ViperEkura
c6a82a5029
- 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
69 lines
2.3 KiB
Python
69 lines
2.3 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_tensor, 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). weight is the leaf
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# parameter, so its dtype is the model-precision baseline; cast everything
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# to it (bf16 model -> bf16 GEMMs, fp32 model -> fp32, no branch):
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# grad_input = g @ W, grad_weight = g^T @ X, grad_bias = sum(g, dim=0)
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compute_dtype = weight.dtype
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grad = grad_output.to(compute_dtype)
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grad_2d = grad.reshape(-1, weight.size(0))
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input_2d = input_tensor.reshape(-1, input_tensor.size(-1)).to(compute_dtype)
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grad_input = (
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torch.mm(grad_2d, weight)
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if output_mask[0]
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else torch.empty(0, device=input_tensor.device, dtype=input_tensor.dtype)
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)
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grad_weight = (
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torch.mm(grad_2d.t(), input_2d)
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if output_mask[1]
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else torch.empty(0, device=input_tensor.device, dtype=input_tensor.dtype)
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)
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grad_bias = (
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grad.sum(dim=0)
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if output_mask[2]
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else torch.empty(0, device=input_tensor.device, dtype=input_tensor.dtype)
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)
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return grad_input.reshape_as(input_tensor), grad_weight, grad_bias
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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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