[Bug]: Guided decoding is broken because tokenizers can't be pickled

[maxdebayser]

Your current environment

The output of `python collect_env.py`

Collecting environment information...
PyTorch version: 2.3.1+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Fedora release 39 (Thirty Nine) (x86_64)
GCC version: (GCC) 13.3.1 20240522 (Red Hat 13.3.1-1)
Clang version: 17.0.6 (Fedora 17.0.6-2.fc39)
CMake version: version 3.29.6
Libc version: glibc-2.38

Python version: 3.11.8 (main, Mar 27 2024, 15:03:48) [GCC 13.2.1 20231205 (Red Hat 13.2.1-6)] (64-bit runtime)
Python platform: Linux-6.7.11-200.fc39.x86_64-x86_64-with-glibc2.38
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce MX330
Nvidia driver version: 545.23.08
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:                        39 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               8
On-line CPU(s) list:                  0-7
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz
CPU family:                           6
Model:                                142
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
Stepping:                             12
CPU(s) scaling MHz:                   69%
CPU max MHz:                          4900.0000
CPU min MHz:                          400.0000
BogoMIPS:                             4599.93
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 est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust sgx bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi md_clear flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            128 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             1 MiB (4 instances)
L3 cache:                             8 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-7
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 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
Vulnerability Srbds:                  Mitigation; Microcode
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.3.100
[pip3] mypy==1.11.1
[pip3] mypy-extensions==1.0.0
[pip3] mypy-protobuf==3.5.0
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] pyzmq==26.1.0
[pip3] torch==2.3.1+cpu
[pip3] torchvision==0.18.1+cpu
[pip3] transformers==4.43.4
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.4@1f26efbb3a5e6dad0b98421dd697167c42a50629
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-7	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

If I run a small model like this python -m vllm.entrypoints.openai.api_server --model gpt2 and call it like this

curl http://localhost:8000/v1/completions   -H "Content-Type: application/json"   -d '{
    "model": "gpt2",
    "prompt": ["An example of a json document: ", "Another example of a json document: "],
    "max_tokens": 100,
    "temperature": 0,
    "guided_decoding_backend": "outlines",
    "response_format": {"type":"json_object"}
  }'

I get the following error in the server log:

Traceback (most recent call last):
  File "/home/mbayser/.pyenv/versions/3.11.8/lib/python3.11/multiprocessing/queues.py", line 244, in _feed
    obj = _ForkingPickler.dumps(obj)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mbayser/.pyenv/versions/3.11.8/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
AttributeError: Can't pickle local object 'get_cached_tokenizer.<locals>.CachedTokenizer'

I've tried to disable frontend multiprocessing, but that only changes the place where the error happens:

Traceback (most recent call last):
  File "/home/mbayser/.pyenv/versions/3.11.8/lib/python3.11/multiprocessing/queues.py", line 244, in _feed
    obj = _ForkingPickler.dumps(obj)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mbayser/.pyenv/versions/3.11.8/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
AttributeError: Can't pickle local object 'get_cached_tokenizer.<locals>.CachedTokenizer'

It seems that some of the refactorings to use multiprocessing have broken the guided decoding feature.

[github-actions]

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[AstroSayan]

Still facing issue with this. Is there any update on the fix?