[Bug]: No output on WSL (Debian, Windows 11)

[DragonBeast829]

Your current environment

PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version: (Debian 12.2.0-14) 12.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.36

Python version: 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 SUPER
Nvidia driver version: 546.65
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
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             12
On-line CPU(s) list:                0-11
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen 5 4600G with Radeon Graphics
CPU family:                         23
Model:                              96
Thread(s) per core:                 2
Core(s) per socket:                 6
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           7385.89
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 pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip rdpid
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          192 KiB (6 instances)
L1i cache:                          192 KiB (6 instances)
L2 cache:                           3 MiB (6 instances)
L3 cache:                           4 MiB (1 instance)
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:             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
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.1.2
[pip3] triton==2.1.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.3.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X                              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

No output when running the offline_inference.py example code with mistralai/Mistral-7B-Instruct-v0.2. This does not happen with transformers.

I also have to use dtype="half" because I can't use bfloat16.

The exact code follows:

from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.2", dtype="half")
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

And I don't see any outputs. When running straight from VSCode, I get

WARNING 03-26 17:27:34 config.py:618] Casting torch.bfloat16 to torch.float16.
INFO 03-26 17:27:34 llm_engine.py:87] Initializing an LLM engine with config: model='mistralai/Mistral-7B-Instruct-v0.2', tokenizer='mistralai/Mistral-7B-Instruct-v0.2', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)
INFO 03-26 17:27:37 weight_utils.py:163] Using model weights format ['*.safetensors']

and no generated text however running from the commandline I get

WARNING 03-26 17:28:40 config.py:618] Casting torch.bfloat16 to torch.float16.
INFO 03-26 17:28:40 llm_engine.py:87] Initializing an LLM engine with config: model='mistralai/Mistral-7B-Instruct-v0.2', tokenizer='mistralai/Mistral-7B-Instruct-v0.2', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)
INFO 03-26 17:28:42 weight_utils.py:163] Using model weights format ['*.safetensors']
        Illegal instruction

and no generated text.

Sorry if this is unclear, was in a bit of a rush to post this. I can provide more information if needed.

[youkaichao]

What is the GPU you use?

[DragonBeast829]

Gtx 1660 super. I can generate stuff with the chosen model with Transformers, so I don't think it's a GPU problem.

[youkaichao]

If you are using pre-built wheels, your GPU is not supported. See #2635 .

You can try to build vllm yourself, from source, and it might help.

Please check https://docs.vllm.ai/en/latest/getting_started/installation.html#build-from-source .

[DragonBeast829]

Okay, I'll try to build it from source and see if that works. Thanks!

[youkaichao]

In addition, your GPU seems to lack enough memory to load a 7B model.

[DragonBeast829]

Weird, since I can use 7B models just fine with Transformers with my GPU. I've generated text with Mistral 7B before

[youkaichao]

According to https://www.techpowerup.com/gpu-specs/geforce-gtx-1660-super.c3458 , it just has 6 GB memory.

[DragonBeast829]

My PC has 10 times that amount of memory, at 63.9 GB

[youkaichao]

So you are using CPU with Transformers , while vllm is designed to work with GPU.

[DragonBeast829]

Odd, since I use 'cuda' when generating with Transformers. Wouldn't that make Transformers use the GPU? Sorry, I'm a bit confused.

[youkaichao]

Then you need to dive deep into transformers.

[DragonBeast829]

Im sorry, but what do you mean by that?

[youkaichao]

They have weight offload strategy https://discuss.huggingface.co/t/big-model-inference-cpu-disk-offloading-for-transformers-using-from-pretrained/75165 .

[DragonBeast829]

I see, so I have been using my CPU without realizing. Sorry for the inconvenience! I'll have to come back to vLLM when I have a better GPU, then.