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

[gcalmettes]

Your current environment

The output of `python collect_env.py`

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]

cc @K-Mistele @patrickvonplaten

[K-Mistele]

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]

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]

@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.