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First attempt at fitting one sided directions
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#%% | ||
import einops | ||
from functools import partial | ||
import torch | ||
from torch import Tensor | ||
from torch.utils.data import DataLoader | ||
from datasets import load_dataset | ||
from jaxtyping import Float, Int, Bool | ||
from typing import Dict, Iterable, List, Tuple, Union | ||
from transformer_lens import HookedTransformer | ||
from transformer_lens.utils import get_dataset, tokenize_and_concatenate, get_act_name, test_prompt | ||
from transformer_lens.hook_points import HookPoint | ||
from tqdm.notebook import tqdm | ||
import pandas as pd | ||
import yaml | ||
from utils.store import load_array, save_html, save_array, is_file, get_model_name, clean_label, save_text | ||
#%% | ||
torch.set_grad_enabled(False) | ||
device = "cuda" | ||
model = HookedTransformer.from_pretrained( | ||
"gpt2-small", | ||
) | ||
#%% | ||
BATCH_SIZE = 64 | ||
owt_data = load_dataset("stas/openwebtext-10k", split="train") | ||
dataset = tokenize_and_concatenate(owt_data, model.tokenizer) | ||
data_loader = DataLoader( | ||
dataset, batch_size=BATCH_SIZE, shuffle=False, drop_last=True | ||
) | ||
#%% | ||
ACT_NAME = get_act_name("resid_post", 0) | ||
#%% # Neutral | ||
count = 0 | ||
total = torch.zeros(model.cfg.d_model) | ||
for batch in tqdm(data_loader): | ||
_, cache = model.run_with_cache( | ||
batch['tokens'], | ||
return_type=None, | ||
names_filter = lambda name: name == ACT_NAME | ||
) | ||
count += 1 | ||
total += cache[ACT_NAME][:, 1, :].mean(dim=0).cpu() | ||
neutral_activation = total / count | ||
print(neutral_activation.shape, neutral_activation.norm()) | ||
#%% # Positive | ||
with open("prompts.yaml", "r") as f: | ||
prompt_dict = yaml.safe_load(f) | ||
#%% | ||
positive_str_tokens = ( | ||
prompt_dict['positive_adjectives_train'] + | ||
prompt_dict['positive_comment_adjectives'] + | ||
prompt_dict['positive_nouns'] + | ||
prompt_dict['positive_verbs'] + | ||
prompt_dict['positive_infinitives'] | ||
) | ||
positive_single_tokens = [] | ||
for token in positive_str_tokens: | ||
token = " " + token | ||
if len(model.to_str_tokens(token, prepend_bos=False)) == 1: | ||
positive_single_tokens.append(token) | ||
positive_tokens = model.to_tokens( | ||
positive_single_tokens, | ||
prepend_bos=True, | ||
) | ||
assert positive_tokens.shape[1] == 2 | ||
_, positive_cache = model.run_with_cache( | ||
positive_tokens, | ||
return_type=None, | ||
names_filter = lambda name: name == ACT_NAME | ||
) | ||
positive_activation = positive_cache[ACT_NAME][:, -1].mean(dim=0).cpu() | ||
print(positive_activation.shape, positive_activation.norm()) | ||
#%% # Negative | ||
negative_str_tokens = ( | ||
prompt_dict['negative_adjectives_train'] + | ||
prompt_dict['negative_comment_adjectives'] + | ||
prompt_dict['negative_nouns'] + | ||
prompt_dict['negative_verbs'] + | ||
prompt_dict['negative_infinitives'] | ||
) | ||
negative_single_tokens = [] | ||
for token in negative_str_tokens: | ||
token = " " + token | ||
if len(model.to_str_tokens(token, prepend_bos=False)) == 1: | ||
negative_single_tokens.append(token) | ||
negative_tokens = model.to_tokens( | ||
negative_single_tokens, | ||
prepend_bos=True, | ||
) | ||
assert negative_tokens.shape[1] == 2 | ||
_, negative_cache = model.run_with_cache( | ||
negative_tokens, | ||
return_type=None, | ||
names_filter = lambda name: name == ACT_NAME | ||
) | ||
negative_activation = negative_cache[ACT_NAME][:, -1].mean(dim=0).cpu() | ||
print(negative_activation.shape, negative_activation.norm()) | ||
# %% | ||
positive_direction = positive_activation - neutral_activation | ||
negative_direction = negative_activation - neutral_activation | ||
positive_direction = positive_direction / positive_direction.norm() | ||
negative_direction = negative_direction / negative_direction.norm() | ||
#%% | ||
save_array( | ||
positive_direction.cpu().numpy(), "mean_diff_positive_layer01", model | ||
) | ||
save_array( | ||
negative_direction.cpu().numpy(), "mean_diff_negative_layer01", model | ||
) | ||
#%% # compute cosine similarity | ||
torch.cosine_similarity(positive_direction, negative_direction, dim=0) | ||
|
||
# %% |