File size: 10,395 Bytes
21231ee
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# 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.

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import paddle
import paddle.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..modeling_utils import ModelMixin
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .unet_1d_blocks import get_down_block, get_mid_block, get_out_block, get_up_block


@dataclass
class UNet1DOutput(BaseOutput):
    """
    Args:
        sample (`paddle.Tensor` of shape `(batch_size, num_channels, sample_size)`):
            Hidden states output. Output of last layer of model.
    """

    sample: paddle.Tensor


class UNet1DModel(ModelMixin, ConfigMixin):
    r"""
    UNet1DModel is a 1D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
    implements for all the model (such as downloading or saving, etc.)

    Parameters:
        sample_size (`int`, *optional*): Default length of sample. Should be adaptable at runtime.
        in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 2): Number of channels in the output.
        time_embedding_type (`str`, *optional*, defaults to `"fourier"`): Type of time embedding to use.
        freq_shift (`float`, *optional*, defaults to 0.0): Frequency shift for fourier time embedding.
        flip_sin_to_cos (`bool`, *optional*, defaults to :
            obj:`False`): Whether to flip sin to cos for fourier time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to :
            obj:`("DownBlock1D", "DownBlock1DNoSkip", "AttnDownBlock1D")`): Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to :
            obj:`("UpBlock1D", "UpBlock1DNoSkip", "AttnUpBlock1D")`): Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to :
            obj:`(32, 32, 64)`): Tuple of block output channels.
        mid_block_type (`str`, *optional*, defaults to "UNetMidBlock1D"): block type for middle of UNet.
        out_block_type (`str`, *optional*, defaults to `None`): optional output processing of UNet.
        act_fn (`str`, *optional*, defaults to None): optional activitation function in UNet blocks.
        norm_num_groups (`int`, *optional*, defaults to 8): group norm member count in UNet blocks.
        layers_per_block (`int`, *optional*, defaults to 1): added number of layers in a UNet block.
        downsample_each_block (`int`, *optional*, defaults to False:
            experimental feature for using a UNet without upsampling.
    """

    @register_to_config
    def __init__(
        self,
        sample_size: int = 65536,
        sample_rate: Optional[int] = None,
        in_channels: int = 2,
        out_channels: int = 2,
        extra_in_channels: int = 0,
        time_embedding_type: str = "fourier",
        flip_sin_to_cos: bool = True,
        use_timestep_embedding: bool = False,
        freq_shift: float = 0.0,
        down_block_types: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),
        up_block_types: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),
        mid_block_type: Tuple[str] = "UNetMidBlock1D",
        out_block_type: str = None,
        block_out_channels: Tuple[int] = (32, 32, 64),
        act_fn: str = None,
        norm_num_groups: int = 8,
        layers_per_block: int = 1,
        downsample_each_block: bool = False,
    ):
        super().__init__()
        self.sample_size = sample_size

        # time
        if time_embedding_type == "fourier":
            self.time_proj = GaussianFourierProjection(
                embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
            )
            timestep_input_dim = 2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            self.time_proj = Timesteps(
                block_out_channels[0], flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=freq_shift
            )
            timestep_input_dim = block_out_channels[0]

        if use_timestep_embedding:
            time_embed_dim = block_out_channels[0] * 4
            self.time_mlp = TimestepEmbedding(
                in_channels=timestep_input_dim,
                time_embed_dim=time_embed_dim,
                act_fn=act_fn,
                out_dim=block_out_channels[0],
            )

        self.down_blocks = nn.LayerList([])
        self.mid_block = None
        self.up_blocks = nn.LayerList([])
        self.out_block = None

        # down
        output_channel = in_channels
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]

            if i == 0:
                input_channel += extra_in_channels

            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=block_out_channels[0],
                add_downsample=not is_final_block or downsample_each_block,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = get_mid_block(
            mid_block_type,
            in_channels=block_out_channels[-1],
            mid_channels=block_out_channels[-1],
            out_channels=block_out_channels[-1],
            embed_dim=block_out_channels[0],
            num_layers=layers_per_block,
            add_downsample=downsample_each_block,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        if out_block_type is None:
            final_upsample_channels = out_channels
        else:
            final_upsample_channels = block_out_channels[0]

        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = (
                reversed_block_out_channels[i + 1] if i < len(up_block_types) - 1 else final_upsample_channels
            )

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block,
                in_channels=prev_output_channel,
                out_channels=output_channel,
                temb_channels=block_out_channels[0],
                add_upsample=not is_final_block,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
        self.out_block = get_out_block(
            out_block_type=out_block_type,
            num_groups_out=num_groups_out,
            embed_dim=block_out_channels[0],
            out_channels=out_channels,
            act_fn=act_fn,
            fc_dim=block_out_channels[-1] // 4,
        )

    def forward(
        self,
        sample: paddle.Tensor,
        timestep: Union[paddle.Tensor, float, int],
        return_dict: bool = True,
    ) -> Union[UNet1DOutput, Tuple]:
        r"""
        Args:
            sample (`paddle.Tensor`): `(batch_size, sample_size, num_channels)` noisy inputs tensor
            timestep (`paddle.Tensor` or `float` or `int): (batch) timesteps
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_1d.UNet1DOutput`] instead of a plain tuple.

        Returns:
            [`~models.unet_1d.UNet1DOutput`] or `tuple`: [`~models.unet_1d.UNet1DOutput`] if `return_dict` is True,
            otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
        """

        # 1. time
        timesteps = timestep
        if not paddle.is_tensor(timesteps):
            timesteps = paddle.to_tensor([timesteps], dtype="int64")
        elif paddle.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None]

        timestep_embed = self.time_proj(timesteps)
        if self.config.use_timestep_embedding:
            timestep_embed = self.time_mlp(timestep_embed)
        else:
            timestep_embed = timestep_embed[..., None]
            timestep_embed = timestep_embed.tile([1, 1, sample.shape[2]]).cast(sample.dtype)
            timestep_embed = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

        # 2. down
        down_block_res_samples = ()
        for downsample_block in self.down_blocks:
            sample, res_samples = downsample_block(hidden_states=sample, temb=timestep_embed)
            down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
            sample = self.mid_block(sample, timestep_embed)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks):
            res_samples = down_block_res_samples[-1:]
            down_block_res_samples = down_block_res_samples[:-1]
            sample = upsample_block(sample, res_hidden_states_tuple=res_samples, temb=timestep_embed)

        # 5. post-process
        if self.out_block:
            sample = self.out_block(sample, timestep_embed)

        if not return_dict:
            return (sample,)

        return UNet1DOutput(sample=sample)