366 lines
13 KiB
Python
366 lines
13 KiB
Python
# Copyright 2022 The HuggingFace Team. 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.
|
|
|
|
import flax.linen as nn
|
|
import jax.numpy as jnp
|
|
|
|
from .attention_flax import FlaxTransformer2DModel
|
|
from .resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D
|
|
|
|
|
|
class FlaxCrossAttnDownBlock2D(nn.Module):
|
|
r"""
|
|
Cross Attention 2D Downsizing block - original architecture from Unet transformers:
|
|
https://arxiv.org/abs/2103.06104
|
|
|
|
Parameters:
|
|
in_channels (:obj:`int`):
|
|
Input channels
|
|
out_channels (:obj:`int`):
|
|
Output channels
|
|
dropout (:obj:`float`, *optional*, defaults to 0.0):
|
|
Dropout rate
|
|
num_layers (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention blocks layers
|
|
attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention heads of each spatial transformer block
|
|
add_downsample (:obj:`bool`, *optional*, defaults to `True`):
|
|
Whether to add downsampling layer before each final output
|
|
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
|
|
Parameters `dtype`
|
|
"""
|
|
in_channels: int
|
|
out_channels: int
|
|
dropout: float = 0.0
|
|
num_layers: int = 1
|
|
attn_num_head_channels: int = 1
|
|
add_downsample: bool = True
|
|
use_linear_projection: bool = False
|
|
only_cross_attention: bool = False
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
resnets = []
|
|
attentions = []
|
|
|
|
for i in range(self.num_layers):
|
|
in_channels = self.in_channels if i == 0 else self.out_channels
|
|
|
|
res_block = FlaxResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=self.out_channels,
|
|
dropout_prob=self.dropout,
|
|
dtype=self.dtype,
|
|
)
|
|
resnets.append(res_block)
|
|
|
|
attn_block = FlaxTransformer2DModel(
|
|
in_channels=self.out_channels,
|
|
n_heads=self.attn_num_head_channels,
|
|
d_head=self.out_channels // self.attn_num_head_channels,
|
|
depth=1,
|
|
use_linear_projection=self.use_linear_projection,
|
|
only_cross_attention=self.only_cross_attention,
|
|
dtype=self.dtype,
|
|
)
|
|
attentions.append(attn_block)
|
|
|
|
self.resnets = resnets
|
|
self.attentions = attentions
|
|
|
|
if self.add_downsample:
|
|
self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
|
|
|
|
def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
|
|
output_states = ()
|
|
|
|
for resnet, attn in zip(self.resnets, self.attentions):
|
|
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
|
|
hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
|
|
output_states += (hidden_states,)
|
|
|
|
if self.add_downsample:
|
|
hidden_states = self.downsamplers_0(hidden_states)
|
|
output_states += (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class FlaxDownBlock2D(nn.Module):
|
|
r"""
|
|
Flax 2D downsizing block
|
|
|
|
Parameters:
|
|
in_channels (:obj:`int`):
|
|
Input channels
|
|
out_channels (:obj:`int`):
|
|
Output channels
|
|
dropout (:obj:`float`, *optional*, defaults to 0.0):
|
|
Dropout rate
|
|
num_layers (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention blocks layers
|
|
add_downsample (:obj:`bool`, *optional*, defaults to `True`):
|
|
Whether to add downsampling layer before each final output
|
|
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
|
|
Parameters `dtype`
|
|
"""
|
|
in_channels: int
|
|
out_channels: int
|
|
dropout: float = 0.0
|
|
num_layers: int = 1
|
|
add_downsample: bool = True
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
resnets = []
|
|
|
|
for i in range(self.num_layers):
|
|
in_channels = self.in_channels if i == 0 else self.out_channels
|
|
|
|
res_block = FlaxResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=self.out_channels,
|
|
dropout_prob=self.dropout,
|
|
dtype=self.dtype,
|
|
)
|
|
resnets.append(res_block)
|
|
self.resnets = resnets
|
|
|
|
if self.add_downsample:
|
|
self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
|
|
|
|
def __call__(self, hidden_states, temb, deterministic=True):
|
|
output_states = ()
|
|
|
|
for resnet in self.resnets:
|
|
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
|
|
output_states += (hidden_states,)
|
|
|
|
if self.add_downsample:
|
|
hidden_states = self.downsamplers_0(hidden_states)
|
|
output_states += (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class FlaxCrossAttnUpBlock2D(nn.Module):
|
|
r"""
|
|
Cross Attention 2D Upsampling block - original architecture from Unet transformers:
|
|
https://arxiv.org/abs/2103.06104
|
|
|
|
Parameters:
|
|
in_channels (:obj:`int`):
|
|
Input channels
|
|
out_channels (:obj:`int`):
|
|
Output channels
|
|
dropout (:obj:`float`, *optional*, defaults to 0.0):
|
|
Dropout rate
|
|
num_layers (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention blocks layers
|
|
attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention heads of each spatial transformer block
|
|
add_upsample (:obj:`bool`, *optional*, defaults to `True`):
|
|
Whether to add upsampling layer before each final output
|
|
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
|
|
Parameters `dtype`
|
|
"""
|
|
in_channels: int
|
|
out_channels: int
|
|
prev_output_channel: int
|
|
dropout: float = 0.0
|
|
num_layers: int = 1
|
|
attn_num_head_channels: int = 1
|
|
add_upsample: bool = True
|
|
use_linear_projection: bool = False
|
|
only_cross_attention: bool = False
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
resnets = []
|
|
attentions = []
|
|
|
|
for i in range(self.num_layers):
|
|
res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
|
|
resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
|
|
|
|
res_block = FlaxResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=self.out_channels,
|
|
dropout_prob=self.dropout,
|
|
dtype=self.dtype,
|
|
)
|
|
resnets.append(res_block)
|
|
|
|
attn_block = FlaxTransformer2DModel(
|
|
in_channels=self.out_channels,
|
|
n_heads=self.attn_num_head_channels,
|
|
d_head=self.out_channels // self.attn_num_head_channels,
|
|
depth=1,
|
|
use_linear_projection=self.use_linear_projection,
|
|
only_cross_attention=self.only_cross_attention,
|
|
dtype=self.dtype,
|
|
)
|
|
attentions.append(attn_block)
|
|
|
|
self.resnets = resnets
|
|
self.attentions = attentions
|
|
|
|
if self.add_upsample:
|
|
self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
|
|
|
|
def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):
|
|
for resnet, attn in zip(self.resnets, self.attentions):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
|
|
|
|
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
|
|
hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
|
|
|
|
if self.add_upsample:
|
|
hidden_states = self.upsamplers_0(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class FlaxUpBlock2D(nn.Module):
|
|
r"""
|
|
Flax 2D upsampling block
|
|
|
|
Parameters:
|
|
in_channels (:obj:`int`):
|
|
Input channels
|
|
out_channels (:obj:`int`):
|
|
Output channels
|
|
prev_output_channel (:obj:`int`):
|
|
Output channels from the previous block
|
|
dropout (:obj:`float`, *optional*, defaults to 0.0):
|
|
Dropout rate
|
|
num_layers (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention blocks layers
|
|
add_downsample (:obj:`bool`, *optional*, defaults to `True`):
|
|
Whether to add downsampling layer before each final output
|
|
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
|
|
Parameters `dtype`
|
|
"""
|
|
in_channels: int
|
|
out_channels: int
|
|
prev_output_channel: int
|
|
dropout: float = 0.0
|
|
num_layers: int = 1
|
|
add_upsample: bool = True
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
resnets = []
|
|
|
|
for i in range(self.num_layers):
|
|
res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
|
|
resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
|
|
|
|
res_block = FlaxResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=self.out_channels,
|
|
dropout_prob=self.dropout,
|
|
dtype=self.dtype,
|
|
)
|
|
resnets.append(res_block)
|
|
|
|
self.resnets = resnets
|
|
|
|
if self.add_upsample:
|
|
self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
|
|
|
|
def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):
|
|
for resnet in self.resnets:
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
|
|
|
|
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
|
|
|
|
if self.add_upsample:
|
|
hidden_states = self.upsamplers_0(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class FlaxUNetMidBlock2DCrossAttn(nn.Module):
|
|
r"""
|
|
Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104
|
|
|
|
Parameters:
|
|
in_channels (:obj:`int`):
|
|
Input channels
|
|
dropout (:obj:`float`, *optional*, defaults to 0.0):
|
|
Dropout rate
|
|
num_layers (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention blocks layers
|
|
attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
|
|
Number of attention heads of each spatial transformer block
|
|
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
|
|
Parameters `dtype`
|
|
"""
|
|
in_channels: int
|
|
dropout: float = 0.0
|
|
num_layers: int = 1
|
|
attn_num_head_channels: int = 1
|
|
use_linear_projection: bool = False
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
# there is always at least one resnet
|
|
resnets = [
|
|
FlaxResnetBlock2D(
|
|
in_channels=self.in_channels,
|
|
out_channels=self.in_channels,
|
|
dropout_prob=self.dropout,
|
|
dtype=self.dtype,
|
|
)
|
|
]
|
|
|
|
attentions = []
|
|
|
|
for _ in range(self.num_layers):
|
|
attn_block = FlaxTransformer2DModel(
|
|
in_channels=self.in_channels,
|
|
n_heads=self.attn_num_head_channels,
|
|
d_head=self.in_channels // self.attn_num_head_channels,
|
|
depth=1,
|
|
use_linear_projection=self.use_linear_projection,
|
|
dtype=self.dtype,
|
|
)
|
|
attentions.append(attn_block)
|
|
|
|
res_block = FlaxResnetBlock2D(
|
|
in_channels=self.in_channels,
|
|
out_channels=self.in_channels,
|
|
dropout_prob=self.dropout,
|
|
dtype=self.dtype,
|
|
)
|
|
resnets.append(res_block)
|
|
|
|
self.resnets = resnets
|
|
self.attentions = attentions
|
|
|
|
def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
|
|
hidden_states = self.resnets[0](hidden_states, temb)
|
|
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
|
hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
|
|
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
|
|
|
|
return hidden_states
|