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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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| .. | ||
| _C | ||
| distributed | ||
| engine | ||
| entrypoints | ||
| hugging_face | ||
| model_executor | ||
| neuron_ops | ||
| npu_ops | ||
| structure_output | ||
| ve | ||
| worker | ||
| __init__.py | ||
| attrdict.py | ||
| config.py | ||
| environ.py | ||
| load_class.py | ||
| logger.py | ||
| model_config_loader.py | ||
| multimodal_tokens.py | ||
| platform.py | ||
| py.typed | ||
| sampling_params.py | ||
| sequence.py | ||
| shared_memory.py | ||
| testing.py | ||
| timer.py | ||
| utils.py | ||