Revert "[dynamo][guards] 1/N Guard selectively for DTensor (#165824)"

This reverts commit ee7434be82.

Reverted https://github.com/pytorch/pytorch/pull/165824 on behalf of https://github.com/anijain2305 due to internal job failed ([comment](https://github.com/pytorch/pytorch/pull/165824#issuecomment-3462667536))

test/distributed/tensor/test_dtensor_compile.py

@@ -464,25 +464,6 @@ def forward(self, b_parametrizations_buffer_original0, x):
         run(g, 64, 8)
         self.assertEqual(cnt.frame_count, 2)
 
-    def test_dtensor_requires_grad_recompile(self):
-        cnt = torch._dynamo.testing.CompileCounterWithBackend("aot_eager")
-        mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
-
-        @torch.compile(backend=cnt, fullgraph=True)
-        def f(x):
-            y = x * x
-            return y.to_local()
-
-        full_x = torch.randn(8, 8, requires_grad=False)
-        x = distribute_tensor(full_x, mesh, [Shard(0)])
-        f(x)
-
-        full_x = torch.randn(8, 8, requires_grad=True)
-        x = distribute_tensor(full_x, mesh, [Shard(0)])
-        f(x)
-
-        self.assertEqual(cnt.frame_count, 2)
-
     def test_dtensor_attribute_access_on_intermediate(self):
         mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
 

torch/_dynamo/guards.py

@@ -2150,19 +2150,6 @@ class GuardBuilder(GuardBuilderBase):
             metadata_checker, get_verbose_code_parts(global_name, guard)
         )
 
-    def DTENSOR_SPEC_MATCH(self, guard: Guard) -> None:
-        # Copied from DTensor __metadata_guard__
-        # TODO - Consider moving this to C++ if stable
-        value = deepcopy(self.get(guard.name))
-
-        def guard_fn(x: Any) -> bool:
-            return x._check_equals(value, skip_shapes=True)
-
-        code = f"__dtensor_spec_{id(guard_fn)}"
-        self.get_guard_manager(guard).add_lambda_guard(
-            guard_fn, get_verbose_code_parts(code, guard)
-        )
-
     def EQUALS_MATCH(self, guard: Guard, recompile_hint: Optional[str] = None) -> None:
         ref = self.arg_ref(guard)
         val = self.get(guard.name)

torch/_dynamo/variables/builder.py

@@ -2229,12 +2229,6 @@ class VariableBuilder:
         if isinstance(source, GradSource) and is_from_optimizer_source(source):
             guard_type = GuardBuilder.NOT_NONE_MATCH
 
-        is_dtensor = torch.distributed.is_available() and isinstance(
-            value, torch.distributed.tensor.DTensor
-        )
-        if not is_dtensor:
-            # We guard on the _local_tensor and the _spec, and therefore we dont
-            # have to guard on the outer DTensor.
         self.install_guards(
             functools.partial(
                 guard_type,
@@ -2249,45 +2243,6 @@ class VariableBuilder:
         # We install TYPE_MATCH guards for traceable wrapper subclass object,
         # and recursively install corresponding guard for each inner attribute.
         if is_traceable_wrapper_subclass(value):
-            # Tensor subclass guards are very expensive because they are
-            # implemented in Python. Since DTensor is PyTorch-maintained class,
-            # we can skip a lot of these guards.
-            if is_dtensor:
-                self.install_guards(GuardBuilder.TYPE_MATCH)
-
-                # The inner tensor name is always _local_tensor. If its not, we
-                # raise assertion to update the check accordingly.
-                inner_tensor_name = value.__tensor_flatten__()[0][0]
-                if inner_tensor_name != "_local_tensor":
-                    raise RuntimeError(
-                        "Expecting Dtensor inner tensor name to be _local_tensor"
-                    )
-
-                # Now selectively guard on the flattening context
-                flattening_ctx = value.__tensor_flatten__()[1]
-                # This is supposed to be (self._spec, self.requires_grad)
-                if not (
-                    len(flattening_ctx) == 2
-                    and flattening_ctx[0] == value._spec
-                    and flattening_ctx[1] == value.requires_grad
-                ):
-                    # If not, raise an assertion to update to the new guards
-                    raise RuntimeError(
-                        "Expecting Dtensor flattening ctx to be _spec, requires_grad"
-                    )
-                # Guard on the dtensor spec
-                install_guard(
-                    AttrSource(self.source, "_spec").make_guard(
-                        GuardBuilder.DTENSOR_SPEC_MATCH
-                    )
-                )
-                # Move this to C++
-                install_guard(
-                    AttrSource(self.source, "requires_grad").make_guard(
-                        GuardBuilder.EQUALS_MATCH
-                    )
-                )
-            else:
             self.install_guards(GuardBuilder.TENSOR_SUBCLASS_METADATA_MATCH)
             self.install_guards(GuardBuilder.TYPE_MATCH)
             install_guard(

torch/distributed/tensor/_api.py

@@ -671,8 +671,6 @@ class DTensor(torch.Tensor):
     def __metadata_guard__(
         cls, orig: tuple[DTensorSpec, bool], other: tuple[DTensorSpec, bool]
     ) -> bool:
-        # TODO - delete this - This is now unused after the PR -
-        # https://github.com/pytorch/pytorch/pull/165824
         orig_spec, orig_requires_grad = orig
         other_spec, other_requires_grad = other
         return (