SolitaryThinker
9ae9eaacb7
fastvideo-kernel/benchmarks/bench_attn_kernels.py benches all four attention kernels on the real logged Wan2.1-1.3B shapes plus an L sweep (1k/4k/16k/32760, H=12 D=128 B=1): bf16_flash_attn|bf16_sdpa high-precision baseline (labels which ran) fp8_sageattn SageAttention qk-int8/pv-fp8 (PR #1496 path) nvfp4_local attn_qat_infer sageattn_blackwell (sm_120a) nvfp4_flashinfer flashinfer nvfp4_attention_sm120 (SM 12.0/12.1) Per kernel x shape: CUDA-event latency (mean/std), TFLOPS (4*B*H*L^2*D), and accuracy (cos / max abs err / mean rel err) vs a query-chunked fp32 SDPA reference. Inputs are biased (+1.0 mean shift on q/k) by default because zero-mean randn is blind to the flashinfer per-block-mean qk-correction head-broadcast bug; --include-zeromean adds the zero-mean rows for contrast. Lanes probe availability + arch gates and skip with a printed reason; oversized shapes are preflighted (chunked reference, qk_correction workspace check) instead of OOMing. Replaces bench_nvfp4_fp8_attention.py (logic folded in; docs pointer updated, CPU unit tests renamed to test_attn_kernels_bench.py and extended for the new --shapes/--bias surface).
FastVideo Documentation
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