modules.py 7.63 KB
Newer Older
1
import open_clip
2
3
import torch
import torch.nn as nn
4
from ldm.util import count_params
Fazzie's avatar
Fazzie committed
5
from torch.utils.checkpoint import checkpoint
6
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
7
8
9
10
11
12
13
14
15
16


class AbstractEncoder(nn.Module):
    def __init__(self):
        super().__init__()

    def encode(self, *args, **kwargs):
        raise NotImplementedError


Fazzie's avatar
Fazzie committed
17
18
19
20
class IdentityEncoder(AbstractEncoder):
    def encode(self, x):
        return x

21
22

class ClassEmbedder(nn.Module):
23
    def __init__(self, embed_dim, n_classes=1000, key="class", ucg_rate=0.1):
24
25
26
        super().__init__()
        self.key = key
        self.embedding = nn.Embedding(n_classes, embed_dim)
Fazzie's avatar
Fazzie committed
27
28
        self.n_classes = n_classes
        self.ucg_rate = ucg_rate
29

Fazzie's avatar
Fazzie committed
30
    def forward(self, batch, key=None, disable_dropout=False):
31
32
33
34
        if key is None:
            key = self.key
        # this is for use in crossattn
        c = batch[key][:, None]
35
36
37
        if self.ucg_rate > 0.0 and not disable_dropout:
            mask = 1.0 - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
            c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
Fazzie's avatar
Fazzie committed
38
            c = c.long()
39
40
41
        c = self.embedding(c)
        return c

Fazzie's avatar
Fazzie committed
42
43
44
45
46
    def get_unconditional_conditioning(self, bs, device="cuda"):
        uc_class = self.n_classes - 1  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
        uc = torch.ones((bs,), device=device) * uc_class
        uc = {self.key: uc}
        return uc
47
48


Fazzie's avatar
Fazzie committed
49
50
51
52
def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self
53
54


Fazzie's avatar
Fazzie committed
55
56
class FrozenT5Embedder(AbstractEncoder):
    """Uses the T5 transformer encoder for text"""
57
58
59
60

    def __init__(
        self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True
    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
61
        super().__init__()
Fazzie's avatar
Fazzie committed
62
63
        self.tokenizer = T5Tokenizer.from_pretrained(version)
        self.transformer = T5EncoderModel.from_pretrained(version)
64
        self.device = device
65
        self.max_length = max_length  # TODO: typical value?
Fazzie's avatar
Fazzie committed
66
67
68
69
70
        if freeze:
            self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()
71
        # self.train = disabled_train
Fazzie's avatar
Fazzie committed
72
73
        for param in self.parameters():
            param.requires_grad = False
74
75

    def forward(self, text):
76
77
78
79
80
81
82
83
84
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
85
        tokens = batch_encoding["input_ids"].to(self.device)
Fazzie's avatar
Fazzie committed
86
        outputs = self.transformer(input_ids=tokens)
87

Fazzie's avatar
Fazzie committed
88
        z = outputs.last_hidden_state
89
90
91
92
93
94
95
        return z

    def encode(self, text):
        return self(text)


class FrozenCLIPEmbedder(AbstractEncoder):
Fazzie's avatar
Fazzie committed
96
    """Uses the CLIP transformer encoder for text (from huggingface)"""
97
98
99
100
101
102
103
104
105
106
107
108

    LAYERS = ["last", "pooled", "hidden"]

    def __init__(
        self,
        version="openai/clip-vit-large-patch14",
        device="cuda",
        max_length=77,
        freeze=True,
        layer="last",
        layer_idx=None,
    ):  # clip-vit-base-patch32
109
        super().__init__()
Fazzie's avatar
Fazzie committed
110
        assert layer in self.LAYERS
111
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
Fazzie's avatar
Fazzie committed
112
        self.transformer = CLIPTextModel.from_pretrained(version)
113
114
        self.device = device
        self.max_length = max_length
Fazzie's avatar
Fazzie committed
115
116
117
118
119
120
121
        if freeze:
            self.freeze()
        self.layer = layer
        self.layer_idx = layer_idx
        if layer == "hidden":
            assert layer_idx is not None
            assert 0 <= abs(layer_idx) <= 12
122
123
124

    def freeze(self):
        self.transformer = self.transformer.eval()
125
        # self.train = disabled_train
126
127
128
129
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
130
131
132
133
134
135
136
137
138
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
139
        tokens = batch_encoding["input_ids"].to(self.device)
140
        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
Fazzie's avatar
Fazzie committed
141
142
143
144
145
146
        if self.layer == "last":
            z = outputs.last_hidden_state
        elif self.layer == "pooled":
            z = outputs.pooler_output[:, None, :]
        else:
            z = outputs.hidden_states[self.layer_idx]
147
148
149
150
151
152
        return z

    def encode(self, text):
        return self(text)


Fazzie's avatar
Fazzie committed
153
class FrozenOpenCLIPEmbedder(AbstractEncoder):
154
    """
Fazzie's avatar
Fazzie committed
155
    Uses the OpenCLIP transformer encoder for text
156
    """
157

Fazzie's avatar
Fazzie committed
158
    LAYERS = [
159
        # "pooled",
Fazzie's avatar
Fazzie committed
160
        "last",
161
        "penultimate",
Fazzie's avatar
Fazzie committed
162
    ]
163
164
165
166

    def __init__(
        self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, freeze=True, layer="last"
    ):
167
        super().__init__()
Fazzie's avatar
Fazzie committed
168
        assert layer in self.LAYERS
169
        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device("cpu"), pretrained=version)
Fazzie's avatar
Fazzie committed
170
171
172
        del model.visual
        self.model = model

173
174
        self.device = device
        self.max_length = max_length
Fazzie's avatar
Fazzie committed
175
176
177
178
179
180
181
182
183
        if freeze:
            self.freeze()
        self.layer = layer
        if self.layer == "last":
            self.layer_idx = 0
        elif self.layer == "penultimate":
            self.layer_idx = 1
        else:
            raise NotImplementedError()
184
185
186
187
188
189
190

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
Fazzie's avatar
Fazzie committed
191
192
        tokens = open_clip.tokenize(text)
        z = self.encode_with_transformer(tokens.to(self.device))
193
194
        return z

Fazzie's avatar
Fazzie committed
195
196
197
198
199
200
201
202
    def encode_with_transformer(self, text):
        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
        x = x + self.model.positional_embedding
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.model.ln_final(x)
        return x
203

204
    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
Fazzie's avatar
Fazzie committed
205
206
207
208
209
210
211
212
        for i, r in enumerate(self.model.transformer.resblocks):
            if i == len(self.model.transformer.resblocks) - self.layer_idx:
                break
            if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint(r, x, attn_mask)
            else:
                x = r(x, attn_mask=attn_mask)
        return x
213

Fazzie's avatar
Fazzie committed
214
215
    def encode(self, text):
        return self(text)
216
217


Fazzie's avatar
Fazzie committed
218
class FrozenCLIPT5Encoder(AbstractEncoder):
219
220
221
222
223
224
225
226
    def __init__(
        self,
        clip_version="openai/clip-vit-large-patch14",
        t5_version="google/t5-v1_1-xl",
        device="cuda",
        clip_max_length=77,
        t5_max_length=77,
    ):
Fazzie's avatar
Fazzie committed
227
228
229
        super().__init__()
        self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
        self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
230
231
232
233
        print(
            f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
            f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params."
        )
234

Fazzie's avatar
Fazzie committed
235
236
    def encode(self, text):
        return self(text)
237

Fazzie's avatar
Fazzie committed
238
239
240
241
    def forward(self, text):
        clip_z = self.clip_encoder.encode(text)
        t5_z = self.t5_encoder.encode(text)
        return [clip_z, t5_z]