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# MIT License
# Copyright (c) Microsoft
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Copyright (c) [2025] [Microsoft]
# SPDX-License-Identifier: MIT
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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