[Bug]: In function calls, when outputting Chinese, a backslash character "\" appears before Chinese characters.

yhhit commented 4 weeks ago

Your current environment

The output of `python collect_env.py`

```text (myenv) root@I1cf76ef41800201311:~# python collect_env.py Collecting environment information... /usr/local/miniconda3/envs/myenv/lib/python3.10/site-packages/vllm/connections.py:8: RuntimeWarning: Failed to read commit hash: No module named 'vllm._version' from vllm.version import __version__ as VLLM_VERSION PyTorch version: 2.4.0+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 3.16.3 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-5.4.0-147-generic-x86_64-with-glibc2.31 Is CUDA available: False CUDA runtime version: 12.1.105 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No devices found. Nvidia driver version: Could not collect cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0 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): 104 On-line CPU(s) list: 0-103 Thread(s) per core: 2 Core(s) per socket: 26 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz Stepping: 7 CPU MHz: 1000.003 CPU max MHz: 4000.0000 CPU min MHz: 1000.0000 BogoMIPS: 4200.00 Virtualization: VT-x L1d cache: 1.6 MiB L1i cache: 1.6 MiB L2 cache: 52 MiB L3 cache: 71.5 MiB NUMA node0 CPU(s): 0-25,52-77 NUMA node1 CPU(s): 26-51,78-103 Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages 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 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 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-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==9.1.0.70 [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==12.560.30 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.68 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchaudio==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.6.68 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchaudio 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.45.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A (dev) vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: Could not collect ```

Model Input Dumps

No response

🐛 Describe the bug

start vllm

vllm serve meta-llama/Llama-3.1-70B-Instruct --dtype bfloat16  --enable-auto-tool-choice --tool-call-parser llama3_json

send message

{
    "model": "meta-llama/Llama-3.1-70B-Instruct",
    "messages": [
        {
            "role": "user",
            "content": "查询scene_code为“YQ0204”，scene_name为“文明养宠”的sop"
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "query_sop_by_scene",
                "description": "Retrieve the Standard Operating Procedure (SOP) based on a specific scenario code. Use this when you need SOP details for a given scene.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "scene_code": {
                            "type": "string",
                            "description": "The encoded representation of the scenario for which the SOP is being queried."
                        },
                        "scene_name": {
                            "type": "string",
                            "description": "scene name"
                        }
                    },
                    "required": [
                        "scene_code",
                        "scene_name"
                    ],
                    "additionalProperties": false
                }
            }
        }
    ],
    "stream": false,
    "logprobs": true
}

problem in logprob, no backslash character "\" appears before Chinese characters. but in output there is a backslash character "\" appears before Chinese characters. unicode chinese is same.

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yhhit commented 3 weeks ago

When I tried offline inference, everything worked fine, so the issue likely lies with the OpenAI Compatible Server.

yhhit commented 3 weeks ago

已解决，解决方案如下修改 vllm/vllm/entrypoints/openai/tool_parsers/llama_tool_parser.py def extract_tool_calls里面

            tool_calls: List[ToolCall] = [
                ToolCall(
                    type="function",
                    function=FunctionCall(
                        name=raw_function_call["name"],
                        # function call args are JSON but as a string
                        arguments=json.dumps(raw_function_call["arguments"] \
                                if "arguments" in raw_function_call \
                                else raw_function_call["parameters"])))
                for raw_function_call in function_call_arr
            ]

改为

            tool_calls: List[ToolCall] = [
                ToolCall(
                    type="function",
                    function=FunctionCall(
                        name=raw_function_call["name"],
                        # function call args are JSON but as a string
                        arguments=json.dumps(raw_function_call["arguments"] \
                                if "arguments" in raw_function_call \
                                else raw_function_call["parameters"], ensure_ascii=False)))
                for raw_function_call in function_call_arr
            ]

具体原因怀疑是ensure_ascii编码在返回时被错误去转义引起的，这可能是某些库的内部的错误，在这里强制不使用ensure_ascii来直接返回utf8编码避免问题

yhhit commented 3 weeks ago

We want contributors to help integrate into the main branch

vllm-project / vllm