap_activation_checkpoint")
 
     def __call__(self, function, *args, **kwargs):
@@ -113,7 +113,7 @@ class TagActivationCheckpoint(HigherOrderOperator):
     the forward and recomputed forward in backward.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__("tag_activation_checkpoint")
 
     @staticmethod

diff --git a/torch/_inductor/codegen/common.py b/torch/_inductor/codegen/common.py
@@ -1560,7 +1560,7 @@ def newvar(self, bounds: ValueRanges[Any] = ValueRanges.unknown()) -> CSEVariabl
 
 
 class CodeGen:
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.exit_stack = contextlib.ExitStack()
 

diff --git a/torch/_inductor/codegen/cpp_wrapper_cuda.py b/torch/_inductor/codegen/cpp_wrapper_cuda.py
@@ -29,7 +29,7 @@ class CppWrapperCuda(CppWrapperCpu):
     Generates cpp wrapper for running on GPU and calls CUDA kernels
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.device = "cuda"
         super().__init__()
         self.grid_id = count()

diff --git a/torch/_inductor/codegen/triton.py b/torch/_inductor/codegen/triton.py
@@ -1113,7 +1113,7 @@ class HelperFunctions:
     _templates_seen: Dict[str, str]  # Template code to function name
     finalized_helpers: List[str]
 
-    def __init__(self):
+    def __init__(self) -> None:
         self._templates_seen = {}
         self.finalized_helpers = []
 

diff --git a/torch/_inductor/dependencies.py b/torch/_inductor/dependencies.py
@@ -588,7 +588,7 @@ def canonicalization_prefix():
 class FreeUnbackedSymbolsOpsHandler:
     symbols: Set[sympy.Symbol]
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.symbols = set()
 
     def __getattr__(self, name: str) -> Callable[..., Any]: