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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from functools import partial
from ...functional import avg_pool2d, interpolate, silu, view
from ...layers import (AvgPool2d, Conv2d, ConvTranspose2d, GroupNorm, Linear,
Mish)
from ...module import Module
class Upsample2D(Module):
def __init__(self,
channels: int,
use_conv=False,
use_conv_transpose=False,
out_channels=None) -> None:
super().__init__()
self.channels = channels
self.out_channels = out_channels
self.use_conv_transpose = use_conv_transpose
self.use_conv = use_conv
if self.use_conv_transpose:
self.conv = ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
elif use_conv:
self.conv = Conv2d(self.channels,
self.out_channels, (3, 3),
padding=(1, 1))
else:
self.conv = None
def forward(self, hidden_states, output_size=None):
assert not hidden_states.is_dynamic()
batch, channels, _, _ = hidden_states.size()
assert channels == self.channels
if self.use_conv_transpose:
return self.conv(hidden_states)
if output_size is None:
hidden_states = interpolate(hidden_states,
scale_factor=2.0,
mode="nearest")
else:
hidden_states = interpolate(hidden_states,
size=output_size,
mode="nearest")
if self.use_conv:
hidden_states = self.conv(hidden_states)
return hidden_states
class Downsample2D(Module):
def __init__(self,
channels,
use_conv=False,
out_channels=None,
padding=1) -> None:
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = (2, 2)
if use_conv:
self.conv = Conv2d(self.channels,
self.out_channels, (3, 3),
stride=stride,
padding=(padding, padding))
else:
assert self.channels == self.out_channels
self.conv = AvgPool2d(kernel_size=stride, stride=stride)
def forward(self, hidden_states):
assert not hidden_states.is_dynamic()
batch, channels, _, _ = hidden_states.size()
assert channels == self.channels
#TODO add the missing pad function
hidden_states = self.conv(hidden_states)
return hidden_states
class ResnetBlock2D(Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
kernel=None,
output_scale_factor=1.0,
use_in_shortcut=None,
up=False,
down=False,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
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.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
self.norm1 = GroupNorm(num_groups=groups,
num_channels=in_channels,
eps=eps,
affine=True)
self.conv1 = Conv2d(in_channels,
out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1))
if temb_channels is not None:
self.time_emb_proj = Linear(temb_channels, out_channels)
else:
self.time_emb_proj = None
self.norm2 = GroupNorm(num_groups=groups_out,
num_channels=out_channels,
eps=eps,
affine=True)
self.conv2 = Conv2d(out_channels,
out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1))
if non_linearity == "swish":
self.nonlinearity = lambda x: silu(x)
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = silu
self.upsample = self.downsample = None
#TODO (guomingz) add the fir kernel supporting.
if self.up:
if kernel == "sde_vp":
self.upsample = partial(interpolate,
scale_factor=2.0,
mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "sde_vp":
self.downsample = partial(avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels,
use_conv=False,
padding=1,
name="op")
self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
if self.use_in_shortcut:
self.conv_shortcut = Conv2d(in_channels,
out_channels,
kernel_size=(1, 1),
stride=(1, 1),
padding=(0, 0))
else:
self.conv_shortcut = None
def forward(self, input_tensor, temb):
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
input_tensor = self.upsample(input_tensor)
hidden_states = self.upsample(hidden_states)
elif self.downsample is not None:
input_tensor = self.downsample(input_tensor)
hidden_states = self.downsample(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
temb = self.time_emb_proj(self.nonlinearity(temb))
new_shape = list(temb.size())
new_shape.extend([1, 1]) #[:,:,None,None] ->view
hidden_states = hidden_states + view(temb, new_shape)
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = (input_tensor +
hidden_states) / self.output_scale_factor
return output_tensor
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