[Bugfix] Fix the abnormal NPU memory usage in full graph mode. (#3331)

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

In the full graph mode, since paged attention operators updates are
required, the parameters of this operators needs to be retained.
However, the tensor such as query、key cache、value cache, does not need
to be persistently saved, and we can manually release this space by
`weak_ref_tensor` to save the memory.

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

### How was this patch tested?

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

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

vllm_ascend/attention/attention_v1.py

@@ -39,6 +39,8 @@ from vllm_ascend.ops.attention import vanilla_chunked_prefill
 from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, aligned_16, is_310p,
                                nd_to_nz_2d, nd_to_nz_spec)
 
+from ..utils import weak_ref_tensors
+
 
 class AscendAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
@@ -402,15 +404,15 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 graph_params.events[num_tokens].append(event)
 
                 graph_params.attn_params[num_tokens].append((
-                    query,
-                    self.key_cache,
-                    self.value_cache,
+                    weak_ref_tensors(query),
+                    weak_ref_tensors(self.key_cache),
+                    weak_ref_tensors(self.value_cache),
                     self.num_kv_heads,
                     self.num_heads,
                     self.scale,
-                    attn_metadata.block_tables,
+                    weak_ref_tensors(attn_metadata.block_tables),
                     attn_metadata.seq_lens,
-                    output,
+                    weak_ref_tensors(output),
                 ))
 
                 torch.npu.graph_task_group_begin(stream)