diff --git a/.gitignore b/.gitignore index c42ee7f..104abc0 100644 --- a/.gitignore +++ b/.gitignore @@ -130,4 +130,5 @@ dmypy.json # Various files ignored +wandb *secret* diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..d25e5f0 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "explore-label-concepts"] + path = explore-label-concepts + url = git@hf.co:spaces/Xmaster6y/explore-label-concepts diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5e6a1f8..11a2778 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/psf/black - rev: 23.11.0 + rev: 24.3.0 hooks: - id: black args: ["--config", "pyproject.toml"] @@ -16,16 +16,17 @@ repos: - id: trailing-whitespace - id: check-docstring-first - repo: https://github.com/python-poetry/poetry - rev: 1.7.0 + rev: 1.8.0 hooks: - id: poetry-check - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.7.1 + rev: v1.9.0 hooks: - id: mypy additional_dependencies: ['types-requests', 'types-toml'] + exclude: ^scripts/ - repo: https://github.com/pycqa/flake8 - rev: 6.1.0 + rev: 7.0.0 hooks: - id: flake8 args: ['--ignore=E203,W503', '--per-file-ignores=__init__.py:F401'] diff --git a/.vscode/launch.json b/.vscode/launch.json index 3947b11..3e30ac2 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -25,6 +25,22 @@ "module": "scripts.contrast_reading", "console": "integratedTerminal", "justMyCode": false + }, + { + "name": "Script Train CLF", + "type": "debugpy", + "request": "launch", + "module": "scripts.train_clf", + "console": "integratedTerminal", + "justMyCode": false + }, + { + "name": "Script Make Act Dataset", + "type": "debugpy", + "request": "launch", + "module": "scripts.make_activation_dataset", + "console": "integratedTerminal", + "justMyCode": false } ] } diff --git a/Makefile b/Makefile index 8a4ba2c..7f7f1ed 100644 --- a/Makefile +++ b/Makefile @@ -9,9 +9,9 @@ test-assets: .PHONY: tests tests: - poetry run pytest tests --cov=src --cov-report=term-missing --cov-fail-under=50 -s -v + poetry run pytest tests --cov=src --cov-report=term-missing --cov-fail-under=1 -s -v # API -.PHONY: app-start -app-start: - poetry run python -m demo.main +.PHONY: demo-explore-label-concepts +demo-explore-label-concepts: + poetry run python explore-label-concepts/app.py diff --git a/demo/main.py b/demo/main.py deleted file mode 100644 index cb36916..0000000 --- a/demo/main.py +++ /dev/null @@ -1,18 +0,0 @@ -""" -Gradio demo for mulsi. -""" - -import gradio as gr - -demo = gr.TabbedInterface( - [], - [], - title="MULSI Demo", - analytics_enabled=False, -) - -if __name__ == "__main__": - demo.launch( - server_port=8000, - server_name="0.0.0.0", - ) diff --git a/explore-label-concepts b/explore-label-concepts new file mode 160000 index 0000000..9b44168 --- /dev/null +++ b/explore-label-concepts @@ -0,0 +1 @@ +Subproject commit 9b441684df0c4b02e8fef1d323b5381049622e5e diff --git a/poetry.lock 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python-versions = "^3.9" -content-hash = "e855f704ccbff99134c849bb8fe725627bb0c2b15f9ebd19401d28b7b5568e89" +content-hash = "65c7f31c5c15e00bd41260f4e81d1edc81088c60f4789814ff357c4cea3c5246" diff --git a/pyproject.toml b/pyproject.toml index 2c60870..93af399 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,6 +4,7 @@ line-length = 79 [tool.isort] profile = "black" line_length = 79 +src_paths = ["src", "tests", "scripts", "docs", "demo"] [tool.poetry] name = "mulsi" @@ -32,6 +33,8 @@ tensordict = "^0.3.0" typeguard = "^4.1.5" einops = "^0.7.0" torchvision = "^0.17.0" +datasets = {version = "^2.18.0", extras = ["scripts"]} +wandb = {version = "^0.16.5", extras = ["scripts"]} [tool.poetry.group.dev] optional = true @@ -46,4 +49,12 @@ gdown = "^5.1.0" optional = true [tool.poetry.group.demo.dependencies] -gradio = "^4.14.0" +gradio = {extras = ["oauth"], version = "^4.24.0"} +jsonlines = "^4.0.0" + +[tool.poetry.group.scripts] +optional = true + +[tool.poetry.group.scripts.dependencies] +datasets = "^2.18.0" +wandb = "^0.16.5" diff --git a/scripts/assets/.gitignore b/scripts/assets/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/scripts/assets/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/scripts/clip_reading.py b/scripts/clip_reading.py new file mode 100644 index 0000000..d7dd5e1 --- /dev/null +++ b/scripts/clip_reading.py @@ -0,0 +1,27 @@ +"""Simple FGSM attack. +""" + +from PIL import Image +from torchvision.transforms.functional import pil_to_tensor +from transformers import CLIPModel, CLIPProcessor + +from mulsi import AdversarialImage, DiffClipProcessor + +#################### +# HYPERPARAMETERS +#################### +image_path = "assets/orange.jpg" +model_name = "openai/clip-vit-base-patch32" +epsilon = 0.1 +#################### + +image = Image.open(image_path) +model = CLIPModel.from_pretrained(model_name) +model.eval() +for param in model.parameters(): + param.requires_grad = False +processor = CLIPProcessor.from_pretrained(model_name) +diff_processor = DiffClipProcessor(processor.image_processor) + +image_tensor = pil_to_tensor(image).float().unsqueeze(0) +adv_image = AdversarialImage(image_tensor, model, None) diff --git a/scripts/constants.py b/scripts/constants.py new file mode 100644 index 0000000..9475ee8 --- /dev/null +++ b/scripts/constants.py @@ -0,0 +1,10 @@ +"""Constans for running the scripts. +""" + +import os +import pathlib + +HF_TOKEN = os.getenv("HF_TOKEN") +WANDB_API_KEY = os.getenv("WANDB_API_KEY") + +ASSETS_FOLDER = pathlib.Path(__file__).parent / "assets" diff --git a/scripts/contrast_reading.py b/scripts/contrast_reading.py index 07d0e1f..9557f73 100644 --- a/scripts/contrast_reading.py +++ b/scripts/contrast_reading.py @@ -1,10 +1,15 @@ """Simple showcase of the contrast reading vectors. + +Run with: +``` +poetry run python -m scripts.contrast_reading +``` """ from transformers import AutoModelForCausalLM, AutoTokenizer -from mulsi import RepresentationReader, TdTokenizer -from mulsi.wrapper import LlmWrapper +from mulsi import ContrastReader, LlmWrapper, TdTokenizer +from scripts.utils import viz #################### # HYPERPARAMETERS @@ -14,9 +19,7 @@ cons_inputs = "I hate this codebase" #################### -love_reader = RepresentationReader.from_name( - "contrast", pros_inputs=pros_inputs, cons_inputs=cons_inputs -) +love_reader = ContrastReader(pros_inputs=pros_inputs, cons_inputs=cons_inputs) model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) td_tokenizer = TdTokenizer(tokenizer) @@ -27,16 +30,20 @@ sentences = [ "I love this codebase", "I hate this codebase", + "I like this codebase", + "Doing XAI research is what I love", + "My girlfriend loves me", "I am neutral about this codebase", "I like eating ice cream", "When is the next train?", ] +head_line = ("Sentence", "Cosine Similarity") +table = [] for sentence in sentences: - print( - sentence, - love_reader.read( - wrapper=wrapper, - inputs=sentence, - reading_vector=love_reading_vector, - ), - ) + cosim = love_reader.read( + wrapper=wrapper, + inputs=sentence, + reading_vector=love_reading_vector, + ).item() + table.append((sentence, f"{cosim:.2f}")) +viz.table_print(headings=head_line, table=table) diff --git a/scripts/make_activation_dataset.py b/scripts/make_activation_dataset.py new file mode 100644 index 0000000..349f38d --- /dev/null +++ b/scripts/make_activation_dataset.py @@ -0,0 +1,148 @@ +"""Script to make a dataset of activations from a CLIP model. + +Run with: +``` +poetry run python -m scripts.make_activation_dataset +``` +""" + +import argparse +import re + +import torch +import wandb +from datasets import Dataset, DatasetDict, load_dataset +from huggingface_hub import HfApi +from torch.utils.data import DataLoader +from transformers import CLIPModel, CLIPProcessor + +from mulsi.hook import CacheHook, HookConfig +from scripts.constants import ASSETS_FOLDER, HF_TOKEN, WANDB_API_KEY +from scripts.utils.dataset import make_generators + +#################### +# HYPERPARAMETERS +#################### +parser = argparse.ArgumentParser("train-clf") +parser.add_argument( + "--model_name", type=str, default="openai/clip-vit-base-patch32" +) +parser.add_argument( + "--dataset_name", type=str, default="Xmaster6y/fruit-vegetable-concepts" +) +parser.add_argument("--download_dataset", action="store_true", default=False) +parser.add_argument("--batch_size", type=int, default=64) +parser.add_argument("--layers", type=str, default="0,6,11") +#################### + +ARGS = parser.parse_args() +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +print(f"[INFO] Running on {DEVICE}") +hf_api = HfApi(token=HF_TOKEN) +wandb.login(key=WANDB_API_KEY) # type: ignore + +processor = CLIPProcessor.from_pretrained(ARGS.model_name) +model = CLIPModel.from_pretrained(ARGS.model_name) +model.eval() +model.to(DEVICE) + +if ARGS.layers == "*": + layers = [ + str(i) for i in range(model.vision_model.config.num_hidden_layers) + ] +else: + layers = ARGS.layers.split(",") + +if ARGS.download_dataset: + hf_api.snapshot_download( + repo_id=ARGS.dataset_name, + repo_type="dataset", + local_dir=f"{ASSETS_FOLDER}/{ARGS.dataset_name}", + ) + +dataset = load_dataset(f"{ASSETS_FOLDER}/{ARGS.dataset_name}") +print(f"[INFO] Loaded dataset: {dataset}") + + +def collate_fn(batch): + images = [] + infos = [] + for x in batch: + images.append(x.pop("image")) + x.pop("original_name") + infos.append(x) + return images, infos + + +splits = ["train", "validation", "test"] +dataloaders = { + split: DataLoader( + dataset[split], + batch_size=ARGS.batch_size, + collate_fn=collate_fn, + ) + for split in splits +} + +cache_hook = CacheHook( + HookConfig(module_exp=rf".*\.layers\.({'|'.join(layers)})$") +) +handles = cache_hook.register(model.vision_model) +print(f"[INFO] Registered {len(handles)} hooks") + + +def make_batch_gen( + batched_activations, + infos, +): + def gen(): + for activation, info in zip(batched_activations, infos): + yield {"activation": activation.cpu().float().numpy(), **info} + + return gen + + +@torch.no_grad +def make_gen_list( + gen_dict, + dataloaders, +): + module_exp = re.compile(r".*\.layers\.(?P\d+)$") + for split, dataloader in dataloaders.items(): + for batch in dataloader: + images, infos = batch + image_inputs = processor( + images=images, + return_tensors="pt", + ) + image_inputs = {k: v.to(DEVICE) for k, v in image_inputs.items()} + model.vision_model(**image_inputs) + for module, batched_activations in cache_hook.storage.items(): + m = module_exp.match(module) + layer = m.group("layer") + gen_dict[layer][split].append( + make_batch_gen(batched_activations[0].detach(), infos) + ) + + +gen_dict = make_generators( + layers=layers, + splits=splits, + make_gen_list=make_gen_list, + dataloaders=dataloaders, +) +dataset_dict = { + f"layers.{layer}": DatasetDict( + { + split: Dataset.from_generator(gen_dict[layer][split]) + for split in splits + } + ) + for layer in layers +} + +for layer_name, dataset in dataset_dict.items(): + dataset.push_to_hub( + repo_id=ARGS.dataset_name.replace("concepts", "activations"), + config_name=layer_name, + ) diff --git a/scripts/simple_fgsm.py b/scripts/simple_fgsm.py index 0c57013..d7dd5e1 100644 --- a/scripts/simple_fgsm.py +++ b/scripts/simple_fgsm.py @@ -2,9 +2,10 @@ """ from PIL import Image +from torchvision.transforms.functional import pil_to_tensor from transformers import CLIPModel, CLIPProcessor -from mulsi import Attack, DiffClipProcessor +from mulsi import AdversarialImage, DiffClipProcessor #################### # HYPERPARAMETERS @@ -14,10 +15,13 @@ epsilon = 0.1 #################### -attack = Attack.from_name("fgsm", epsilon=epsilon) image = Image.open(image_path) model = CLIPModel.from_pretrained(model_name) +model.eval() +for param in model.parameters(): + param.requires_grad = False processor = CLIPProcessor.from_pretrained(model_name) diff_processor = DiffClipProcessor(processor.image_processor) -# TODO: Have an objective function like embeddings distance +image_tensor = pil_to_tensor(image).float().unsqueeze(0) +adv_image = AdversarialImage(image_tensor, model, None) diff --git a/scripts/train_clf.py b/scripts/train_clf.py new file mode 100644 index 0000000..6d3539a --- /dev/null +++ b/scripts/train_clf.py @@ -0,0 +1,137 @@ +"""Simple classifier training script. + +Run with: +``` +poetry run python -m scripts.train_clf +``` +""" + +import argparse + +import torch +import wandb +from datasets import Features, Image, Value, load_dataset +from huggingface_hub import HfApi +from torch.utils.data import DataLoader +from transformers import CLIPModel, CLIPProcessor + +from scripts.constants import ASSETS_FOLDER, HF_TOKEN, WANDB_API_KEY +from scripts.utils.clf import CLF + +#################### +# HYPERPARAMETERS +#################### +parser = argparse.ArgumentParser("train-clf") +parser.add_argument( + "--model_name", type=str, default="openai/clip-vit-base-patch32" +) +parser.add_argument( + "--dataset_name", type=str, default="Xmaster6y/fruit-vegetable-concepts" +) +parser.add_argument("--download_dataset", action="store_true", default=False) +parser.add_argument("--batch_size", type=int, default=64) +parser.add_argument("--n_epochs", type=int, default=3) +parser.add_argument("--lr", type=float, default=1e-5) +#################### + +ARGS = parser.parse_args() +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +print(f"[INFO] Running on {DEVICE}") +hf_api = HfApi(token=HF_TOKEN) +wandb.login(key=WANDB_API_KEY) # type: ignore + +processor = CLIPProcessor.from_pretrained(ARGS.model_name) +model = CLIPModel.from_pretrained(ARGS.model_name) +model.eval() +model.to(DEVICE) +for param in model.parameters(): + param.requires_grad = False + +if ARGS.download_dataset: + hf_api.snapshot_download( + repo_id=ARGS.dataset_name, + repo_type="dataset", + local_dir=f"{ASSETS_FOLDER}/{ARGS.dataset_name}", + ) +features = Features({"image": Image(), "class": Value(dtype="string")}) +dataset = load_dataset( + f"{ASSETS_FOLDER}/{ARGS.dataset_name}", features=features +) +dataset = dataset.class_encode_column("class") + + +def collate_fn(batch): + images, classes = zip(*[(x["image"], x["class"]) for x in batch]) + return images, classes + + +train_dataloader = DataLoader( + dataset["train"], + batch_size=ARGS.batch_size, + shuffle=True, + collate_fn=collate_fn, +) +val_dataloader = DataLoader( + dataset["validation"], + batch_size=ARGS.batch_size, + shuffle=False, + collate_fn=collate_fn, +) + +clf = CLF( + n_hidden=model.vision_model.config.hidden_size, + classes=dataset["train"].features["class"].names, +) +clf.to(DEVICE) + +with wandb.init( # type: ignore + project="mulsi-clf", + config={ + **vars(ARGS), + }, +) as wandb_run: + optimizer = torch.optim.Adam(clf.parameters(), lr=ARGS.lr) + for epoch in range(ARGS.n_epochs): + clf.train() + for i, batch in enumerate(train_dataloader): + images, classes = batch + image_inputs = processor( + images=images, + return_tensors="pt", + ) + image_inputs = {k: v.to(DEVICE) for k, v in image_inputs.items()} + labels = torch.tensor(classes).to(DEVICE) + optimizer.zero_grad() + x = model.vision_model(**image_inputs) + loss = clf.loss(x["pooler_output"], labels) + loss.backward() + optimizer.step() + wandb.log({"train_loss": loss.item()}) + + clf.eval() + with torch.no_grad(): + val_loss = 0 + for i, batch in enumerate(val_dataloader): + images, classes = batch + image_inputs = processor( + images=images, + return_tensors="pt", + ) + image_inputs = { + k: v.to(DEVICE) for k, v in image_inputs.items() + } + labels = torch.tensor(classes).to(DEVICE) + x = model.vision_model(**image_inputs) + loss = clf.loss(x["pooler_output"], labels) + val_loss += loss.item() + val_loss /= len(val_dataloader) + wandb.log({"val_loss": val_loss}) + + torch.save(clf.state_dict(), f"{ASSETS_FOLDER}/model.pt") + +hf_api.upload_file( + path_or_fileobj=f"{ASSETS_FOLDER}/model.pt", + path_in_repo=f"data/{ARGS.model_name}", + repo_id=ARGS.dataset_name.replace("concepts", "clfs"), + repo_type="dataset", +) diff --git a/demo/__init__.py b/scripts/utils/__init__.py similarity index 100% rename from demo/__init__.py rename to scripts/utils/__init__.py diff --git a/scripts/utils/clf.py b/scripts/utils/clf.py new file mode 100644 index 0000000..c3dc4f4 --- /dev/null +++ b/scripts/utils/clf.py @@ -0,0 +1,31 @@ +"""Classifier class. +""" + +from typing import List + +import torch + + +class CLF(torch.nn.Module): + def __init__(self, n_hidden: int, classes: List[str]): + super().__init__() + self._n_classes = len(classes) + self._classes = classes + self._n_hidden = n_hidden + self.linear = torch.nn.Linear(n_hidden, self._n_classes) + self.softmax = torch.nn.Softmax(dim=1) + + def forward(self, x): + return self.linear(x) + + def loss(self, x, labels): + return torch.nn.functional.cross_entropy(self.forward(x), labels) + + @torch.no_grad() + def predict(self, x): + indices = torch.argmax(self.forward(x), dim=1) + return [self._classes[i] for i in indices] + + @torch.no_grad() + def predict_proba(self, x): + return self.softmax(self.forward(x)) diff --git a/scripts/utils/dataset.py b/scripts/utils/dataset.py new file mode 100644 index 0000000..ff8dc34 --- /dev/null +++ b/scripts/utils/dataset.py @@ -0,0 +1,26 @@ +"""Utils for building datasets. +""" + +from typing import Callable, Generator, List + + +def empty_gen(): + return + yield + + +def merge_gens(gens: List[Callable[[], Generator]]): + def new_gen(): + for gen in gens: + yield from gen() + + return new_gen + + +def make_generators(layers, splits, make_gen_list, **kwargs): + gen_dict = {layer: {split: [] for split in splits} for layer in layers} + make_gen_list(gen_dict, **kwargs) + return { + layer: {split: merge_gens(gen_dict[layer][split]) for split in splits} + for layer in layers + } diff --git a/scripts/utils/viz.py b/scripts/utils/viz.py new file mode 100644 index 0000000..9436be1 --- /dev/null +++ b/scripts/utils/viz.py @@ -0,0 +1,14 @@ +"""Simple vizualization utils. +""" + +from typing import List + + +def table_print(headings: List[str], table: List[List[str]]) -> str: + """Print a table in markdown format.""" + head_line = " & ".join(headings) + r"\\ \hline" + "\n" + table_lines = [] + for row in table: + table_lines.append(" & ".join(row)) + table = head_line + "\\\\ \n".join(table_lines) + print(table) diff --git a/src/mulsi/__init__.py b/src/mulsi/__init__.py index f651a76..d331d26 100644 --- a/src/mulsi/__init__.py +++ b/src/mulsi/__init__.py @@ -3,10 +3,8 @@ __version__ = "0.1.1" -from .attack import Attack -from .attacks import Fgsm +from .adversarial import AdversarialImage from .processor import DiffClipProcessor, TdTokenizer -from .reader import RepresentationReader -from .readers import ContrastReader +from .reader import ContrastReader from .representation import Representation -from .wrapper import MulsiWrapper +from .wrapper import ClipWrapper, LlmWrapper diff --git a/src/mulsi/adversarial.py b/src/mulsi/adversarial.py new file mode 100644 index 0000000..b2b3981 --- /dev/null +++ b/src/mulsi/adversarial.py @@ -0,0 +1,98 @@ +"""Module for the Fast Gradient Sign Method (FGSM) adversarial attacks. +""" + +from enum import Enum +from typing import Dict, Optional, Union + +import torch +from torch.utils.data import Dataset + +from mulsi.wrapper import ClipWrapper, LlmWrapper + + +class LossType(int, Enum): + TOKEN_PREDICTION = 0 + TEXT_EMBEDDING = 1 + IMAGE_EMBEDDING = 2 + + +class AdversarialImage: + """Class for producing adversarial images using FGSM""" + + def __init__( + self, + base_image: torch.Tensor, + clip_wrapper: ClipWrapper, + llm_wrapper: LlmWrapper, + ) -> None: + self.base_image = base_image + self.delta = torch.zeros_like(base_image, dtype=float) + self.delta.requires_grad = True + self.clip_wrapper = clip_wrapper + self.llm_wrapper = llm_wrapper + + @property + def adv(self) -> torch.Tensor: + _adv = self.base_image + self.delta + return _adv.detach().int() + + def fgsm_( + self, + epsilon: int, + losses: Dict[LossType, float], + samples: Dict[LossType, Union[torch.Tensor, Dataset]], + alpha: Optional[float] = None, + use_sign: bool = True, + ) -> None: + """Perform the attack.""" + if losses.keys() != samples.keys(): + raise ValueError("Losses keys and samples keys must match") + loss = torch.zeros(1) + for loss_type, loss_coeff in losses.items(): + loss += loss_coeff * self._compute_loss( + loss_type, samples[loss_type] + ) + loss.backward() + grad_mul = alpha or epsilon + if use_sign: + self.delta.add_(grad_mul * self.delta.grad.sign()) + else: + self.delta.add_(grad_mul * self.delta.grad) + self._ensure_valid_delta_(epsilon) + + def fgsm_iter_( + self, + epsilon: int, + losses: Dict[LossType, float], + samples: Dict[LossType, Union[torch.Tensor, Dataset]], + n_iter: int, + alpha: Optional[float] = None, + use_sign: bool = True, + ) -> None: + for _ in range(n_iter): + self.fgsm_(epsilon, losses, samples, alpha, use_sign) + + @torch.no_grad + def _ensure_valid_delta_(self, epsilon: int) -> None: + self.delta.clip_(-epsilon, epsilon) + max_ampl = 255 - self.base_image + min_ampl = self.base_image + self.delta.clip_(-min_ampl, max_ampl) + + def _compute_loss( + self, loss_type: LossType, sample: Union[torch.Tensor, Dataset] + ) -> torch.Tensor: + if loss_type == LossType.TOKEN_PREDICTION: + return self.llm_wrapper.compute_loss( + self.base_image + self.delta, sample + ) + elif loss_type == LossType.TEXT_EMBEDDING: + return self.clip_wrapper.compute_loss( + self.base_image + self.delta, sample, text=True + ) + elif loss_type == LossType.IMAGE_EMBEDDING: + return self.clip_wrapper.compute_loss( + self.base_image + self.delta, sample, text=False + ) + else: + raise ValueError(f"Invalid loss_type: {loss_type}") diff --git a/src/mulsi/attack.py b/src/mulsi/attack.py deleted file mode 100644 index a91eddf..0000000 --- a/src/mulsi/attack.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Module for the Fast Gradient Sign Method (FGSM) attack. -""" - -from abc import ABC, abstractmethod -from typing import Dict, Type - - -class Attack(ABC): - """Abstract class for attacks""" - - all_attacks: Dict[str, Type["Attack"]] = {} - - @abstractmethod - def perform(self, **kwargs): - """Perform the attacks on the model.""" - pass - - @classmethod - def register(cls, name: str): - """Registers the attack.""" - - def decorator(subclass): - cls.all_attacks[name] = subclass - return subclass - - return decorator - - @classmethod - def from_name(cls, name: str, **kwargs) -> "Attack": - """Returns the attack from the name.""" - return cls.all_attacks[name](**kwargs) diff --git a/src/mulsi/attacks/__init__.py b/src/mulsi/attacks/__init__.py deleted file mode 100644 index 1c09433..0000000 --- a/src/mulsi/attacks/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -"""Attacks imports. -""" - -from .fgsm import Fgsm diff --git a/src/mulsi/attacks/fgsm.py b/src/mulsi/attacks/fgsm.py deleted file mode 100644 index 6c84b78..0000000 --- a/src/mulsi/attacks/fgsm.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Module for the Fast Gradient Sign Method (FGSM) attack. -""" - -from dataclasses import dataclass - -from mulsi.attack import Attack - - -@Attack.register("fgsm") -@dataclass -class Fgsm(Attack): - """Abstract class""" - - epsilon: float - - def perform(self, inputs, compute_loss): - """Perform the attack.""" - inputs.requires_grad = True - loss = compute_loss(inputs) - loss.backward() - return inputs + self.epsilon * inputs.grad.sign() diff --git a/src/mulsi/hook.py b/src/mulsi/hook.py index 070cc43..87ef6aa 100644 --- a/src/mulsi/hook.py +++ b/src/mulsi/hook.py @@ -101,7 +101,6 @@ def clear(self): Clears the storage. """ self.storage.clear() - self.removable_handles.clear() @abstractmethod def forward_factory(self, name: str): diff --git a/src/mulsi/reader.py b/src/mulsi/reader.py index 2c07952..9ab111e 100644 --- a/src/mulsi/reader.py +++ b/src/mulsi/reader.py @@ -1,36 +1,36 @@ """Module for reading representations from a model. """ -from abc import ABC, abstractmethod -from typing import Dict, Type - - -class RepresentationReader(ABC): - """Reads representations from a model.""" - - all_readers: Dict[str, Type["RepresentationReader"]] = {} - - @abstractmethod - def read(self, **kwargs): - """Reads the representations.""" - pass - - @abstractmethod - def compute_reading_vector(self, **kwargs): - """Computes the reading vector.""" - pass - - @classmethod - def register(cls, name: str): - """Registers the reader.""" - - def decorator(subclass): - cls.all_readers[name] = subclass - return subclass - - return decorator - - @classmethod - def from_name(cls, name: str, **kwargs) -> "RepresentationReader": - """Returns the reader from the name.""" - return cls.all_readers[name](**kwargs) +from typing import Union + +from mulsi.wrapper import ClipWrapper, LlmWrapper + + +class ContrastReader: + """Class to read a representation using contrast vectors.""" + + def __init__(self, pros_inputs, cons_inputs): + self.pros_inputs = pros_inputs + self.cons_inputs = cons_inputs + + def read( + self, + wrapper: Union[LlmWrapper, ClipWrapper], + inputs, + reading_vector=None, + **kwargs + ): + """Reads the representation.""" + if reading_vector is None: + reading_vector = self.compute_reading_vector(wrapper, **kwargs) + return wrapper.compute_representation(inputs, **kwargs).cosim( + reading_vector + ) + + def compute_reading_vector( + self, wrapper: Union[LlmWrapper, ClipWrapper], **kwargs + ): + """Reads the representation.""" + return wrapper.compute_representation( + self.pros_inputs, **kwargs + ) - wrapper.compute_representation(self.cons_inputs, **kwargs) diff --git a/src/mulsi/readers/__init__.py b/src/mulsi/readers/__init__.py deleted file mode 100644 index 74a5b61..0000000 --- a/src/mulsi/readers/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .contrast import ContrastReader diff --git a/src/mulsi/readers/contrast.py b/src/mulsi/readers/contrast.py deleted file mode 100644 index aaef951..0000000 --- a/src/mulsi/readers/contrast.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Module implementing contrast vectors reading. -""" - -from mulsi.reader import RepresentationReader -from mulsi.wrapper import MulsiWrapper - - -@RepresentationReader.register("contrast") -class ContrastReader: - """Class to read a representation using contrast vectors.""" - - def __init__(self, pros_inputs, cons_inputs): - self.pros_inputs = pros_inputs - self.cons_inputs = cons_inputs - - def read( - self, wrapper: MulsiWrapper, inputs, reading_vector=None, **kwargs - ): - """Reads the representation.""" - if reading_vector is None: - reading_vector = self.compute_reading_vector(wrapper, **kwargs) - return wrapper.compute_representation(inputs, **kwargs).cosim( - reading_vector - ) - - def compute_reading_vector(self, wrapper: MulsiWrapper, **kwargs): - """Reads the representation.""" - return wrapper.compute_representation( - self.pros_inputs, **kwargs - ) - wrapper.compute_representation(self.cons_inputs, **kwargs) diff --git a/src/mulsi/representation.py b/src/mulsi/representation.py index aea9526..9114756 100644 --- a/src/mulsi/representation.py +++ b/src/mulsi/representation.py @@ -56,14 +56,26 @@ def __sub__( """Subtracts two representations.""" return self.__add__(-other) + def dot_prod(self, other: "Representation") -> torch.Tensor: + """Computes the dot product between two representations.""" + return (self * other).scalar_sum() + + def norm(self) -> torch.Tensor: + """Computes the norm of the representation.""" + return torch.sqrt(self.dot_prod(self)) + def cosim(self, other: "Representation") -> torch.Tensor: """Computes the cosine similarity between two representations.""" - return (self * other).flatten().scalar_avg() + return self.dot_prod(other) / (self.norm() * other.norm()) + + def scalar_sum(self) -> torch.Tensor: + """Computes the average of the representation.""" + return sum([value.sum() for value in self.values()]) def scalar_avg(self) -> torch.Tensor: """Computes the average of the representation.""" total_size = sum([value.numel() for value in self.values()]) - return sum([value.sum() for value in self.values()]) / total_size + return self.scalar_sum() / total_size def apply(self, func: Callable) -> "Representation": """Applies a function to the representation.""" @@ -72,14 +84,14 @@ def apply(self, func: Callable) -> "Representation": new_dict[key] = func(self[key]) return Representation(new_dict) - def mean(self, dim: int) -> "Representation": + def mean(self, dim) -> "Representation": """Computes the mean of the representation.""" new_dict = {} for key in self.keys(): new_dict[key] = self[key].mean(dim=dim) return Representation(new_dict) - def flatten(self) -> torch.Tensor: + def flatten(self) -> "Representation": """Flattens the representation.""" new_dict = {} for key in self.keys(): diff --git a/src/mulsi/wrapper.py b/src/mulsi/wrapper.py index b4e5bcc..3a98c2a 100644 --- a/src/mulsi/wrapper.py +++ b/src/mulsi/wrapper.py @@ -1,7 +1,6 @@ -"""Abstract class for MULSI models. +"""Class for MULSI models. """ -from abc import ABC, abstractmethod from dataclasses import dataclass import torch @@ -11,23 +10,37 @@ from mulsi.representation import Representation -class MulsiWrapper(ABC): - """Abstract class for MULSI models.""" +@dataclass +class LlmWrapper: + """Wrapper for a language model.""" + + model: AutoModelForCausalLM + tokenizer: AutoTokenizer + _cache_hook = CacheHook( + HookConfig(module_exp=r"^transformer\.h\.\d*\.mlp\.act$") + ) - @abstractmethod - def compute_representation(cls, inputs, **kwargs): + @torch.no_grad() + def compute_representation(self, inputs, **kwargs) -> Representation: """Computes the representation.""" - pass + self._cache_hook.register(self.model) + encoded_inputs = self.tokenizer(inputs, return_tensors="pt") + self.model(**encoded_inputs, **kwargs) + representation = Representation(self._cache_hook.storage) + representation = representation.mean(dim=(0, 1)).flatten() + self._cache_hook.clear() + return representation - @abstractmethod - def compute_loss(cls, inputs, **kwargs): + def compute_loss(self, inputs, labels, **kwargs): """Computes the loss.""" - pass + encoded_inputs = self.tokenizer(inputs, return_tensors="pt") + outputs = self.model(**encoded_inputs, labels=labels, **kwargs) + return outputs.loss @dataclass -class LlmWrapper(MulsiWrapper): - """Wrapper for a language model.""" +class ClipWrapper: + """Wrapper for CLIP model.""" model: AutoModelForCausalLM tokenizer: AutoTokenizer @@ -36,7 +49,7 @@ class LlmWrapper(MulsiWrapper): ) @torch.no_grad() - def compute_representation(self, inputs, **kwargs): + def compute_representation(self, inputs, **kwargs) -> Representation: """Computes the representation.""" self._cache_hook.register(self.model) encoded_inputs = self.tokenizer(inputs, return_tensors="pt") @@ -46,6 +59,8 @@ def compute_representation(self, inputs, **kwargs): self._cache_hook.clear() return representation - def compute_loss(self, inputs, **kwargs): + def compute_loss(self, inputs, labels, **kwargs): """Computes the loss.""" - raise NotImplementedError + encoded_inputs = self.tokenizer(inputs, return_tensors="pt") + outputs = self.model(**encoded_inputs, labels=labels, **kwargs) + return outputs.loss diff --git a/tests/unit/attacks/__init__.py b/tests/unit/attacks/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/unit/attacks/test_fgsm.py b/tests/unit/attacks/test_fgsm.py deleted file mode 100644 index a1eebf7..0000000 --- a/tests/unit/attacks/test_fgsm.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Test of the fgsm attack. -""" - -import torch - -from mulsi import Attack - - -class TestFgsm: - def test_basic_fgsm(self): - """ - Test the fgsm attack. - """ - - attack = Attack.from_name("fgsm", epsilon=0.1) - assert attack.epsilon == 0.1 - inputs = torch.zeros(2, 4, 4) - - def compute_loss(inputs): - return (inputs**2).sum() - - adv = attack.perform(inputs, compute_loss) - assert torch.allclose(adv, inputs) diff --git a/tests/unit/test_adversarial.py b/tests/unit/test_adversarial.py new file mode 100644 index 0000000..86ecaea --- /dev/null +++ b/tests/unit/test_adversarial.py @@ -0,0 +1,19 @@ +"""Test of the fgsm attack. +""" + +import torch + +from mulsi import AdversarialImage + + +class TestFgsm: + def test_define_fgsm(self): + """ + Test the fgsm attack. + """ + base_image = torch.zeros(1, 3, 224, 224, dtype=torch.int) + clip_wrapper = None + llm_wrapper = None + adv_image = AdversarialImage(base_image, clip_wrapper, llm_wrapper) + adv = adv_image.adv + assert torch.allclose(adv, base_image) diff --git a/tests/unit/test_attack.py b/tests/unit/test_attack.py deleted file mode 100644 index b9c02b4..0000000 --- a/tests/unit/test_attack.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Module for testing the attack. -""" - -from mulsi.attack import Attack - - -class TestLoading: - def test_register_attack(self): - """ - Test register attack. - """ - - assert "test" not in Attack.all_attacks - - @Attack.register("test") - class TestAttack(Attack): - def perform(self, **kwargs): - """Perform the attack.""" - pass - - assert "test" in Attack.all_attacks - assert Attack.all_attacks["test"] == TestAttack diff --git a/tests/unit/test_reader.py b/tests/unit/test_reader.py deleted file mode 100644 index 55262c9..0000000 --- a/tests/unit/test_reader.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Module for testing the reader. -""" - -from mulsi.reader import RepresentationReader - - -class TestLoading: - def test_register_reader(self): - """ - Test register reader. - """ - - assert "test" not in RepresentationReader.all_readers - - @RepresentationReader.register("test") - class TestReader(RepresentationReader): - def read(self, **kwargs): - """Read the representations.""" - pass - - assert "test" in RepresentationReader.all_readers - assert RepresentationReader.all_readers["test"] == TestReader