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on
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Running
on
Zero
File size: 2,151 Bytes
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import torch
import torch.nn as nn
from . import SparseTensor
from . import DEBUG
__all__ = [
'SparseGroupNorm',
'SparseLayerNorm',
'SparseGroupNorm32',
'SparseLayerNorm32',
]
class SparseGroupNorm(nn.GroupNorm):
def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine)
def forward(self, input: SparseTensor) -> SparseTensor:
nfeats = torch.zeros_like(input.feats)
for k in range(input.shape[0]):
if DEBUG:
assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch"
bfeats = input.feats[input.layout[k]]
bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
bfeats = super().forward(bfeats)
bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
nfeats[input.layout[k]] = bfeats
return input.replace(nfeats)
class SparseLayerNorm(nn.LayerNorm):
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine)
def forward(self, input: SparseTensor) -> SparseTensor:
nfeats = torch.zeros_like(input.feats)
for k in range(input.shape[0]):
bfeats = input.feats[input.layout[k]]
bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
bfeats = super().forward(bfeats)
bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
nfeats[input.layout[k]] = bfeats
return input.replace(nfeats)
class SparseGroupNorm32(SparseGroupNorm):
"""
A GroupNorm layer that converts to float32 before the forward pass.
"""
def forward(self, x: SparseTensor) -> SparseTensor:
return super().forward(x.float()).type(x.dtype)
class SparseLayerNorm32(SparseLayerNorm):
"""
A LayerNorm layer that converts to float32 before the forward pass.
"""
def forward(self, x: SparseTensor) -> SparseTensor:
return super().forward(x.float()).type(x.dtype)
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