Distributed inference runtime error

[cascade812]

🐛 Describe the bug

When trying to run distributed/run_dist_inference.sh . It has below error.
[rank0]:[rank0]: model = _load_model(builder_args)
[rank0]:[rank0]: File "/scratch/grace/torchchat/torchchat/cli/builder.py", line 473, in _load_model
[rank0]:[rank0]: model = _maybe_parellelize_model(model, builder_args, world_mesh, parallel_dims)
[rank0]:[rank0]: File "/scratch/grace/torchchat/torchchat/cli/builder.py", line 460, in _maybe_parellelize_model
[rank0]:[rank0]: parallelize_llama(model, world_mesh, parallel_dims)
[rank0]:[rank0]: File "/scratch/grace/torchchat/distributed/parallelize_llama.py", line 124, in parallelize_llama
[rank0]:[rank0]: model = apply_tp(model, world_mesh)
[rank0]:[rank0]: File "/scratch/grace/torchchat/distributed/parallelize_llama.py", line 69, in apply_tp
[rank0]:[rank0]: for transformer_block in model.layers:
[rank0]:[rank0]: File "/opt/conda/envs/ptca/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1729, in getattr
[rank0]:[rank0]: raise AttributeError(f"'{type(self).name}' object has no attribute '{name}'")
[rank0]:[rank0]: AttributeError: 'TextOnlyModel' object has no attribute 'layers'

Versions

PyTorch version: 2.4.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.0
Libc version: glibc-2.31

Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1045-azure-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe
GPU 2: NVIDIA A100 80GB PCIe
GPU 3: NVIDIA A100 80GB PCIe

Nvidia driver version: 535.86.10
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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
Byte Order: Little Endian
Address sizes: 48 bits physical, 48 bits virtual
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 1
Core(s) per socket: 48
Socket(s): 2
NUMA node(s): 4
Vendor ID: AuthenticAMD
CPU family: 25
Model: 1
Model name: AMD EPYC 7V13 64-Core Processor
Stepping: 1
CPU MHz: 2445.443
BogoMIPS: 4890.88
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 3 MiB
L1i cache: 3 MiB
L2 cache: 48 MiB
L3 cache: 384 MiB
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 Retbleed: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
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 pcid 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 invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] onnx==1.16.2
[pip3] onnxruntime-training==1.18.0
[pip3] pytorch-lightning==1.9.5
[pip3] torch==2.4.1
[pip3] torch-nebula==0.16.13
[pip3] torch-ort==1.18.0
[pip3] torch-tb-profiler==0.4.3
[pip3] torchao==0.5.0
[pip3] torchaudio==2.4.1
[pip3] torchdata==0.7.1
[pip3] torchmetrics==1.2.0
[pip3] torchsnapshot==0.1.0
[pip3] torchtune==0.3.0
[pip3] torchvision==0.19.1
[pip3] triton==3.0.0
[conda] magma-cuda121 2.6.1 1 pytorch
[conda] mkl 2022.2.1 pypi_0 pypi
[conda] mkl-include 2022.2.1 pypi_0 pypi
[conda] numpy 1.23.5 pypi_0 pypi
[conda] pytorch-lightning 1.9.5 pypi_0 pypi
[conda] torch 2.4.1 pypi_0 pypi
[conda] torch-nebula 0.16.13 pypi_0 pypi
[conda] torch-ort 1.18.0 pypi_0 pypi
[conda] torch-tb-profiler 0.4.3 pypi_0 pypi
[conda] torchao 0.5.0 pypi_0 pypi
[conda] torchaudio 2.4.1 pypi_0 pypi
[conda] torchdata 0.7.1 pypi_0 pypi
[conda] torchmetrics 1.2.0 pypi_0 pypi
[conda] torchsnapshot 0.1.0 pypi_0 pypi
[conda] torchtune 0.3.0 pypi_0 pypi
[conda] torchvision 0.19.1 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi

[Jack-Khuu]

Thanks for flagging @guijuzhang. Can you share command you ran as well?

cc: @kwen2501 for distrubuted