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Merge pull request #55 from huggingface/refactor_glide

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[Resnet] Merge glide resnet into general resnet
This commit is contained in:
Patrick von Platen
2022-07-01 00:26:05 +02:00
committed by GitHub
parent c1c4dea98d efe1e60e12
commit fb188cd3f5
3 changed files with 101 additions and 287 deletions
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284
src/diffusers/models/resnet.py
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@@ -1,4 +1,3 @@
import string
from abc import abstractmethod
import numpy as np
@@ -162,221 +161,7 @@ class Downsample(nn.Module):
# RESNETS
# unet_glide.py
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param
use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing
on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for
downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
up=False,
down=False,
overwrite=False, # TODO(Patrick) - use for glide at later stage
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels, swish=1.0),
nn.Identity(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
# if self.updown:
# import ipdb; ipdb.set_trace()
if up:
self.h_upd = Upsample(channels, use_conv=False, dims=dims)
self.x_upd = Upsample(channels, use_conv=False, dims=dims)
elif down:
self.h_upd = Downsample(channels, use_conv=False, dims=dims, padding=1, name="op")
self.x_upd = Downsample(channels, use_conv=False, dims=dims, padding=1, name="op")
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
nn.SiLU() if use_scale_shift_norm else nn.Identity(),
nn.Dropout(p=dropout),
zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
self.overwrite = overwrite
self.is_overwritten = False
if self.overwrite:
in_channels = channels
out_channels = self.out_channels
conv_shortcut = False
dropout = 0.0
temb_channels = emb_channels
groups = 32
pre_norm = True
eps = 1e-5
non_linearity = "silu"
self.pre_norm = pre_norm
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
if self.pre_norm:
self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
else:
self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = nonlinearity
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
if self.in_channels != self.out_channels:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def set_weights(self):
# TODO(Patrick): use for glide at later stage
self.norm1.weight.data = self.in_layers[0].weight.data
self.norm1.bias.data = self.in_layers[0].bias.data
self.conv1.weight.data = self.in_layers[-1].weight.data
self.conv1.bias.data = self.in_layers[-1].bias.data
self.temb_proj.weight.data = self.emb_layers[-1].weight.data
self.temb_proj.bias.data = self.emb_layers[-1].bias.data
self.norm2.weight.data = self.out_layers[0].weight.data
self.norm2.bias.data = self.out_layers[0].bias.data
self.conv2.weight.data = self.out_layers[-1].weight.data
self.conv2.bias.data = self.out_layers[-1].bias.data
if self.in_channels != self.out_channels:
self.nin_shortcut.weight.data = self.skip_connection.weight.data
self.nin_shortcut.bias.data = self.skip_connection.bias.data
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
if self.overwrite:
# TODO(Patrick): use for glide at later stage
self.set_weights()
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
result = self.skip_connection(x) + h
# TODO(Patrick) Use for glide at later stage
# result = self.forward_2(x, emb)
return result
def forward_2(self, x, temb, mask=1.0):
if self.overwrite and not self.is_overwritten:
self.set_weights()
self.is_overwritten = True
h = x
if self.pre_norm:
h = self.norm1(h)
h = self.nonlinearity(h)
h = self.conv1(h)
if not self.pre_norm:
h = self.norm1(h)
h = self.nonlinearity(h)
h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
if self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if not self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(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
# unet.py and unet_grad_tts.py
# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
class ResnetBlock(nn.Module):
def __init__(
self,
@@ -390,8 +175,12 @@ class ResnetBlock(nn.Module):
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
up=False,
down=False,
overwrite_for_grad_tts=False,
overwrite_for_ldm=False,
overwrite_for_glide=False,
):
super().__init__()
self.pre_norm = pre_norm
@@ -399,6 +188,9 @@ class ResnetBlock(nn.Module):
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
if self.pre_norm:
self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
@@ -406,10 +198,16 @@ class ResnetBlock(nn.Module):
self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
if time_embedding_norm == "default":
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
elif time_embedding_norm == "scale_shift":
self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = nonlinearity
elif non_linearity == "mish":
@@ -417,16 +215,21 @@ class ResnetBlock(nn.Module):
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
# TODO(Patrick) - this branch is never used I think => can be deleted!
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)
if up:
self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
elif down:
self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
if self.in_channels != self.out_channels:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
# TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
self.is_overwritten = False
self.overwrite_for_glide = overwrite_for_glide
self.overwrite_for_grad_tts = overwrite_for_grad_tts
self.overwrite_for_ldm = overwrite_for_ldm
self.overwrite_for_ldm = overwrite_for_ldm or overwrite_for_glide
if self.overwrite_for_grad_tts:
dim = in_channels
dim_out = out_channels
@@ -458,7 +261,7 @@ class ResnetBlock(nn.Module):
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
2 * self.out_channels if self.time_embedding_norm == "scale_shift" else self.out_channels,
),
)
self.out_layers = nn.Sequential(
@@ -469,8 +272,6 @@ class ResnetBlock(nn.Module):
)
if self.out_channels == in_channels:
self.skip_connection = nn.Identity()
# elif use_conv:
# self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
@@ -513,6 +314,8 @@ class ResnetBlock(nn.Module):
self.nin_shortcut.bias.data = self.skip_connection.bias.data
def forward(self, x, temb, mask=1.0):
# TODO(Patrick) eventually this class should be split into multiple classes
# too many if else statements
if self.overwrite_for_grad_tts and not self.is_overwritten:
self.set_weights_grad_tts()
self.is_overwritten = True
@@ -526,6 +329,10 @@ class ResnetBlock(nn.Module):
h = self.norm1(h)
h = self.nonlinearity(h)
if self.up or self.down:
x = self.x_upd(x)
h = self.h_upd(h)
h = self.conv1(h)
if not self.pre_norm:
@@ -533,12 +340,20 @@ class ResnetBlock(nn.Module):
h = self.nonlinearity(h)
h = h * mask
h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
if self.time_embedding_norm == "scale_shift":
scale, shift = torch.chunk(temb, 2, dim=1)
h = h * mask
if self.pre_norm:
h = self.norm2(h)
h = h + h * scale + shift
h = self.nonlinearity(h)
elif self.time_embedding_norm == "default":
h = h + temb
h = h * mask
if self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
@@ -550,10 +365,7 @@ class ResnetBlock(nn.Module):
x = x * mask
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
x = self.nin_shortcut(x)
return x + h
@@ -566,10 +378,6 @@ class Block(torch.nn.Module):
torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
)
def forward(self, x, mask):
output = self.block(x * mask)
return output * mask
# unet_score_estimation.py
class ResnetBlockBigGANpp(nn.Module):

100
src/diffusers/models/unet_glide.py
View File

@@ -6,7 +6,7 @@ from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample, ResBlock, TimestepBlock, Upsample
from .resnet import Downsample, ResnetBlock, TimestepBlock, Upsample
def convert_module_to_f16(l):
@@ -101,7 +101,7 @@ class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
def forward(self, x, emb, encoder_out=None):
for layer in self:
if isinstance(layer, TimestepBlock):
if isinstance(layer, TimestepBlock) or isinstance(layer, ResnetBlock):
x = layer(x, emb)
elif isinstance(layer, AttentionBlock):
x = layer(x, encoder_out)
@@ -190,14 +190,15 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
ResnetBlock(
in_channels=ch,
out_channels=mult * model_channels,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
time_embedding_norm="scale_shift" if use_scale_shift_norm else "default",
overwrite_for_glide=True,
)
]
ch = int(mult * model_channels)
@@ -218,14 +219,15 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
ResnetBlock(
in_channels=ch,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
time_embedding_norm="scale_shift" if use_scale_shift_norm else "default",
overwrite_for_glide=True,
down=True,
)
if resblock_updown
@@ -240,13 +242,14 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
ResnetBlock(
in_channels=ch,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
time_embedding_norm="scale_shift" if use_scale_shift_norm else "default",
overwrite_for_glide=True,
),
AttentionBlock(
ch,
@@ -255,13 +258,14 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
num_head_channels=num_head_channels,
encoder_channels=transformer_dim,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
ResnetBlock(
in_channels=ch,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
time_embedding_norm="scale_shift" if use_scale_shift_norm else "default",
overwrite_for_glide=True,
),
)
self._feature_size += ch
@@ -271,15 +275,16 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
ResnetBlock(
in_channels=ch + ich,
out_channels=model_channels * mult,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
time_embedding_norm="scale_shift" if use_scale_shift_norm else "default",
overwrite_for_glide=True,
),
]
ch = int(model_channels * mult)
if ds in attention_resolutions:
@@ -295,14 +300,15 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
if level and i == num_res_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
ResnetBlock(
in_channels=ch,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
time_embedding_norm="scale_shift" if use_scale_shift_norm else "default",
overwrite_for_glide=True,
up=True,
)
if resblock_updown

4
tests/test_modeling_utils.py
View File

@@ -259,7 +259,7 @@ class UnetModelTests(ModelTesterMixin, unittest.TestCase):
# fmt: off
expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
@@ -795,7 +795,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
sizes = (32, 32)
noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [9.]).to(torch_device)
time_step = torch.tensor(batch_size * [9.0]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)