[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,
-                                               weak_ref_tensors(workspace))
+                update_graph_params_workspaces(num_tokens, workspace)
 
             # Handle graph capturing mode
             stream = torch_npu.npu.current_stream()