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import torch
import torch.nn.functional as F
class LPLayerNorm(torch.nn.LayerNorm):
def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None):
super().__init__(
normalized_shape=normalized_shape,
eps=eps,
elementwise_affine=elementwise_affine,
device=device,
dtype=dtype,
)
def forward(self, x):
module_device = x.device
downcast_x = _cast_if_autocast_enabled(x)
downcast_weight = _cast_if_autocast_enabled(
self.weight) if self.weight is not None else self.weight
downcast_bias = _cast_if_autocast_enabled(
self.bias) if self.bias is not None else self.bias
with torch.autocast(enabled=False, device_type=module_device.type):
return F.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)
def _cast_if_autocast_enabled(tensor):
if torch.is_autocast_enabled():
if tensor.device.type == 'cuda':
dtype = torch.get_autocast_gpu_dtype()
elif tensor.device.type == 'cpu':
dtype = torch.get_autocast_cpu_dtype()
else:
raise NotImplementedError()
return tensor.to(dtype=dtype)
return tensor
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