The overflow decision was rank-local, and the dense rerun has collectives of
its own: one rank rerunning left the others waiting in its all-gather (NRT
'Collective operation pending'). The flag is now max-all-reduced over the EP
group. The bench gave every rank the same prompt, which routes 64 copies of
each token to the same experts (overflow on every layer); real requests
differ, so the prompts now take the rank in their seed.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- serve/: the minimal OpenAI-compatible server of deepseek-ai/DeepInfer#147,
with a Neuron worker setup (basic mode env, per-rank NeuronCore, fixed KV
pool via --num-kv-blocks, no MTP lookahead) and a /v1/completions endpoint
(prompt text or token ids, no chat template): what vllm bench serve
--backend openai drives, as vllm-neuron#71's run_bench.py does.
- examples/neuron/bench_neuron.py: the same TTFT/TPOT matrix offline.
- static MoE: one packed fp32 all-gather instead of three (each 64-rank
collective is ~2 ms of fixed latency a layer); a capacity-grouped expert
path for prefill chunks (DEEPINFER_NEURON_MOE_CAPACITY, dense rerun on
overflow; not yet validated on device).
- Sub-region timings at split() boundaries under DEEPINFER_NEURON_REGION_TIMING.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
