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A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
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[Bug]: ray cluster Segmentation fault #6106

Open warlockedward opened 3 months ago

warlockedward commented 3 months ago

Your current environment


The output of `python collect_env.py`
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.6
Libc version: glibc-2.35

Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-113-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: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB
GPU 4: Tesla V100-SXM2-32GB
GPU 5: Tesla V100-SXM2-32GB
GPU 6: Tesla V100-SXM2-32GB
GPU 7: Tesla V100-SXM2-32GB

Nvidia driver version: 535.183.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
Stepping:                           7
BogoMIPS:                           5200.00
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 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 cdp_l3 invpcid_single intel_ppin 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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp_epp pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.5 MiB (48 instances)
L1i cache:                          1.5 MiB (48 instances)
L2 cache:                           48 MiB (48 instances)
L3 cache:                           66 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-23,48-71
NUMA node1 CPU(s):                  24-47,72-95
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
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 IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] transformers==4.42.1
[pip3] triton==2.3.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] torch                     2.3.0                    pypi_0    pypi
[conda] transformers              4.42.1                   pypi_0    pypi
[conda] triton                    2.3.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.0.post1
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    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV1     NV2     NV1     SYS     SYS     SYS     NV2     NODE    NODE    SYS     SYS     0-23,48-71      0               N/A
GPU1    NV1      X      NV1     NV2     SYS     SYS     NV2     SYS     NODE    NODE    SYS     SYS     0-23,48-71      0               N/A
GPU2    NV2     NV1      X      NV2     SYS     NV1     SYS     SYS     PIX     PIX     SYS     SYS     0-23,48-71      0               N/A
GPU3    NV1     NV2     NV2      X      NV1     SYS     SYS     SYS     PIX     PIX     SYS     SYS     0-23,48-71      0               N/A
GPU4    SYS     SYS     SYS     NV1      X      NV2     NV2     NV1     SYS     SYS     PIX     PIX     24-47,72-95     1               N/A
GPU5    SYS     SYS     NV1     SYS     NV2      X      NV1     NV2     SYS     SYS     PIX     PIX     24-47,72-95     1               N/A
GPU6    SYS     NV2     SYS     SYS     NV2     NV1      X      NV1     SYS     SYS     NODE    NODE    24-47,72-95     1               N/A
GPU7    NV2     SYS     SYS     SYS     NV1     NV2     NV1      X      SYS     SYS     NODE    NODE    24-47,72-95     1               N/A
NIC0    NODE    NODE    PIX     PIX     SYS     SYS     SYS     SYS      X      PIX     SYS     SYS
NIC1    NODE    NODE    PIX     PIX     SYS     SYS     SYS     SYS     PIX      X      SYS     SYS
NIC2    SYS     SYS     SYS     SYS     PIX     PIX     NODE    NODE    SYS     SYS      X      PIX
NIC3    SYS     SYS     SYS     SYS     PIX     PIX     NODE    NODE    SYS     SYS     PIX      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

### 🐛 Describe the bug

vllm) root@ubuntu:/model# python -u -m vllm.entrypoints.openai.api_server --model /model/models/Mixtral-8x22B-Instruct-v0.1/ --port xxxxxxx --api-key xxxxxxxxxxxxxxxxx -tp 24 --served-model-name  Mixtral-8x22B-Instruct-v0.1 --dtype float32 --gpu-memory-utilization 0.98 --disable-custom-all-reduce --enforce-eager --worker-use-ray --engine-use-ray --trust-remote-code 
INFO 07-03 14:42:55 api_server.py:177] vLLM API server version 0.5.0.post1
INFO 07-03 14:42:55 api_server.py:178] args: Namespace(host=None, port=xxxxxxx, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key='sk-xxxxxxxxxxxxxxxxxx, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='/model/models/Mixtral-8x22B-Instruct-v0.1/', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='float32', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=True, pipeline_parallel_size=1, tensor_parallel_size=24, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.98, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=True, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, device='auto', image_input_type=None, image_token_id=None, image_input_shape=None, image_feature_size=None, image_processor=None, image_processor_revision=None, disable_image_processor=False, scheduler_delay_factor=0.0, enable_chunked_prefill=False, speculative_model=None, num_speculative_tokens=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, model_loader_extra_config=None, preemption_mode=None, served_model_name=['Mixtral-8x22B-Instruct-v0.1'], qlora_adapter_name_or_path=None, engine_use_ray=True, disable_log_requests=False, max_log_len=None)
INFO 07-03 14:42:55 config.py:1214] Upcasting torch.bfloat16 to torch.float32.
2024-07-03 14:42:55,264 INFO worker.py:1586 -- Connecting to existing Ray cluster at address: 128.1.219.178:6379...
2024-07-03 14:42:55,271 INFO worker.py:1771 -- Connected to Ray cluster.
(_AsyncLLMEngine pid=3954, ip=128.1.219.179) INFO 07-03 14:42:57 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='/model/models/Mixtral-8x22B-Instruct-v0.1/', speculative_config=None, tokenizer='/model/models/Mixtral-8x22B-Instruct-v0.1/', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float32, max_seq_len=65536, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=24, disable_custom_all_reduce=True, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=Mixtral-8x22B-Instruct-v0.1)
(RayWorkerWrapper pid=4501, ip=128.1.219.179) INFO 07-03 14:43:47 selector.py:131] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(RayWorkerWrapper pid=4501, ip=128.1.219.179) INFO 07-03 14:43:47 selector.py:51] Using XFormers backend.
(RayWorkerWrapper pid=4139, ip=128.1.219.180) INFO 07-03 14:43:49 utils.py:637] Found nccl from library libnccl.so.2
(RayWorkerWrapper pid=4139, ip=128.1.219.180) INFO 07-03 14:43:49 pynccl.py:63] vLLM is using nccl==2.20.5
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148] Error executing method init_device. This might cause deadlock in distributed execution.
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148] Traceback (most recent call last):
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/worker/worker_base.py", line 140, in execute_method
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     return executor(*args, **kwargs)
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]            ^^^^^^^^^^^^^^^^^^^^^^^^^
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/worker/worker.py", line 115, in init_device
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     init_worker_distributed_environment(self.parallel_config, self.rank,
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/worker/worker.py", line 357, in init_worker_distributed_environment
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 655, in ensure_model_parallel_initialized
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     initialize_model_parallel(tensor_model_parallel_size,
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 616, in initialize_model_parallel
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     _TP = GroupCoordinator(
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]           ^^^^^^^^^^^^^^^^^
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 147, in __init__
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     self.pynccl_comm = PyNcclCommunicator(
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]                        ^^^^^^^^^^^^^^^^^^^
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/distributed/device_communicators/pynccl.py", line 89, in __init__
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     self.comm: ncclComm_t = self.nccl.ncclCommInitRank(
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/distributed/device_communicators/pynccl_wrapper.py", line 244, in ncclCommInitRank
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     self.NCCL_CHECK(self._funcs["ncclCommInitRank"](ctypes.byref(comm),
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]   File "/model/anaconda3/envs/vllm/lib/python3.11/site-packages/vllm/distributed/device_communicators/pynccl_wrapper.py", line 223, in NCCL_CHECK
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148]     raise RuntimeError(f"NCCL error: {error_str}")
(RayWorkerWrapper pid=4139, ip=128.1.219.180) ERROR 07-03 14:43:50 worker_base.py:148] RuntimeError: NCCL error: internal error - please report this issue to the NCCL developers
(RayWorkerWrapper pid=10387) ERROR 07-03 14:43:50 worker_base.py:148] RuntimeError: NCCL error: remote process exited or there was a network error
(RayWorkerWrapper pid=9821) *** SIGSEGV received at time=1720017831 on cpu 54 ***
(RayWorkerWrapper pid=9821) PC: @     0x7f0a10469996  (unknown)  ncclProxyServiceUDS()
(RayWorkerWrapper pid=9821)     @     0x7f2757e09520       3384  (unknown)
(RayWorkerWrapper pid=9821)     @ ... and at least 1 more frames
(RayWorkerWrapper pid=9821) [2024-07-03 14:43:51,034 E 9821 10625] logging.cc:440: *** SIGSEGV received at time=1720017831 on cpu 54 ***
(RayWorkerWrapper pid=9821) [2024-07-03 14:43:51,034 E 9821 10625] logging.cc:440: PC: @     0x7f0a10469996  (unknown)  ncclProxyServiceUDS()
(RayWorkerWrapper pid=9821) [2024-07-03 14:43:51,034 E 9821 10625] logging.cc:440:     @     0x7f2757e09520       3384  (unknown)
(RayWorkerWrapper pid=9821) [2024-07-03 14:43:51,034 E 9821 10625] logging.cc:440:     @ ... and at least 1 more frames
(RayWorkerWrapper pid=9821) Fatal Python error: Segmentation fault
(RayWorkerWrapper pid=9821) 
(RayWorkerWrapper pid=9821) 
(RayWorkerWrapper pid=9821) Extension modules: msgpack._cmsgpack, google._upb._message, psutil._psutil_linux, psutil._psutil_posix, setproctitle, yaml._yaml, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, uvloop.loop, ray._raylet, numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, sentencepiece._sentencepiece, pyarrow.lib, pyarrow._json, PIL._imaging, scipy._lib._ccallback_c, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_update, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.optimize._minpack2, scipy.optimize._group_columns, scipy._lib.messagestream, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.linalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.spatial._ckdtree, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.special._ufuncs_cxx, scipy.special._cdflib, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial.transform._rotation, scipy.optimize._direct (total: 94)
(RayWorkerWrapper pid=10387) 
(RayWorkerWrapper pid=10387) 
(RayWorkerWrapper pid=4139, ip=128.1.219.180) 
(RayWorkerWrapper pid=4139, ip=128.1.219.180) 
(RayWorkerWrapper pid=4139, ip=128.1.219.180) Extension modules: msgpack._cmsgpack, google._upb._message, psutil._psutil_linux, psutil._psutil_posix, setproctitle, yaml._yaml, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, uvloop.loop, ray._raylet, numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, sentencepiece._sentencepiece, pyarrow.lib, pyarrow._json, PIL._imaging, PIL._imagingft (total: 36)
(raylet) A worker died or was killed while executing a task by an unexpected system error. To troubleshoot the problem, check the logs for the dead worker. RayTask ID: ffffffffffffffffde157c4cf1f6b23423e20a4102000000 Worker ID: 87ef81da3a43bb79f5c81f96798469a8517e0c262856c1c3925d593f Node ID: 72fcc31105b5f056ddf7cf05cca422a06bcf0a40c3076bf4bb2b7f2c Worker IP address: 128.1.219.178 Worker port: 10115 Worker PID: 10387 Worker exit type: SYSTEM_ERROR Worker exit detail: Worker unexpectedly exits with a connection error code 2. End of file. There are some potential root causes. (1) The process is killed by SIGKILL by OOM killer due to high memory usage. (2) ray stop --force is called. (3) The worker is crashed unexpectedly due to SIGSEGV or other unexpected errors.
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