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A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
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[Usage]: PeftModelForCausalLM is not JSON serializable #6469

Closed jazzisfuture closed 1 month ago

jazzisfuture commented 2 months ago

Your current environment

ollecting environment information...
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.30.0
Libc version: glibc-2.31

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-113-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
GPU 2: NVIDIA GeForce RTX 4090
GPU 3: NVIDIA GeForce RTX 4090

Nvidia driver version: 555.42.02
cuDNN version: Probably one of the following:
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn.so.8.9.2
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.2
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.2
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.2
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.2
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.2
/data/jiangyulong/software/cuda121/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.2
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
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
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 48 bits virtual
CPU(s):                             80
On-line CPU(s) list:                0-79
Thread(s) per core:                 2
Core(s) per socket:                 20
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz
Stepping:                           7
CPU MHz:                            3440.546
CPU max MHz:                        4000.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4200.00
Virtualization:                     VT-x
L1d cache:                          1.3 MiB
L1i cache:                          1.3 MiB
L2 cache:                           40 MiB
L3 cache:                           55 MiB
NUMA node0 CPU(s):                  0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78
NUMA node1 CPU(s):                  1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
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 IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled
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 cdp_l3 invpcid_single intel_ppin 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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.1
[pip3] torchvision==0.18.1
[pip3] transformers==4.42.4
[pip3] triton==2.3.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] torch                     2.3.1                    pypi_0    pypi
[conda] torchvision               0.18.1                   pypi_0    pypi
[conda] transformers              4.42.4                   pypi_0    pypi
[conda] triton                    2.3.1                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.2
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     0,2,4,6,8,10    0               N/A
GPU1    NODE     X      SYS     SYS     0,2,4,6,8,10    0               N/A
GPU2    SYS     SYS      X      NODE    1,3,5,7,9,11    1               N/A
GPU3    SYS     SYS     NODE     X      1,3,5,7,9,11    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

How would you like to use vllm

I want to run inference of a unsloth/Phi-3-mini-4k-instruct-bnb-4bit.I used unsloth to fine-tune the model and followed unsloth's method to export the model saving-to-vllm. It can be deployed successfully, but after initiating a request using openai, an error occurs.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[117], line 1
----> 1 res = client.chat.completions.create(
      2     model=model,
      3    messages=message_phi
      4 )

File envs/unsloth_env/lib/python3.10/site-packages/openai/_utils/_utils.py:277, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    275             msg = f"Missing required argument: {quote(missing[0])}"
    276     raise TypeError(msg)
--> 277 return func(*args, **kwargs)

File envs/unsloth_env/lib/python3.10/site-packages/openai/resources/chat/completions.py:643, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    609 @required_args(["messages", "model"], ["messages", "model", "stream"])
    610 def create(
    611     self,
   (...)
    641     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    642 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
--> 643     return self._post(
    644         "/chat/completions",
    645         body=maybe_transform(
    646             {
    647                 "messages": messages,
...
    178     """
--> 179     raise TypeError(f'Object of type {o.__class__.__name__} '
    180                     f'is not JSON serializable')

TypeError: Object of type PeftModelForCausalLM is not JSON serializable

on the sever-side not have any error

DarkLight1337 commented 1 month ago

Sorry for the delay, can you show the code which you've used? Note that you are supposed to pass the model name/path to the OpenAI client, rather than passing the model instance directly.