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A world all-reduce costs ~3.2 ms per graph that holds one, whatever it carries (microbenchmark at 64 ranks: one all-reduce a graph 3.5 ms, ten chained in one graph 3.6 ms; fp32 / bf16 and 1 / 6 rows alike), so the step pays per collective-bearing graph, not per byte. The MoE's gate, experts, all-reduce and shared expert now stay one graph at decode sizes (prompt chunks still split). bench62 (512 in, TP=64, nextn=5): engine step 805 -> 740 ms. Random-token prompts make greedy outputs chaotic, so a graph-boundary change that moves bf16 rounding changes the generated tokens and the acceptance; the step time is the comparable figure. 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 | ||