debugging.rst

.. _debugging:

===============
Debugging Tips
===============

This document outlines some debugging strategies you can consider. If you think you've discovered a bug, please `search existing issues <https://github.com/vllm-project/vllm/issues?q=is%3Aissue>`_ first to see if it has already been reported. If not, please `file a new issue <https://github.com/vllm-project/vllm/issues/new/choose>`_, providing as much relevant information as possible.

.. note::

    Once you've debugged a problem, remember to turn off any debugging environment variables defined, or simply start a new shell to avoid being affected by lingering debugging settings. Otherwise, the system might be slow with debugging functionalities left activated.

Hangs downloading a model 
----------------------------------------
If the model isn't already downloaded to disk, vLLM will download it from the internet which can take time and depend on your internet connection. 
It's recommended to download the model first using the `huggingface-cli <https://huggingface.co/docs/huggingface_hub/en/guides/cli>`_ and passing the local path to the model to vLLM. This way, you can isolate the issue.

Hangs loading a model from disk
----------------------------------------
If the model is large, it can take a long time to load it from disk. Pay attention to where you store the model. Some clusters have shared filesystems across nodes, e.g. a distributed filesystem or a network filesystem, which can be slow. 
It'd be better to store the model in a local disk. Additionally, have a look at the CPU memory usage, when the model is too large it might take a lot of CPU memory, slowing down the operating system because it needs to frequently swap between disk and memory.

Model is too large
----------------------------------------
If the model is too large to fit in a single GPU, you might want to `consider tensor parallelism <https://docs.vllm.ai/en/latest/serving/distributed_serving.html#distributed-inference-and-serving>`_ to split the model across multiple GPUs. In that case, every process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism). You can convert the model checkpoint to a sharded checkpoint using `this example <https://docs.vllm.ai/en/latest/getting_started/examples/save_sharded_state.html>`_ . The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.

Enable more logging 
----------------------------------------
If other strategies don't solve the problem, it's likely that the vLLM instance is stuck somewhere. You can use the following environment variables to help debug the issue:

- ``export VLLM_LOGGING_LEVEL=DEBUG`` to turn on more logging.
- ``export CUDA_LAUNCH_BLOCKING=1`` to identify which CUDA kernel is causing the problem.
- ``export NCCL_DEBUG=TRACE`` to turn on more logging for NCCL.
- ``export VLLM_TRACE_FUNCTION=1`` to record all function calls for inspection in the log files to tell which function crashes or hangs.

Incorrect network setup
----------------------------------------
The vLLM instance cannot get the correct IP address if you have a complicated network config. You can find a log such as ``DEBUG 06-10 21:32:17 parallel_state.py:88] world_size=8 rank=0 local_rank=0 distributed_init_method=tcp://xxx.xxx.xxx.xxx:54641 backend=nccl`` and the IP address should be the correct one. 
If it's not, override the IP address using the environment variable ``export VLLM_HOST_IP=<your_ip_address>``. 

You might also need to set ``export NCCL_SOCKET_IFNAME=<your_network_interface>`` and ``export GLOO_SOCKET_IFNAME=<your_network_interface>`` to specify the network interface for the IP address.

Error near ``self.graph.replay()`` 
----------------------------------------
If vLLM crashes and the error trace captures it somewhere around ``self.graph.replay()`` in ``vllm/worker/model_runner.py``, it is a CUDA error inside CUDAGraph. 
To identify the particular CUDA operation that causes the error, you can add ``--enforce-eager`` to the command line, or ``enforce_eager=True`` to the :class:`~vllm.LLM` class to disable the CUDAGraph optimization and isolate the exact CUDA operation that causes the error.

Incorrect hardware/driver
----------------------------------------
If GPU/CPU communication cannot be established, you can use the following Python script and follow the instructions below to confirm whether the GPU/CPU communication is working correctly.

.. code-block:: python

    # Test PyTorch NCCL
    import torch
    import torch.distributed as dist
    dist.init_process_group(backend="nccl")
    local_rank = dist.get_rank() % torch.cuda.device_count()
    torch.cuda.set_device(local_rank)
    data = torch.FloatTensor([1,] * 128).to("cuda")
    dist.all_reduce(data, op=dist.ReduceOp.SUM)
    torch.cuda.synchronize()
    value = data.mean().item()
    world_size = dist.get_world_size()
    assert value == world_size, f"Expected {world_size}, got {value}"

    print("PyTorch NCCL is successful!")

    # Test PyTorch GLOO
    gloo_group = dist.new_group(ranks=list(range(world_size)), backend="gloo")
    cpu_data = torch.FloatTensor([1,] * 128)
    dist.all_reduce(cpu_data, op=dist.ReduceOp.SUM, group=gloo_group)
    value = cpu_data.mean().item()
    assert value == world_size, f"Expected {world_size}, got {value}"

    print("PyTorch GLOO is successful!")

    if world_size <= 1:
        exit()

    # Test vLLM NCCL, with cuda graph
    from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator

    pynccl = PyNcclCommunicator(group=gloo_group, device=local_rank)
    pynccl.disabled = False

    s = torch.cuda.Stream()
    with torch.cuda.stream(s):
        data.fill_(1)
        pynccl.all_reduce(data, stream=s)
        value = data.mean().item()
        assert value == world_size, f"Expected {world_size}, got {value}"

    print("vLLM NCCL is successful!")

    g = torch.cuda.CUDAGraph()
    with torch.cuda.graph(cuda_graph=g, stream=s):
        pynccl.all_reduce(data, stream=torch.cuda.current_stream())

    data.fill_(1)
    g.replay()
    torch.cuda.current_stream().synchronize()
    value = data.mean().item()
    assert value == world_size, f"Expected {world_size}, got {value}"

    print("vLLM NCCL with cuda graph is successful!")

    dist.destroy_process_group(gloo_group)
    dist.destroy_process_group()

If you are testing with a single node, adjust ``--nproc-per-node`` to the number of GPUs you want to use:

.. code-block:: console

    $ NCCL_DEBUG=TRACE torchrun --nproc-per-node=<number-of-GPUs> test.py

If you are testing with multi-nodes, adjust ``--nproc-per-node`` and ``--nnodes`` according to your setup and set ``MASTER_ADDR`` to the correct IP address of the master node, reachable from all nodes. Then, run:

.. code-block:: console

    $ NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR test.py

If the script runs successfully, you should see the message ``sanity check is successful!``.

If the test script hangs or crashes, usually it means the hardware/drivers are broken in some sense. You should try to contact your system administrator or hardware vendor for further assistance. As a common workaround, you can try to tune some NCCL environment variables, such as ``export NCCL_P2P_DISABLE=1`` to see if it helps. Please check `their documentation <https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html>`__ for more information. Please only use these environment variables as a temporary workaround, as they might affect the performance of the system. The best solution is still to fix the hardware/drivers so that the test script can run successfully.

.. note::

    A multi-node environment is more complicated than a single-node one. If you see errors such as ``torch.distributed.DistNetworkError``, it is likely that the network/DNS setup is incorrect. In that case, you can manually assign node rank and specify the IP via command line arguments:

    - In the first node, run ``NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --node-rank 0 --master_addr $MASTER_ADDR test.py``.
    - In the second node, run ``NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --node-rank 1 --master_addr $MASTER_ADDR test.py``.

    Adjust ``--nproc-per-node``, ``--nnodes``, and ``--node-rank`` according to your setup, being sure to execute different commands (with different ``--node-rank``) on different nodes.

Known Issues
----------------------------------------
- In ``v0.5.2``, ``v0.5.3``, and ``v0.5.3.post1``, there is a bug caused by `zmq <https://github.com/zeromq/pyzmq/issues/2000>`_ , which can occasionally cause vLLM to hang depending on the machine configuration. The solution is to upgrade to the latest version of ``vllm`` to include the `fix <https://github.com/vllm-project/vllm/pull/6759>`_.