prevent setattr pytorch module to register on the Chain class

This commit is contained in:
Benjamin Trom 2023-10-09 17:51:25 +02:00
parent d02be0d10e
commit a663375dc7
4 changed files with 106 additions and 63 deletions

View file

@ -143,6 +143,14 @@ class Chain(ContextModule):
if isinstance(module, ContextModule) and module.parent != self: if isinstance(module, ContextModule) and module.parent != self:
module._set_parent(self) module._set_parent(self)
def __setattr__(self, name: str, value: Any) -> None:
if isinstance(value, torch.nn.Module):
raise ValueError(
"Chain does not support setting modules by attribute. Instead, use a mutation method like `append` or"
" wrap it within a single element list to prevent pytorch from registering it as a submodule."
)
super().__setattr__(name, value)
@property @property
def provider(self) -> ContextProvider: def provider(self) -> ContextProvider:
return self._provider return self._provider

View file

@ -11,7 +11,7 @@ from torch.nn.modules.module import Module as TorchModule
from refiners.fluxion.utils import load_from_safetensors from refiners.fluxion.utils import load_from_safetensors
from refiners.fluxion.context import Context, ContextProvider from refiners.fluxion.context import Context, ContextProvider
from typing import Callable, TYPE_CHECKING, Sequence from typing import TYPE_CHECKING, Sequence
if TYPE_CHECKING: if TYPE_CHECKING:
from refiners.fluxion.layers.chain import Chain from refiners.fluxion.layers.chain import Chain
@ -26,11 +26,14 @@ class Module(TorchModule):
_buffers: dict[str, Any] _buffers: dict[str, Any]
_tag: str = "" _tag: str = ""
__getattr__: Callable[["Module", str], Any] # type: ignore
__setattr__: Callable[["Module", str, Any], None] # type: ignore
def __init__(self, *args: Any, **kwargs: Any) -> None: def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, *kwargs) # type: ignore super().__init__(*args, *kwargs) # type: ignore[reportUnknownMemberType]
def __getattr__(self, name: str) -> Any:
return super().__getattr__(name=name)
def __setattr__(self, name: str, value: Any) -> None:
return super().__setattr__(name=name, value=value)
def load_from_safetensors(self, tensors_path: str | Path, strict: bool = True) -> "Module": def load_from_safetensors(self, tensors_path: str | Path, strict: bool = True) -> "Module":
state_dict = load_from_safetensors(tensors_path) state_dict = load_from_safetensors(tensors_path)

View file

@ -10,64 +10,6 @@ from torch.nn import Parameter
import re import re
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 refiners.foundationals.clip.concepts import ConceptExtender
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
from 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, self.token_encoder_parent = next(target.walk(TokenEncoder))
except StopIteration:
raise RuntimeError("TokenEncoder not found.")
try:
clip_tokenizer, self.clip_tokenizer_parent = next(target.walk(CLIPTokenizer))
except StopIteration:
raise RuntimeError("Tokenizer not found.")
self.embedding_extender = EmbeddingExtender(token_encoder)
self.token_extender = TokenExtender(clip_tokenizer)
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()
class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]): class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
old_weight: Parameter old_weight: Parameter
new_weight: Parameter new_weight: Parameter
@ -122,3 +64,84 @@ class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]):
tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE) tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE)
# Define the keyword as its own smallest subtoken # Define the keyword as its own smallest subtoken
tokenizer.byte_pair_encoding_cache[token] = token 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 refiners.foundationals.clip.concepts import ConceptExtender
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
from 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()

View file

@ -217,3 +217,12 @@ def test_chain_structural_copy() -> None:
y2 = m2(x) y2 = m2(x)
assert y2.shape == (7, 12) assert y2.shape == (7, 12)
torch.equal(y2, y) torch.equal(y2, y)
def test_setattr_dont_register() -> None:
chain = fl.Chain(fl.Linear(in_features=1, out_features=1), fl.Linear(in_features=1, out_features=1))
with pytest.raises(expected_exception=ValueError):
chain.foo = fl.Linear(in_features=1, out_features=1)
assert module_keys(chain=chain) == ["Linear_1", "Linear_2"]