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The compressed-KV scopes are fp4 records; their decode was nibble masks and shifts plus the integer e2m1 / e4m3 decoders on GpSimd (three such chains were the largest cost of the ratio-2 attention graph, device profile bench72). Now: nibbles from floor(b / 16), e2m1 from a piecewise-linear map of the magnitude code, scales through the hardware fp8 conversion. Bit-identical to the integer decode for every byte (host check). bench73 (vllm-sonnet 512, TP=64, nextn=5): TPOT 243.5 -> 228.6 ms, TTFT 4.84 -> 4.35 s, step 699 -> 666 ms, tokens per step identical. 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 | ||