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| # Copyright 2022 DeepMind Technologies Limited. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """RASP Evaluator which applies causal masks to selectors.""" | |
| from typing import Sequence, Union | |
| import numpy as np | |
| from tracr.rasp import rasp | |
| class CausalEvaluator(rasp.DefaultRASPEvaluator): | |
| """Evaluates RASP with causal masking.""" | |
| def evaluate( | |
| self, expr: rasp.RASPExpr, xs: Sequence[rasp.Value] | |
| ) -> Union[Sequence[rasp.Value], rasp.SelectorValue]: | |
| out = super().evaluate(expr, xs) | |
| if not isinstance(expr, rasp.Selector): | |
| return out | |
| out = np.array(out) | |
| causal_mask = np.tril(np.full(out.shape, 1)) | |
| return np.logical_and(causal_mask, out).tolist() | |
| evaluate = CausalEvaluator().evaluate | |