74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
"""FP8 CUDA kernel interface adapter (the only module touching the pybind.
|
|
|
|
Isolates the ``fp8_mm`` CUDA extension behind stable Python functions:
|
|
- availability / dtype checks and clear errors
|
|
- torch.library ``custom::fp8_mm`` registration (meta + CPU fallback)
|
|
- quantize-in-GEMM primitives used by ``fp8.py`` training state
|
|
|
|
Policy (scales, amax history, delayed scaling, autocast) lives in ``fp8.py``;
|
|
this module is stateless.
|
|
"""
|
|
|
|
import torch
|
|
from torch.library import custom_op
|
|
|
|
from astrai.extension.loader import get_module, is_available
|
|
|
|
|
|
def _mod():
|
|
if not is_available("fp8_mm"):
|
|
raise RuntimeError(
|
|
"CUDA kernel 'fp8_mm' is not available. Build with CSRC_KERNELS=true."
|
|
)
|
|
return get_module("fp8_mm")
|
|
|
|
|
|
@custom_op("custom::fp8_mm", mutates_args=())
|
|
def fp8_mm(
|
|
a: torch.Tensor, b: torch.Tensor, sx: torch.Tensor, sw: torch.Tensor
|
|
) -> torch.Tensor:
|
|
"""FP8 e4m3 GEMM: a[M,K] x b[N,K] -> bf16[M,N] (pre-scaled inputs)."""
|
|
|
|
|
|
@fp8_mm.register_fake
|
|
def _fp8_mm_fake(a, b, sx, sw):
|
|
return torch.empty((a.size(0), b.size(1)), device=a.device, dtype=torch.bfloat16)
|
|
|
|
|
|
@fp8_mm.register_kernel("cuda")
|
|
def _fp8_mm_cuda(a, b, sx, sw):
|
|
return _mod().fp8_mm(a, b)
|
|
|
|
|
|
@fp8_mm.register_kernel("cpu")
|
|
def _fp8_mm_cpu(a, b, sx, sw):
|
|
return torch.mm(a.float(), b.float().t()).to(torch.bfloat16)
|
|
|
|
|
|
def linear_forward_scaled(x, w, bias, sx, sw, sx_inv, sw_inv, amax_x, amax_w):
|
|
"""Quantize x/w with per-tensor scales + cuBLASLt GEMM + bias -> bf16.
|
|
|
|
x/w: [..., K] / [N, K] bf16; sx/sw: f32 scale tensors (device scalars);
|
|
sx_inv/sw_inv: 1/scale; amax_x/amax_w: f32 buffers receiving max-abs.
|
|
"""
|
|
if not (x.dtype == torch.bfloat16 and w.dtype == torch.bfloat16):
|
|
raise TypeError(f"fp8 forward requires bf16 inputs, got {x.dtype}/{w.dtype}")
|
|
return _mod().fp8_linear_forward_scaled(
|
|
x, w, bias, sx, sw, sx_inv, sw_inv, amax_x, amax_w
|
|
)
|
|
|
|
|
|
def linear_backward_scaled(g, x, w, masks, sg, sw, sx, sg_inv, sw_inv, sx_inv, amax_g):
|
|
"""dX = g @ W, dW = g^T @ X, dB = sum(g) with per-tensor scales."""
|
|
if not (
|
|
g.dtype == torch.bfloat16
|
|
and x.dtype == torch.bfloat16
|
|
and w.dtype == torch.bfloat16
|
|
):
|
|
raise TypeError(
|
|
f"fp8 backward requires bf16 inputs, got {g.dtype}/{x.dtype}/{w.dtype}"
|
|
)
|
|
return _mod().fp8_linear_backward_scaled(
|
|
g, x, w, masks, sg, sw, sx, sg_inv, sw_inv, sx_inv, amax_g
|
|
)
|