import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
from einops import rearrange
from typing import Optional, Any
from model.distributions import DiagonalGaussianDistribution
from model.config import Config, AttnMode
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=0)
def forward(self, x):
if self.with_conv:
pad = (0,1,0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x+h
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
print(f"building AttnBlock (vanilla) with {in_channels} in_channels")
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
class MemoryEfficientAttnBlock(nn.Module):
"""
Uses xformers efficient implementation,
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
Note: this is a single-head self-attention operation
"""
#
def __init__(self, in_channels):
super().__init__()
print(f"building MemoryEfficientAttnBlock (xformers) with {in_channels} in_channels")
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.attention_op: Optional[Any] = None
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
B, C, H, W = q.shape
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(B, t.shape[1], 1, C)
.permute(0, 2, 1, 3)
.reshape(B * 1, t.shape[1], C)
.contiguous(),
(q, k, v),
)
out = Config.xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
out = (
out.unsqueeze(0)
.reshape(B, 1, out.shape[1], C)
.permute(0, 2, 1, 3)
.reshape(B, out.shape[1], C)
)
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
out = self.proj_out(out)
return x+out
class SDPAttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
print(f"building SDPAttnBlock (sdp) with {in_channels} in_channels")
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
B, C, H, W = q.shape
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(B, t.shape[1], 1, C)
.permute(0, 2, 1, 3)
.reshape(B * 1, t.shape[1], C)
.contiguous(),
(q, k, v),
)
out = F.scaled_dot_product_attention(q, k, v)
out = (
out.unsqueeze(0)
.reshape(B, 1, out.shape[1], C)
.permute(0, 2, 1, 3)
.reshape(B, out.shape[1], C)
)
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
out = self.proj_out(out)
return x+out
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
assert attn_type in ["vanilla", "sdp", "xformers", "linear", "none"], f'attn_type {attn_type} unknown'
if attn_type == "vanilla":
assert attn_kwargs is None
return AttnBlock(in_channels)
elif attn_type == "sdp":
return SDPAttnBlock(in_channels)
elif attn_type == "xformers":
return MemoryEfficientAttnBlock(in_channels)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
raise NotImplementedError()
class Encoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, double_z=True, use_linear_attn=False,
**ignore_kwargs):
super().__init__()
### setup attention type
if Config.attn_mode == AttnMode.SDP:
attn_type = "sdp"
elif Config.attn_mode == AttnMode.XFORMERS:
attn_type = "xformers"
else:
attn_type = "vanilla"
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.in_ch_mult = in_ch_mult
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
2*z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
# timestep embedding
temb = None
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class Decoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
**ignorekwargs):
super().__init__()
### setup attention type
if Config.attn_mode == AttnMode.SDP:
attn_type = "sdp"
elif Config.attn_mode == AttnMode.XFORMERS:
attn_type = "xformers"
else:
attn_type = "vanilla"
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.give_pre_end = give_pre_end
self.tanh_out = tanh_out
self.controller = None
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,)+tuple(ch_mult)
block_in = ch*ch_mult[self.num_resolutions-1]
curr_res = resolution // 2**(self.num_resolutions-1)
self.z_shape = (1,z_channels,curr_res,curr_res)
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels,
block_in,
kernel_size=3,
stride=1,
padding=1)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
print(f"attn")
attn.append(make_attn(block_in, attn_type=attn_type))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, z):
#assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb)
''' ToMe '''
# tome_info = {
# "size": None,
# "hooks": [],
# "args": {
# "generator": None,
# "max_downsample": 2,
# "min_downsample": 1,
# "generator": None,
# "seed": 123,
# "batch_size": 1,
# "align_batch": False,
# "merge_global": False,
# "global_merge_ratio": 0,
# "local_merge_ratio": 0.9,
# "global_rand": 0.1,
# "target_stride": 4,
# "current_step": 0,
# "frame_ids": [0],
# "label": "Decoder_up",
# "downsample": 1,
# "controller": self.controller,
# }
# }
# B, C, H, W = h.shape
# h = rearrange(h, 'b c h w -> b (h w) c')
# if tome_info["args"]["controller"] is None:
# non_pad_ratio_h, non_pad_ratio_w = 1, 1
# print(f"[INFO] no padding removal")
# else:
# non_pad_ratio_h, non_pad_ratio_w = self.controller.non_pad_ratio
# padding_size_w = W - int(W * non_pad_ratio_w)
# padding_size_h = H - int(H * non_pad_ratio_h)
# padding_mask = torch.zeros((H, W), device=h.device, dtype=torch.bool)
# if padding_size_w:
# padding_mask[:, -padding_size_w:] = 1
# if padding_size_h:
# padding_mask[-padding_size_h:, :] = 1
# padding_mask = rearrange(padding_mask, 'h w -> (h w)')
# idx_buffer = torch.arange(H * W, device=h.device, dtype=torch.int64)
# non_pad_idx = idx_buffer[None, ~padding_mask, None]
# del idx_buffer, padding_mask
# x_non_pad = torch.gather(h, dim=1, index=non_pad_idx.expand(B, -1, C))
# tome_info["size"] = (int(H * non_pad_ratio_h), int(W * non_pad_ratio_w))
# from vidtome.patch import compute_merge
# m_a, u_a, merged_tokens = compute_merge(
# self, x_non_pad, tome_info)
# x_non_pad = u_a(merged_tokens)
# h.scatter_(dim=1, index=non_pad_idx.expand(B, -1, C), src=x_non_pad)
# h = rearrange(h, 'b (h w) c -> b c h w', h=H, w=W)
''' ToMe ended'''
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
''' ToMe '''
# print(f"[INFO] before merging h mean: {torch.mean(h)} h std: {torch.std(h)}")
# B, C, H, W = h.shape
# h = rearrange(h, 'b c h w -> b (h w) c')
# padding_size_w = W - int(W * non_pad_ratio_w)
# padding_size_h = H - int(H * non_pad_ratio_h)
# padding_mask = torch.zeros((H, W), device=h.device, dtype=torch.bool)
# if padding_size_w:
# padding_mask[:, -padding_size_w:] = 1
# if padding_size_h:
# padding_mask[-padding_size_h:, :] = 1
# padding_mask = rearrange(padding_mask, 'h w -> (h w)')
# idx_buffer = torch.arange(H * W, device=h.device, dtype=torch.int64)
# non_pad_idx = idx_buffer[None, ~padding_mask, None]
# del idx_buffer, padding_mask
# x_non_pad = torch.gather(h, dim=1, index=non_pad_idx.expand(B, -1, C))
# tome_info["size"] = (int(H * non_pad_ratio_h), int(W * non_pad_ratio_w))
# m_a, u_a, merged_tokens = compute_merge(
# self, x_non_pad, tome_info)
# x_non_pad = u_a(merged_tokens)
# h.scatter_(dim=1, index=non_pad_idx.expand(B, -1, C), src=x_non_pad)
# h = rearrange(h, 'b (h w) c -> b c h w', h=H, w=W)
# print(f"[INFO] after merging h mean: {torch.mean(h)} h std: {torch.std(h)}")
''' ToMe ended '''
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
# print(f"i_level {i_level} i_block {i_block} with shape {h.shape}")
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# import ipdb; ipdb.set_trace()
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
if self.tanh_out:
h = torch.tanh(h)
return h
class AutoencoderKL(nn.Module):
def __init__(self, ddconfig, embed_dim):
super().__init__()
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
self.embed_dim = embed_dim
def encode(self, x, batch_size=0):
if batch_size:
h = []
batch_x = x.split(batch_size, dim=0)
for x_ in batch_x:
h_ = self.encoder(x_)
h += [h_]
torch.cuda.empty_cache()
h = torch.cat(h)
else:
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z, batch_size=0):
z = self.post_quant_conv(z)
if batch_size:
dec = []
batch_z = z.split(batch_size, dim=0)
for z_ in batch_z:
# decode
z_ = self.decoder(z_)
dec += [z_]
torch.cuda.empty_cache()
dec = torch.cat(dec)
else:
dec = self.decoder(z)
# import ipdb; ipdb.set_trace()
return dec
def forward(self, input, sample_posterior=True):
posterior = self.encode(input)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior