[Bug]: Running llama2-7b on H20, Floating point exception (core dumped) appears on float16

[yk1012664593]

Your current environment

PyTorch version: 2.2.1+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 3.29.2 Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.4.0-106-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.4.131 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H20 GPU 1: NVIDIA H20 GPU 2: NVIDIA H20 GPU 3: NVIDIA H20 GPU 4: NVIDIA H20 GPU 5: NVIDIA H20 GPU 6: NVIDIA H20 GPU 7: NVIDIA H20

Nvidia driver version: 550.54.15 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0 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 Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 192 On-line CPU(s) list: 0-191 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8469C CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 48 Socket(s): 2 Stepping: 8 CPU max MHz: 3800.0000 CPU min MHz: 800.0000 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 tsc_known_freq pni pclmulqdq dtes64 monitor 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 cat_l2 cdp_l3 invpcid_single cdp_l2 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 4.5 MiB (96 instances) L1i cache: 3 MiB (96 instances) L2 cache: 192 MiB (96 instances) L3 cache: 195 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-47,96-143 NUMA node1 CPU(s): 48-95,144-191 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: 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 Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.19.3 [pip3] torch==2.2.1 [pip3] triton==2.2.0 [pip3] vllm-nccl-cu12==2.18.1.0.4.0 [conda] Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 0-47,96-143 0 N/A GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 0-47,96-143 0 N/A GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 0-47,96-143 0 N/A GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 0-47,96-143 0 N/A GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 48-95,144-191 1 N/A GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 48-95,144-191 1 N/A GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 48-95,144-191 1 N/A GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X 48-95,144-191 1 N/A

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

🐛 Describe the bug

Running llama2-7b on H20, vllm0.4.1, Floating point exception (core dumped) with float16 accuracy, float32 accuracy can be executed normally The error screenshot is as follows

[yk1012664593]

llama model init start INFO 04-26 17:03:13 llm_engine.py:98] Initializing an LLM engine (v0.4.1) with config: model='/mnt/deep_learning_test/testsuite/dataset/llms_inference_llama7b-v2_accelerate/checkpoint/7B-V2/', speculative_config=None, tokenizer='/mnt/deep_learning_test/testsuite/dataset/llms_inference_llama7b-v2_accelerate/checkpoint/7B-V2/', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=Falsequantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0) INFO 04-26 17:03:13 utils.py:613] Found nccl from library /root/.config/vllm/nccl/cu12/libnccl.so.2.18.1 INFO 04-26 17:03:16 selector.py:77] Cannot use FlashAttention-2 backend because the flash_attn package is not found. Please install it for better performance. INFO 04-26 17:03:16 selector.py:33] Using XFormers backend. INFO 04-26 17:03:25 model_runner.py:173] Loading model weights took 12.5523 GB INFO 04-26 17:03:25 gpu_executor.py:119] # GPU blocks: 9217, # CPU blocks: 512 INFO 04-26 17:03:26 model_runner.py:977] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. INFO 04-26 17:03:26 model_runner.py:981] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing gpu_memory_utilization or enforcing eager mode. You can also reduce the max_num_seqs as needed to decrease memory usage. Floating point exception (core dumped)

[youkaichao]

Does it occur everytime? Or only for certain prompt?

In addition:

If you experienced crashes or hangs, it would be helpful to run vllm with export VLLM_TRACE_FUNCTION=1 . All the function calls in vllm will be recorded. Inspect these log files, and tell which function crashes or hangs.

[yk1012664593]

它每次都会发生吗？还是只针对某些提示？

另外：

如果您遇到崩溃或挂起，使用 .vllm 中的所有函数调用都将被记录下来。检查这些日志文件，并判断哪个函数崩溃或挂起。export VLLM_TRACE_FUNCTION=1

Yes, this issue is inevitable. On the H20 model, all vllm versions with float16 accuracy will experience this error

[pipecat]

Met the same bug, and it's useful to add --enforce-eager to avoid it.

Additionally, some models (for example facebook/opt-125m) with float16 won't meet this bug.

[ElvisWai]

Met the same bug with Qwen1.5-14B-Chat on the H20, and I was able to solve it with float32. But using float16 and add --enforce-eager does not solve the problem.

[tianbiai]

Met the same bug with almost all LLM on the H20, but --enforce-eager does not solve the problem.

[chk4991]

After testing, the --enforce-eager parameter does not work, but setting dtype to float32 works. However, this means that the quantized model cannot be deployed on h20.

[caddfa31434]

Try to build vllm from source using the nvcr.io/nvidia/pytorch:24.04-py3 container to avoid the bug related to cuBLAS on specific shapes (on H20).

[mir-of]

I am using nvcr.io/nvidia/pytorch:23.10-py3，it's all right when I use the float32 and float16, but floating point exception when I use bfloat16. I tried:

• build vllm from source using the nvcr.io/nvidia/pytorch:24.04-py3 container on H20 (still , float16 good, bfloat16 floating point exception)
• nvcr.io/nvidia/pytorch:23.10-py3 on H800 (all good)

[Harryking1999]

Met the same bug with Qwen1.5-14B-Chat on the H20, and I was able to solve it with float32. But using float16 and add --enforce-eager does not solve the problem.

How can I use float32 with vllm?

[TobyYang7]

I met the same bug recently on H20. My torch version is: 2.3.1+cu121 You need to check your conda list | grep cublas first. Ensure there is only one cublas and that the version is greater than 12.3, then run pip install nvidia-cublas-cu12==12.4.5.8. It works for me.

[finger92]

for me, both bfloat16 and float16 failed but float32 works

btw @TobyYang7 's solution works