[Triton] Centralize Ascend extension op dispatch in triton_utils (#6937)

### What this PR does / why we need it?

This pull request refactors the dispatch mechanism for the
**triton-ascend-specific operators** `insert_slice`, `extract_slice`,
and `get_element` to ensure compatibility with both CANN 8.5 and 9.0.

A unified helper function, `_resolve_triton_ascend_op`, has been
introduced in `vllm_ascend/ops/triton/triton_utils.py`. This function
dynamically resolves these operators by first attempting to import them
from the `triton.language.extra.cann.extension` module, which is present
in newer CANN versions. If that fails, it falls back to the standard
`triton.language` module.

This approach centralizes operator dispatch logic, allowing individual
Triton kernels to use these functions without being aware of the
underlying Triton/CANN version. All call sites have been updated to use
these new unified functions.

### Does this PR introduce _any_ user-facing change?

No. This is an internal refactoring of operator implementations and does
not introduce any user-facing changes.

### How was this patch tested?

CI is expected to pass with existing tests.

**Testing Context:**
- vLLM version: v0.16.0
- vLLM main: `15d76f74e2fdb12a95ea00f0ca283acf6219a2b7`

Signed-off-by: linfeng-yuan <1102311262@qq.com>

vllm_ascend/ops/triton/activation/swiglu_quant.py

@@ -1,7 +1,7 @@
 import torch
 from vllm.triton_utils import tl, triton
 
-from vllm_ascend.ops.triton.triton_utils import get_vectorcore_num
+from vllm_ascend.ops.triton.triton_utils import extract_slice, get_vectorcore_num
 
 
 @triton.jit
@@ -40,8 +40,8 @@ def _swiglu_quant_kernel(
         # swiglu
         x_offsets = row_idx * TOTAL_COLS + tl.arange(0, TOTAL_COLS)
         cur_x = tl.load(x_ptr + x_offsets)
-        x1 = tl.extract_slice(cur_x, offsets=(0,), sizes=(HALF_COLS,), strides=(1,))
-        x2 = tl.extract_slice(cur_x, offsets=(HALF_COLS,), sizes=(HALF_COLS,), strides=(1,))
+        x1 = extract_slice(cur_x, offsets=(0,), sizes=(HALF_COLS,), strides=(1,))
+        x2 = extract_slice(cur_x, offsets=(HALF_COLS,), sizes=(HALF_COLS,), strides=(1,))
         out = x1 * tl.sigmoid(x1) * x2
 
         # quant
@@ -50,7 +50,7 @@ def _swiglu_quant_kernel(
             # store scale
             tl.store(scale_ptr + row_idx, scale.to(scale_ptr.dtype.element_ty))
             for col_blk_idx in range(0, HALF_COLS, COL_BLOCK_SIZE):
-                tmp_out = tl.extract_slice(out, offsets=(col_blk_idx,), sizes=(COL_BLOCK_SIZE,), strides=(1,))
+                tmp_out = extract_slice(out, offsets=(col_blk_idx,), sizes=(COL_BLOCK_SIZE,), strides=(1,))
                 tmp_out = (tmp_out.to(tl.float32) / scale).to(x_ptr.dtype.element_ty)
                 tmp_out = tmp_out.cast(tl.int8, overflow_mode="saturate")