# Dataclasses and enums for storing neuron explanations, their scores, and related data. Also, # related helper functions. from __future__ import annotations import json from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union import blobfile as bf import boostedblob as bbb from neuron_explainer.activations.activations import NeuronId from neuron_explainer.fast_dataclasses import FastDataclass, loads, register_dataclass class ActivationScale(str, Enum): """Which "units" are stored in the expected_activations/distribution_values fields of a SequenceSimulation. This enum identifies whether the values represent real activations of the neuron or something else. Different scales are not necessarily related by a linear transformation. """ NEURON_ACTIVATIONS = "neuron_activations" """Values represent real activations of the neuron.""" SIMULATED_NORMALIZED_ACTIVATIONS = "simulated_normalized_activations" """ Values represent simulated activations of the neuron, normalized to the range [0, 10]. This scale is arbitrary and should not be interpreted as a neuron activation. """ @register_dataclass @dataclass class SequenceSimulation(FastDataclass): """The result of a simulation of neuron activations on one text sequence.""" tokens: list[str] """The sequence of tokens that was simulated.""" expected_activations: list[float] """Expected value of the possibly-normalized activation for each token in the sequence.""" activation_scale: ActivationScale """What scale is used for values in the expected_activations field.""" distribution_values: list[list[float]] """ For each token in the sequence, a list of values from the discrete distribution of activations produced from simulation. Tokens will be included here if and only if they are in the top K=15 tokens predicted by the simulator, and excluded otherwise. May be transformed to another unit by calibration. When we simulate a neuron, we produce a discrete distribution with values in the arbitrary discretized space of the neuron, e.g. 10% chance of 0, 70% chance of 1, 20% chance of 2. Which we store as distribution_values = [0, 1, 2], distribution_probabilities = [0.1, 0.7, 0.2]. When we transform the distribution to the real activation units, we can correspondingly transform the values of this distribution to get a distribution in the units of the neuron. e.g. if the mapping from the discretized space to the real activation unit of the neuron is f(x) = x/2, then the distribution becomes 10% chance of 0, 70% chance of 0.5, 20% chance of 1. Which we store as distribution_values = [0, 0.5, 1], distribution_probabilities = [0.1, 0.7, 0.2]. """ distribution_probabilities: list[list[float]] """ For each token in the sequence, the probability of the corresponding value in distribution_values. """ uncalibrated_simulation: Optional["SequenceSimulation"] = None """The result of the simulation before calibration.""" @register_dataclass @dataclass class ScoredSequenceSimulation(FastDataclass): """ SequenceSimulation result with a score (for that sequence only) and ground truth activations. """ simulation: SequenceSimulation """The result of a simulation of neuron activations.""" true_activations: List[float] """Ground truth activations on the sequence (not normalized)""" ev_correlation_score: float """ Correlation coefficient between the expected values of the normalized activations from the simulation and the unnormalized true activations of the neuron on the text sequence. """ rsquared_score: Optional[float] = None """R^2 of the simulated activations.""" absolute_dev_explained_score: Optional[float] = None """ Score based on absolute difference between real and simulated activations. absolute_dev_explained_score = 1 - mean(abs(real-predicted))/ mean(abs(real)) """ @register_dataclass @dataclass class ScoredSimulation(FastDataclass): """Result of scoring a neuron simulation on multiple sequences.""" scored_sequence_simulations: List[ScoredSequenceSimulation] """ScoredSequenceSimulation for each sequence""" ev_correlation_score: Optional[float] = None """ Correlation coefficient between the expected values of the normalized activations from the simulation and the unnormalized true activations on a dataset created from all score_results. (Note that this is not equivalent to averaging across sequences.) """ rsquared_score: Optional[float] = None """R^2 of the simulated activations.""" absolute_dev_explained_score: Optional[float] = None """ Score based on absolute difference between real and simulated activations. absolute_dev_explained_score = 1 - mean(abs(real-predicted))/ mean(abs(real)). """ def get_preferred_score(self) -> Optional[float]: """ This method may return None in cases where the score is undefined, for example if the normalized activations were all zero, yielding a correlation coefficient of NaN. """ return self.ev_correlation_score @register_dataclass @dataclass class ScoredExplanation(FastDataclass): """Simulator parameters and the results of scoring it on multiple sequences""" explanation: str """The explanation used for simulation.""" scored_simulation: ScoredSimulation """Result of scoring the neuron simulator on multiple sequences.""" def get_preferred_score(self) -> Optional[float]: """ This method may return None in cases where the score is undefined, for example if the normalized activations were all zero, yielding a correlation coefficient of NaN. """ return self.scored_simulation.get_preferred_score() @register_dataclass @dataclass class NeuronSimulationResults(FastDataclass): """Simulation results and scores for a neuron.""" neuron_id: NeuronId scored_explanations: list[ScoredExplanation] def load_neuron_explanations( explanations_path: str, layer_index: Union[str, int], neuron_index: Union[str, int] ) -> Optional[NeuronSimulationResults]: """Load scored explanations for the specified neuron.""" file = bf.join(explanations_path, str(layer_index), f"{neuron_index}.jsonl") if not bf.exists(file): return None with bf.BlobFile(file) as f: for line in f: return loads(line) return None @bbb.ensure_session async def load_neuron_explanations_async( explanations_path: str, layer_index: Union[str, int], neuron_index: Union[str, int] ) -> Optional[NeuronSimulationResults]: """Load scored explanations for the specified neuron, asynchronously.""" return await read_explanation_file( bf.join(explanations_path, str(layer_index), f"{neuron_index}.jsonl") ) @bbb.ensure_session async def read_file(filename: str) -> Optional[str]: """Read the contents of the given file as a string, asynchronously.""" try: raw_contents = await bbb.read.read_single(filename) except FileNotFoundError: print(f"Could not read {filename}") return None lines = [] for line in raw_contents.decode("utf-8").split("\n"): if len(line) > 0: lines.append(line) assert len(lines) == 1, filename return lines[0] @bbb.ensure_session async def read_explanation_file(explanation_filename: str) -> Optional[NeuronSimulationResults]: """Load scored explanations from the given filename, asynchronously.""" line = await read_file(explanation_filename) return loads(line) if line is not None else None @bbb.ensure_session async def read_json_file(filename: str) -> Optional[dict]: """Read the contents of the given file as a JSON object, asynchronously.""" line = await read_file(filename) return json.loads(line) if line is not None else None def get_numerical_subdirs(dataset_path: str) -> list[str]: """Return the names of all numbered subdirectories in the specified directory. Used to get all layer directories in an explanation directory. """ return [ str(x) for x in sorted( [ int(x) for x in bf.listdir(dataset_path) if bf.isdir(bf.join(dataset_path, x)) and x.isnumeric() ] ) ] def get_sorted_neuron_indices_from_explanations( explanations_path: str, layer: Union[str, int] ) -> list[int]: """Return the indices of all neurons in this layer, in ascending order.""" layer_dir = bf.join(explanations_path, str(layer)) return sorted( [int(f.split(".")[0]) for f in bf.listdir(layer_dir) if f.split(".")[0].isnumeric()] )