Revert "add and fix OpInfo tests for the default partitioner (#165372)"

This reverts commit bcfea48ab7fd489218289693b98c1a6a6582d079.

Reverted https://github.com/pytorch/pytorch/pull/165372 on behalf of https://github.com/malfet due to Looks like it broke slow jobs, see 331b7cc054/1 ([comment](https://github.com/pytorch/pytorch/pull/165372#issuecomment-3407567748))

test/functorch/test_aotdispatch.py

@@ -8044,7 +8044,7 @@ symbolic_aot_autograd_failures = {
 }
 
 
-def _test_aot_autograd_helper(self, device, dtype, op, dynamic=False, use_min_cut=True):
+def _test_aot_autograd_helper(self, device, dtype, op, dynamic=False):
     if not op.supports_autograd:
         self.skipTest("Op does not support autograd")
 
@@ -8075,7 +8075,6 @@ def _test_aot_autograd_helper(self, device, dtype, op, dynamic=False, use_min_cu
                 check_gradients=True,
                 try_check_data_specialization=try_check_data_specialization,
                 skip_correctness_check=op.skip_correctness_check_compile_vs_eager,
-                use_min_cut=use_min_cut,
             )
         except DynamicOutputShapeException:
             self.skipTest("Dynamic output shape operation in trace")
@@ -8176,29 +8175,6 @@ class TestEagerFusionOpInfo(AOTTestCase):
     def test_aot_autograd_symbolic_exhaustive(self, device, dtype, op):
         _test_aot_autograd_helper(self, device, dtype, op, dynamic=True)
 
-    @ops(op_db + hop_db, allowed_dtypes=(torch.float,))
-    @skipOps(
-        "TestEagerFusionOpInfo",
-        "test_aot_autograd_default_partition_exhaustive",
-        aot_autograd_failures,
-    )
-    def test_aot_autograd_default_partition_exhaustive(self, device, dtype, op):
-        _test_aot_autograd_helper(self, device, dtype, op, use_min_cut=False)
-
-    @ops(op_db + hop_db, allowed_dtypes=(torch.float,))
-    @patch("functorch.compile.config.debug_assert", True)
-    @skipOps(
-        "TestEagerFusionOpInfo",
-        "test_aot_autograd_symbolic_default_partition_exhaustive",
-        aot_autograd_failures | symbolic_aot_autograd_failures,
-    )
-    def test_aot_autograd_symbolic_default_partition_exhaustive(
-        self, device, dtype, op
-    ):
-        _test_aot_autograd_helper(
-            self, device, dtype, op, dynamic=True, use_min_cut=False
-        )
-
 
 aot_autograd_module_failures = set(
     {

torch/_functorch/partitioners.py

@@ -1025,11 +1025,7 @@ def default_partition(
             # Symints must be kept separate from tensors so that PythonFunction only calls
             # save_for_backward on tensors and stashes symints in autograd .ctx
             saved_sym_nodes.append(node)
-        elif (
-            "tensor_meta" not in node.meta
-            and node.op == "call_function"
-            and not isinstance(node.meta.get("val"), torch._subclasses.FakeTensor)
-        ):
+        elif "tensor_meta" not in node.meta and node.op == "call_function":
             # Since we can't save tuple of tensor values, we need to flatten out what we're saving
             users = node.users
             assert all(user.target == operator.getitem for user in users)

torch/testing/_internal/optests/aot_autograd.py

@@ -3,7 +3,7 @@
 import torch
 import torch.utils._pytree as pytree
 from torch.testing._utils import wrapper_set_seed
-from functorch.compile import compiled_function, min_cut_rematerialization_partition, default_partition, nop
+from functorch.compile import compiled_function, min_cut_rematerialization_partition, nop
 from .make_fx import randomize
 import re
 
@@ -38,7 +38,6 @@ def aot_autograd_check(
         assert_equals_fn=torch.testing.assert_close,
         check_gradients=True,
         try_check_data_specialization=False,
-        use_min_cut=True,
         skip_correctness_check=False):
     """Compares func(*args, **kwargs) in eager-mode to under AOTAutograd.
 
@@ -64,24 +63,14 @@ def aot_autograd_check(
         c_args, c_kwargs = pytree.tree_unflatten(reconstructed_flat_args, args_spec)
         return func(*c_args, **c_kwargs)
 
-    if use_min_cut:
-        compiled_f = compiled_function(
-            func_no_tensors,
-            nop,
-            nop,
-            dynamic=dynamic,
-            partition_fn=min_cut_rematerialization_partition,
-            keep_inference_input_mutations=True
-        )
-    else:
-        compiled_f = compiled_function(
-            func_no_tensors,
-            nop,
-            nop,
-            dynamic=dynamic,
-            partition_fn=default_partition,
-            keep_inference_input_mutations=True
-        )
+    compiled_f = compiled_function(
+        func_no_tensors,
+        nop,
+        nop,
+        dynamic=dynamic,
+        partition_fn=min_cut_rematerialization_partition,
+        keep_inference_input_mutations=True
+    )
 
     out = wrapper_set_seed(func_no_tensors, args)
     if check_gradients == "auto":