[Bug]: vLLM produces non-deterministic output due to Triton autotuner #25194
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : 14.0.0-1ubuntu1.1
CMake version : version 3.22.1
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.8.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-5.15.0-143-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3
Nvidia driver version : 575.57.08
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
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 8474C
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: 4200.00
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 intel_ppin 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 split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp 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 la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 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 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: Not affected
Vulnerability Spec rstack overflow: 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 / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.13.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pynccl==0.1.2
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.0.1
[pip3] torch==2.8.0
[pip3] torchaudio==2.8.0
[pip3] torchvision==0.23.0
[pip3] transformers==4.57.0.dev0
[pip3] triton==3.4.0
[pip3] triton_kernels==1.0.0
[conda] numpy 2.2.6 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi
[conda] nvidia-cudnn-frontend 1.13.0 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.13.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi
[conda] nvidia-ml-py 12.575.51 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-nvshmem-cu12 3.3.20 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi
[conda] pynccl 0.1.2 pypi_0 pypi
[conda] pynvml 12.0.0 pypi_0 pypi
[conda] pyzmq 27.0.1 pypi_0 pypi
[conda] torch 2.8.0 pypi_0 pypi
[conda] torchaudio 2.8.0 pypi_0 pypi
[conda] torchvision 0.23.0 pypi_0 pypi
[conda] transformers 4.57.0.dev0 pypi_0 pypi
[conda] triton 3.4.0 pypi_0 pypi
[conda] triton-kernels 1.0.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.10.2rc2.dev59+g6d80ae83e (git sha: 6d80ae83e)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 NIC10 NIC11 NIC12 NIC13 NIC14 NIC15 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX PIX NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS 0-47,96-143 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE PIX PIX NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS 0-47,96-143 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE NODE NODE PIX PIX NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS 0-47,96-143 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE NODE NODE NODE PIX PIX SYS SYS SYS SYS SYS SYS SYS SYS 0-47,96-143 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS SYS SYS SYS SYS PIX PIX NODE NODE NODE NODE NODE NODE 48-95,144-191 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE PIX PIX NODE NODE NODE NODE 48-95,144-191 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE PIX PIX NODE NODE 48-95,144-191 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE PIX PIX 48-95,144-191 1 N/A
NIC0 PIX NODE NODE NODE SYS SYS SYS SYS X PIX NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS
NIC1 PIX NODE NODE NODE SYS SYS SYS SYS PIX X NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS
NIC2 NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE X PIX NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS
NIC3 NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE PIX X NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS
NIC4 NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE NODE NODE X PIX NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS
NIC5 NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE NODE NODE PIX X NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS
NIC6 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE X PIX SYS SYS SYS SYS SYS SYS SYS SYS
NIC7 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE PIX X SYS SYS SYS SYS SYS SYS SYS SYS
NIC8 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS X PIX NODE NODE NODE NODE NODE NODE
NIC9 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS PIX X NODE NODE NODE NODE NODE NODE
NIC10 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE X PIX NODE NODE NODE NODE
NIC11 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE PIX X NODE NODE NODE NODE
NIC12 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE X PIX NODE NODE
NIC13 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE PIX X NODE NODE
NIC14 SYS SYS SYS SYS NODE NODE NODE PIX SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE X PIX
NIC15 SYS SYS SYS SYS NODE NODE NODE PIX SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE PIX 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_0
NIC1: mlx5_1
NIC2: mlx5_2
NIC3: mlx5_3
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6
NIC7: mlx5_7
NIC8: mlx5_8
NIC9: mlx5_9
NIC10: mlx5_10
NIC11: mlx5_11
NIC12: mlx5_12
NIC13: mlx5_13
NIC14: mlx5_14
NIC15: mlx5_15
==============================
Environment Variables
==============================
CUDA_VISIBLE_DEVICES=1
CUDA_VISIBLE_DEVICES=1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
TLDR: Triton auto-tuning can cause vLLM to have non-deterministic output
While debugging prefix caching for mamba, we encountered a test-case that produces non-deterministic behaviour on main.
Running the script below multiple times (on an H100) produces non-deterministic output, even though there is (a) no batching (b) no prefix caching (c) no chunked prefill and (d) temperature is zero. Please excuse the bizarre prompt...
from vllm import LLM, SamplingParams
prompt = """vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
Translate the following English sentence into Japanese, French, and Swahili: 'The early bird catches the worm.'
"""
prompts = [prompt]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.0, max_tokens=3, logprobs=1)
# Create an LLM.
llm = LLM(model="ibm-granite/granite-4.0-tiny-preview", enforce_eager=True,
max_num_seqs=1, gpu_memory_utilization=0.4)
# 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.
print("\nGenerated Outputs:\n" + "-" * 60)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
logprobs = output.outputs[0].logprobs
print(f"Num prompt tokens: {len(output.prompt_token_ids)}")
print(f"Output: {generated_text!r}")
print(f"Logprobs: {logprobs!r}")
print(f"Cached tokens: {output.num_cached_tokens}")
print("-" * 60)Sometime it produces:
Generated Outputs:
------------------------------------------------------------
Num prompt tokens: 2644
Output: '\n- Japanese'
Logprobs: [{203: Logprob(logprob=-0.3451612591743469, rank=1, decoded_token='\n')}, {31: Logprob(logprob=-1.0327364206314087, rank=1, decoded_token='-')}, {48032: Logprob(logprob=-1.6438578367233276, rank=1, decoded_token=' Japanese')}]
Cached tokens: 0
------------------------------------------------------------
and other times it produces:
Generated Outputs:
------------------------------------------------------------
Num prompt tokens: 2644
Output: '\n- v'
Logprobs: [{203: Logprob(logprob=-0.3099258542060852, rank=1, decoded_token='\n')}, {31: Logprob(logprob=-0.9378877282142639, rank=1, decoded_token='-')}, {354: Logprob(logprob=-1.3365482091903687, rank=1, decoded_token=' v')}]
Cached tokens: 0
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After extensive debugging we have determined that this non-determinism is coming from the Triton auto-tuning that happens for this kernel. It seems like sometimes {'BLOCK_SIZE_H': 1} gets selected and sometimes {'BLOCK_SIZE_H': 2} gets selected. This can happen since optimized configurations are chosen based on runtime and there can be fluctuations/outliers. However, it is somewhat surprising that this can lead to numerically different output.
In this specific kernel it seems like tl.cumsum produces numerically different results for BLOCK_SIZE_H=1 vs. BLOCK_SIZE_H>1. Our hypothesis is that in the former case it implements some kind of parallel reduction in the chunk size dimension, whereas in the latter case it just exploits parallelism along the head dimension.
We will put up a fix for this specific case (simply removing BLOCK_SIZE_H=1 as a configuration should be fine) but wanted to open this issue to raise awareness that auto-tuning can be an additional source of non-deterministic output in vLLM (since this is a topic of quite some discussion at the moment).
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