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Zero
Running
on
Zero
File size: 1,470 Bytes
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# Copyright (c) 2023-2024, Zexin He
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
class ModLN(nn.Module):
"""
Modulation with adaLN.
References:
DiT: https://github.com/facebookresearch/DiT/blob/main/models.py#L101
"""
def __init__(self, inner_dim: int, mod_dim: int, eps: float):
super().__init__()
self.norm = nn.LayerNorm(inner_dim, eps=eps)
self.mlp = nn.Sequential(
nn.SiLU(),
nn.Linear(mod_dim, inner_dim * 2),
)
@staticmethod
def modulate(x, shift, scale):
# x: [N, L, D]
# shift, scale: [N, D]
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor:
shift, scale = self.mlp(mod).chunk(2, dim=-1) # [N, D]
return self.modulate(self.norm(x), shift, scale) # [N, L, D]
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