[Feature] adapt to uva buffer and main2main (#6657)

### What this PR does / why we need it?
vllm model runner v2 use uva buffer to prepare input data, but npu
doesn't support uva yet, this pr implement a uvawrapper class to mimic
gpu's uva backend. what's more, this pr make some modifications to adapt
to the newer main branch.

### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM main:
13397841ab

---------

Signed-off-by: Ronald1995 <ronaldautomobile@163.com>

vllm_ascend/worker/v2/attn_utils.py

@@ -42,17 +42,21 @@ def get_attn_mask_builder(device: torch.device):
 
 
 def build_attn_metadata(
+    *,
     attn_metadata_builders: list[AttentionMetadataBuilder],
     num_reqs: int,
     num_tokens: int,
     query_start_loc_gpu: torch.Tensor,
     query_start_loc_cpu: torch.Tensor,
+    max_query_len: int,
     seq_lens: torch.Tensor,
-    seq_lens_np: np.ndarray,
-    num_computed_tokens_cpu: torch.Tensor | None,
+    max_seq_len: int,
     block_tables: Sequence[torch.Tensor],
     slot_mappings: torch.Tensor,
     kv_cache_config: KVCacheConfig,
+    # extra attributes for ascend npus.
+    seq_lens_np: np.ndarray | None = None,
+    num_computed_tokens_cpu: torch.Tensor | None = None,
     positions: torch.Tensor | None = None,
     attn_state: Any | None = None,
     graph_pad_size: int = -1,
@@ -61,9 +65,13 @@ def build_attn_metadata(
 ) -> dict[str, Any]:
     """Build attention metadata for Ascend NPUs."""
     # TODO(Ronald1995): optimize AscendCommonAttentionMetadata.
-    max_query_len = int(query_start_loc_cpu.max())
-    seq_lens_cpu = torch.from_numpy(seq_lens_np)
-    max_seq_len = int(seq_lens_cpu.max())
+
+    # seq_lens_np is used for ascend npus, it maybe None in spec_decode case,
+    # we fill it with max_seq_len in case `attn_metadata_builder.build` raise
+    # an error.
+    if seq_lens_np is None:
+        seq_lens_np = np.full(num_reqs, max_seq_len, dtype=np.int32)
+    seq_lens_cpu = torch.from_numpy(seq_lens_np)[:num_reqs]
     # torch_npu._reshape_and_cache operator requires slot_mappings to
     # be torch.int32.
     slot_mappings = slot_mappings.to(torch.int32)
@@ -77,7 +85,7 @@ def build_attn_metadata(
         common_attn_metadata = AscendCommonAttentionMetadata(
             query_start_loc=query_start_loc_gpu,
             query_start_loc_cpu=query_start_loc_cpu,
-            seq_lens_cpu=seq_lens_cpu[:num_reqs],
+            seq_lens_cpu=seq_lens_cpu,
             seq_lens=seq_lens[:num_reqs],
             num_reqs=num_reqs,
             num_actual_tokens=num_tokens,