chris-brightbeam
commented
1 month ago i expected skip_special_tokens=True as a sampling_arg to do the same. Can't get the model to output text not statrting with headers
Open erickrf opened 2 months ago
chris-brightbeam
commented
1 month ago i expected skip_special_tokens=True as a sampling_arg to do the same. Can't get the model to output text not statrting with headers
Your current environment
The output of `python collect_env.py`
```text 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 22.04.3 LTS (x86_64) GCC version: Could not collect 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-6.1.85+-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 L4 Nvidia driver version: 550.54.15 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, 48 bits virtual Byte Order: Little Endian CPU(s): 32 On-line CPU(s) list: 0-31 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU @ 2.20GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 7 BogoMIPS: 4400.43 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 512 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 16 MiB (16 instances) L3 cache: 38.5 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-31 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: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl 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 SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Versions of relevant libraries: [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==8.9.2.26 [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==12.555.43 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.4.127 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] torch==2.3.0 [pip3] transformers==4.40.2 [pip3] triton==2.3.0 [pip3] vllm-nccl-cu12==2.18.1.0.4.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-31 0 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
I'm running vLLM via kserve using the following yaml:
YAML file
``` apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: name: huggingface-nous-research-llama spec: predictor: serviceAccountName: "default-editor" tolerations: - key: "l4-gpu" operator: Equal value: "present" - key: "nvidia.com/gpu" operator: Equal value: "present" model: modelFormat: name: huggingface args: - --model_name=llama3 - --model_id=NousResearch/Meta-Llama-3-8B-Instruct resources: limits: cpu: "26" memory: 40Gi nvidia.com/gpu: "1" requests: cpu: "26" memory: 40Gi nvidia.com/gpu: "1" ```I've also tried other sources of Llama 3, including the original from Meta.
Whenever I send a request to vLLM, the response content starts with
<|start_header_id|>assistant<|end_header_id|>. I am pretty sure this is because the tokenizer is not adding them to the prompt and the model does it itself.I have tried setting
"add_generation_prompt": Truein my requests (which should be the default anyway), and even copying the tokenizer chat template tochat_template, but the result is the same.It is surprising that I couldn't find any other reports of this behavior with vLLM, so maybe there is something wrong with my setup.