[BugFix]converting pa get_workspace back to capturing (#5833)

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

This helps to fix a bug in for pa get_workspace. In earlier
implementation, we use `_npu_paged_attention_get_workspace` in
`_update_pa_attn_params`. However, this might cause some potential
memory problems as it dynamically allocate new memory for workspace when
calling this api. Therefor, we move this back to capturing, and use a
fixed `SEQ_LEN_WITH_MAX_PA_WORKSPACE` to get max workspace.

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

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

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

vllm_ascend/compilation/acl_graph.py

@@ -229,25 +229,6 @@ def _update_attn_pa_params(update_stream, forward_context, runtime_shape):
             ) = param
             seq_lens = forward_context.attn_metadata[key].seq_lens
 
-            # 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,
-                num_kv_heads=num_kv_heads,
-                num_heads=num_heads,
-                scale_value=scale,
-                block_table=block_table,
-                context_lens=seq_lens,
-                out=output,
-            )
             torch.npu.graph_task_update_begin(update_stream, handle)
             torch_npu._npu_paged_attention(
                 query=query,
@@ -259,7 +240,7 @@ def _update_attn_pa_params(update_stream, forward_context, runtime_shape):
                 block_table=block_table,
                 context_lens=seq_lens,
                 out=output,
-                workspace=workspace,
+                workspace=graph_params.workspaces.get(runtime_shape),
             )
             torch.npu.graph_task_update_end(update_stream)