2 Commits

Include renames

Author	SHA1	Message	Date
ViperEkura	f9efb705b8	perf: output fp8 gemm in bf16 instead of fp32 ... - cublasLt C layout and buffer switched to CUDA_R_16BF, halving output bandwidth - downstream ops (RMSNorm etc.) keep matching bf16 dtype, fused kernels stay - numeric error unchanged (0.19% vs fp32 ref on quantized inputs)	2026-08-14 01:08:26 +08:00
ViperEkura	a5b238dd86	feat: add fp8 training via cublasLt dispatch ... - fp8_mm kernel (csrc): cublasLt fp8 e4m3 gemm, TN layout mapped zero-copy - custom::fp8_mm custom op: meta/cuda/cpu kernels + scale-corrected bf16 autograd - aten::linear and linear_backward dispatch on CUDA key, zero model changes - per-tensor scale or raw cast; single-GPU smoke loss matches bf16	2026-08-14 00:39:49 +08:00