DEEPINFER_NEURON_MOE_FLAG_FORCE=0|1 forces the gated experts' flags (timing only, wrong results);
DEEPINFER_NEURON_PROFILE_RANKS keeps NEURON_RT_INSPECT on for the listed followers.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- fused_sample._host_flags: the top-p/top-k and greedy flags of the step's
sampling parameters are read from the device once a step (a DSpark step
samples the same tensors six times); reset_host_flags at each sample step.
- NEURON_RT_INSPECT_* stays on the driver (64 ranks would overwrite one
output directory).
- VocabParallelEmbedding.ids_in_range: one F.embedding instead of the
zero-filling gather (~5 eager NEFF launches).
- bench33 (512 in, TP=64, nextn=5): TPOT 272.6 -> 268.2 ms.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- Followers claim core cp_rank (DEEPINFER_NEURON_LOCAL_RANK at spawn) and get
the graph's host metadata through shm (no in-graph broadcast on Neuron),
including the prompt-as-decode bucket and the context limit; they skip the
sampler. max_num_prefilling_tokens is not divided by cp on Neuron (prompt
chunks run replicated).
- DEEPINFER_NEURON_NUM_KV_BLOCKS: the fixed KV pool as an env var, so spawned
CP followers take it too.
- static MoE: when the EP group is the CP group the tokens are already
replicated: no all-gather, all-reduce instead of reduce-scatter (64x fewer
expert rows a rank at cp=ep=64).
- run_deepinfer_tiny.py --cp. Host tiny: cp=2 and cp=4 match the cp=1 tokens
(short 8/9 at a near tie, long 9/9).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
