[core] Support capture custom ops into aclgraph (#2113)

### What this PR does / why we need it?
Thanks to the PR https://github.com/vllm-project/vllm-ascend/pull/426
make vllm-ascend support the aclgraph inference to reduce the host
overhead. However, the capability of aclgraph strongly relies on the
functionality provided by `torch.compile`, which is the key feature
supported in torch 2.x . Therefore, capture custom op into aclgraph is
only possible when it can be recognize and captured by `torch.compile`.

In this PR, we register the meta implementation of current custom ops to
enable the fx graph capture. And by doing that, insert those custom ops
into aclgraph become a natural thing to the ascend runtime.

### Does this PR introduce _any_ user-facing change?
No user face change.

### How was this patch tested?
Tested in unittest, we will integrate the `rotary_embedding` op into a
small custom model and use `torch.compile` and aclgraph to capture and
replay it to verify its functionality.

- vLLM version: v0.10.0
- vLLM main:
1b99028069

---------

Signed-off-by: ganyi <pleaplusone.gy@gmail.com>

vllm_ascend/utils.py

@@ -214,8 +214,12 @@ def enable_custom_op():
     if _CUSTOM_OP_ENABLED is not None:
         return _CUSTOM_OP_ENABLED
     try:
+        # isort: off
         # register custom ops into torch_library here
         import vllm_ascend.vllm_ascend_C  # type: ignore  # noqa: F401
+        # register the meta implementation for custom kernel if necessary
+        import vllm_ascend.meta_registration  # type: ignore  # noqa: F401
+        # isort: on
         _CUSTOM_OP_ENABLED = True
     except ImportError:
         _CUSTOM_OP_ENABLED = False