......lib/python3.12/site-packages/vllm/_C.abi3.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSsb

[Rex818]

......lib/python3.12/site-packages/vllm/_C.abi3.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSsb
vllm serve. NOT RUN ,

[sudeshmu]

This looks like a PyTorch error. Check your PyTorch Version

python -c "import torch; print(torch.__version__)"
Could you share more on the issue? vllm version and how this occurred?

[xXMrNidaXx]

This undefined symbol error is a classic ABI mismatch!

Root cause:
The vLLM binary was compiled with a different PyTorch version than what's currently installed.

Fixes:

1. Reinstall vLLM with matching PyTorch

pip uninstall vllm
pip install vllm --no-cache-dir

2. Check PyTorch version

import torch
print(torch.__version__)
print(torch.version.cuda)

vLLM needs specific CUDA + PyTorch combos.

3. Full clean install

pip uninstall vllm torch
pip cache purge
pip install torch==2.2.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html
pip install vllm

4. Build from source (if weird setup)

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e .

Common causes:

• Installing vLLM before PyTorch
• Conda/pip environment mixing
• Multiple CUDA versions on system

We've debugged these ABI issues at RevolutionAI many times. The clean reinstall usually fixes it.

What's your pip list | grep torch output?

[smqd19]

This error usually points to a mismatch between the PyTorch version installed in your environment and the version vLLM was built or linked against. Specifically, it's failing to resolve a TorchScript function, parseSchemaOrName, which suggests ABI incompatibility.

From experience, these kinds of issues often occur when using pre-built binaries (_C.abi3.so) that rely on specific PyTorch versions. Here’s how I’d debug and resolve this:

1. Check PyTorch version: Ensure the version installed matches the one supported by the vLLM release you're using. For example:

   pip show torch

   Verify it aligns with the requirements in vLLM’s setup.py or documentation.

2. Rebuild vLLM: If PyTorch was upgraded after vLLM was installed, you'll need to rebuild vLLM to re-link against the correct ABI:

   pip uninstall vllm
   pip install --no-cache-dir vllm

3. Downgrade PyTorch: If rebuilding isn’t an option or introduces other issues, consider downgrading PyTorch to a compatible version:

   pip install torch==<target_version>

4. Environment isolation: Sometimes, package conflicts lead to such errors. Use a clean virtual environment:

   python3.12 -m venv vllm_env
   source vllm_env/bin/activate
   pip install vllm

If none of these steps work, check whether vLLM uses any custom compiled components. In such cases, ensuring your environment matches the compiler/runtime versions used for building those components (e.g., GCC or Clang) might help. Let me know if you’re still stuck, and I can dig deeper into specifics.

[reallyticsai]

The error message indicates a symbol lookup failure for _ZN5torch3jit17parseSchemaOrNameERKSsb in vllm/_C.abi3.so. This suggests a compatibility issue between the PyTorch version used to build vllm and the one used at runtime.
We've seen similar issues in production when PyTorch versions mismatch between build and runtime environments.
To troubleshoot, check PyTorch versions in both environments using python -c "import torch; print(torch.__version__)".
Ensure they match or are compatible. You can also try rebuilding vllm with the same PyTorch version used at runtime.
If issues persist, examine the symbol demangling using c++filt -n _ZN5torch3jit17parseSchemaOrNameERKSsb, which should output torch::jit::parseSchemaOrName. This confirms the symbol is related to PyTorch JIT.
Verify that your PyTorch installation is not corrupted and consider using a virtual environment to isolate dependencies.

[demirciberk]

This is a native-extension ABI mismatch: vllm/_C.abi3.so is trying to resolve a PyTorch JIT symbol that is not present in the PyTorch library loaded at runtime. The usual causes are:

• PyTorch was upgraded or downgraded after vLLM was installed.
• vLLM and PyTorch came from incompatible CUDA/PyTorch builds.
• Multiple environments or mixed pip/Conda installations are being used.

Please first collect the versions from the same environment in which vllm serve is run:

bash
python -c "import sys, torch; print(sys.executable); print(torch.version); print(torch.version.cuda); print(torch.file)"
python -m pip show vllm torch
python -m pip check

Do not use a manually selected PyTorch version without checking the requirements for the vLLM release. For example, vLLM v0.8.5 explicitly required PyTorch 2.6.0 in its CUDA requirements:
https://github.com/vllm-project/vllm/blob/v0.8.5/requirements/cuda.txt

The safest fix is a fresh environment and an installation method that selects a compatible backend:

bash
uv venv --python 3.12
source .venv/bin/activate
uv pip install vllm --torch-backend=auto
python -c "import torch, vllm; print(torch.version, torch.version.cuda)"
vllm serve

Current vLLM documentation specifically recommends a fresh environment because the compiled binaries can be incompatible with different PyTorch or CUDA builds:
https://docs.vllm.ai/en/stable/getting_started/installation/gpu/

If you must keep an existing PyTorch installation, rebuild vLLM from source against that exact installation rather than using a prebuilt wheel:
https://docs.vllm.ai/en/stable/getting_started/installation/gpu/#build-wheel-from-source

The exact correction cannot be determined from the report alone. Please provide the vLLM version, PyTorch version, CUDA/ROCm version, operating system, GPU, and the complete traceback.