214 lines
7.4 KiB
Python
214 lines
7.4 KiB
Python
import torch
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import torch.nn as nn
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from torch.utils.checkpoint import checkpoint
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from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
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import open_clip
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from ldm.util import default, count_params
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class AbstractEncoder(nn.Module):
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def __init__(self):
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super().__init__()
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def encode(self, *args, **kwargs):
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raise NotImplementedError
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class IdentityEncoder(AbstractEncoder):
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def encode(self, x):
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return x
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class ClassEmbedder(nn.Module):
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def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
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super().__init__()
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self.key = key
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self.embedding = nn.Embedding(n_classes, embed_dim)
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self.n_classes = n_classes
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self.ucg_rate = ucg_rate
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def forward(self, batch, key=None, disable_dropout=False):
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if key is None:
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key = self.key
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# this is for use in crossattn
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c = batch[key][:, None]
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if self.ucg_rate > 0. and not disable_dropout:
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mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
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c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
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c = c.long()
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c = self.embedding(c)
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return c
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def get_unconditional_conditioning(self, bs, device="cuda"):
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uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
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uc = torch.ones((bs,), device=device) * uc_class
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uc = {self.key: uc}
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return uc
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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class FrozenT5Embedder(AbstractEncoder):
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"""Uses the T5 transformer encoder for text"""
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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
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super().__init__()
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self.tokenizer = T5Tokenizer.from_pretrained(version)
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self.transformer = T5EncoderModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length # TODO: typical value?
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if freeze:
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self.freeze()
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def freeze(self):
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self.transformer = self.transformer.eval()
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#self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
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return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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return z
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def encode(self, text):
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return self(text)
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class FrozenCLIPEmbedder(AbstractEncoder):
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"""Uses the CLIP transformer encoder for text (from huggingface)"""
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LAYERS = [
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"last",
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"pooled",
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"hidden"
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]
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def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
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freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
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super().__init__()
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assert layer in self.LAYERS
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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self.layer_idx = layer_idx
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if layer == "hidden":
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assert layer_idx is not None
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assert 0 <= abs(layer_idx) <= 12
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def freeze(self):
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self.transformer = self.transformer.eval()
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#self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
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return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
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if self.layer == "last":
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z = outputs.last_hidden_state
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elif self.layer == "pooled":
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z = outputs.pooler_output[:, None, :]
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else:
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z = outputs.hidden_states[self.layer_idx]
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return z
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def encode(self, text):
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return self(text)
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class FrozenOpenCLIPEmbedder(AbstractEncoder):
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"""
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Uses the OpenCLIP transformer encoder for text
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"""
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LAYERS = [
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#"pooled",
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"last",
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"penultimate"
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]
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def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
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freeze=True, layer="last"):
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super().__init__()
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assert layer in self.LAYERS
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model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
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del model.visual
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self.model = model
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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if self.layer == "last":
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self.layer_idx = 0
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elif self.layer == "penultimate":
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self.layer_idx = 1
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else:
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raise NotImplementedError()
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def freeze(self):
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self.model = self.model.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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tokens = open_clip.tokenize(text)
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z = self.encode_with_transformer(tokens.to(self.device))
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return z
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def encode_with_transformer(self, text):
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x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
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x = x + self.model.positional_embedding
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.model.ln_final(x)
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return x
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def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
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for i, r in enumerate(self.model.transformer.resblocks):
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if i == len(self.model.transformer.resblocks) - self.layer_idx:
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break
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if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint(r, x, attn_mask)
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else:
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x = r(x, attn_mask=attn_mask)
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return x
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def encode(self, text):
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return self(text)
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class FrozenCLIPT5Encoder(AbstractEncoder):
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def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
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clip_max_length=77, t5_max_length=77):
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super().__init__()
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self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
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self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
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print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
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f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
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def encode(self, text):
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return self(text)
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def forward(self, text):
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clip_z = self.clip_encoder.encode(text)
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t5_z = self.t5_encoder.encode(text)
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return [clip_z, t5_z]
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