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69 lines
2.0 KiB
69 lines
2.0 KiB
from typing import List, Optional
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import torch
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from torch.nn import functional as F
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try:
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from apex.normalization.fused_layer_norm import fused_layer_norm_affine
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has_apex = True
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except ImportError:
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has_apex = False
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# fast (ie lower precision LN) can be disabled with this flag if issues crop up
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_USE_FAST_NORM = False # defaulting to False for now
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def is_fast_norm():
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return _USE_FAST_NORM
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def set_fast_norm(enable=True):
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global _USE_FAST_NORM
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_USE_FAST_NORM = enable
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def fast_group_norm(
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x: torch.Tensor,
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num_groups: int,
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weight: Optional[torch.Tensor] = None,
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bias: Optional[torch.Tensor] = None,
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eps: float = 1e-5
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) -> torch.Tensor:
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if torch.jit.is_scripting():
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# currently cannot use is_autocast_enabled within torchscript
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return F.group_norm(x, num_groups, weight, bias, eps)
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if torch.is_autocast_enabled():
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# normally native AMP casts GN inputs to float32
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# here we use the low precision autocast dtype
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dt = torch.get_autocast_gpu_dtype()
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x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt)
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with torch.cuda.amp.autocast(enabled=False):
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return F.group_norm(x, num_groups, weight, bias, eps)
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def fast_layer_norm(
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x: torch.Tensor,
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normalized_shape: List[int],
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weight: Optional[torch.Tensor] = None,
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bias: Optional[torch.Tensor] = None,
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eps: float = 1e-5
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) -> torch.Tensor:
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if torch.jit.is_scripting():
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# currently cannot use is_autocast_enabled within torchscript
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return F.layer_norm(x, normalized_shape, weight, bias, eps)
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if has_apex:
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return fused_layer_norm_affine(x, weight, bias, normalized_shape, eps)
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if torch.is_autocast_enabled():
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# normally native AMP casts LN inputs to float32
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# apex LN does not, this is behaving like Apex
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dt = torch.get_autocast_gpu_dtype()
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x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt)
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with torch.cuda.amp.autocast(enabled=False):
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return F.layer_norm(x, normalized_shape, weight, bias, eps)
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