vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
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[Bug]: Inconsistent Output from OPT-x models #5833

Open NihalPotdar opened 4 months ago

NihalPotdar commented 4 months ago

Your current environment

Collecting environment information... PyTorch version: 2.3.0+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 3.29.3 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-5.15.0-112-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: GPU 0: NVIDIA RTX A6000 GPU 1: NVIDIA RTX A6000

Nvidia driver version: 545.23.08 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): 24 On-line CPU(s) list: 0-23 Vendor ID: AuthenticAMD Model name: AMD Ryzen Threadripper PRO 5945WX 12-Cores CPU family: 25 Model: 8 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 1 Stepping: 2 Frequency boost: enabled CPU max MHz: 7014.8428 CPU min MHz: 1800.0000 BogoMIPS: 8184.12 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 pcid sse4_1 sse4_2 x2apic 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 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid 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 pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm Virtualization: AMD-V L1d cache: 384 KiB (12 instances) L1i cache: 384 KiB (12 instances) L2 cache: 6 MiB (12 instances) L3 cache: 64 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-11 NUMA node1 CPU(s): 12-23 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 rstack overflow: Mitigation; safe RET, no microcode 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; IBRS_FW; 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] fast-pytorch-kmeans==0.2.0.1 [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] optree==0.11.0 [pip3] torch==2.3.0 [pip3] torchprofile==0.0.4 [pip3] torchvision==0.18.0 [pip3] transformers==4.41.2 [pip3] triton==2.3.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.3 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV4 0-23 0-1 N/A GPU1 NV4 X 0-23 0-1 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

Recently, I have been experimenting with the opt models on VLLM and I am noticing that their output is non-sensical. For comparison, I have tested the same models by loading them with fast-api and hugging face. While the output produced by VLLM is odd and does not make sense relative to the prompt, the outputs produced by these other sources are consistent with what is expected. From my testing, this seems to be the same for any of the opt-x models and not only the opt-13b in the code below. On the same hardware and configurations, I have also tested other models through vllm and these were okay. Seems like the wrong model is being loaded?

My code is attached below for validation:

VLLM: python -m vllm.entrypoints.openai.api_server --model facebook/opt-13b --dtype auto

Fast-API:

from transformers import AutoModelForCausalLM, TFAutoModelForCausalLM,  AutoTokenizer, AutoModelForMaskedLM, BitsAndBytesConfig
from huggingface_hub import login
from fastapi import FastAPI, HTTPException

# make sure you're logged in to HF
device = "cuda" # the device to load the model onto

app = FastAPI()

model_str = "facebook/opt-13b"
model_optimized = AutoModelForCausalLM.from_pretrained(model_str, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_str)

@app.post("/generate")
async def generate_text(prompt: str):
    inputs = tokenizer([prompt], return_tensors="pt")
    model_inputs = inputs.to(device)
    generated_ids = model_optimized.generate(**model_inputs, do_sample=True)
    decoded = tokenizer.batch_decode(generated_ids)
    return decoded

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Request for server:

from requests import get, post

url = "http://localhost:8000"

response = get(url)

print(response.status_code)
print(response.text)

response = post(url + "/generate?prompt=tell me about mayonaisse")
print(response.status_code)
print(response.text)
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="test",
)

completion = client.chat.completions.create(
    model="facebook/opt-13b",
    messages=[
        {"role": "user", "content": "tell me about mayonaisse"}
    ],
    max_tokens=100
)

print(completion.choices[0].message)

VLLM output: ChatCompletionMessage(content="It's not a bug, you can disable it in the settings.\nI know, but you can still get it if you swim too fast/far and your screen is shaking.\nI know, but the OP said you could only get it if you were swimming and not moving...", role='assistant', function_call=None, tool_calls=None) fast-api output: ["</s>tell me about mayonaisse\nI like to dip my pizza rolls in it.</s>"]

Hardware: Nvidia RTX-A6000

github-actions[bot] commented 3 weeks ago

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!