385 lines
15 KiB
Python
385 lines
15 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
from torch.utils.checkpoint import checkpoint
|
||
|
|
|
||
|
|
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoProcessor, CLIPVisionModelWithProjection
|
||
|
|
|
||
|
|
from iopaint.model.anytext.ldm.util import count_params
|
||
|
|
|
||
|
|
|
||
|
|
def _expand_mask(mask, dtype, tgt_len=None):
|
||
|
|
"""
|
||
|
|
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
||
|
|
"""
|
||
|
|
bsz, src_len = mask.size()
|
||
|
|
tgt_len = tgt_len if tgt_len is not None else src_len
|
||
|
|
|
||
|
|
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
||
|
|
|
||
|
|
inverted_mask = 1.0 - expanded_mask
|
||
|
|
|
||
|
|
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
||
|
|
|
||
|
|
|
||
|
|
def _build_causal_attention_mask(bsz, seq_len, dtype):
|
||
|
|
# lazily create causal attention mask, with full attention between the vision tokens
|
||
|
|
# pytorch uses additive attention mask; fill with -inf
|
||
|
|
mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
|
||
|
|
mask.fill_(torch.tensor(torch.finfo(dtype).min))
|
||
|
|
mask.triu_(1) # zero out the lower diagonal
|
||
|
|
mask = mask.unsqueeze(1) # expand mask
|
||
|
|
return mask
|
||
|
|
|
||
|
|
class AbstractEncoder(nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
def encode(self, *args, **kwargs):
|
||
|
|
raise NotImplementedError
|
||
|
|
|
||
|
|
|
||
|
|
class IdentityEncoder(AbstractEncoder):
|
||
|
|
|
||
|
|
def encode(self, x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class ClassEmbedder(nn.Module):
|
||
|
|
def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
|
||
|
|
super().__init__()
|
||
|
|
self.key = key
|
||
|
|
self.embedding = nn.Embedding(n_classes, embed_dim)
|
||
|
|
self.n_classes = n_classes
|
||
|
|
self.ucg_rate = ucg_rate
|
||
|
|
|
||
|
|
def forward(self, batch, key=None, disable_dropout=False):
|
||
|
|
if key is None:
|
||
|
|
key = self.key
|
||
|
|
# this is for use in crossattn
|
||
|
|
c = batch[key][:, None]
|
||
|
|
if self.ucg_rate > 0. and not disable_dropout:
|
||
|
|
mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
|
||
|
|
c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
|
||
|
|
c = c.long()
|
||
|
|
c = self.embedding(c)
|
||
|
|
return c
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
def disabled_train(self, mode=True):
|
||
|
|
"""Overwrite model.train with this function to make sure train/eval mode
|
||
|
|
does not change anymore."""
|
||
|
|
return self
|
||
|
|
|
||
|
|
|
||
|
|
class FrozenT5Embedder(AbstractEncoder):
|
||
|
|
"""Uses the T5 transformer encoder for text"""
|
||
|
|
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
|
||
|
|
super().__init__()
|
||
|
|
self.tokenizer = T5Tokenizer.from_pretrained(version)
|
||
|
|
self.transformer = T5EncoderModel.from_pretrained(version)
|
||
|
|
self.device = device
|
||
|
|
self.max_length = max_length # TODO: typical value?
|
||
|
|
if freeze:
|
||
|
|
self.freeze()
|
||
|
|
|
||
|
|
def freeze(self):
|
||
|
|
self.transformer = self.transformer.eval()
|
||
|
|
#self.train = disabled_train
|
||
|
|
for param in self.parameters():
|
||
|
|
param.requires_grad = False
|
||
|
|
|
||
|
|
def forward(self, text):
|
||
|
|
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")
|
||
|
|
tokens = batch_encoding["input_ids"].to(self.device)
|
||
|
|
outputs = self.transformer(input_ids=tokens)
|
||
|
|
|
||
|
|
z = outputs.last_hidden_state
|
||
|
|
return z
|
||
|
|
|
||
|
|
def encode(self, text):
|
||
|
|
return self(text)
|
||
|
|
|
||
|
|
|
||
|
|
class FrozenCLIPEmbedder(AbstractEncoder):
|
||
|
|
"""Uses the CLIP transformer encoder for text (from huggingface)"""
|
||
|
|
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
|
||
|
|
super().__init__()
|
||
|
|
assert layer in self.LAYERS
|
||
|
|
self.tokenizer = CLIPTokenizer.from_pretrained(version)
|
||
|
|
self.transformer = CLIPTextModel.from_pretrained(version)
|
||
|
|
self.device = device
|
||
|
|
self.max_length = max_length
|
||
|
|
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
|
||
|
|
|
||
|
|
def freeze(self):
|
||
|
|
self.transformer = self.transformer.eval()
|
||
|
|
# self.train = disabled_train
|
||
|
|
for param in self.parameters():
|
||
|
|
param.requires_grad = False
|
||
|
|
|
||
|
|
def forward(self, text):
|
||
|
|
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")
|
||
|
|
tokens = batch_encoding["input_ids"].to(self.device)
|
||
|
|
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
|
||
|
|
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]
|
||
|
|
return z
|
||
|
|
|
||
|
|
def encode(self, text):
|
||
|
|
return self(text)
|
||
|
|
|
||
|
|
|
||
|
|
class FrozenOpenCLIPEmbedder(AbstractEncoder):
|
||
|
|
"""
|
||
|
|
Uses the OpenCLIP transformer encoder for text
|
||
|
|
"""
|
||
|
|
LAYERS = [
|
||
|
|
# "pooled",
|
||
|
|
"last",
|
||
|
|
"penultimate"
|
||
|
|
]
|
||
|
|
|
||
|
|
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
|
||
|
|
freeze=True, layer="last"):
|
||
|
|
super().__init__()
|
||
|
|
assert layer in self.LAYERS
|
||
|
|
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
|
||
|
|
del model.visual
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
self.device = device
|
||
|
|
self.max_length = max_length
|
||
|
|
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()
|
||
|
|
|
||
|
|
def freeze(self):
|
||
|
|
self.model = self.model.eval()
|
||
|
|
for param in self.parameters():
|
||
|
|
param.requires_grad = False
|
||
|
|
|
||
|
|
def forward(self, text):
|
||
|
|
tokens = open_clip.tokenize(text)
|
||
|
|
z = self.encode_with_transformer(tokens.to(self.device))
|
||
|
|
return z
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
|
||
|
|
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
|
||
|
|
|
||
|
|
def encode(self, text):
|
||
|
|
return self(text)
|
||
|
|
|
||
|
|
|
||
|
|
class FrozenCLIPT5Encoder(AbstractEncoder):
|
||
|
|
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):
|
||
|
|
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)
|
||
|
|
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.")
|
||
|
|
|
||
|
|
def encode(self, text):
|
||
|
|
return self(text)
|
||
|
|
|
||
|
|
def forward(self, text):
|
||
|
|
clip_z = self.clip_encoder.encode(text)
|
||
|
|
t5_z = self.t5_encoder.encode(text)
|
||
|
|
return [clip_z, t5_z]
|
||
|
|
|
||
|
|
|
||
|
|
class FrozenCLIPEmbedderT3(AbstractEncoder):
|
||
|
|
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
|
||
|
|
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, freeze=True, use_vision=False):
|
||
|
|
super().__init__()
|
||
|
|
self.tokenizer = CLIPTokenizer.from_pretrained(version)
|
||
|
|
self.transformer = CLIPTextModel.from_pretrained(version)
|
||
|
|
if use_vision:
|
||
|
|
self.vit = CLIPVisionModelWithProjection.from_pretrained(version)
|
||
|
|
self.processor = AutoProcessor.from_pretrained(version)
|
||
|
|
self.device = device
|
||
|
|
self.max_length = max_length
|
||
|
|
if freeze:
|
||
|
|
self.freeze()
|
||
|
|
|
||
|
|
def embedding_forward(
|
||
|
|
self,
|
||
|
|
input_ids=None,
|
||
|
|
position_ids=None,
|
||
|
|
inputs_embeds=None,
|
||
|
|
embedding_manager=None,
|
||
|
|
):
|
||
|
|
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
|
||
|
|
if position_ids is None:
|
||
|
|
position_ids = self.position_ids[:, :seq_length]
|
||
|
|
if inputs_embeds is None:
|
||
|
|
inputs_embeds = self.token_embedding(input_ids)
|
||
|
|
if embedding_manager is not None:
|
||
|
|
inputs_embeds = embedding_manager(input_ids, inputs_embeds)
|
||
|
|
position_embeddings = self.position_embedding(position_ids)
|
||
|
|
embeddings = inputs_embeds + position_embeddings
|
||
|
|
return embeddings
|
||
|
|
|
||
|
|
self.transformer.text_model.embeddings.forward = embedding_forward.__get__(self.transformer.text_model.embeddings)
|
||
|
|
|
||
|
|
def encoder_forward(
|
||
|
|
self,
|
||
|
|
inputs_embeds,
|
||
|
|
attention_mask=None,
|
||
|
|
causal_attention_mask=None,
|
||
|
|
output_attentions=None,
|
||
|
|
output_hidden_states=None,
|
||
|
|
return_dict=None,
|
||
|
|
):
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
output_hidden_states = (
|
||
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
|
)
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
encoder_states = () if output_hidden_states else None
|
||
|
|
all_attentions = () if output_attentions else None
|
||
|
|
hidden_states = inputs_embeds
|
||
|
|
for idx, encoder_layer in enumerate(self.layers):
|
||
|
|
if output_hidden_states:
|
||
|
|
encoder_states = encoder_states + (hidden_states,)
|
||
|
|
layer_outputs = encoder_layer(
|
||
|
|
hidden_states,
|
||
|
|
attention_mask,
|
||
|
|
causal_attention_mask,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
)
|
||
|
|
hidden_states = layer_outputs[0]
|
||
|
|
if output_attentions:
|
||
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
||
|
|
if output_hidden_states:
|
||
|
|
encoder_states = encoder_states + (hidden_states,)
|
||
|
|
return hidden_states
|
||
|
|
|
||
|
|
self.transformer.text_model.encoder.forward = encoder_forward.__get__(self.transformer.text_model.encoder)
|
||
|
|
|
||
|
|
def text_encoder_forward(
|
||
|
|
self,
|
||
|
|
input_ids=None,
|
||
|
|
attention_mask=None,
|
||
|
|
position_ids=None,
|
||
|
|
output_attentions=None,
|
||
|
|
output_hidden_states=None,
|
||
|
|
return_dict=None,
|
||
|
|
embedding_manager=None,
|
||
|
|
):
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
output_hidden_states = (
|
||
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
|
)
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
if input_ids is None:
|
||
|
|
raise ValueError("You have to specify either input_ids")
|
||
|
|
input_shape = input_ids.size()
|
||
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
||
|
|
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager)
|
||
|
|
bsz, seq_len = input_shape
|
||
|
|
# CLIP's text model uses causal mask, prepare it here.
|
||
|
|
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
|
||
|
|
causal_attention_mask = _build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
|
||
|
|
hidden_states.device
|
||
|
|
)
|
||
|
|
# expand attention_mask
|
||
|
|
if attention_mask is not None:
|
||
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||
|
|
attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
|
||
|
|
last_hidden_state = self.encoder(
|
||
|
|
inputs_embeds=hidden_states,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
causal_attention_mask=causal_attention_mask,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
last_hidden_state = self.final_layer_norm(last_hidden_state)
|
||
|
|
return last_hidden_state
|
||
|
|
|
||
|
|
self.transformer.text_model.forward = text_encoder_forward.__get__(self.transformer.text_model)
|
||
|
|
|
||
|
|
def transformer_forward(
|
||
|
|
self,
|
||
|
|
input_ids=None,
|
||
|
|
attention_mask=None,
|
||
|
|
position_ids=None,
|
||
|
|
output_attentions=None,
|
||
|
|
output_hidden_states=None,
|
||
|
|
return_dict=None,
|
||
|
|
embedding_manager=None,
|
||
|
|
):
|
||
|
|
return self.text_model(
|
||
|
|
input_ids=input_ids,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
embedding_manager=embedding_manager
|
||
|
|
)
|
||
|
|
|
||
|
|
self.transformer.forward = transformer_forward.__get__(self.transformer)
|
||
|
|
|
||
|
|
def freeze(self):
|
||
|
|
self.transformer = self.transformer.eval()
|
||
|
|
for param in self.parameters():
|
||
|
|
param.requires_grad = False
|
||
|
|
|
||
|
|
def forward(self, text, **kwargs):
|
||
|
|
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")
|
||
|
|
tokens = batch_encoding["input_ids"].to(self.device)
|
||
|
|
z = self.transformer(input_ids=tokens, **kwargs)
|
||
|
|
return z
|
||
|
|
|
||
|
|
def encode(self, text, **kwargs):
|
||
|
|
return self(text, **kwargs)
|