Make MoE models non-strict tracing friendly (#2612)

## Summary
- `kernels.py`: wrap `fill_indices` as `custom_op` + `register_fake` for
FakeTensor compatibility during `make_fx` tracing
- `test_trace_module.py`: add model-specific tests (llama3, qwen3,
qwen3_moe, deepseek_v3, llama4, gpt_oss) and FSDP tests

Stacked on #2553

## Test plan
- [x] `pytest
torchtitan/experiments/graph_trainer/tests/test_trace_module.py -k
TestTraceModels` — all 6 model tests pass
- [x] `pre-commit run --all-files` passes

Author: ydwu4

torchtitan/experiments/graph_trainer/make_fx_tracer.py

@@ -23,6 +23,23 @@
 from torch.utils._python_dispatch import is_traceable_wrapper_subclass
 
 
+@contextmanager
+def _skip_nested_compile() -> Generator[None, None, None]:
+    """Tell dynamo to skip torch.compile calls encountered during make_fx tracing.
+
+    make_fx cannot trace through torch.compile'd functions (e.g. compiled
+    flex_attention in FlexAttentionWrapper). Setting error_on_nested_fx_trace
+    to False makes dynamo silently inline the wrapped function instead of
+    raising, so make_fx traces the underlying ops normally.
+    """
+    prev = torch._dynamo.config.error_on_nested_fx_trace
+    torch._dynamo.config.error_on_nested_fx_trace = False
+    try:
+        yield
+    finally:
+        torch._dynamo.config.error_on_nested_fx_trace = prev
+
+
 @dataclass
 class SubclassMeta:
     cls: type
@@ -219,7 +236,7 @@ def _copy_fwd_metadata_to_bw_nodes(fx_g: torch.fx.GraphModule) -> None:
     seq_nr_to_fwd_node: dict[int, torch.fx.Node] = {}
 
     for node in fx_g.graph.nodes:
-        if node.op != "call_function" or "seq_nr" not in node.meta:
+        if node.op not in ("call_function", "get_attr") or "seq_nr" not in node.meta:
             continue
         seq_nr = node.meta["seq_nr"]
         if seq_nr not in seq_nr_to_fwd_node:
@@ -275,11 +292,14 @@ def functional_call(*all_args):
             unwrapped_args.append(arg)
             input_layouts.append(SubclassLayout(1, None))
 
-    fake_mode = FakeTensorMode(allow_non_fake_inputs=True)
+    fake_mode = FakeTensorMode(
+        allow_non_fake_inputs=True,
+        shape_env=torch.fx.experimental.symbolic_shapes.ShapeEnv(),
+    )
 
     def to_fake(t):
         if isinstance(t, torch.Tensor):
-            return fake_mode.from_tensor(t)
+            return fake_mode.from_tensor(t, static_shapes=True)
         return t
 
     fake_args = tuple(to_fake(a) for a in unwrapped_args)
@@ -315,7 +335,7 @@ def fn_with_subclass_handling(*plain_args):
         return unwrapped_outputs
 
     # preserve_node_meta propagates fx.traceback.annotate metadata to traced nodes
-    with fake_mode, preserve_node_meta():
+    with fake_mode, preserve_node_meta(), _skip_nested_compile():
         traced = make_fx(
             fn_with_subclass_handling,
             record_stack_traces=True,