Repository: pytorch
Issue: `_native_batch_norm_legit`: `aot_eager` backend incorrectly raising an error.
Labels: triaged,oncall: pt2,module: pt2-dispatcher

### 🐛 Describe the bug

`out=` variant of `_native_batch_norm_legit` operation does not work with `aot_eager` backend.

```python
args = (
    torch.rand(5, 5, 5),
    torch.rand(5),
    torch.rand(5),
    torch.rand(5),
    torch.rand(5),
    True,
    0.5,
    0.6,
)

out = (
    torch.empty(5, 5, 5),
    torch.empty(5),
    torch.empty(5),
)

>>> torch.compile(torch._native_batch_norm_legit, backend="aot_eager")(*args, out=out)
Traceback (most recent call last):
  File "examples/bn.py", line 20, in <module>
    torch.compile(torch._native_batch_norm_legit, backend="aot_eager")(*args, out=out)
  File "torch/_dynamo/eval_frame.py", line 487, in _fn
    return fn(*args, **kwargs)
  File "torch/_dynamo/convert_frame.py", line 1350, in __call__
    return self._torchdynamo_orig_callable(
  File "torch/_dynamo/convert_frame.py", line 1141, in __call__
    result = self._inner_convert(
  File "torch/_dynamo/convert_frame.py", line 543, in __call__
    return _compile(
  File "torch/_dynamo/convert_frame.py", line 963, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "torch/_dynamo/convert_frame.py", line 694, in compile_inner
    return _compile_inner(code, one_graph, hooks, transform)
  File "torch/_utils_internal.py", line 87, in wrapper_function
    return function(*args, **kwargs)
  File "torch/_dynamo/convert_frame.py", line 727, in _compile_inner
    out_code = transform_code_object(code, transform)
  File "torch/_dynamo/bytecode_transformation.py", line 1337, in transform_code_object
    transformations(instructions, code_options)
  File "torch/_dynamo/convert_frame.py", line 228, in _fn
    return fn(*args, **kwargs)
  File "torch/_dynamo/convert_frame.py", line 656, in transform
    tracer.run()
  File "torch/_dynamo/symbolic_convert.py", line 2794, in run
    super().run()
  File "torch/_dynamo/symbolic_convert.py", line 996, in run
    while self.step():
  File "torch/_dynamo/symbolic_convert.py", line 908, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "torch/_dynamo/symbolic_convert.py", line 2985, in RETURN_VALUE
    self._return(inst)
  File "torch/_dynamo/symbolic_convert.py", line 2970, in _return
    self.output.compile_subgraph(
  File "torch/_dynamo/output_graph.py", line 1143, in compile_subgraph
    self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root)
  File "torch/_dynamo/output_graph.py", line 1371, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
  File "torch/_dynamo/output_graph.py", line 1418, in call_user_compiler
    return self._call_user_compiler(gm)
  File "torch/_dynamo/output_graph.py", line 1467, in _call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "torch/_dynamo/output_graph.py", line 1448, in _call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "torch/_dynamo/repro/after_dynamo.py", line 130, in __call__
    compiled_gm = compiler_fn(gm, example_inputs)
  File "torch/__init__.py", line 2309, in __call__
    return self.compiler_fn(model_, inputs_, **self.kwargs)
  File "torch/_dynamo/backends/common.py", line 72, in __call__
    cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
  File "torch/_functorch/aot_autograd.py", line 1087, in aot_module_simplified
    compiled_fn = dispatch_and_compile()
  File "torch/_functorch/aot_autograd.py", line 1063, in dispatch_and_compile
    compiled_fn, _ = create_aot_dispatcher_function(
  File "torch/_functorch/aot_autograd.py", line 524, in create_aot_dispatcher_function
    return _create_aot_dispatcher_function(
  File "torch/_functorch/aot_autograd.py", line 762, in _create_aot_dispatcher_function
    compiled_fn, fw_metadata = compiler_fn(
  File "torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 145, in aot_dispatch_base
    fw_module, updated_flat_args, maybe_subclass_meta = aot_dispatch_base_graph(  # type: ignore[misc]
  File "torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py", line 135, in aot_dispatch_base_graph
    fw_module = _create_graph(
  File "torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py", line 54, in _create_graph
    fx_g = make_fx(
  File "torch/fx/experimental/proxy_tensor.py", line 2159, in wrapped
    return make_fx_tracer.trace(f, *args)
  File "torch/fx/experimental/proxy_tensor.py", line 2097, in trace
    return self._trace_inner(f, *args)
  File "torch/fx/experimental/proxy_tensor.py", line 2068, in _trace_inner
    t = dispatch_trace(
  File "torch/_compile.py", line 32, in inner
    return disable_fn(*args, **kwargs)
  File "torch/_dynamo/eval_frame.py", line 654, in _fn
    return fn(*args, **kwargs)
  File "torch/fx/experimental/proxy_tensor.py", line 1133, in dispatch_trace
    graph = tracer.trace(root, concrete_args)  # type: ignore[arg-type]
  File "torch/_dynamo/eval_frame.py", line 654, in _fn
    return fn(*args, **kwargs)
  File "torch/fx/_symbolic_trace.py", line 823, in trace
    (self.create_arg(fn(*args)),),
  File "torch/fx/experimental/proxy_tensor.py", line 1188, in wrapped
    out = f(*tensors)
  File "<string>", line 1, in <lambda>
  File "torch/_functorch/_aot_autograd/traced_function_transforms.py", line 693, in inner_fn
    outs = fn(*args)
  File "torch/_functorch/_aot_autograd/traced_function_transforms.py", line 614, in _functionalized_f_helper
    inpt_old.copy_(inpt_new)
  File "torch/fx/experimental/proxy_tensor.py", line 1236, in __torch_function__
    return func(*args, **kwargs)
  File "torch/_subclasses/functional_tensor.py", line 541, in __torch_dispatch__
    outs_unwrapped = func._op_dk(
  File "torch/_meta_registrations.py", line 399, in meta_copy_
    aten.expand_copy.default(intermediate, self.size())
  File "torch/_ops.py", line 723, in __call__
    return self._op(*args, **kwargs)
  File "torch/_refs/__init__.py", line 2227, in _fn
    result = fn(*args, out=out, **kwargs)
  File "torch/_prims_common/wrappers.py", line 289, in _fn
    result = fn(*args, **kwargs)
  File "torch/_ops.py", line 1123, in __call__
    return self._op(*args, **(kwargs or {}))
  File "torch/_refs/__init__.py", line 2983, in expand
    torch._check(
  File "torch/__init__.py", line 1594, in _check
    _check_with(RuntimeError, cond, message)
  File "torch/__init__.py", line 1576, in _check_with
    raise error_type(message_evaluated)
torch._dynamo.exc.BackendCompilerFailed: backend='aot_eager' raised:
RuntimeError: expand: the requested shape has too few dimensions!

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
```

Note: since `native_batch_norm` may call `_native_batch_norm_legit`, #137807 may be related.

### Versions

PyTorch version: 2.5.0a0+git7128504
Is debug build: True
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

cc @ezyang @chauhang @penguinwu @zou3519 @bdhirsh @yf225