[Bug]: AMD Instinct MI210 + vllm fail to run the official deepseek-r1 model: ValueError("type fp8e4b8 not supported in this architecture. The supported fp8 dtypes are ('fp8e5',)") #16394
Description
Your current environment
The output of `python collect_env.py`
INFO 04-10 07:17:55 [__init__.py:207] Automatically detected platform rocm.
Collecting environment information...
PyTorch version: 2.7.0a0+git6c0e746
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.3.42133-1b9c17779
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.1 24491 1e0fda770a2079fbd71e4b70974d74f62fd3af10)
CMake version: version 3.31.4
Libc version: glibc-2.35
Python version: 3.12.9 (main, Feb 5 2025, 08:49:00) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-136-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: AMD Instinct MI210 (gfx90a:sramecc+:xnack-)
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.3.42133
MIOpen runtime version: 3.3.0
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 192
On-line CPU(s) list: 0-191
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7K62 48-Core Processor
CPU family: 23
Model: 49
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
Stepping: 0
Frequency boost: enabled
CPU max MHz: 2600.0000
CPU min MHz: 1500.0000
BogoMIPS: 5200.36
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 nonstop_tsc cpuid extd_apicid aperfmperf rapl 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_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
Virtualization: AMD-V
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): 2
NUMA node0 CPU(s): 0-47,96-143
NUMA node1 CPU(s): 48-95,144-191
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 enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; IBPB conditional; STIBP always-on; 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] pyzmq==26.2.1
[pip3] torch==2.7.0a0+git6c0e746
[pip3] torchvision==0.21.0+7af6987
[pip3] transformers==4.49.0
[pip3] triton==3.2.0+gite5be006a
[conda] Could not collect
ROCM Version: 6.3.42133-1b9c17779
Neuron SDK Version: N/A
vLLM Version: 0.7.4.dev49+gc0dd5adf6
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
============================ ROCm System Management Interface ============================
================================ Weight between two GPUs =================================
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7
GPU0 0 15 15 15 72 72 72 72
GPU1 15 0 15 15 72 72 72 72
GPU2 15 15 0 15 72 72 72 72
GPU3 15 15 15 0 72 72 72 72
GPU4 72 72 72 72 0 15 15 15
GPU5 72 72 72 72 15 0 15 15
GPU6 72 72 72 72 15 15 0 15
GPU7 72 72 72 72 15 15 15 0
================================= Hops between two GPUs ==================================
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7
GPU0 0 1 1 1 3 3 3 3
GPU1 1 0 1 1 3 3 3 3
GPU2 1 1 0 1 3 3 3 3
GPU3 1 1 1 0 3 3 3 3
GPU4 3 3 3 3 0 1 1 1
GPU5 3 3 3 3 1 0 1 1
GPU6 3 3 3 3 1 1 0 1
GPU7 3 3 3 3 1 1 1 0
=============================== Link Type between two GPUs ===============================
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7
GPU0 0 XGMI XGMI XGMI PCIE PCIE PCIE PCIE
GPU1 XGMI 0 XGMI XGMI PCIE PCIE PCIE PCIE
GPU2 XGMI XGMI 0 XGMI PCIE PCIE PCIE PCIE
GPU3 XGMI XGMI XGMI 0 PCIE PCIE PCIE PCIE
GPU4 PCIE PCIE PCIE PCIE 0 XGMI XGMI XGMI
GPU5 PCIE PCIE PCIE PCIE XGMI 0 XGMI XGMI
GPU6 PCIE PCIE PCIE PCIE XGMI XGMI 0 XGMI
GPU7 PCIE PCIE PCIE PCIE XGMI XGMI XGMI 0
======================================= Numa Nodes =======================================
GPU[0] : (Topology) Numa Node: 0
GPU[0] : (Topology) Numa Affinity: 0
GPU[1] : (Topology) Numa Node: 0
GPU[1] : (Topology) Numa Affinity: 0
GPU[2] : (Topology) Numa Node: 0
GPU[2] : (Topology) Numa Affinity: 0
GPU[3] : (Topology) Numa Node: 0
GPU[3] : (Topology) Numa Affinity: 0
GPU[4] : (Topology) Numa Node: 1
GPU[4] : (Topology) Numa Affinity: 1
GPU[5] : (Topology) Numa Node: 1
GPU[5] : (Topology) Numa Affinity: 1
GPU[6] : (Topology) Numa Node: 1
GPU[6] : (Topology) Numa Affinity: 1
GPU[7] : (Topology) Numa Node: 1
GPU[7] : (Topology) Numa Affinity: 1
================================== End of ROCm SMI Log ===================================
NCCL_P2P_DISABLE=1
NCCL_IB_HCA=mlx5
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
PYTORCH_ROCM_ARCH=gfx90a;gfx942
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/opt/rocm/lib:/usr/local/lib:
VLLM_HOST_IP=10.41.18.47
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
I run the deepseek-r1 model on two node with docker+vllm+ray with each node having 8 AMD MI210 gpu cards.
My commands are:
-
Start a ray cluster on head node:
bash run_cluster2.sh vllm-dsr1:v1 $my_ip_head --head /root -e VLLM_HOST_IP=$my_ip_head --privileged -e NCCL_IB_HCA=mlx5 -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 -e NCCL_P2P_DISABLE=1 -
Join the ray cluster on worker node:
bash run_cluster2.sh vllm-dsr1:v1 $my_ip_head --worker /root -e VLLM_HOST_IP=$my_ip_worker --privileged -e NCCL_IB_HCA=mlx5 -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 -e NCCL_P2P_DISABLE=1 -
Enter the container via either node:
docker exec -it node2 /bin/bash -
Run deepseek-r1 model:
python -m vllm.entrypoints.openai.api_server --model /models/DeepSeek-R1 --tensor-parallel-size 8 --port 1001 --enforce_eager --distributed-executor-backend ray --pipeline-parallel-size 2 --max-model-len 1024 --max-num-batched-tokens 1024 --trust-remote-code --enable-prefix-caching
After loading all the 163 .safetensors models, it raises the error: ValueError("type fp8e4b8 not supported in this architecture. The supported fp8 dtypes are ('fp8e5',)").
How to solve this problems? Thanks!
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