[main][bugfix] Fix a rare bug triggered by _npu_paged_attention in FULL_DECODE_ONLY mode (#3986)

### What this PR does / why we need it?
This PR fixes a bug where the workspace of `_npu_paged_attention` in
setup is smaller than execution. For current implementation of
FULL_DECODE_ONLY with `_npu_paged_attention`, we use
`_npu_paged_attention_get_workspace` when capturing with `max_model_len`
as `seq_lens`. This assumes that PA with larger `seq_lens` inputs should
have larger workspace than smaller `seq_lens`. However, there are rare
cases where PA with smaller `seq_lens` incurs larger space. So I add
`get_workspace` directly into `update_attn_params`.
This change might introduce small(≈1%) performance degradation for low
num_tokens(such as 1) in decode phase, and there is no other known
memory issues. So I think this change is acceptable. We can remove this
if new attention op (such as `npu_fused_infer_attention_score`) does not
have such problems.

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

No.

### How was this patch tested?


- vLLM version: v0.11.0
- vLLM main:
83f478bb19

Signed-off-by: Angazenn <supperccell@163.com>

vllm_ascend/compilation/acl_graph.py

@@ -214,8 +214,16 @@ def update_attn_params(update_stream, forward_context, runtime_shape):
                 output,
             ) = param
             seq_lens = forward_context.attn_metadata[key].seq_lens
-            torch.npu.graph_task_update_begin(update_stream, handle)
-            torch_npu._npu_paged_attention(
+
+            # When using FULL_DECODE_ONLY, there are some rare bugs for FULL_DECODE_ONLY
+            # mode with GQA. This is triggered by getting workspace for _npu_paged_attention
+            # in torch_npu. On some rare cases, _npu_paged_attention with smaller seq_lens
+            # might encounter a bigger workspace, while currently we use max_model_len to
+            # calculate max workspace in capturing. So additional get_workspace is added
+            # here to avoid such bugs.
+            # TODO(Angazenn): we will remove this once _npu_paged_attention is fully
+            # replaced by npu_fused_infer_attention_score which does not contain such bugs.
+            workspace = torch_npu._npu_paged_attention_get_workspace(
                 query=query,
                 key_cache=key_cache,
                 value_cache=value_cache,
@@ -224,8 +232,18 @@ def update_attn_params(update_stream, forward_context, runtime_shape):
                 scale_value=scale,
                 block_table=block_table,
                 context_lens=seq_lens,
-                out=output,
-                workspace=graph_params.workspaces.get(runtime_shape))
+                out=output)
+            torch.npu.graph_task_update_begin(update_stream, handle)
+            torch_npu._npu_paged_attention(query=query,
+                                           key_cache=key_cache,
+                                           value_cache=value_cache,
+                                           num_kv_heads=num_kv_heads,
+                                           num_heads=num_heads,
+                                           scale_value=scale,
+                                           block_table=block_table,
+                                           context_lens=seq_lens,
+                                           out=output,
+                                           workspace=workspace)
             torch.npu.graph_task_update_end(update_stream)
 
             event.record(update_stream)