ViperEkura 41cd40363a perf: post-multiply attention scale in float instead of pre-scaling Q in bf16 ... - Replace bf16 pre-scale Q loading with direct 32-bit aligned bf16x2 reads - Apply scale in float32 after Q@K^T, before online softmax - Reduces causal max error from 2^-6 to 2^-8 with zero perf cost		2026-07-11 00:13:32 +08:00
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kernels	perf: post-multiply attention scale in float instead of pre-scaling Q in bf16	2026-07-11 00:13:32 +08:00
tests	refactor: rename gqa_* to attn_*, split-KV for all decode paths	2026-07-10 23:35:14 +08:00
__init__.py	feat: add optional CUDA kernel system (csrc/) + fused GQA decode attention	2026-07-06 12:09:58 +08:00
build.py	refactor: rename gqa_* to attn_*, split-KV for all decode paths	2026-07-10 23:35:14 +08:00