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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import torch
import torch.nn as nn
from timm.models.vision_transformer import Mlp
from diffusion.model.act import build_act, get_act_name
from diffusion.model.norms import build_norm, get_norm_name
from diffusion.model.utils import get_same_padding, val2tuple
class ConvLayer(nn.Module):
def __init__(
self,
in_dim: int,
out_dim: int,
kernel_size=3,
stride=1,
dilation=1,
groups=1,
padding: int or None = None,
use_bias=False,
dropout=0.0,
norm="bn2d",
act="relu",
):
super().__init__()
if padding is None:
padding = get_same_padding(kernel_size)
padding *= dilation
self.in_dim = in_dim
self.out_dim = out_dim
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.groups = groups
self.padding = padding
self.use_bias = use_bias
self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None
self.conv = nn.Conv2d(
in_dim,
out_dim,
kernel_size=(kernel_size, kernel_size),
stride=(stride, stride),
padding=padding,
dilation=(dilation, dilation),
groups=groups,
bias=use_bias,
)
self.norm = build_norm(norm, num_features=out_dim)
self.act = build_act(act)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.dropout is not None:
x = self.dropout(x)
x = self.conv(x)
if self.norm:
x = self.norm(x)
if self.act:
x = self.act(x)
return x
class GLUMBConv(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: int,
out_feature=None,
kernel_size=3,
stride=1,
padding: int or None = None,
use_bias=False,
norm=(None, None, None),
act=("silu", "silu", None),
dilation=1,
):
out_feature = out_feature or in_features
super().__init__()
use_bias = val2tuple(use_bias, 3)
norm = val2tuple(norm, 3)
act = val2tuple(act, 3)
self.glu_act = build_act(act[1], inplace=False)
self.inverted_conv = ConvLayer(
in_features,
hidden_features * 2,
1,
use_bias=use_bias[0],
norm=norm[0],
act=act[0],
)
self.depth_conv = ConvLayer(
hidden_features * 2,
hidden_features * 2,
kernel_size,
stride=stride,
groups=hidden_features * 2,
padding=padding,
use_bias=use_bias[1],
norm=norm[1],
act=None,
dilation=dilation,
)
self.point_conv = ConvLayer(
hidden_features,
out_feature,
1,
use_bias=use_bias[2],
norm=norm[2],
act=act[2],
)
# from IPython import embed; embed(header='debug dilate conv')
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
x = self.inverted_conv(x)
x = self.depth_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.point_conv(x)
x = x.reshape(B, C, N).permute(0, 2, 1)
return x
class SlimGLUMBConv(GLUMBConv):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# 移除 self.inverted_conv 层
del self.inverted_conv
self.out_dim = self.point_conv.out_dim
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
# 直接使用 x，跳过 self.inverted_conv 层的调用
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
# x = self.inverted_conv(x)
x = self.depth_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.point_conv(x)
x = x.reshape(B, self.out_dim, N).permute(0, 2, 1)
return x
class MBConvPreGLU(nn.Module):
def __init__(
self,
in_dim: int,
out_dim: int,
kernel_size=3,
stride=1,
mid_dim=None,
expand=6,
padding: int or None = None,
use_bias=False,
norm=(None, None, "ln2d"),
act=("silu", "silu", None),
):
super().__init__()
use_bias = val2tuple(use_bias, 3)
norm = val2tuple(norm, 3)
act = val2tuple(act, 3)
mid_dim = mid_dim or round(in_dim * expand)
self.inverted_conv = ConvLayer(
in_dim,
mid_dim * 2,
1,
use_bias=use_bias[0],
norm=norm[0],
act=None,
)
self.glu_act = build_act(act[0], inplace=False)
self.depth_conv = ConvLayer(
mid_dim,
mid_dim,
kernel_size,
stride=stride,
groups=mid_dim,
padding=padding,
use_bias=use_bias[1],
norm=norm[1],
act=act[1],
)
self.point_conv = ConvLayer(
mid_dim,
out_dim,
1,
use_bias=use_bias[2],
norm=norm[2],
act=act[2],
)
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
x = self.inverted_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.depth_conv(x)
x = self.point_conv(x)
x = x.reshape(B, C, N).permute(0, 2, 1)
return x
@property
def module_str(self) -> str:
_str = f"{self.depth_conv.kernel_size}{type(self).__name__}("
_str += f"in={self.inverted_conv.in_dim},mid={self.depth_conv.in_dim},out={self.point_conv.out_dim},s={self.depth_conv.stride}"
_str += (
f",norm={get_norm_name(self.inverted_conv.norm)}"
f"+{get_norm_name(self.depth_conv.norm)}"
f"+{get_norm_name(self.point_conv.norm)}"
)
_str += (
f",act={get_act_name(self.inverted_conv.act)}"
f"+{get_act_name(self.depth_conv.act)}"
f"+{get_act_name(self.point_conv.act)}"
)
_str += f",glu_act={get_act_name(self.glu_act)})"
return _str
class DWMlp(Mlp):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.0,
kernel_size=3,
stride=1,
dilation=1,
padding=None,
):
super().__init__(
in_features=in_features,
hidden_features=hidden_features,
out_features=out_features,
act_layer=act_layer,
bias=bias,
drop=drop,
)
hidden_features = hidden_features or in_features
self.hidden_features = hidden_features
if padding is None:
padding = get_same_padding(kernel_size)
padding *= dilation
self.conv = nn.Conv2d(
hidden_features,
hidden_features,
kernel_size=(kernel_size, kernel_size),
stride=(stride, stride),
padding=padding,
dilation=(dilation, dilation),
groups=hidden_features,
bias=bias,
)
def forward(self, x, HW=None):
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = x.reshape(B, H, W, self.hidden_features).permute(0, 3, 1, 2)
x = self.conv(x)
x = x.reshape(B, self.hidden_features, N).permute(0, 2, 1)
x = self.fc2(x)
x = self.drop2(x)
return x
class Mlp(Mlp):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.0):
super().__init__(
in_features=in_features,
hidden_features=hidden_features,
out_features=out_features,
act_layer=act_layer,
bias=bias,
drop=drop,
)
def forward(self, x, HW=None):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
if __name__ == "__main__":
model = GLUMBConv(
1152,
1152 * 4,
1152,
use_bias=(True, True, False),
norm=(None, None, None),
act=("silu", "silu", None),
).cuda()
input = torch.randn(4, 256, 1152).cuda()
output = model(input)
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