[Bug]: Outputs are different at separate runs

Your current environment

The output of `python collect_env.py`

```text PyTorch version: 2.4.0+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: Could not collect Libc version: glibc-2.35 Python version: 3.12.7 (main, Oct 1 2024, 08:52:12) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.10.134-007.ali5000.al8.x86_64-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 A100-SXM4-80GB GPU 1: NVIDIA A100-SXM4-80GB GPU 2: NVIDIA A100-SXM4-80GB GPU 3: NVIDIA A100-SXM4-80GB GPU 4: NVIDIA A100-SXM4-80GB GPU 5: NVIDIA A100-SXM4-80GB GPU 6: NVIDIA A100-SXM4-80GB GPU 7: NVIDIA A100-SXM4-80GB Nvidia driver version: 535.129.03 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: 46 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 128 On-line CPU(s) list: 0-127 Vendor ID: GenuineIntel BIOS Vendor ID: Intel(R) Corporation Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz BIOS Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 Stepping: 6 CPU max MHz: 3500.0000 CPU min MHz: 800.0000 BogoMIPS: 5800.00 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 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 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 3 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 80 MiB (64 instances) L3 cache: 96 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-127 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: 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, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] flashinfer==0.1.6+cu121torch2.4 [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.77 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.3.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 PXB 0-127 0 N/A GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 PXB 0-127 0 N/A GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 SYS 0-127 0 N/A GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 SYS 0-127 0 N/A GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS 0-127 0 N/A GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS 0-127 0 N/A GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS 0-127 0 N/A GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS 0-127 0 N/A NIC0 PXB PXB SYS SYS 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 ```

Model Input Dumps

No response

🐛 Describe the bug

The outputs of Lllma3.1 are different at separate runs. I provide a script to reproduce with official docker image vllm/vllm-openai:v0.6.3.post1. Similar issue #8531

import time
import numpy as np
from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
from vllm.utils import FlexibleArgumentParser

def create_test_prompts():
    test_case = 10 * ["help me create a rust app that supports the elevenlabs.io api "
                      "and that can read the contents of clipboard aloud using tts"]
    sampling_params = SamplingParams(temperature=0.0,max_tokens=770)
    return [(prompt, sampling_params) for prompt in test_case]

def process_requests(engine, test_prompts):
    np.random.seed(0)
    intervals = np.random.exponential(1.0, size = len(test_prompts)).tolist()

    all_outputs = []
    last_time = time.time()
    request_id = 0
    while test_prompts or engine.has_unfinished_requests():
        cur_time = time.time()
        if test_prompts and cur_time - last_time > intervals[0]:
            prompt, sampling_params = test_prompts.pop(0)
            intervals.pop(0)
            engine.add_request(str(request_id), prompt, sampling_params)
            request_id += 1

        request_outputs = engine.step()
        for request_output in request_outputs:
            if request_output.finished:
                all_outputs.append(request_output)

    return all_outputs

def print_outlen(outputs):
    all_outlen = []
    for output in outputs:
        all_outlen.append(len(output.outputs[0].token_ids))
    print(f"{all_outlen=}")
    print(f"{sum(all_outlen)=}")

def initialize_engine(args):
    engine_args = EngineArgs.from_cli_args(args)
    return LLMEngine.from_engine_args(engine_args)

def main(args):
    engine = initialize_engine(args)
    test_prompts = create_test_prompts()
    outputs = process_requests(engine, test_prompts)
    print_outlen(outputs)

if __name__ == '__main__':
    parser = FlexibleArgumentParser(description='Bug repro')
    parser = EngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    main(args)

You can run like

python3 this_scripy.py --model meta-llama/Llama-3.1-70B-Instruct -tp 4

At the end of the output, you may see something like:

all_outlen=[644, 644, 653, 656, 644, 653, 708, 683, 770, 770]
sum(all_outlen)=6825

These are the lengths of the generated text that resulted from 10 requests. If you run this script multiple times, you'll observe different output lengths each time. However, this inconsistency NEVER occurs with meta-llama/Llama-3-70B-Instruct. Additional details: this issue does not appear to be related to chunked prefill, which might differentiate Llama3 from Llama3.1 in vllm. Even if you add the official flag --enable-chunked-prefill=False(this script accepts all official engine arguments), the outputs for Llama3.1 still vary.

cc @simon-mo

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