TRELLIS/trellis/modules/sparse/norm.py

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)