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initial commit
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import torch
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
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 torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)
def rms_norm(x, weight=None, eps=1e-05):
output = (x / torch.rsqrt((x.pow(2).mean((- 1), keepdim=True) + eps)))
if (weight is not None):
return (output * weight)
return output
class RMSNorm(torch.nn.Module):
def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
super().__init__()
self.eps = eps
if weight:
self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
else:
self.register_parameter('weight', None)
def forward(self, x):
return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
class LPRMSNorm(RMSNorm):
def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)
def forward(self, x):
downcast_x = _cast_if_autocast_enabled(x)
downcast_weight = (_cast_if_autocast_enabled(self.weight) if (self.weight is not None) else self.weight)
with torch.autocast(enabled=False, device_type=x.device.type):
return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)
NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}