[Bug]: llama3.1-70B RuntimeError: Inplace update to inference tensor outside InferenceMode is not allowed.You can make a clone to get a normal tensor before doing inplace update.

[duguwanglong]

Your current environment

Collecting environment information...
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: CentOS Linux 7 (Core) (x86_64)
GCC version: (GCC) 11.2.1 20220127 (Red Hat 11.2.1-9)
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.17

Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB

Nvidia driver version: 550.54.15
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
Byte Order:            Little Endian
CPU(s):                192
On-line CPU(s) list:   0-191
Thread(s) per core:    2
Core(s) per socket:    48
Socket(s):             2
NUMA node(s):          2
Vendor ID:             AuthenticAMD
CPU family:            23
Model:                 49
Model name:            AMD EPYC 7K62 48-Core Processor
Stepping:              0
CPU MHz:               2600.000
CPU max MHz:           2600.0000
CPU min MHz:           1500.0000
BogoMIPS:              5190.28
Virtualization:        AMD-V
L1d cache:             32K
L1i cache:             32K
L2 cache:              512K
L3 cache:              16384K
NUMA node0 CPU(s):     0-47,96-143
NUMA node1 CPU(s):     48-95,144-191
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 art rep_good nopl nonstop_tsc extd_apicid aperfmperf eagerfpu pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_l2 cpb cat_l3 cdp_l3 hw_pstate retpoline_amd ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip overflow_recov succor smca

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] pyzmq==26.1.0
[pip3] torch==2.3.1
[pip3] torchvision==0.18.1
[pip3] transformers==4.43.3
[pip3] triton==2.3.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] pyzmq                     26.1.0                   pypi_0    pypi
[conda] torch                     2.3.1                    pypi_0    pypi
[conda] torchvision               0.18.1                   pypi_0    pypi
[conda] transformers              4.43.3                   pypi_0    pypi
[conda] triton                    2.3.1                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.3.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV12    NV12    NV12    NV12    NV12    NV12    NV12    SYS     0-47,96-143     0               N/A
GPU1    NV12     X      NV12    NV12    NV12    NV12    NV12    NV12    SYS     0-47,96-143     0               N/A
GPU2    NV12    NV12     X      NV12    NV12    NV12    NV12    NV12    PXB     0-47,96-143     0               N/A
GPU3    NV12    NV12    NV12     X      NV12    NV12    NV12    NV12    PXB     0-47,96-143     0               N/A
GPU4    NV12    NV12    NV12    NV12     X      NV12    NV12    NV12    SYS     48-95,144-191   1               N/A
GPU5    NV12    NV12    NV12    NV12    NV12     X      NV12    NV12    SYS     48-95,144-191   1               N/A
GPU6    NV12    NV12    NV12    NV12    NV12    NV12     X      NV12    SYS     48-95,144-191   1               N/A
GPU7    NV12    NV12    NV12    NV12    NV12    NV12    NV12     X      SYS     48-95,144-191   1               N/A
NIC0    SYS     SYS     PXB     PXB     SYS     SYS     SYS     SYS      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_bond_0

🐛 Describe the bug

model_param.update({"tensor_parallel_size": 4, "gpu_memory_utilization": 0.99})

[plageon]

same problem

[plageon]

https://github.com/vllm-project/vllm/issues/1968. The solution to a former issue may apply to this as well. Try unset TORCH_DISTRIBUTED_DEBUG

[duguwanglong]

Reference in n No changed. Also has the issue. I use cmdline to infer model. [root@localhost ~]# unset TORCH_DISTRIBUTED_DEBUG I also make the TORCH_DISTRIBUTED_DEBUG==INFO or OFF or DETAIL, but it dosn`t matter.