[Bug]: data_parallel.py not working in multi-node case

[yiz-liu]

Your current environment

The output of python collect_env.py

INFO 05-22 05:44:27 [__init__.py:248] Automatically detected platform cuda.
Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.3 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : version 3.31.4
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.7.0+cu126
Is debug build               : False
CUDA used to build PyTorch   : 12.6
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.9 (main, Feb  5 2025, 08:49:00) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.15.0-25-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.1.105
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : 
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20
GPU 4: NVIDIA H20
GPU 5: NVIDIA H20
GPU 6: NVIDIA H20
GPU 7: NVIDIA H20

Nvidia driver version        : 550.127.08
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          224
On-line CPU(s) list:             0-223
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8480+
CPU family:                      6
Model:                           143
Thread(s) per core:              2
Core(s) per socket:              56
Socket(s):                       2
Stepping:                        8
Frequency boost:                 enabled
CPU max MHz:                     2001.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4000.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr avx512_fp16 flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       5.3 MiB (112 instances)
L1i cache:                       3.5 MiB (112 instances)
L2 cache:                        224 MiB (112 instances)
L3 cache:                        210 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-55,112-167
NUMA node1 CPU(s):               56-111,168-223
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          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 IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==26.2.1
[pip3] torch==2.7.0
[pip3] torchaudio==2.7.0
[pip3] torchvision==0.22.0
[pip3] transformers==4.52.1
[pip3] triton==3.3.0
[pip3] tritonclient==2.55.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
Neuron SDK Version           : N/A
vLLM Version                 : 0.9.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
  	�[4mGPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	NIC9	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	PIX	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU1	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	PIX	NODE	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU2	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NODE	PIX	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU3	NV18	NV18	NV18	 X 	NV18	NV18	NV18	NV18	PIX	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU4	NV18	NV18	NV18	NV18	 X 	NV18	NV18	NV18	SYS	SYS	SYS	SYS	NODE	PIX	NODE	NODE	NODE	NODE	56-111,168-223	1		N/A
GPU5	NV18	NV18	NV18	NV18	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	PIX	NODE	NODE	NODE	NODE	NODE	56-111,168-223	1		N/A
GPU6	NV18	NV18	NV18	NV18	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	PIX	56-111,168-223	1		N/A
GPU7	NV18	NV18	NV18	NV18	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	PIX	NODE	56-111,168-223	1		N/A
NIC0	NODE	NODE	NODE	PIX	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS				
NIC1	NODE	NODE	PIX	NODE	SYS	SYS	SYS	SYS	NODE	 X 	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS				
NIC2	NODE	PIX	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS				
NIC3	PIX	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS				
NIC4	SYS	SYS	SYS	SYS	NODE	PIX	NODE	NODE	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	NODE	NODE				
NIC5	SYS	SYS	SYS	SYS	PIX	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	 X 	NODE	NODE	NODE	NODE				
NIC6	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	 X 	PIX	NODE	NODE				
NIC7	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	PIX	 X 	NODE	NODE				
NIC8	SYS	SYS	SYS	SYS	NODE	NODE	NODE	PIX	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	 X 	NODE				
NIC9	SYS	SYS	SYS	SYS	NODE	NODE	PIX	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	 X 				

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.1 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526
NCCL_VERSION=2.17.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
NVIDIA_CUDA_END_OF_LIFE=1
CUDA_VERSION=12.1.0
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

We basically run the data_parallel.py by

python3 data_parallel.py \
        --model="path/to/weight" \
        --enforce-eager \
        --dp-size=2 \
        --tp-size=2 \
        --node-size=2 \
        --node-rank=0 \
        --master-addr=${addr} \
        --master-port=13345

and the output are as follows:

root@glusterfs-05:workspace/codehub/vllm/examples/offline_inference# bash run_dp.sh                             
INFO 05-22 05:33:36 [__init__.py:248] Automatically detected platform cuda.
DP rank 1 needs to process 200 prompts
INFO 05-22 05:33:39 [__init__.py:30] Available plugins for group vllm.general_plugins:
INFO 05-22 05:33:39 [__init__.py:32] name=lora_filesystem_resolver, value=vllm.plugins.lora_resolvers.filesystem_resolver:register_filesystem_resolver
INFO 05-22 05:33:39 [__init__.py:34] all available plugins for group vllm.general_plugins will be loaded.
INFO 05-22 05:33:39 [__init__.py:36] set environment variable VLLM_PLUGINS to control which plugins to load.
INFO 05-22 05:33:39 [__init__.py:44] plugin lora_filesystem_resolver loaded.
INFO 05-22 05:33:47 [config.py:787] This model supports multiple tasks: {'classify', 'reward', 'score', 'embed', 'generate'}. Defaulting to 'generate'.
INFO 05-22 05:33:50 [config.py:1869] Defaulting to use mp for distributed inference
INFO 05-22 05:33:50 [config.py:2112] Chunked prefill is enabled with max_num_batched_tokens=16384.
WARNING 05-22 05:33:50 [cuda.py:87] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used
(EngineCore_1 pid=14081) INFO 05-22 05:33:51 [core.py:431] Waiting for init message from front-end.
Process Process-1:
Traceback (most recent call last):
  File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "workspace/codehub/vllm/examples/offline_inference/data_parallel.py", line 118, in main
    llm = LLM(
          ^^^^
  File "workspace/codehub/vllm/vllm/utils.py", line 1177, in inner
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/entrypoints/llm.py", line 250, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/engine/llm_engine.py", line 511, in from_engine_args
    return engine_cls.from_vllm_config(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/v1/engine/llm_engine.py", line 115, in from_vllm_config
    return cls(vllm_config=vllm_config,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/v1/engine/llm_engine.py", line 92, in __init__
    self.engine_core = EngineCoreClient.make_client(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/v1/engine/core_client.py", line 75, in make_client
    return SyncMPClient(vllm_config, executor_class, log_stats)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/v1/engine/core_client.py", line 580, in __init__
    super().__init__(
  File "workspace/codehub/vllm/vllm/v1/engine/core_client.py", line 418, in __init__
    self._wait_for_engine_startup(output_address, parallel_config)
  File "workspace/codehub/vllm/vllm/v1/engine/core_client.py", line 495, in _wait_for_engine_startup
    raise RuntimeError(f"Message from engine with unexpected data "
RuntimeError: Message from engine with unexpected data parallel rank: 1

After some debugging, we found that PR #15977 may have introduced this bug by adding the following code:

engine = next(
    (e for e in self.core_engines if e.identity == eng_identity),
    None)

Here, e.identity is derived from local_dp_rank in data_parallel.py (which should be b'\x00\x00 in both the master and worker nodes), while eng_identity comes from dp_rank (which will be b'\x00\x01 on the worker node).

We are working on fixing this. Is this issue already being addressed? Please let us know, thank you.

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[NaNAGISaSA]

same problem here

[rjg-lyh]

As this bug is currently causing some blockage in our project, we would greatly appreciate it if you could take a look at it when you have the chance. @njhill @youkaichao @DarkLight1337

[rjg-lyh]

Apart from that, I followed the first example mentioned in PR #15977 to start the online dual-node data parallel distributed across two servers, but the following error occurred on the second node:

log

root@glusterfs-05:workspace/codehub/vllm/examples/offline_inference# bash run_dp.sh 
INFO 05-23 00:12:34 [__init__.py:248] Automatically detected platform cuda.
INFO 05-23 00:12:37 [__init__.py:30] Available plugins for group vllm.general_plugins:
INFO 05-23 00:12:37 [__init__.py:32] name=lora_filesystem_resolver, value=vllm.plugins.lora_resolvers.filesystem_resolver:register_filesystem_resolver
INFO 05-23 00:12:37 [__init__.py:34] all available plugins for group vllm.general_plugins will be loaded.
INFO 05-23 00:12:37 [__init__.py:36] set environment variable VLLM_PLUGINS to control which plugins to load.
INFO 05-23 00:12:37 [__init__.py:44] plugin lora_filesystem_resolver loaded.
INFO 05-23 00:12:46 [config.py:787] This model supports multiple tasks: {'score', 'embed', 'reward', 'generate', 'classify'}. Defaulting to 'generate'.
Traceback (most recent call last):
  File "/usr/local/bin/vllm", line 8, in <module>
    sys.exit(main())
             ^^^^^^
  File "workspace/codehub/vllm/vllm/entrypoints/cli/main.py", line 53, in main
    args.dispatch_function(args)
  File "workspace/codehub/vllm/vllm/entrypoints/cli/serve.py", line 38, in cmd
    run_headless(args)
  File "workspace/codehub/vllm/vllm/entrypoints/cli/serve.py", line 92, in run_headless
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "workspace/codehub/vllm/vllm/engine/arg_utils.py", line 1063, in create_engine_config
    parallel_config = ParallelConfig(
                      ^^^^^^^^^^^^^^^
  File "<string>", line 23, in __init__
  File "workspace/codehub/vllm/vllm/config.py", line 1810, in __post_init__
    raise ValueError(
ValueError: data_parallel_size_local (2) must be <= data_parallel_size (1)

[GGKOP]

same problem

[njhill]

Sorry about this, I will look into it.

Apart from that, I followed the first example mentioned in PR #15977 to start the online dual-node data parallel distributed across two servers, but the following error occurred on the second node:

@rjg-lyh re this, I have updated those examples. You need to specify the same --data-parallel-size arg on the secondary nodes too (also sorry about that, this hadn't been required in the first iteration of the implementation).

[yiz-liu]

@njhill, a straightforward quick fix for offline inference data_parallel.py would be to modify a single line in core_client.py. Specifically, replace:

CoreEngine(index=local_start_index, local=True)

with:

CoreEngine(index=start_index, local=True)

However, I am not certain whether this change aligns with your original design intent. And we are still working on online serving,

[yiz-liu]

@njhill, a straightforward quick fix for offline inference data_parallel.py would be to modify a single line in core_client.py. Specifically, replace:

CoreEngine(index=local_start_index, local=True)
with:

CoreEngine(index=start_index, local=True)
However, I am not certain whether this change aligns with your original design intent. And we are still working on online serving,

@NaNAGISaSA @GGKOP You can try this fix and see if it's working.

[GGKOP]

@njhill, a straightforward quick fix for offline inference data_parallel.py would be to modify a single line in core_client.py. Specifically, replace:
CoreEngine(index=local_start_index, local=True)
with:
CoreEngine(index=start_index, local=True)
However, I am not certain whether this change aligns with your original design intent. And we are still working on online serving,

@NaNAGISaSA @GGKOP You can try this fix and see if it's working.

The method you provided is very useful!

[vadimkantorov]

same problem :(

also, in data_parallel.py, shouldn't chunking of input data round up? currently it's rounding down, likely skipping some input data at the end

[njhill]

Thanks @yiz-liu your fix is correct, I have opened #18553 and included you as co-author.

[njhill]

same problem :(

also, in data_parallel.py, shouldn't chunking of input data round up? currently it's rounding down, likely skipping some input data at the end

Thanks @vadimkantorov, I think data_parallel.py is only an "example" of how to do it, but in any case I've updated it in the same PR to not skip any prompts in case they aren't divisible by the dp size.