[Bug]: ToolCall IDs generated by Mistral tool call parser do not comply with Mistral tool calls and template constraints

gcalmettes commented 1 month ago

Your current environment

The output of `python collect_env.py`

```text Collecting environment information... 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: Could not collect Libc version: glibc-2.31 Python version: 3.12.6 (main, Sep 10 2024, 00:05:17) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-5.15.0-113-generic-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe GPU 1: NVIDIA H100 PCIe Nvidia driver version: 550.90.07 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 Byte Order: Little Endian Address sizes: 52 bits physical, 57 bits virtual CPU(s): 48 On-line CPU(s) list: 0-47 Thread(s) per core: 1 Core(s) per socket: 48 Socket(s): 1 NUMA node(s): 1 Vendor ID: AuthenticAMD CPU family: 25 Model: 17 Model name: AMD EPYC 9334 32-Core Processor Stepping: 1 CPU MHz: 2695.950 BogoMIPS: 5391.90 Virtualization: AMD-V Hypervisor vendor: KVM Virtualization type: full L1d cache: 3 MiB L1i cache: 3 MiB L2 cache: 24 MiB L3 cache: 16 MiB NUMA node0 CPU(s): 0-47 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 Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode 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; Retpolines; IBPB conditional; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected 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 rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean pausefilter pfthreshold v_vmsave_vmload vgif avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm arch_capabilities Versions of relevant libraries: [pip3] flashinfer==0.1.6+cu121torch2.4 [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] torchvision==0.19.0 [pip3] transformers==4.45.0 [pip3] triton==3.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.1.dev238+ge2c6e0a82 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X PHB 0-47 0 N/A GPU1 PHB X 0-47 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 ```

Model Input Dumps

No response

🐛 Describe the bug

IDs generated for tool calls parsed by the Mistral parser (flag --tool-call-parser=mistral) do not respect the Mistral ToolCall id naming constraints and therefore cannot be used in subsequent function call workflow (the error from the template is raised).

ERROR 10-02 05:13:21 serving_chat.py:155] Error in applying chat template from request: Tool call IDs should be alphanumeric strings with length 9!
INFO:     172.17.0.1:59530 - "POST /v1/chat/completions HTTP/1.1" 400 Bad Request

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DarkLight1337 commented 1 month ago

cc @K-Mistele @patrickvonplaten

K-Mistele commented 1 month ago

Hi! Yes this is correct - vLLM's standard tool call ID format is incompatible with Mistral's 9-digit (I think?) alphanumeric tool call ID.

Instead of trying to alter vLLM's internal standard for tool call IDs to be compatible with Mistral, I opted to transform them in the provided chat templates to be compatible with Mistral's format. Details on mistral tool calling & chat templates are here in the dpcs

To resolve this, i recommend using the examples/tool_chat_template_mistral.jinja chat template, the examples/tool_chat_template_mistral_parallel.jinja chat template, or providing your own that processes vLLM's tool call ID properly.

K-Mistele commented 1 month ago

since before my implementation, tool calls have been generated using the following:

class ToolCall(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
    type: Literal["function"] = "function"
    function: FunctionCall

I opted not to change this, but it could probably be overridden in the mistral tool parser for anyone who wants to rely on the default mistral chat template.

gcalmettes commented 1 month ago

@K-Mistele , thanks for providing details !

The examples/tool_chat_template_mistral.jinja templates actually also enforces the 9-size constraint for the ID.

I proposed in this PR to use a dedicated MistralToolCall class (that directly inherits from the ToolCall vllm class, so the only change is that it overrides the id generation when the Mistral tool parser is used). This would prevent the user to have to provide its own template or to have to fiddle with the ids, still benefiting from the original ToolCall implementation.

vllm-project / vllm