Simple Python library/structure to ablate features in LLMs which are supported by TransformerLens.
Most of its advantage in workflow comes from being able to enter temporary contexts, quickly cache activations with N samples, refusal direction calculation built-in, and tokenizer utilities. As well as wrapping around certain quirks of TransformerLens.
If you're interested in notebooking your own orthgonalized model, this library will help save you a LOT of time in performing and measuring experiments to find your best orthogonalization.
This is ultimately just bits and pieces to make it so to process and experiment with ablation direction turns into shorter, hopefully clearer code without losing track of where things are at; encapsulating a lot of useful logic that you'll find yourself writing if you're looking to do this more.
This library is so exceedingly barebones for right now, and documentation is slim at the moment (WYSIWYG!). Right now, it feels like a glorified IPython notebook rather than something more broadly useful. I want to publish this now to lay out the template, and hopefully bring this up to snuff over time. Ideally with the help of the community!
Right now, this works very well for my own personal workflow, but I would like to systematize and ideally automate this further, and broaden out from the pure "harmless / harmful" feature ablation, to augmentation, and adding additional features
import abliterator
model = "meta-llama/Meta-Llama-3-70B-Instruct" # the huggingface or path to the model you're interested in loading in
dataset = [abliterator.get_harmful_instructions(), abliterator.get_harmless_instructions()] # datasets to be used for caching and testing, split by harmful/harmless
device = 'cuda' # optional: defaults to cuda
n_devices = None # optional: when set to None, defaults to `device.cuda.device_count`
cache_fname = 'my_cached_point.pth' # optional: if you need to save where you left off, you can use `save_activations(filename)` which will write out a file. This is how you load that back in.
activation_layers = None # optional: defaults to ['resid_pre', 'resid_mid', 'resid_post'] which are the residual streams. Setting to None will cache ALL activation layer types
chat_template = None # optional: defaults to Llama-3 instruction template. You can use a format string e.g. ("<system>{instruction}<end><assistant>") or a custom class with format function -- it just needs an '.format(instruction="")` function. See abliterator.ChatTemplate for a very basic structure.
negative_toks = [4250] # optional, but highly recommended: ' cannot' in Llama's tokenizer. Tokens you don't want to be seeing. Defaults to my preset for Llama-3 models
positive_toks = [23371, 40914] # optional, but highly recommended: ' Sure' and 'Sure' in Llama's tokenizer. Tokens you want to be seeing, basically. Defaults to my preset for Llama-3 models
my_model = abliterator.ModelAbliterator(
model,
dataset,
device='cuda',
n_devices=None,
cache_fname=None,
activation_layers=['resid_pre', 'resid_post', 'attn_out', 'mlp_out'],
chat_template="<system>\n{instruction}<end><assistant>",
positive_toks=positive_toks,
negative_toks=negative_toks
)Once loaded in, run the model against N samples of harmful, and N samples of harmless so it has some data to work with:
my_model.cache_activations(N=512,reset=True,preserve_harmless=True)preserve_harmless=True is generally useful, as it keeps the "desired behaviour" unaltered from any stacked modifications if you run it after some mods.
Most of the advantage of this is a lot of groundwork has been laid to make it so you aren't repeating yourself 1000 times just to try one little experiment.
save_activations('file.pth') will save your cached activations, and any currently applied modifications to the model's weights to a file so you can restore them next time you load up with cache_fname='file.pth' in your ModelAbliterator initialization.
Speaking of modding, here's a simple representation of how to pick, test, and actually apply a direction from a layer's activations:
refusal_dirs = my_model.refusal_dirs()
testing_dir = refusal_dirs['blocks.18.hook_resid_pre']
my_model.test_dir(testing_dir, N=32, use_hooks=True) # I recommend use_hooks=True for large models as it can slow things down otherwise, but use_hooks=False can give you more precise scoring to an actual weights modificationtest_dir will apply your refusal_dir to the model temporarily, and run against N samples of test data, and return a composite (negative_score, positive_score) from those runs. Generally, you want negative_score to go down, positive_score to go up.
This is one of the functions included in the library, but it's also useful for showing how this can be generalized to test a whole bunch of directions.
def find_best_refusal_dir(N=4, use_hooks=True, invert=False):
dirs = self.refusal_dirs(invert=invert)
scores = []
for direction in tqdm(dirs.items()):
score = self.test_dir(direction[1],N=N,use_hooks=use_hooks)[0]
scores.append((score,direction))
return sorted(scores,key=lambda x:x[0])And now, to apply it!
my_amazing_dir = find_best_refusal_dir()[0]
my_model.apply_refusal_dirs([my_amazing_dir],layers=None)Note the layers=None. You can supply a list here to specify which layers you want to apply the refusal direction to. None will apply it to all writable layers.
Sometimes some layers are troublesome no matter what you do. If you're worried about accidentally replacing it, you can blacklist it to prevent any alteration from occurring:
my_model.blacklist_layer(27)
my_model.blacklist_layer([i for i in range(27,30)]) # it also accepts lists!And naturally, to undo this and make sure a layer can be overwritten:
my_model.whitelist_layer(27)
By default, all layers are whitelisted. I recommend blacklisting the first and last couple layers, as those can and will have dramatic effects on outputs.
Neither of these will provide success/failure states. They will just assure the desired state in running it at that instant.
Now to make sure you've not damaged the model dramatically after applying some stuff, you can do a test run:
with my_model: # loads a temporary context with the model
ortho.apply_refusal_dir([my_new_precious_dir]) # Because this was applied in the 'with my_model:', it will be unapplied after coming out.
print(my_model.mse_harmless(N=128)) # While we've got the dir applied, this tells you the Mean Squared Error using the current cached harmless runs as "ground truth" (loss function, effectively)ortho.test(N=16,batch_size = 4) # runs N samples from the harmful test set and prints them for the user. Good way to check the model hasn't completely derailed.
# Note that by default if a test run produces a negative token, it will stop the whole batch and move on to the next. (it will show lots of '!!!!' in Llama-3's case, as that's token ID 0)
ortho.generate("How much wood could a woodchuck chuck if a woodchuck could chuck wood?") # runs and prints the prompt!Documentation coming soon.
Functionality coming soon. For now, use PyTorch's saving method, or see my notebook for an idea of how to do this yourself.
