Add type annotations for higher order ops/flex_attention (#137065)

Chillee · pytorchmergebot · commit 2854d157de7d · 2024-10-02T04:39:25.000Z
Pull Request resolved: #137065 Approved by: https://github.com/drisspg, https://github.com/Skylion007 ghstack dependencies: #136826, #137043, #137049
diff --git a/torch/_higher_order_ops/auto_functionalize.py b/torch/_higher_order_ops/auto_functionalize.py
@@ -586,7 +586,9 @@ def auto_functionalized_fake(
     **kwargs: Any,
 ) -> Tuple[Any, Tuple[Tensor, ...]]:
     with mode:
-        result = auto_functionalized_dense(_mutable_op, **kwargs)
+        result = auto_functionalized_dense(
+            _mutable_op, _only_clone_these_tensors=None, **kwargs
+        )
         return result
 
 
@@ -681,7 +683,9 @@ def auto_functionalized_v2_fake(
     **kwargs: Dict[str, Any],
 ) -> Tuple[Any, Tuple[Tensor, ...]]:
     with mode:
-        result = auto_functionalized_v2_dense(_mutable_op, **kwargs)
+        result = auto_functionalized_v2_dense(
+            _mutable_op, _only_clone_these_bases=None, **kwargs
+        )
         return result
 
 
diff --git a/torch/_higher_order_ops/flex_attention.py b/torch/_higher_order_ops/flex_attention.py
@@ -1,18 +1,17 @@
-# mypy: allow-untyped-decorators
-# mypy: allow-untyped-defs
 import math
-from typing import Any, Callable, Dict, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
 
 import torch
 import torch.utils._pytree as pytree
+from torch import Tensor
 from torch._C import DispatchKey
 from torch._higher_order_ops.utils import (
     _has_potential_branch_input_mutation,
     autograd_not_implemented,
     reenter_make_fx,
     UnsupportedAliasMutationException,
 )
-from torch._ops import HigherOrderOperator
+from torch._ops import HigherOrderOperator, OpOverload
 from torch._subclasses import FakeTensorMode
 from torch.fx.experimental.proxy_tensor import (
     make_fx,
@@ -77,7 +76,13 @@ class TransformGetItemToIndex(TorchFunctionMode):
     # scalar and create a view. We do not want that behavior in this case, so we
     # use this torchfunctionmode to override that behavior for score_mod
     # wherever we're running it.
-    def __torch_function__(self, func, types, args=(), kwargs=None):
+    def __torch_function__(
+        self,
+        func: OpOverload,
+        types: Tuple[torch._C._TensorMeta, ...],
+        args: Tuple[object, ...] = (),
+        kwargs: Optional[Dict[str, object]] = None,
+    ) -> object:
         if func == torch.Tensor.__getitem__:
             index_args = pytree.tree_leaves(args[1])
             if all(isinstance(x, torch.Tensor) for x in index_args):
@@ -485,7 +490,11 @@ def flex_attention_fake_tensor_mode(
 
 
 # ---------------------------- Autograd Implementation ----------------------------
-def create_fw_bw_graph(score_mod, index_values, other_buffers):
+def create_fw_bw_graph(
+    score_mod: Callable,
+    index_values: Tuple[Tensor, Tensor, Tensor, Tensor, Tensor],
+    other_buffers: Tuple[Tensor, ...],
+) -> Tuple[Callable, Callable]:
     # See Note:[HOP create fw_bw graph]
 
     # All of these imports need to be here in order to avoid circular dependencies
@@ -508,7 +517,7 @@ def create_fw_bw_graph(score_mod, index_values, other_buffers):
     with suspend_functionalization(), disable_functional_mode():
         with disable_proxy_modes_tracing():
 
-            def _from_fun(t):
+            def _from_fun(t: Tensor) -> Tensor:
                 return torch.empty_strided(
                     t.size(),
                     t.stride(),
@@ -544,8 +553,18 @@ def _from_fun(t):
                 )
             example_grad = _from_fun(example_flat_out)
 
-        def joint_f(score, b, h, m, n, example_grad, *other_buffers):
-            def fw_with_masks(*args):
+        def joint_f(
+            score: Tensor,
+            b: Tensor,
+            h: Tensor,
+            m: Tensor,
+            n: Tensor,
+            example_grad: Tensor,
+            *other_buffers: Tuple[Tensor, ...],
+        ) -> Tuple[Tensor, ...]:
+            def fw_with_masks(
+                *args: Tuple[Tensor, ...]
+            ) -> Tuple[Tuple[Tensor], Tuple[bool]]:
                 fw_out = score_mod(*args)
                 out_requires_grad = fw_out.requires_grad
                 return ((fw_out,), (out_requires_grad,))
@@ -566,17 +585,17 @@ def fw_with_masks(*args):
 class FlexAttentionAutogradOp(torch.autograd.Function):
     @staticmethod
     def forward(
-        ctx,
-        query,
-        key,
-        value,
-        fw_graph,
-        joint_graph,
-        block_mask,
-        scale,
-        kernel_options,
-        score_mod_other_buffers,
-        mask_mod_other_buffers,
+        ctx: Any,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        fw_graph: Callable,
+        joint_graph: Callable,
+        block_mask: Tuple[Any, ...],
+        scale: float,
+        kernel_options: Dict[str, Any],
+        score_mod_other_buffers: Tuple[Any, ...],
+        mask_mod_other_buffers: Tuple[Any, ...],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         any_buffer_requires_grad = any(
             buffer.requires_grad
@@ -620,7 +639,7 @@ def forward(
         return out, logsumexp
 
     @staticmethod
-    def backward(ctx, grad_out, grad_logsumexp):
+    def backward(ctx: Any, grad_out: Tensor, grad_logsumexp: Tensor) -> Tuple[Optional[Tensor], ...]:  # type: ignore[override]
         fw_args = ctx.saved_tensors
         (
             query,
@@ -693,15 +712,19 @@ def flex_attention_autograd(
     block_mask: Tuple,
     scale: float,
     kernel_options: Dict[str, Any],
-    score_mod_other_buffers: Tuple = (),
-    mask_mod_other_buffers: Tuple = (),
+    score_mod_other_buffers: Tuple[Tensor, ...] = (),
+    mask_mod_other_buffers: Tuple[Tensor, ...] = (),
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     with TransformGetItemToIndex():
         input_requires_grad = any(t.requires_grad for t in (query, key, value))
         if torch.is_grad_enabled() and input_requires_grad:
-            example_vals = [
-                torch.zeros((), dtype=query.dtype, requires_grad=input_requires_grad)
-            ] + [torch.zeros((), dtype=torch.int) for _ in range(4)]
+            example_vals = (
+                torch.zeros((), dtype=query.dtype, requires_grad=input_requires_grad),
+                torch.zeros((), dtype=torch.int),
+                torch.zeros((), dtype=torch.int),
+                torch.zeros((), dtype=torch.int),
+                torch.zeros((), dtype=torch.int),
+            )
             fw_graph, bw_graph = create_fw_bw_graph(
                 score_mod, example_vals, score_mod_other_buffers
             )
diff --git a/torch/_inductor/fx_passes/post_grad.py b/torch/_inductor/fx_passes/post_grad.py
@@ -845,7 +845,9 @@ def _(match: Match, *args, **kwargs):
         # tracing a function with kwargs.
         def decomp(*flat_args):
             args, kwargs = pytree.tree_unflatten(flat_args, spec)
-            return auto_functionalized_dense(*args, only_clone_these_tensors, **kwargs)
+            assert len(args) == 1
+            mode = args[0]
+            return auto_functionalized_dense(mode, only_clone_these_tensors, **kwargs)
 
         match.replace_by_example(decomp, flat_args, run_functional_passes=False)
 
@@ -889,7 +891,11 @@ def _(match: Match, *args, **kwargs):
         # tracing a function with kwargs.
         def decomp(*flat_args):
             args, kwargs = pytree.tree_unflatten(flat_args, spec)
-            return auto_functionalized_v2_dense(*args, only_clone_these_bases, **kwargs)
+            assert len(args) == 1
+            mutable_op = args[0]
+            return auto_functionalized_v2_dense(
+                mutable_op, only_clone_these_bases, **kwargs
+            )
 
         match.replace_by_example(decomp, flat_args, run_functional_passes=False)
 
diff --git a/torch/_ops.py b/torch/_ops.py
@@ -6,7 +6,7 @@
 import inspect
 import sys
 import types
-from typing import Any, Callable, Dict, List, Set, Type, Union
+from typing import Any, Callable, Dict, List, Set, Type, TypeVar, Union
 
 import torch
 import torch.utils._pytree as pytree
@@ -16,6 +16,9 @@
 from torch.utils._python_dispatch import TorchDispatchMode
 
 
+_F = TypeVar("_F", bound=Callable[..., Any])
+
+
 # Query `hasattr` only once.
 _SET_GLOBAL_FLAGS = hasattr(sys, "getdlopenflags") and hasattr(sys, "setdlopenflags")
 
@@ -99,8 +102,8 @@ def has_kernel_for_any_dispatch_key(self, ks):
                 return True
         return False
 
-    def py_impl(self, k):
-        def inner(fn):
+    def py_impl(self, k: Any) -> Callable[[_F], _F]:
+        def inner(fn: _F) -> _F:
             if inspect.isclass(k) and (
                 issubclass(k, TorchDispatchMode) or issubclass(k, torch.Tensor)
             ):
@@ -141,7 +144,7 @@ def inner(fn):
     #       with ctx.redispatch_to_next():
     #           out = ctx.functionalize(inner_f)(*args_unwrapped)
     #           return ctx.wrap_tensors(out)
-    def py_functionalize_impl(self, fn):
+    def py_functionalize_impl(self, fn: _F) -> _F:
         from torch._subclasses.functional_tensor import (
             CppFunctionalizeAPI as _CppFunctionalizeAPI,
             FunctorchFunctionalizeAPI as _FunctorchFunctionalizeAPI,
@@ -273,7 +276,7 @@ def __init__(self, name, *, cacheable=False):
         # it to next key. This is only safe to do when PreDispatch key stack has no
         # active modes.
 
-    def py_impl(self, k):
+    def py_impl(self, k: Any) -> Callable[[_F], _F]:
         if isinstance(k, DispatchKey) and not self.non_fallthrough_keys.has(k):
             self.non_fallthrough_keys = self.non_fallthrough_keys.add(k)
         return super().py_impl(k)
