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fc32096 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import argparse
import json
import numpy as np
import torch
from common import inside, save_json, permit_compute
from weights import load, project_writers, save_edit
def matched_direction(productive, repetitive):
a, b = np.asarray(productive), np.asarray(repetitive)
if a.shape != b.shape or a.ndim != 2:
raise ValueError('Нужны попарные средние активации одинаковой формы [contexts, width]')
direction = (b - a).mean(0)
norm = np.linalg.norm(direction)
if not np.isfinite(norm) or norm == 0:
raise ValueError('Направление не определено')
return direction / norm
def writer_scores(weight, direction, productive_mlp, repetitive_mlp):
coefficients = np.asarray(direction) @ weight.float().numpy()
deltas = np.asarray(repetitive_mlp) - np.asarray(productive_mlp)
values = deltas * coefficients
return values.mean(0), (values > 0).mean(0)
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument('--model', required=True)
p.add_argument('--tensor', required=True)
p.add_argument('--productive', required=True)
p.add_argument('--repetitive', required=True)
p.add_argument('--productive-mlp', required=True)
p.add_argument('--repetitive-mlp', required=True)
p.add_argument('--columns', type=int, required=True)
p.add_argument('--alpha', type=float, required=True)
p.add_argument('--out', required=True)
p.add_argument('--hypothesis', required=True)
a = p.parse_args()
permit_compute()
direction = matched_direction(np.load(inside(a.productive)), np.load(inside(a.repetitive)))
weight = load(a.model, a.tensor)
score, consistency = writer_scores(weight, direction, np.load(inside(a.productive_mlp)), np.load(inside(a.repetitive_mlp)))
if not 1 <= a.columns < weight.shape[1]:
raise ValueError('Неверное количество столбцов')
columns = np.argsort(score)[-a.columns:]
edited = project_writers(weight, direction, columns, a.alpha)
record = save_edit(a.model, a.out, a.tensor, edited, a.hypothesis)
save_json(inside(a.out) / (a.tensor + '.analysis.json'), {'columns': columns.tolist(), 'alpha': a.alpha,
'scores': score[columns].tolist(),
'consistency': consistency[columns].tolist(),
'input_files': [a.productive, a.repetitive, a.productive_mlp, a.repetitive_mlp]})
np.save(inside(a.out) / (a.tensor + '.direction.npy'), direction)
print(json.dumps(record, ensure_ascii=False))
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