mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-24 07:08:45 +00:00
prevent setattr pytorch module to register on the Chain class
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d02be0d10e
commit
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@ -143,6 +143,14 @@ class Chain(ContextModule):
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if isinstance(module, ContextModule) and module.parent != self:
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module._set_parent(self)
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def __setattr__(self, name: str, value: Any) -> None:
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if isinstance(value, torch.nn.Module):
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raise ValueError(
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"Chain does not support setting modules by attribute. Instead, use a mutation method like `append` or"
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" wrap it within a single element list to prevent pytorch from registering it as a submodule."
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)
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super().__setattr__(name, value)
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@property
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def provider(self) -> ContextProvider:
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return self._provider
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@ -11,7 +11,7 @@ from torch.nn.modules.module import Module as TorchModule
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from refiners.fluxion.utils import load_from_safetensors
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from refiners.fluxion.context import Context, ContextProvider
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from typing import Callable, TYPE_CHECKING, Sequence
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from typing import TYPE_CHECKING, Sequence
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if TYPE_CHECKING:
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from refiners.fluxion.layers.chain import Chain
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@ -26,11 +26,14 @@ class Module(TorchModule):
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_buffers: dict[str, Any]
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_tag: str = ""
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__getattr__: Callable[["Module", str], Any] # type: ignore
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__setattr__: Callable[["Module", str, Any], None] # type: ignore
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, *kwargs) # type: ignore
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super().__init__(*args, *kwargs) # type: ignore[reportUnknownMemberType]
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def __getattr__(self, name: str) -> Any:
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return super().__getattr__(name=name)
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def __setattr__(self, name: str, value: Any) -> None:
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return super().__setattr__(name=name, value=value)
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def load_from_safetensors(self, tensors_path: str | Path, strict: bool = True) -> "Module":
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state_dict = load_from_safetensors(tensors_path)
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@ -10,64 +10,6 @@ from torch.nn import Parameter
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import re
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class ConceptExtender(fl.Chain, Adapter[CLIPTextEncoder]):
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"""
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Extends the vocabulary of a CLIPTextEncoder with one or multiple new concepts, e.g. obtained via the Textual Inversion technique.
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Example:
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import torch
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from refiners.foundationals.clip.concepts import ConceptExtender
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from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
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from refiners.fluxion.utils import load_from_safetensors
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encoder = CLIPTextEncoderL(device="cuda")
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tensors = load_from_safetensors("CLIPTextEncoderL.safetensors")
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encoder.load_state_dict(tensors)
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cat_embedding = torch.load("cat_embedding.bin")["<this-cat>"]
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dog_embedding = torch.load("dog_embedding.bin")["<that-dog>"]
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extender = ConceptExtender(encoder)
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extender.add_concept(token="<this-cat>", embedding=cat_embedding)
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extender.inject()
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# New concepts can be added at any time
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extender.add_concept(token="<that-dog>", embedding=dog_embedding)
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# Now the encoder can be used with the new concepts
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"""
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def __init__(self, target: CLIPTextEncoder) -> None:
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with self.setup_adapter(target):
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super().__init__(target)
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try:
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token_encoder, self.token_encoder_parent = next(target.walk(TokenEncoder))
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except StopIteration:
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raise RuntimeError("TokenEncoder not found.")
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try:
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clip_tokenizer, self.clip_tokenizer_parent = next(target.walk(CLIPTokenizer))
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except StopIteration:
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raise RuntimeError("Tokenizer not found.")
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self.embedding_extender = EmbeddingExtender(token_encoder)
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self.token_extender = TokenExtender(clip_tokenizer)
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def add_concept(self, token: str, embedding: Tensor) -> None:
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self.embedding_extender.add_embedding(embedding)
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self.token_extender.add_token(token, self.embedding_extender.num_embeddings - 1)
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def inject(self: "ConceptExtender", parent: fl.Chain | None = None) -> "ConceptExtender":
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self.embedding_extender.inject(self.token_encoder_parent)
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self.token_extender.inject(self.clip_tokenizer_parent)
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return super().inject(parent)
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def eject(self) -> None:
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self.embedding_extender.eject()
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self.token_extender.eject()
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super().eject()
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class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
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old_weight: Parameter
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new_weight: Parameter
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@ -122,3 +64,84 @@ class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]):
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tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE)
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# Define the keyword as its own smallest subtoken
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tokenizer.byte_pair_encoding_cache[token] = token
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class ConceptExtender(fl.Chain, Adapter[CLIPTextEncoder]):
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"""
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Extends the vocabulary of a CLIPTextEncoder with one or multiple new concepts, e.g. obtained via the Textual Inversion technique.
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Example:
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import torch
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from refiners.foundationals.clip.concepts import ConceptExtender
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from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
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from refiners.fluxion.utils import load_from_safetensors
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encoder = CLIPTextEncoderL(device="cuda")
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tensors = load_from_safetensors("CLIPTextEncoderL.safetensors")
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encoder.load_state_dict(tensors)
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cat_embedding = torch.load("cat_embedding.bin")["<this-cat>"]
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dog_embedding = torch.load("dog_embedding.bin")["<that-dog>"]
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extender = ConceptExtender(encoder)
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extender.add_concept(token="<this-cat>", embedding=cat_embedding)
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extender.inject()
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# New concepts can be added at any time
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extender.add_concept(token="<that-dog>", embedding=dog_embedding)
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# Now the encoder can be used with the new concepts
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"""
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def __init__(self, target: CLIPTextEncoder) -> None:
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with self.setup_adapter(target):
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super().__init__(target)
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try:
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token_encoder, token_encoder_parent = next(target.walk(TokenEncoder))
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self._token_encoder_parent = [token_encoder_parent]
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except StopIteration:
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raise RuntimeError("TokenEncoder not found.")
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try:
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clip_tokenizer, clip_tokenizer_parent = next(target.walk(CLIPTokenizer))
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self._clip_tokenizer_parent = [clip_tokenizer_parent]
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except StopIteration:
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raise RuntimeError("Tokenizer not found.")
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self._embedding_extender = [EmbeddingExtender(token_encoder)]
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self._token_extender = [TokenExtender(clip_tokenizer)]
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@property
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def embedding_extender(self) -> EmbeddingExtender:
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assert len(self._embedding_extender) == 1, "EmbeddingExtender not found."
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return self._embedding_extender[0]
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@property
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def token_extender(self) -> TokenExtender:
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assert len(self._token_extender) == 1, "TokenExtender not found."
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return self._token_extender[0]
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@property
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def token_encoder_parent(self) -> fl.Chain:
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assert len(self._token_encoder_parent) == 1, "TokenEncoder parent not found."
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return self._token_encoder_parent[0]
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@property
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def clip_tokenizer_parent(self) -> fl.Chain:
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assert len(self._clip_tokenizer_parent) == 1, "Tokenizer parent not found."
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return self._clip_tokenizer_parent[0]
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def add_concept(self, token: str, embedding: Tensor) -> None:
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self.embedding_extender.add_embedding(embedding)
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self.token_extender.add_token(token, self.embedding_extender.num_embeddings - 1)
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def inject(self: "ConceptExtender", parent: fl.Chain | None = None) -> "ConceptExtender":
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self.embedding_extender.inject(self.token_encoder_parent)
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self.token_extender.inject(self.clip_tokenizer_parent)
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return super().inject(parent)
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def eject(self) -> None:
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self.embedding_extender.eject()
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self.token_extender.eject()
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super().eject()
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@ -217,3 +217,12 @@ def test_chain_structural_copy() -> None:
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y2 = m2(x)
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assert y2.shape == (7, 12)
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torch.equal(y2, y)
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def test_setattr_dont_register() -> None:
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chain = fl.Chain(fl.Linear(in_features=1, out_features=1), fl.Linear(in_features=1, out_features=1))
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with pytest.raises(expected_exception=ValueError):
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chain.foo = fl.Linear(in_features=1, out_features=1)
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assert module_keys(chain=chain) == ["Linear_1", "Linear_2"]
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