diff --git a/torchtitan/models/llama4/infra/parallelize.py b/torchtitan/models/llama4/infra/parallelize.py
index 9325a3cf4e..5836ffd1a7 100644
--- a/torchtitan/models/llama4/infra/parallelize.py
+++ b/torchtitan/models/llama4/infra/parallelize.py
@@ -639,13 +639,20 @@ def apply_compile(model: nn.Module, compile_config: CompileConfig, ep_enabled: b
                     # by wrapping each submod's forward instead of their __call__
                     moe = submod
                     for attr_name, submod in moe.named_children():
-                        setattr(
-                            moe,
-                            attr_name,
-                            torch.compile(
-                                submod, backend=compile_config.backend, fullgraph=True
-                            ),
-                        )
+                        # temp workaround: compile everything except GroupedExperts.
+                        # Context: https://github.com/pytorch/torchtitan/pull/2250#discussion_r2713296483
+                        # TL;DR is this avoids a tensor metadata mismatch issue between forward output and
+                        # backward() input (upstream grad).
+                        if not isinstance(submod, moe_module.GroupedExperts):
+                            setattr(
+                                moe,
+                                attr_name,
+                                torch.compile(
+                                    submod,
+                                    backend=compile_config.backend,
+                                    fullgraph=True,
+                                ),
+                            )
                 else:
                     setattr(
                         block,
@@ -667,35 +674,6 @@ def apply_compile(model: nn.Module, compile_config: CompileConfig, ep_enabled: b
         # pyrefly: ignore [missing-attribute]
         model.layers.register_module(layer_id, transformer_block)
 
-    # Patch some globals only once (apply_compile is called multiple times for PP setup)
-    already_patched = (
-        "_run_experts_grouped_mm_dynamic"
-        in moe_module._run_experts_grouped_mm.__qualname__
-    )
-    if not already_patched:
-        moe_module._run_experts_grouped_mm = torch.compile(
-            moe_module._run_experts_grouped_mm,
-            backend=compile_config.backend,
-            fullgraph=True,
-        )
-
-        if ep_enabled:
-            compiled_fn = moe_module._run_experts_grouped_mm
-
-            # keep function logic in sync with `already_patched` above
-            def _run_experts_grouped_mm_dynamic(
-                w1: torch.Tensor,
-                w2: torch.Tensor,
-                w3: torch.Tensor,
-                x: torch.Tensor,
-                num_tokens_per_expert: torch.Tensor,
-            ) -> torch.Tensor:
-                # dynamic number of tokens in expert parallel
-                torch._dynamo.mark_dynamic(x, 0)
-                return compiled_fn(w1, w2, w3, x, num_tokens_per_expert)
-
-            moe_module._run_experts_grouped_mm = _run_experts_grouped_mm_dynamic
-
     # NOTE: We don't compile for loop code path due to an issue with unbacked symints:
     # https://github.com/pytorch/pytorch/issues/166460