[Feature] Optimize Qwen3.5/Qwen3Next GDN prefill by prebuilding chunk metadata (#7487)

### What this PR does / why we need it?
This PR optimizes the Qwen3.5 and Qwen3Next GDN prefill path on Ascend
by reducing host/device synchronization overhead.

The current implementation of the `chunk_gated_delta_rule` path for
variable-length sequences prepares chunk metadata during the forward
pass. This approach triggers frequent CPU intervention and host/device
round-trips. When running prefill-heavy workloads with asynchronous
scheduling enabled, these synchronizations result in execution "bubbles"
and prefill stalling (stuttering). **Note that this does not cause
asynchronous scheduling to fail; rather, it prevents the system from
reaching its theoretical throughput due to these unnecessary stalls.**

To resolve this, the patch moves metadata preparation out of the hot
path:
- **Prebuilt Metadata:** All non-speculative varlen chunk metadata for
GDN is now prebuilt on the CPU.
- **Asynchronous Transfer:** Staging buffers are kept in pinned memory
and transferred to the NPU asynchronously.
- **Integration:** The prebuilt bundle is attached to GDN attention
metadata via `patch_gdn_attn.py` and passed into Triton wrappers.
- **Backward Compatibility:** Triton wrappers fall back to the legacy
preparation path if no prebuilt metadata is provided.

- vLLM version: v0.17.0
- vLLM main:
8b6325758c
---------
Signed-off-by: maoxx241 <maomaoyu870@gmail.com>

vllm_ascend/ops/triton/fla/cumsum.py

@@ -81,6 +81,7 @@ def chunk_local_cumsum_scalar(
     reverse: bool = False,
     scale: float = None,
     cu_seqlens: torch.Tensor | None = None,
+    block_indices: torch.Tensor | None = None,
     head_first: bool = False,
     output_dtype: torch.Tensor | None = torch.float,
 ):
@@ -90,7 +91,8 @@ def chunk_local_cumsum_scalar(
         B, T, H = g.shape
     assert chunk_size == 2 ** (chunk_size.bit_length() - 1), "chunk_size must be a power of 2"
     OPTIM_BLOCK_SIZE = triton.next_power_of_2((2**18) // (H * chunk_size))
-    block_indices = prepare_chunk_indices(cu_seqlens, chunk_size=OPTIM_BLOCK_SIZE) if cu_seqlens is not None else None
+    if cu_seqlens is not None and block_indices is None:
+        block_indices = prepare_chunk_indices(cu_seqlens, chunk_size=OPTIM_BLOCK_SIZE)
     num_blocks = len(block_indices) if cu_seqlens is not None else triton.cdiv(T, OPTIM_BLOCK_SIZE)
     g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
     grid = (num_blocks, B)
@@ -132,6 +134,7 @@ def chunk_local_cumsum(
             reverse=reverse,
             scale=scale,
             cu_seqlens=cu_seqlens,
+            block_indices=kwargs.get("block_indices"),
             head_first=head_first,
             output_dtype=output_dtype,
         )