ViperEkura
5244f1a8fc
- 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)
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
"""FP8 matrix-multiply op (torch.library custom_op) and FP8 linear replacement.
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Dispatch table:
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- Meta (register_fake): shapes only, for torch.compile / dynamic shapes
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- CUDA: csrc fp8_mm kernel (cuBLASLt TN fp8 GEMM, e4m3 in, fp32 acc/out)
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- CPU: fp32 fallback (testing)
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- AutogradCUDA (register_autograd): bf16 backward, scale-corrected
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"""
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import torch
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from torch.library import custom_op
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from astrai.extension.loader import get_module, is_available
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@custom_op("custom::fp8_mm", mutates_args=())
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def fp8_mm(
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a: torch.Tensor, b: torch.Tensor, sx: torch.Tensor, sw: torch.Tensor
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) -> torch.Tensor:
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"""FP8 e4m3 GEMM: a[M,K] x b[N,K] -> fp32[M,N], scales applied by the caller.
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a/b arrive pre-scaled (divided by sx/sw) fp8 tensors; the op returns the
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unscaled fp32 result so scale math stays in autograd-land.
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"""
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@fp8_mm.register_fake
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def _fp8_mm_fake(a, b, sx, sw):
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return torch.empty((a.size(0), b.size(1)), device=a.device, dtype=torch.float32)
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@fp8_mm.register_kernel("cuda")
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def _fp8_mm_cuda(a, b, sx, sw):
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if not is_available("fp8_mm"):
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raise RuntimeError(
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"CUDA kernel 'fp8_mm' is not available. Build with CSRC_KERNELS=true."
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)
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return get_module("fp8_mm").fp8_mm(a, b)
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@fp8_mm.register_kernel("cpu")
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def _fp8_mm_cpu(a, b, sx, sw):
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return torch.mm(a.float(), b.float().t())
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def _fp8_mm_setup_context(ctx, inputs, output):
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ctx.save_for_backward(*inputs)
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def _fp8_mm_backward(ctx, g):
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"""Scale-corrected straight-through gradients.
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out = F(a, b) * (sx * sw) with F(a, b) = a @ b^T, a = x/sx, b = w/sw:
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dx = g * sw @ b (dout/dx = dF/da * 1/sx * sx*sw)
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dW = (g * sx)^T @ a (dout/dw = dF/db * 1/sw * sx*sw)
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bf16 GEMMs keep gradients in range (e4m3 saturates at 448).
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"""
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a, b, sx, sw = ctx.saved_tensors
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ga = torch.mm(g * sw, b.float())
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gb = torch.mm((g * sx).t(), a.float())
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return ga.to(torch.bfloat16), gb.to(torch.bfloat16), None, None
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fp8_mm.register_autograd(_fp8_mm_backward, setup_context=_fp8_mm_setup_context)
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def fp8_linear_forward(x: torch.Tensor, w: torch.Tensor, bias=None):
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"""TE-style scaled fp8 linear forward (delegates to fp8_state)."""
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from astrai.extension.fp8_state import fp8_linear_forward as _f
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return _f(x, w, bias)
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def fp8_linear_backward(g, x, w, masks):
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"""TE-style scaled fp8 linear backward (delegates to fp8_state)."""
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from astrai.extension.fp8_state import fp8_linear_backward as _b
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return _b(g, x, w, masks)
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def fp8_available() -> bool:
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return is_available("fp8_mm")
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