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- 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> |
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| .. | ||
| __init__.py | ||
| base.py | ||
| cache_engine.py | ||
| cuda_graph_runner.py | ||
| decode_perf_cache.py | ||
| dflash_cuda_graph_runner.py | ||
| model_runner.py | ||
| neuron_graph_runner.py | ||
| runtime_config.py | ||
| worker.py | ||
| zos_status.py | ||