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[Bug]: Bug while using deepspeed with TRL with vLLM #16867

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abeerag opened this issue Apr 18, 2025 · 0 comments
Open
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[Bug]: Bug while using deepspeed with TRL with vLLM #16867

abeerag opened this issue Apr 18, 2025 · 0 comments
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@abeerag
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abeerag commented Apr 18, 2025

Your current environment

The output of `python collect_env.py`

INFO 04-18 19:54:26 [init.py:239] Automatically detected platform cuda.
Collecting environment information...
/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/_distutils_hack/init.py:30: UserWarning: Setuptools is replacing distutils. Support for replacing an already imported distutils is deprecated. In the future, this condition will fail. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml
warnings.warn(
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: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:19:12) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-1073-azure-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB

Nvidia driver version: 535.216.03
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: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7V12 64-Core Processor
CPU family: 23
Model: 49
Thread(s) per core: 1
Core(s) per socket: 48
Socket(s): 2
Stepping: 0
BogoMIPS: 4890.90
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 3 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 48 MiB (96 instances)
L3 cache: 384 MiB (24 instances)
NUMA node(s): 4
NUMA node0 CPU(s): 0-23
NUMA node1 CPU(s): 24-47
NUMA node2 CPU(s): 48-71
NUMA node3 CPU(s): 72-95
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: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled
Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode
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; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[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==26.4.0
[pip3] torch==2.6.0
[pip3] torchaudio==2.6.0
[pip3] torchvision==0.21.0
[pip3] transformers==4.51.3
[pip3] triton==3.2.0
[conda] blas 2.16 mkl conda-forge
[conda] cuda 12.2.2 0 nvidia
[conda] cuda-cccl 12.8.90 0 nvidia
[conda] cuda-cccl_linux-64 12.8.90 0 nvidia
[conda] cuda-command-line-tools 12.4.1 0 nvidia
[conda] cuda-compiler 12.6.2 0 nvidia
[conda] cuda-cudart 12.4.127 0 nvidia
[conda] cuda-cudart-dev 12.4.127 0 nvidia
[conda] cuda-cudart-static 12.8.90 0 nvidia
[conda] cuda-cudart-static_linux-64 12.8.90 0 nvidia
[conda] cuda-cuobjdump 12.8.90 0 nvidia
[conda] cuda-cupti 12.4.127 0 nvidia
[conda] cuda-cuxxfilt 12.8.90 0 nvidia
[conda] cuda-demo-suite 12.4.127 0 nvidia
[conda] cuda-documentation 12.4.127 0 nvidia
[conda] cuda-driver-dev 12.8.90 0 nvidia
[conda] cuda-driver-dev_linux-64 12.8.90 0 nvidia
[conda] cuda-gdb 12.8.90 0 nvidia
[conda] cuda-libraries 12.4.1 0 nvidia
[conda] cuda-libraries-dev 12.6.2 0 nvidia
[conda] cuda-libraries-static 12.8.1 0 nvidia
[conda] cuda-nsight 12.8.90 0 nvidia
[conda] cuda-nvcc 12.2.140 0 nvidia/label/cuda-12.2.2
[conda] cuda-nvdisasm 12.8.90 0 nvidia
[conda] cuda-nvml-dev 12.8.90 0 nvidia
[conda] cuda-nvprof 12.8.90 0 nvidia
[conda] cuda-nvprune 12.8.90 0 nvidia
[conda] cuda-nvrtc 12.4.127 0 nvidia
[conda] cuda-nvrtc-dev 12.4.127 0 nvidia
[conda] cuda-nvrtc-static 12.8.93 0 nvidia
[conda] cuda-nvtx 12.4.127 0 nvidia
[conda] cuda-nvvp 12.8.93 0 nvidia
[conda] cuda-opencl 12.8.90 0 nvidia
[conda] cuda-opencl-dev 12.8.90 0 nvidia
[conda] cuda-profiler-api 12.8.90 0 nvidia
[conda] cuda-runtime 12.4.1 0 nvidia
[conda] cuda-sanitizer-api 12.8.93 0 nvidia
[conda] cuda-toolkit 12.4.1 0 nvidia
[conda] cuda-tools 12.4.1 0 nvidia
[conda] cuda-version 12.8 3 nvidia
[conda] cuda-visual-tools 12.6.2 0 nvidia
[conda] gds-tools 1.13.1.3 0 nvidia
[conda] libblas 3.8.0 16_mkl conda-forge
[conda] libcblas 3.8.0 16_mkl conda-forge
[conda] libcublas 12.4.5.8 0 nvidia
[conda] libcublas-dev 12.4.5.8 0 nvidia
[conda] libcublas-static 12.8.4.1 0 nvidia
[conda] libcufft 11.2.1.3 0 nvidia
[conda] libcufft-dev 11.2.1.3 0 nvidia
[conda] libcufft-static 11.3.3.83 0 nvidia
[conda] libcufile 1.13.1.3 0 nvidia
[conda] libcufile-dev 1.13.1.3 0 nvidia
[conda] libcufile-static 1.13.1.3 0 nvidia
[conda] libcurand 10.3.9.90 0 nvidia
[conda] libcurand-dev 10.3.9.90 0 nvidia
[conda] libcurand-static 10.3.9.90 0 nvidia
[conda] libcusolver 11.6.1.9 0 nvidia
[conda] libcusolver-dev 11.6.1.9 0 nvidia
[conda] libcusolver-static 11.7.3.90 0 nvidia
[conda] libcusparse 12.3.1.170 0 nvidia
[conda] libcusparse-dev 12.3.1.170 0 nvidia
[conda] libcusparse-static 12.5.8.93 0 nvidia
[conda] liblapack 3.8.0 16_mkl conda-forge
[conda] liblapacke 3.8.0 16_mkl conda-forge
[conda] libnpp 12.2.5.30 0 nvidia
[conda] libnpp-dev 12.2.5.30 0 nvidia
[conda] libnpp-static 12.3.3.100 0 nvidia
[conda] libnvfatbin 12.8.90 0 nvidia
[conda] libnvfatbin-dev 12.8.90 0 nvidia
[conda] libnvfatbin-static 12.8.90 0 nvidia
[conda] libnvjitlink 12.4.127 0 nvidia
[conda] libnvjitlink-dev 12.4.127 0 nvidia
[conda] libnvjitlink-static 12.8.93 1 nvidia
[conda] libnvjpeg 12.3.1.117 0 nvidia
[conda] libnvjpeg-dev 12.3.1.117 0 nvidia
[conda] libnvjpeg-static 12.3.5.92 0 nvidia
[conda] mkl 2020.2 256
[conda] nsight-compute 2025.1.1.2 0 nvidia
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi
[conda] nvidia-ml-py 12.570.86 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] pytorch-cuda 12.4 hc786d27_7 pytorch
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] pyzmq 26.4.0 pypi_0 pypi
[conda] torch 2.6.0 pypi_0 pypi
[conda] torchaudio 2.6.0 pypi_0 pypi
[conda] torchvision 0.21.0 pypi_0 pypi
[conda] transformers 4.51.3 pypi_0 pypi
[conda] triton 3.2.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.8.4
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 NODE NODE SYS SYS SYS SYS SYS SYS 24-47 1 N/A
GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 NODE NODE SYS SYS SYS SYS SYS SYS 24-47 1 N/A
GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 SYS SYS NODE NODE SYS SYS SYS SYS 0-23 0 N/A
GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 SYS SYS NODE NODE SYS SYS SYS SYS 0-23 0 N/A
GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS SYS SYS NODE NODE SYS SYS 72-95 3 N/A
GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS SYS SYS NODE NODE SYS SYS 72-95 3 N/A
GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS SYS SYS SYS NODE NODE 48-71 2 N/A
GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS SYS SYS SYS NODE NODE 48-71 2 N/A
NIC0 NODE NODE SYS SYS SYS SYS SYS SYS X NODE SYS SYS SYS SYS SYS SYS
NIC1 NODE NODE SYS SYS SYS SYS SYS SYS NODE X SYS SYS SYS SYS SYS SYS
NIC2 SYS SYS NODE NODE SYS SYS SYS SYS SYS SYS X NODE SYS SYS SYS SYS
NIC3 SYS SYS NODE NODE SYS SYS SYS SYS SYS SYS NODE X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS NODE NODE SYS SYS SYS SYS SYS SYS X NODE SYS SYS
NIC5 SYS SYS SYS SYS NODE NODE SYS SYS SYS SYS SYS SYS NODE X SYS SYS
NIC6 SYS SYS SYS SYS SYS SYS NODE NODE SYS SYS SYS SYS SYS SYS X NODE
NIC7 SYS SYS SYS SYS SYS SYS NODE NODE SYS SYS SYS SYS SYS SYS 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

NCCL_SOCKET_IFNAME=eth0
NCCL_DEBUG_SUBSYS=GRAPH,INIT,ENV
NCCL_DEBUG=INFO
NCCL_IB_TIMEOUT=22
LD_LIBRARY_PATH=/azureml-envs/designer-pytorch-2.3-train/lib:
NCCL_IB_DISABLE=0
NCCL_TOPO_FILE=/opt/microsoft/ndv4-topo.xml
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

import datasets
import functools
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
import trl

from alpha.services.assistant.train import dataset
from alpha.services.assistant.train import reward

if __name__ == "__main__":
    processing_class = AutoTokenizer.from_pretrained('google/gemma-7b', padding_side="left")
    model = AutoModelForCausalLM.from_pretrained('google/gemma-7b')

    train_dataset = dataset.load_dataset(dataset.KnownClass(), datasets.Split.TRAIN)
    eval_dataset = dataset.load_dataset(dataset.KnownClass(), datasets.Split.VALIDATION)

    reward_fn: reward.SingleTurnComponentizedRewardFn = functools.partial(
        reward.stg_rewards,
        processing_class=processing_class,
        max_completion_length=5120 + 1536,
    )
    config = trl.GRPOConfig(
        num_train_epochs=16,
        gradient_accumulation_steps=8,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_generations=16,
        max_prompt_length=5120,
        max_completion_length=1536,
        use_cpu=False,
        beta=0.04,
        temperature=0.9,
        bf16=True,
        bf16_full_eval=True,
        torch_empty_cache_steps=1,
        eval_accumulation_steps=1
        use_vllm=True,
    )
    trainer = trl.GRPOTrainer(
        args=config,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        processing_class=processing_class,
        model=model,
        reward_funcs=reward.total_reward_fn(reward_fn),
    )
    trainer.train()

Launching with:

export VLLM_USE_V1=0
export NCCL_DEBUG=INFO

CUDA_VISIBLE_DEVICES=0 nohup trl vllm-serve --model google/gemma-3-4b-it --max-model-len 6656 --gpu-memory-utilization=0.9 --enable_prefix_caching True > outputs/vllm.txt  & ```

CUDA_VISIBLE_DEVICES="1,2,3,4,5,6,7" nohup accelerate launch --config_file alpha/services/assistant/train/accelerate_config_8xh100.yaml alpha/services/assistant/train/train_standardhf.py > outputs/accelerate.txt &

The accelerate config is as follows:

compute_environment: LOCAL_MACHINE
debug: true
deepspeed_config:
  gradient_accumulation_steps: 8
  gradient_clipping: 1.0
  offload_optimizer_device: cpu
  offload_param_device: cpu
  zero3_init_flag: false
  zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
enable_cpu_affinity: false
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 7
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

outputs:

Traceback (most recent call last):
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/uvicorn/protocols/http/httptools_impl.py", line 409, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/uvicorn/middleware/proxy_headers.py", line 60, in __call__
    return await self.app(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/applications.py", line 112, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/middleware/errors.py", line 187, in __call__
    raise exc
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/middleware/errors.py", line 165, in __call__
    await self.app(scope, receive, _send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 62, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    raise exc
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    await app(scope, receive, sender)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/routing.py", line 714, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/routing.py", line 734, in app
    await route.handle(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/routing.py", line 288, in handle
    await self.app(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/routing.py", line 76, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    raise exc
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    await app(scope, receive, sender)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/routing.py", line 74, in app
    await response(scope, receive, send)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/responses.py", line 160, in __call__
    await self.background()
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/background.py", line 41, in __call__
    await task()
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/background.py", line 28, in __call__
    await run_in_threadpool(self.func, *self.args, **self.kwargs)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/starlette/concurrency.py", line 37, in run_in_threadpool
    return await anyio.to_thread.run_sync(func)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2470, in run_sync_in_worker_thread
    return await future
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 967, in run
    result = context.run(func, *args)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 496, in collective_rpc
    return self.llm_engine.collective_rpc(method, timeout, args, kwargs)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 2132, in collective_rpc
    return self.model_executor.collective_rpc(method, timeout, args,
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/executor/uniproc_executor.py", line 56, in collective_rpc
    answer = run_method(self.driver_worker, method, args, kwargs)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/utils.py", line 2347, in run_method
    return func(*args, **kwargs)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/trl/scripts/vllm_serve.py", line 103, in init_communicator
    self.pynccl_comm = PyNcclCommunicator(pg, device=self.device)
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/distributed/device_communicators/pynccl.py", line 99, in __init__
    self.comm: ncclComm_t = self.nccl.ncclCommInitRank(
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/distributed/device_communicators/pynccl_wrapper.py", line 277, in ncclCommInitRank
    self.NCCL_CHECK(self._funcs["ncclCommInitRank"](ctypes.byref(comm),
  File "/azureml-envs/designer-pytorch-2.3-train/lib/python3.10/site-packages/vllm/distributed/device_communicators/pynccl_wrapper.py", line 256, in NCCL_CHECK
    raise RuntimeError(f"NCCL error: {error_str}")
RuntimeError: NCCL error: unhandled cuda error (run with NCCL_DEBUG=INFO for details)

The error is isolated to deepspeed because if I run with

CUDA_VISIBLE_DEVICES="1,2,3,4,5,6,7" accelerate launch --multi-gpu --num-processes 7 alpha/services/assistant/train/train_standardhf.py

there are no issues.

The error in communication is between GPU 0 and GPU 1

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@abeerag abeerag added the bug Something isn't working label Apr 18, 2025
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