[Bugfix] fix the precision issues that may raise from the inter-layer reuse of the workspace in certain scenarios (#5522)

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

In the current process of implementing attention updates, the FIA
operator shares a single workspace among different layers within the
same computation graph. To enable memory reuse, we adopt the
weak_ref_tensor mechanism. However, this approach may lead to precision
anomalies in certain scenarios. To address this issue, different layers
in the same computation graph are assigned independent workspaces.

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

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1

Signed-off-by: WithHades <244036962@qq.com>

vllm_ascend/attention/attention_v1.py

@@ -440,8 +440,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
                     block_table=attn_metadata.block_tables,
                     context_lens=attn_metadata.seq_lens,
                     out=output)
-                update_graph_params_workspaces(num_tokens,
+                update_graph_params_workspaces(num_tokens, workspace)
-                                               weak_ref_tensors(workspace))
 
             # Handle graph capturing mode
             stream = torch_npu.npu.current_stream()

vllm_ascend/compilation/acl_graph.py

@@ -162,6 +162,13 @@ class ACLGraphWrapper:
                         # any other acl graph.
                         output = weak_ref_tensors(output)
 
+            # here we always use weak ref for the workspaces
+            # to save memory
+            global _graph_params
+            global _draft_graph_params
+            weak_ref_workspaces(_graph_params)
+            weak_ref_workspaces(_draft_graph_params)
+
             # here we always use weak ref for the output
             # to save memory
             entry.output = weak_ref_tensors(output)
@@ -195,6 +202,16 @@ class ACLGraphWrapper:
         return entry.output
 
 
+def weak_ref_workspaces(params):
+    if params is None:
+        return
+    for num_tokens in params.workspaces:
+        if params.workspaces[num_tokens] is None:
+            continue
+        params.workspaces[num_tokens] = weak_ref_tensors(
+            params.workspaces[num_tokens])
+
+
 def _update_attn_pa_params(update_stream, forward_context, runtime_shape):
     graph_params = get_graph_params()
     # FIXME: Behold! We are using a temporary hack here to update the args
@@ -523,7 +540,7 @@ def set_graph_params(aclgraph_capture_sizes: list[int]):
 def update_graph_params_workspaces(num_tokens: int, workspace: torch.Tensor):
     global _graph_params
     if _graph_params is not None:
-        _graph_params.workspaces[num_tokens] = weak_ref_tensors(workspace)
+        _graph_params.workspaces[num_tokens] = workspace
 
 
 def get_graph_params():