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Add norm/norm_act header comments
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@ -1,3 +1,11 @@
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""" 'Fast' Normalization Functions
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For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
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Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
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Hacked together by / Copyright 2022 Ross Wightman
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"""
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from typing import List, Optional
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import torch
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@ -37,6 +45,7 @@ def fast_group_norm(
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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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# FIXME what to do re CPU autocast?
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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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@ -62,6 +71,7 @@ def fast_layer_norm(
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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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# FIXME what to do re CPU autocast?
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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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@ -1,4 +1,8 @@
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""" Normalization layers and wrappers
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Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
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Hacked together by / Copyright 2022 Ross Wightman
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"""
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import torch
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""" Normalization + Activation Layers
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Provides Norm+Act fns for standard PyTorch norm layers such as
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* BatchNorm
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* GroupNorm
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* LayerNorm
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This allows swapping with alternative layers that are natively both norm + act such as
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* EvoNorm (evo_norm.py)
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* FilterResponseNorm (filter_response_norm.py)
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* InplaceABN (inplace_abn.py)
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Hacked together by / Copyright 2022 Ross Wightman
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"""
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from typing import Union, List, Optional, Any
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