imaginAIry/imaginairy/vendored/refiners/foundationals/clip/concepts.py
Bryce 55e27160f5 build: vendorize refiners
so we can still work in conda envs
2024-01-02 22:02:31 -08:00

149 lines
6.0 KiB
Python

import re
from typing import cast
import torch.nn.functional as F
from torch import Tensor, cat, zeros
from torch.nn import Parameter
import imaginairy.vendored.refiners.fluxion.layers as fl
from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter
from imaginairy.vendored.refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder
from imaginairy.vendored.refiners.foundationals.clip.tokenizer import CLIPTokenizer
class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
old_weight: Parameter
new_weight: Parameter
def __init__(
self,
target: TokenEncoder,
) -> None:
with self.setup_adapter(target):
super().__init__(fl.Lambda(func=self.lookup))
self.old_weight = cast(Parameter, target.weight)
self.new_weight = Parameter(
zeros([0, target.embedding_dim], device=target.device, dtype=target.dtype)
) # requires_grad=True by default
# Use F.embedding instead of nn.Embedding to make sure that gradients can only be computed for the new embeddings
def lookup(self, x: Tensor) -> Tensor:
# Concatenate old and new weights for dynamic embedding updates during training
return F.embedding(x, cat([self.old_weight, self.new_weight]))
def add_embedding(self, embedding: Tensor) -> None:
assert embedding.shape == (self.old_weight.shape[1],)
self.new_weight = Parameter(
cat([self.new_weight, embedding.unsqueeze(0).to(self.new_weight.device, self.new_weight.dtype)])
)
@property
def num_embeddings(self) -> int:
return self.old_weight.shape[0] + self.new_weight.shape[0]
class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]):
def __init__(self, target: CLIPTokenizer) -> None:
with self.setup_adapter(target):
super().__init__(
CLIPTokenizer(
vocabulary_path=target.vocabulary_path,
sequence_length=target.sequence_length,
start_of_text_token_id=target.start_of_text_token_id,
end_of_text_token_id=target.end_of_text_token_id,
pad_token_id=target.pad_token_id,
)
)
def add_token(self, token: str, token_id: int) -> None:
token = token.lower()
tokenizer = self.ensure_find(CLIPTokenizer)
assert token_id not in tokenizer.token_to_id_mapping.values()
tokenizer.token_to_id_mapping[token] = token_id
current_pattern = tokenizer.token_pattern.pattern
new_pattern = re.escape(token) + "|" + current_pattern
tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE)
# Define the keyword as its own smallest subtoken
tokenizer.byte_pair_encoding_cache[token] = token
class ConceptExtender(fl.Chain, Adapter[CLIPTextEncoder]):
"""
Extends the vocabulary of a CLIPTextEncoder with one or multiple new concepts, e.g. obtained via the Textual Inversion technique.
Example:
import torch
from imaginairy.vendored.refiners.foundationals.clip.concepts import ConceptExtender
from imaginairy.vendored.refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
from imaginairy.vendored.refiners.fluxion.utils import load_from_safetensors
encoder = CLIPTextEncoderL(device="cuda")
tensors = load_from_safetensors("CLIPTextEncoderL.safetensors")
encoder.load_state_dict(tensors)
cat_embedding = torch.load("cat_embedding.bin")["<this-cat>"]
dog_embedding = torch.load("dog_embedding.bin")["<that-dog>"]
extender = ConceptExtender(encoder)
extender.add_concept(token="<this-cat>", embedding=cat_embedding)
extender.inject()
# New concepts can be added at any time
extender.add_concept(token="<that-dog>", embedding=dog_embedding)
# Now the encoder can be used with the new concepts
"""
def __init__(self, target: CLIPTextEncoder) -> None:
with self.setup_adapter(target):
super().__init__(target)
try:
token_encoder, token_encoder_parent = next(target.walk(TokenEncoder))
self._token_encoder_parent = [token_encoder_parent]
except StopIteration:
raise RuntimeError("TokenEncoder not found.")
try:
clip_tokenizer, clip_tokenizer_parent = next(target.walk(CLIPTokenizer))
self._clip_tokenizer_parent = [clip_tokenizer_parent]
except StopIteration:
raise RuntimeError("Tokenizer not found.")
self._embedding_extender = [EmbeddingExtender(token_encoder)]
self._token_extender = [TokenExtender(clip_tokenizer)]
@property
def embedding_extender(self) -> EmbeddingExtender:
assert len(self._embedding_extender) == 1, "EmbeddingExtender not found."
return self._embedding_extender[0]
@property
def token_extender(self) -> TokenExtender:
assert len(self._token_extender) == 1, "TokenExtender not found."
return self._token_extender[0]
@property
def token_encoder_parent(self) -> fl.Chain:
assert len(self._token_encoder_parent) == 1, "TokenEncoder parent not found."
return self._token_encoder_parent[0]
@property
def clip_tokenizer_parent(self) -> fl.Chain:
assert len(self._clip_tokenizer_parent) == 1, "Tokenizer parent not found."
return self._clip_tokenizer_parent[0]
def add_concept(self, token: str, embedding: Tensor) -> None:
self.embedding_extender.add_embedding(embedding)
self.token_extender.add_token(token, self.embedding_extender.num_embeddings - 1)
def inject(self: "ConceptExtender", parent: fl.Chain | None = None) -> "ConceptExtender":
self.embedding_extender.inject(self.token_encoder_parent)
self.token_extender.inject(self.clip_tokenizer_parent)
return super().inject(parent)
def eject(self) -> None:
self.embedding_extender.eject()
self.token_extender.eject()
super().eject()