ViperEkura
a5b238dd86
- 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
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
"""FP8 linear dispatch: replace aten::linear on the CUDA key, no model changes.
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``F.linear`` -> ``aten::linear`` -> dispatcher -> this CUDA impl (fp8 when
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enabled) or the original composite implementation via ``redispatch``.
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Enabling is per-thread; model code stays untouched.
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"""
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import threading
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import torch
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from torch.library import Library
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from astrai.extension.fp8_ops import fp8_linear_forward
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_state = threading.local()
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def fp8_linear_enable(enabled: bool = True) -> None:
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"""Toggle fp8 dispatch for aten::linear on this thread."""
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_state.enabled = enabled
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def fp8_linear_enabled() -> bool:
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return getattr(_state, "enabled", False)
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def _linear_cuda_impl(x: torch.Tensor, w: torch.Tensor, bias=None):
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if fp8_linear_enabled() and x.dtype in (torch.bfloat16, torch.float32):
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return fp8_linear_forward(x, w, bias)
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return torch.ops.aten.linear.default.redispatch(
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torch._C.DispatchKeySet(torch._C.DispatchKey.CompositeImplicitAutograd),
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x,
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w,
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bias,
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)
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def _linear_backward_cuda_impl(input, grad_output, weight, output_mask):
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# VariableType wraps aten::linear; its backward runs aten::linear_backward
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# with schema (self, grad_output, weight, mask). Implement the bf16
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# gradient math directly (no redispatch), supporting [..., K] inputs:
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# dX = g @ W, dW = g^T @ X, dB = sum(g, dim=0)
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g = grad_output.to(torch.bfloat16)
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g2d = g.reshape(-1, weight.size(0))
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x2d = input.reshape(-1, input.size(-1)).to(torch.bfloat16)
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dX = (
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torch.mm(g2d, weight)
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if output_mask[0]
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else torch.empty(0, device=input.device, dtype=input.dtype)
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)
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dX = dX.reshape_as(input)
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dW = (
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torch.mm(g2d.t(), x2d)
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if output_mask[1]
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else torch.empty(0, device=input.device, dtype=input.dtype)
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)
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dB = (
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g.sum(dim=0)
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if output_mask[2]
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else torch.empty(0, device=input.device, dtype=input.dtype)
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)
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return dX, dW, dB
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_lib = Library("aten", "IMPL", "CUDA")
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_lib.impl("linear", _linear_cuda_impl)
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_lib.impl("linear_backward", _linear_backward_cuda_impl)
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