github-actions[bot]
commented
3 days 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!
Your current environment
The output of `python collect_env.py`
```text PyTorch version: 2.4.0 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 3.22.1 Libc version: glibc-2.35 Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-119-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.1.66 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA L40 GPU 1: NVIDIA L40 GPU 2: NVIDIA L40 GPU 3: NVIDIA L40 Nvidia driver version: 535.183.01 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: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 64 On-line CPU(s) list: 0-63 Vendor ID: AuthenticAMD Model name: AMD EPYC 9124 16-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 2 Stepping: 1 BogoMIPS: 5991.00 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 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 ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d Virtualization: AMD-V L1d cache: 1 MiB (32 instances) L1i cache: 1 MiB (32 instances) L2 cache: 32 MiB (32 instances) L3 cache: 128 MiB (8 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-15,32-47 NUMA node1 CPU(s): 16-31,48-63 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: 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; Enhanced / Automatic IBRS; IBPB conditional; 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] numpy==1.26.4 [pip3] nvidia-ml-py==12.560.30 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchaudio==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.44.2 [pip3] triton==3.0.0 [conda] blas 1.0 mkl [conda] cuda-cudart 12.1.105 0 nvidia [conda] cuda-cupti 12.1.105 0 nvidia [conda] cuda-libraries 12.1.0 0 nvidia [conda] cuda-nvrtc 12.1.105 0 nvidia [conda] cuda-nvtx 12.1.105 0 nvidia [conda] cuda-opencl 12.6.68 0 nvidia [conda] cuda-runtime 12.1.0 0 nvidia [conda] cuda-version 12.6 3 nvidia [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] libcublas 12.1.0.26 0 nvidia [conda] libcufft 11.0.2.4 0 nvidia [conda] libcufile 1.11.1.6 0 nvidia [conda] libcurand 10.3.7.68 0 nvidia [conda] libcusolver 11.4.4.55 0 nvidia [conda] libcusparse 12.0.2.55 0 nvidia [conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch [conda] libnpp 12.0.2.50 0 nvidia [conda] libnvjitlink 12.1.105 0 nvidia [conda] libnvjpeg 12.1.1.14 0 nvidia [conda] mkl 2023.1.0 h213fc3f_46344 [conda] mkl-service 2.4.0 py310h5eee18b_1 [conda] mkl_fft 1.3.8 py310h5eee18b_0 [conda] mkl_random 1.2.4 py310hdb19cb5_0 [conda] numpy 1.26.4 py310h5f9d8c6_0 [conda] numpy-base 1.26.4 py310hb5e798b_0 [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] pytorch 2.4.0 py3.10_cuda12.1_cudnn9.1.0_0 pytorch [conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torchaudio 2.4.0 py310_cu121 pytorch [conda] torchtriton 3.0.0 py310 pytorch [conda] torchvision 0.19.0 py310_cu121 pytorch [conda] transformers 4.44.2 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.5@2684efc4678eb46d1dc7fe4311365a99215e2dc6 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NODE SYS SYS 0-15,32-47 0 N/A GPU1 NODE X SYS SYS 0-15,32-47 0 N/A GPU2 SYS SYS X NODE 16-31,48-63 1 N/A GPU3 SYS SYS NODE X 16-31,48-63 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
vLLM built from source.
Not sure if it is a bug or expected but vLLM fails to generate consistent JSON (with guided_json) if speculative decoding (ngram) is active and temperature > 0.0. This is not the case when speculative decoding is disabled.
Edit: guided_json (outlines) also has a significant speed impact when temp = 0. Generation is about about 3-5 times slower in my use case.
Before submitting a new issue...