diff --git a/vllm_ascend/compilation/acl_graph.py b/vllm_ascend/compilation/acl_graph.py
index f2cab32..d3e779e 100644
--- a/vllm_ascend/compilation/acl_graph.py
+++ b/vllm_ascend/compilation/acl_graph.py
@@ -212,19 +212,37 @@ def update_attn_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)
+
         with torch.npu.stream(update_stream):
             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=graph_params.workspaces.get(runtime_shape))
+            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)