fix: stub entire torchao package on ROCm Windows instead of individual ops

torchao is not supported on ROCm Windows and its import chain transitively
requires torch._C._distributed_c10d (absent from the ROCm Windows wheel).
Rather than stub each missing op one by one, stub the whole torchao package
upfront. Unsloth uses bitsandbytes for quantization, not torchao, so this
has no functional impact. transformers gracefully handles an importable-but-
empty torchao by disabling TorchAoHfQuantizer.

studio/backend/core/training/worker.py

@@ -1148,6 +1148,22 @@ def run_training_process(
         return _StubClassMeta(name, (), {"__init__": lambda self, *a, **kw: None})
 
     if sys.platform == "win32":
+        # torchao is not supported on ROCm Windows and its import chain
+        # transitively pulls in torch._C._distributed_c10d (absent from the
+        # ROCm Windows wheel), causing cascading AttributeErrors.  We don't
+        # use torchao quantization (unsloth uses bitsandbytes), so stub the
+        # entire package up-front.  transformers falls back gracefully when
+        # torchao is importable but empty.
+        for _tao_name in (
+            "torchao",
+            "torchao.quantization",
+            "torchao.dtypes",
+            "torchao.float8",
+            "torchao.utils",
+        ):
+            if _tao_name not in sys.modules:
+                sys.modules[_tao_name] = _make_mod_stub(_tao_name)
+
         _c10d_key = "torch._C._distributed_c10d"
         if _c10d_key not in sys.modules:  # guard: never overwrite real NVIDIA impl
             _c10d_stub = _types.ModuleType(_c10d_key)