text: Title: [Bug]: UserWarning on skipping serialisation of PostGradPassManager
  Body: ### Your current environment
  
  <details>
  <summary>The output of `python collect_env.py`</summary>
  
  ```text
  PyTorch version: 2.6.0+cu124
  Is debug build: False
  CUDA used to build PyTorch: 12.4
  ROCM used to build PyTorch: N/A
  
  OS: Ubuntu 22.04.5 LTS (x86_64)
  GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
  Clang version: 19.1.7
  CMake version: version 3.31.4
  Libc version: glibc-2.35
  
  Python version: 3.11.10 (main, Oct 16 2024, 04:38:48) [Clang 18.1.8 ] (64-bit runtime)
  Python platform: Linux-5.15.0-134-generic-x86_64-with-glibc2.35
  Is CUDA available: True
  CUDA runtime version: Could not collect
  CUDA_MODULE_LOADING set to: LAZY
  GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB
  Nvidia driver version: 565.57.01
  cuDNN version: Could not collect
  HIP runtime version: N/A
  MIOpen runtime version: N/A
  Is XNNPACK available: True
  
  CPU:
  Architecture:                         x86_64
  CPU op-mode(s):                       32-bit, 64-bit
  Address sizes:                        46 bits physical, 48 bits virtual
  Byte Order:                           Little Endian
  CPU(s):                               12
  On-line CPU(s) list:                  0-11
  Vendor ID:                            GenuineIntel
  Model name:                           Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz
  CPU family:                           6
  Model:                                106
  Thread(s) per core:                   1
  Core(s) per socket:                   12
  Socket(s):                            1
  Stepping:                             6
  BogoMIPS:                             6002.58
  Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush acpi mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single intel_ppin ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves umip pku ospke gfni vaes vpclmulqdq rdpid md_clear flush_l1d arch_capabilities
  Hypervisor vendor:                    Xen
  Virtualization type:                  full
  L1d cache:                            576 KiB (12 instances)
  L1i cache:                            384 KiB (12 instances)
  L2 cache:                             15 MiB (12 instances)
  L3 cache:                             216 MiB (12 instances)
  NUMA node(s):                         1
  NUMA node0 CPU(s):                    0-11
  Vulnerability Gather data sampling:   Not affected
  Vulnerability Itlb multihit:          Not affected
  Vulnerability L1tf:                   Not affected
  Vulnerability Mds:                    Not affected
  Vulnerability Meltdown:               Not affected
  Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT Host state unknown
  Vulnerability Reg file data sampling: Not affected
  Vulnerability Retbleed:               Not affected
  Vulnerability Spec rstack overflow:   Not affected
  Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
  Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
  Vulnerability Srbds:                  Not affected
  Vulnerability Tsx async abort:        Not affected
  
  Versions of relevant libraries:
  [pip3] flashinfer-python==0.2.2.post1+cu124torch2.5
  [pip3] mypy==1.15.0
  [pip3] mypy-extensions==1.0.0
  [pip3] numpy==2.1.3
  [pip3] nvidia-cublas-cu12==12.4.5.8
  [pip3] nvidia-cuda-cupti-cu12==12.4.127
  [pip3] nvidia-cuda-nvrtc-cu12==12.4.127
  [pip3] nvidia-cuda-runtime-cu12==12.4.127
  [pip3] nvidia-cudnn-cu12==9.1.0.70
  [pip3] nvidia-cufft-cu12==11.2.1.3
  [pip3] nvidia-curand-cu12==10.3.5.147
  [pip3] nvidia-cusolver-cu12==11.6.1.9
  [pip3] nvidia-cusparse-cu12==12.3.1.170
  [pip3] nvidia-cusparselt-cu12==0.6.2
  [pip3] nvidia-ml-py==12.570.86
  [pip3] nvidia-nccl-cu12==2.21.5
  [pip3] nvidia-nvjitlink-cu12==12.4.127
  [pip3] nvidia-nvtx-cu12==12.4.127
  [pip3] pyzmq==26.2.1
  [pip3] sentence-transformers==3.2.1
  [pip3] torch==2.6.0
  [pip3] torchao==0.9.0
  [pip3] torchaudio==2.5.1
  [pip3] torchvision==0.21.0
  [pip3] transformers==4.49.0
  [pip3] transformers-stream-generator==0.0.5
  [pip3] triton==3.2.0
  [pip3] tritonclient==2.51.0
  [pip3] vector-quantize-pytorch==1.21.2
  [conda] Could not collect
  ROCM Version: Could not collect
  Neuron SDK Version: N/A
  vLLM Version: 0.7.3.dev758+g489b7938
  vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
  GPU Topology:
  GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
  GPU0     X      0-11    0               N/A
  
  Legend:
  
    X    = Self
    SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
    NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
    PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
    PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
    PIX  = Connection traversing at most a single PCIe bridge
    NV#  = Connection traversing a bonded set of # NVLinks
  
  CUDA_PATH=/nix/store/skdw4l72lgrj628l0arj1m3ynlzfksi8-cuda-merged-12.4
  LD_LIBRARY_PATH=/workspace/vllm/.venv/lib/python3.11/site-packages/cv2/../../lib64:/nix/store/lmyyfaz2amcs2an1f6m9h263151jiajy-cuda-merged-12.4/lib
  NCCL_CUMEM_ENABLE=0
  TORCHINDUCTOR_COMPILE_THREADS=1
  CUDA_MODULE_LOADING=LAZY
  ```
  
  </details>
  
  
  ### 🐛 Describe the bug
  
  I have started to see this serialization warning whenever running `vllm serve` from time to time when running with v1:
  
  ```prolog
  /workspace/project/.venv/lib/python3.12/site-packages/torch/utils/_config_module.py:189: UserWarning: Skipping serialization of post_grad_custom_post_pass value <vllm.compilation.pass_manager.PostGradPassManager object at 0x7f23314f7c20>
    warnings.warn(f"Skipping serialization of {k} value {v}")
  ```
  
  Not sure how impactful is this, or we can ignore this.
  
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