diff --git a/autoparallel/activation_checkpointing.py b/autoparallel/activation_checkpointing.py
index 71fe731e..0b19587f 100644
--- a/autoparallel/activation_checkpointing.py
+++ b/autoparallel/activation_checkpointing.py
@@ -3,6 +3,7 @@
 # This source code is licensed under the BSD license found in the
 # LICENSE file in the root directory of this source tree.
 import logging
+import operator
 from collections import defaultdict
 from dataclasses import dataclass
 from typing import Optional, Union
@@ -207,6 +208,26 @@ def mark_nodes_as_must_save_to_stage_recomputation(
         if node.meta.get("recompute", None) is not None:
             # do not mess with allgather nodes that have already been marked recompute!
             continue
+        if node.target is operator.getitem:
+            # we need to be a bit careful: we are trying to manually emulate setting "precompute" tags
+            # in the same way that compiel does when it encounters userland SAC.
+            #
+            # torch.compile does this by using TorchDispatchModes to intercept ops as they are traced,
+            # and setting their "recompute" tag.
+            #
+            # However, TorchDispatchModes *only* intercept OpOverloads (and HOPs)
+            # getitem is neither, and so in vanilla torch.compile usage,
+            # getitem nodes recieve no tags.
+            #
+            # What happens if we blindly set all nodes to PREFER_RECOMPUTE? Example bad outcome:
+            # - user is using attention, so we see this series of ops in the joint graph:
+            #     attention_fw -> getitem -> attention_bw (the getitem is an output used for the bw)
+            # - user runs SAC, and marks attention_fw as MUST_SAVE
+            # - if we mark getitem as PREFER_RECOMPUTE, and attention_fw as MUST_SAVE,
+            #   the partitioner ends up generating an invalid graph.
+            #   Today the partitioner relies on the fact that getitem's recompute behavior
+            #   is implicitly determined by the recompute behavior of the multi-output op preceding it.
+            continue
         node.meta["recompute"] = CheckpointPolicy.PREFER_RECOMPUTE
         # add an arbitrarily large graph id. I'm assuming 100000 here, which should be fine
         # and is the same we add for the all-gather nodes
@@ -327,7 +348,7 @@ def ac_joint_pass(graph: torch.fx.Graph, ac_stage_size_in_GiB: float = 2.0):
     # policy, but this is not working yet
     save_list = {
         torch.ops.aten.mm.default,
-        # torch.ops.aten._scaled_dot_product_efficient_attention.default,
-        # torch.ops.aten._scaled_dot_product_flash_attention.default,
+        torch.ops.aten._scaled_dot_product_efficient_attention.default,
+        torch.ops.aten._scaled_dot_product_flash_attention.default,
     }
     _apply_ac_policy(graph, save_list=save_list)