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[Misc] usage_stats: report more engine, spec-decode, and EP config (#44595)

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Signed-off-by: Zach Xi <zachary.xi@gmail.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Zachary Xi
2026-06-08 15:20:00 -07:00
committed by GitHub
co-authored by Cursor
parent bc941f375d
commit 3f627ebef7
1 changed files with 44 additions and 4 deletions
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vllm/v1/utils.py
+44 -4
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@@ -638,29 +638,54 @@ def report_usage_stats(
from vllm.model_executor.model_loader import get_architecture_class_name
model_config = vllm_config.model_config
scheduler_config = vllm_config.scheduler_config
parallel_config = vllm_config.parallel_config
attention_config = vllm_config.attention_config
compilation_config = vllm_config.compilation_config
speculative_config = vllm_config.speculative_config
# Prepare KV connector string if applicable
kv_connector = None
if vllm_config.kv_transfer_config is not None:
kv_connector = vllm_config.kv_transfer_config.kv_connector
# Attention backend is None when set to "auto" (resolved at runtime per platform).
attention_backend = (
attention_config.backend.name if attention_config.backend is not None else None
)
# CompilationMode is an IntEnum; report the name for readability in dashboards.
compilation_mode = (
compilation_config.mode.name if compilation_config.mode is not None else None
)
# Speculative decoding fields default to None when spec decode is disabled.
spec_decode_method = (
speculative_config.method if speculative_config is not None else None
)
num_speculative_tokens = (
speculative_config.num_speculative_tokens
if speculative_config is not None
else None
)
usage_message.report_usage(
get_architecture_class_name(vllm_config.model_config),
get_architecture_class_name(model_config),
usage_context,
extra_kvs={
# Common configuration
"dtype": str(vllm_config.model_config.dtype),
"dtype": str(model_config.dtype),
"block_size": vllm_config.cache_config.block_size,
"gpu_memory_utilization": vllm_config.cache_config.gpu_memory_utilization,
"kv_cache_memory_bytes": vllm_config.cache_config.kv_cache_memory_bytes,
# Quantization
"quantization": vllm_config.model_config.quantization,
"quantization": model_config.quantization,
"kv_cache_dtype": str(vllm_config.cache_config.cache_dtype),
# Feature flags
"enable_lora": bool(vllm_config.lora_config),
"enable_prefix_caching": vllm_config.cache_config.enable_prefix_caching,
"enforce_eager": vllm_config.model_config.enforce_eager,
"enforce_eager": model_config.enforce_eager,
"disable_custom_all_reduce": parallel_config.disable_custom_all_reduce,
# Distributed parallelism settings
"tensor_parallel_size": parallel_config.tensor_parallel_size,
@@ -671,6 +696,21 @@ def report_usage_stats(
"all2all_backend": parallel_config.all2all_backend,
# KV connector used
"kv_connector": kv_connector,
# Batching limits — tuning knobs operators commonly override
"max_model_len": model_config.max_model_len,
"max_num_seqs": scheduler_config.max_num_seqs,
"max_num_batched_tokens": scheduler_config.max_num_batched_tokens,
# Attention backend (user-requested; None = auto-selected at runtime)
"attention_backend": attention_backend,
# torch.compile mode (e.g. NONE, STOCK_TORCH_COMPILE, VLLM_COMPILE)
"compilation_mode": compilation_mode,
# Speculative decoding configuration
"spec_decode_method": spec_decode_method,
"num_speculative_tokens": num_speculative_tokens,
# Wide expert parallel: load balancer + redundant/total expert counts
"enable_eplb": parallel_config.enable_eplb,
"num_redundant_experts": parallel_config.eplb_config.num_redundant_experts,
"num_experts": model_config.get_num_experts(),
},
)