Fixing two cascading bugs when running the CK MoE tuner

[xaguilar-amd]

Problem

Running the CK MoE 2-stage tuner fails immediately with two cascading errors,
leaving zero shapes tuned:

[aiter] [Failed] Task 4 failed with OSError:
  aiter/jit/module_moe_asm.so: undefined symbol: _ZTVN5torch8autograd12AutogradMetaE

[aiter] error in batch 1 of 1: list index out of range
Traceback (most recent call last):
  File "aiter/utility/base_tuner.py", line 468, in run
    all_results = self.tune(batch, self.tunedf, args)
  File "csrc/ck_gemm_moe_2stages_codegen/gemm_moe_tune.py", line 2311, in tune
    rets = mp_tuner(...)
  File "aiter/utility/mp_tuner.py", line 535, in mp_tuner
    result.append(task_result[0])
IndexError: list index out of range

---

Root causes

1 — module_moe_asm.so was compiled without torch linkage (aiter/jit/core.py)

module_moe_asm is used in two modes: as a pybind extension (loaded via
importlib) and as a raw ctypes library (loaded via ctypes.CDLL).
The ctypes path lives in _ctypes_call._ensure_loaded() and, before this fix,
unconditionally forced:

d_args["torch_exclude"] = True

torch_exclude=True drops all torch link flags (-ltorch_cpu, -lc10, etc.)
from the build. This is intentional for purely standalone C kernel modules —
but module_moe_asm's source list includes csrc/pybind/moe_op_pybind.cu, a
pybind11 file that uses at::Tensor and other PyTorch C++ types. Those types
reference the vtable symbol _ZTVN5torch8autograd12AutogradMetaE, which the
linker left undefined:

$ nm -D aiter/jit/module_moe_asm.so | grep AutogradMeta
                 U _ZTVN5torch8autograd12AutogradMetaE

Because libtorch_cpu.so was absent from NEEDED, ctypes.CDLL failed when
loading the .so in tuner worker subprocesses. The tuner uses
mp.set_start_method("spawn"), so each worker starts a fresh Python
interpreter. Even though import torch runs inside the worker (via
mp_tuner.py's top-level import), PyTorch's own shared libraries are loaded
as transitive dependencies of torch._C.so. On Linux, transitive
dependencies do not inherit RTLD_GLOBAL, so their symbols are invisible to
subsequent ctypes.CDLL calls.

Fix: remove the forced override so each module is built with its own
configured value (which defaults to torch_exclude=False for
module_moe_asm). The rebuilt .so lists libtorch_cpu.so in NEEDED, and
the dynamic linker resolves all symbols automatically.

2 — Failed tasks left result_dict empty, causing IndexError (aiter/utility/mp_tuner.py)

mp_tuner collects worker results into result_dict[k] and reconstructs a
flat list at the end:

for k in range(len(rets)):
    task_result = result_dict.get(k, [])
    ...
    result.append(task_result[0])   # IndexError if task_result is []

When a task raises any exception that is not MPTimeoutError and not
KeyError, the existing handler logged the error and marked the task as
complete — but never wrote a placeholder into result_dict[k]. The loop
then called task_result[0] on an empty list and raised IndexError, crashing
the tuner before it could print which shapes failed.

Fix: add the same add_dummy_result call that already existed for the
timeout branch so every failed task gets a (info, float("inf"), 1.0)
placeholder in result_dict.

---

How the two bugs interact

Bug 1 caused every ASM-stage-1 task to raise OSError in the subprocess.
Bug 2 meant that the parent process then crashed with IndexError when
collecting results — before any summary or diagnostic output could be printed.

Fixing Bug 2 alone allows the tuner to survive and produce a proper
"Failed shapes" summary, but no ASM-stage-1 kernels are tuned. Both fixes are
required for correct operation.

---

Testing

After deleting the stale .so (so it is rebuilt with correct flags on the next
run) and applying both patches, the tuner completes without errors.

---

Notes for reviewers

• The torch_exclude=True override in _ctypes_call._ensure_loaded is safe to
  remove because any module that has torch_exclude explicitly set to True in
  optCompilerConfig.json (e.g. module_aiter_enum) will continue to use that
  value. Only modules that rely on the default (False) are affected.

• If a future module genuinely needs both ctypes loading and a torch-free
  build, its pybind source file should be separated from its pure-C sources into
  a distinct build target so the two concerns do not conflict at link time.

  PR created with Cursor.

[github-actions]

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