ViperEkura
5244f1a8fc
feat: add te-style scaled fp8 training via fp8_autocast
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- per-tensor scales applied inside cublasLt via A_SCALE/B_SCALE
- delayed scaling: weight amax history ring, refresh every 16 steps
- quantize kernels emit atomic amax, device-side scale updates
- fp8_autocast context toggles aten::linear dispatch like torch.autocast
- fallback to bf16 when M/N not 16-aligned (fp8 gemm constraint)
- x/g scales delayed one step, reuse free atomic amax (no abs/max reduce)
2026-08-14 12:14:04 +08:00
ViperEkura
15862d4b56
perf: fuse fp8 linear fwd and bwd into single kernel calls
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- fp8_linear_forward: cast + cublasLt GEMM + transpose + bias in one call
- fp8_linear_backward: scale-free, dtype derived from input tensor
- drops per-op Python dispatch (was ~6-8 launches per linear) and amax syncs
- 1024x1024 linear: 6.8x slow -> 0.67x (36.7us vs 24.8us bf16)
- small-model e2e still 1.71x slow; 15bt estimate ~0.78x (linear-heavy)
2026-08-14 01:24:37 +08:00
ViperEkura
c6a82a5029
refactor: align linear backward dtype with weight
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- cast gradients and inputs to weight.dtype instead of hardcoded bf16
- single code path covers bf16 and fp32 models, no branch needed
- gradient dtype now matches the leaf parameter dtype exactly
2026-08-14 01:01:58 +08:00
ViperEkura
a5b238dd86
feat: add fp8 training via cublasLt dispatch
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- 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
ViperEkura