[Misc] usage_stats: report more engine, spec-decode, and EP config

[zlxi02]

Added fields to usage_stats so we can better understand how people use vLLM in production and which fields to focus efforts on.

Fields added:

• Batching limits: max_model_len, max_num_seqs, max_num_batched_tokens
• Spec decoding spec_decode_method, num_speculative_tokens
• Wide expert parallel: enable_eplb, num_redundant_experts, num_experts
• Backend: attention_backend
• Torch details: compilation_mode, torch_version

On privacy, all information is in aggregate and high level enough to protect user privacy.

[claude]

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[simon-mo]

can you please post the local usage stats json to show what these fields look like when enabled?

[simon-mo]

Typically vLLM version is tied to a given torch version so we do not need to capture this, however, as we move towards Torch stable ABI, this needs to be understood well!

[simon-mo]

Oh should be get the CUDA version? or is cuda_runtime already capturing this, i have vague memory now.

[zlxi02]

1. sounds good, torch version dropped; agreed on the redundancy
2. cuda_runtime already captures it (set to torch.version.cuda)

[simon-mo]

to clarify this is only the value if user explicitly opt-in?

[zlxi02]

Yes: value is set to attention backend name if opt in, and then None if user doesn't specify flag

[zlxi02]

can you please post the local usage stats json to show what these fields look like when enabled?

Ran on a single-GPU GB200 node with LLM(model="facebook/opt-125m", max_model_len=512). Resulting ~/.config/vllm/usage_stats.json:

{
  "uuid": "<redacted>",
  "provider": "GCP",
  "num_cpu": 140,
  "cpu_type": "Neoverse-V2",
  "cpu_family_model_stepping": ",,",
  "total_memory": 947973390336,
  "architecture": "aarch64",
  "platform": "Linux-...aarch64",
  "xpu_runtime": null,
  "cuda_runtime": "13.0",
  "gpu_count": 4,
  "gpu_type": "NVIDIA GB200",
  "gpu_memory_per_device": 197897617408,
  "env_var_json": "{\"VLLM_USE_MODELSCOPE\": false, \"VLLM_USE_FLASHINFER_SAMPLER\": true, \"VLLM_PP_LAYER_PARTITION\": null, \"VLLM_USE_TRITON_AWQ\": false, \"VLLM_ENABLE_V1_MULTIPROCESSING\": true}",
  "model_architecture": "OPTForCausalLM",
  "vllm_version": "0.20.2rc1.dev242+gd7af6b34d",
  "context": "ENGINE_CONTEXT",
  "log_time": 1780688065386652928,
  "source": "production",
  "dtype": "torch.float16",
  "block_size": 16,
  "gpu_memory_utilization": 0.92,
  "kv_cache_memory_bytes": null,
  "quantization": null,
  "kv_cache_dtype": "auto",
  "enable_lora": false,
  "enable_prefix_caching": true,
  "enforce_eager": false,
  "disable_custom_all_reduce": false,
  "tensor_parallel_size": 1,
  "data_parallel_size": 1,
  "pipeline_parallel_size": 1,
  "enable_expert_parallel": false,
  "all2all_backend": "allgather_reducescatter",
  "kv_connector": null,
  "max_model_len": 512,
  "max_num_seqs": 1024,
  "max_num_batched_tokens": 16384,
  "attention_backend": null,
  "compilation_mode": "VLLM_COMPILE",
  "spec_decode_method": null,
  "num_speculative_tokens": null,
  "enable_eplb": false,
  "num_redundant_experts": 0,
  "num_experts": 0
}

New fields at the bottom^

[simon-mo]

can you please post a json with spec decode fields and other fields available?

[zlxi02]

can you please post a json with spec decode fields and other fields available?

yes, new run on Qwen/Qwen1.5-MoE-A2.7B + EPLB + n-gram spec decode + FlashInfer attention (compilation_mode is None because we had to avoid a regression on main)

~/.config/vllm/usage_stats.json:

{
  "uuid": "<redacted>",
  "provider": "GCP",
  "num_cpu": 140,
  "cpu_type": "Neoverse-V2",
  "cpu_family_model_stepping": ",,",
  "total_memory": 947973390336,
  "architecture": "aarch64",
  "platform": "Linux-...aarch64",
  "xpu_runtime": null,
  "cuda_runtime": "13.0",
  "gpu_count": 4,
  "gpu_type": "NVIDIA GB200",
  "gpu_memory_per_device": 197897617408,
  "env_var_json": "{\"VLLM_USE_MODELSCOPE\": false, \"VLLM_USE_FLASHINFER_SAMPLER\": true, \"VLLM_PP_LAYER_PARTITION\": null, \"VLLM_USE_TRITON_AWQ\": false, \"VLLM_ENABLE_V1_MULTIPROCESSING\": true}",
  "model_architecture": "Qwen2MoeForCausalLM",
  "vllm_version": "0.20.2rc1.dev242+gd7af6b34d",
  "context": "ENGINE_CONTEXT",
  "log_time": 1780703520195269120,
  "source": "production",
  "dtype": "torch.bfloat16",
  "block_size": 16,
  "gpu_memory_utilization": 0.92,
  "kv_cache_memory_bytes": null,
  "quantization": null,
  "kv_cache_dtype": "auto",
  "enable_lora": false,
  "enable_prefix_caching": true,
  "enforce_eager": true,
  "disable_custom_all_reduce": false,
  "tensor_parallel_size": 2,
  "data_parallel_size": 1,
  "pipeline_parallel_size": 1,
  "enable_expert_parallel": true,
  "all2all_backend": "allgather_reducescatter",
  "kv_connector": null,
  "max_model_len": 4096,
  "max_num_seqs": 1024,
  "max_num_batched_tokens": 16384,
  "attention_backend": "FLASHINFER",
  "compilation_mode": "NONE",
  "spec_decode_method": "ngram",
  "num_speculative_tokens": 3,
  "enable_eplb": true,
  "num_redundant_experts": 8,
  "num_experts": 60
}