Fix the bugs about operator registration by PyTorch Dispatcher (#2786)

**Background:**

There are two principles about operator registration in PyTorch
- The same namespace can be only registered once by `TORCH_LIBRARY`
- The operator signatures can be only registered once by `def`

Considering that all custom operators defined in the current repo are
only used by Ascend, instead of defining a common operator schema by
vLLM, all accelerators then follow this operator schema and complete the
implementation based on their respective hardware, which is conducive to
functional abstraction.

Therefore, we can rename the operator registration namespace to an
Ascend-specific namespace(**_C_ascend**).

Related ISSUE: https://github.com/vllm-project/vllm-ascend/issues/2742


- vLLM version: main
- vLLM main:
f592b3174b

Signed-off-by: FFFrog <ljw1101.vip@gmail.com>

csrc/torch_binding_meta.cpp

@@ -40,7 +40,7 @@ std::tuple<at::Tensor, at::Tensor> rotary_embedding_meta(
   at::Tensor &positions,
   at::Tensor &query,
   at::Tensor &key,
-  int64_t head_size, 
+  int64_t head_size,
   at::Tensor &cos_sin_cache,
   bool is_neox) {
     auto num_tokens = positions.sym_numel();
@@ -86,9 +86,9 @@ at::Tensor sgmv_expand_meta(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_
 } // namespace vllm_ascend
 
 namespace {
-  // Register the meta implementations of the custom kernels for symbolic tracing, this will also 
+  // Register the meta implementations of the custom kernels for symbolic tracing, this will also
   // the custom kernel been captured into aclgraph
-  TORCH_LIBRARY_IMPL_EXPAND(_C, Meta, ops) {
+  TORCH_LIBRARY_IMPL_EXPAND(CONCAT(_C, _ascend), Meta, ops) {
     // Rotary embedding meta implementation
     ops.impl("rotary_embedding", &vllm_ascend::meta::rotary_embedding_meta);
     // Masked input and mask meta implementation
@@ -99,4 +99,4 @@ namespace {
     ops.impl("sgmv_expand", &vllm_ascend::meta::sgmv_expand_meta);
 
 }
-}
+}