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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) | |