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# Copyright (c) 2024-present, BAAI. All Rights Reserved.
#
# 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
#
# http://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.
# ------------------------------------------------------------------------
"""Normalization Layers."""
from typing import Tuple
import torch
from torch import nn
class AdaLayerNormZero(nn.Module):
"""Adaptive LayerNorm with residual stats."""
def __init__(self, dim, rank=None, num_stats=2, eps=1e-6):
super(AdaLayerNormZero, self).__init__()
self.lora = nn.Linear(dim, rank, bias=False) if rank else nn.Identity()
self.proj = nn.Linear(rank if rank else dim, num_stats * dim)
self.norm = nn.LayerNorm(dim, eps, elementwise_affine=False) if eps else nn.Identity()
self.activation, self.num_stats = nn.SiLU(), num_stats
def forward(self, x, z) -> Tuple[torch.Tensor, Tuple[torch.Tensor]]:
stats = self.proj(self.lora(self.activation(z))).chunk(self.num_stats, dim=-1)
return self.norm(x).mul(1 + stats[0]).add_(stats[1]), stats[2:]
class AdaLayerNorm(AdaLayerNormZero):
"""Adaptive LayerNorm."""
def __init__(self, dim, rank=None, eps=1e-6):
super(AdaLayerNorm, self).__init__(dim, rank, num_stats=2, eps=eps)
def forward(self, x, z) -> torch.Tensor:
return super().forward(x, z)[0]
class AdaLayerNormSingle(nn.Module):
"""Adaptive LayerNorm with shared residual stats."""
def __init__(self, dim, num_stats=2, eps=1e-6):
super(AdaLayerNormSingle, self).__init__()
self.bias = nn.Parameter(torch.randn(num_stats, dim) / dim**0.5)
self.norm = nn.LayerNorm(dim, eps, elementwise_affine=False) if eps else nn.Identity()
self.num_stats = num_stats
def forward(self, x, z) -> Tuple[torch.Tensor, Tuple[torch.Tensor]]:
axis = -2 if z.size(-1) == self.bias.size(-1) else -1
bias = self.bias.flatten(-1 if z.size(-1) == self.bias.size(-1) else 0)
stats = z.add(bias).chunk(self.num_stats, dim=axis)
return self.norm(x).mul(1 + stats[0]).add_(stats[1]), stats[2:]
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