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from typing import Optional, Union
import torch
from .layers_registry import norms
norms.register(name='layernorm', func=torch.nn.LayerNorm)
def _cast_if_autocast_enabled(tensor: torch.Tensor) -> torch.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
@norms.register_class('low_precision_layernorm')
class LPLayerNorm(torch.nn.LayerNorm):
def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, elementwise_affine: bool=True, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None):
super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
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: torch.Tensor, weight: Optional[torch.Tensor]=None, eps: float=1e-05) -> torch.Tensor:
output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
if weight is not None:
return output * weight
return output
@norms.register_class('rmsnorm')
class RMSNorm(torch.nn.Module):
def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, weight: bool=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.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: torch.Tensor) -> torch.Tensor:
return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
@norms.register_class('low_precision_rmsnorm')
class LPRMSNorm(RMSNorm):
def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, weight: bool=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None):
super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)
def forward(self, x: torch.Tensor) -> torch.Tensor:
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)
@norms.register_class('triton_rmsnorm')
class TritonRMSNorm(torch.nn.Module):
def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None):
super().__init__()
self.eps = eps
try:
from flash_attn.ops.triton.layer_norm import rms_norm_fn
except ImportError:
raise ImportError('triton_rms_norm requires Flash Attention to be installed. ' + 'Please pip install flash-attn.')
if not isinstance(normalized_shape, int):
raise ValueError('TritonRMSNorm only supports 1D tensors')
self.rms_norm_fn = rms_norm_fn
self.weight = torch.nn.Parameter(torch.ones(normalized_shape, device=device, dtype=dtype))
def forward(self, x: torch.Tensor):
return self.rms_norm_fn(x, self.weight, None, residual=None, eps=self.eps, dropout_p=0.0, prenorm=False, residual_in_fp32=False) |