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DEEPINFER_NEURON_CTX_BUCKETS: the model runner sets the step's context limit to the smallest bucket that holds every sequence, and the dense kv_ca_index / indexer logits are sized to it instead of max_model_len (one more set of attention graphs a bucket). Prefill cost scaled with the limit: a 128-token chunk ~9 s at 16384 against ~5.3 s at 4352. Host tiny check: same tokens as without buckets up to near ties. 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 | ||