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55f6a76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | 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} |