[Misc] upgrade to vllm main (#6646)

### What this PR does / why we need it?
This PR upgrades the core vLLM dependency to a newer version from the
main branch (`13397841ab469cecf1ed425c3f52a9ffc38139b5`). This is
necessary to keep our project up-to-date with the latest features and
fixes from upstream vLLM.

1.
ac32e66cf9
pass file is moved.

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Co-authored-by: wxsIcey <1790571317@qq.com>

tests/e2e/singlecard/compile/backend.py

@@ -19,10 +19,15 @@ from typing import Any, Callable, List, Optional, Sequence
 
 import torch.fx as fx
 from torch._inductor.decomposition import select_decomp_table
-from vllm.compilation.fx_utils import OpOverload
 from vllm.config import get_current_vllm_config
 
 from vllm_ascend.compilation.compiler_interface import compile_fx
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.15.0"):
+    from vllm.compilation.fx_utils import OpOverload  # type: ignore
+else:
+    from vllm.compilation.passes.fx_utils import OpOverload
 
 
 class TestBackend:

tests/e2e/singlecard/compile/test_norm_quant_fusion.py

@@ -21,7 +21,6 @@ import torch
 import torch.nn as nn
 import torch_npu
 import vllm.config
-from vllm.compilation.fx_utils import OpOverload
 from vllm.config import ModelConfig, VllmConfig
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
@@ -33,6 +32,13 @@ from vllm_ascend.ascend_forward_context import set_ascend_forward_context
 from vllm_ascend.compilation.passes.norm_quant_fusion_pass import \
     AddRMSNormQuantFusionPass
 from vllm_ascend.utils import enable_custom_op
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.15.0"):
+    from vllm.compilation.fx_utils import OpOverload  # type: ignore
+else:
+    from vllm.compilation.passes.fx_utils import OpOverload
+
 
 
 class TestModelWithoutBias(nn.Module):