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bbda22e648
Automated edits performed by FURB118. Operator is implemented in C and way faster when passed to another C method like sorted, max etc as a `key=` Pull Request resolved: https://github.com/pytorch/pytorch/pull/154722 Approved by: https://github.com/jansel
96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
import operator
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from typing import Optional
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from torch._inductor.autoheuristic.autoheuristic_utils import (
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AHContext,
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AHMetadata,
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Choice,
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)
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class LearnedHeuristic:
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"""
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LearnedHeuristic is a base class for all learned heuristics.
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"""
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def __init__(self) -> None:
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pass
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def check_precondition(
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self,
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metadata: AHMetadata,
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context: AHContext,
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) -> bool:
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return True
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def get_decision(
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self, context: AHContext, choices: list[Choice]
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) -> Optional[Choice]:
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return None
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def get_confidence_threshold(self) -> float:
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return 1.0
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def get_name(self) -> str:
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return ""
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def get_decisions_ranked(self, context: AHContext) -> Optional[list[str]]:
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return None
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class LearnedHeuristicRegression(LearnedHeuristic):
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def __init__(self) -> None:
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super().__init__()
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def get_feedback(self, context: AHContext, choice: Choice) -> float:
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return 1.0
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def get_decision(
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self, context: AHContext, choices: list[Choice]
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) -> Optional[Choice]:
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choice2feedback = {}
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for choice in choices:
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predicted_feedback = self.get_feedback(context, choice)
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choice2feedback[choice] = predicted_feedback
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sorted_choices_feedback = sorted(
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choice2feedback.items(), key=operator.itemgetter(1)
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)
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highest_feedback = sorted_choices_feedback[-1][1]
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second_highest_feedback = sorted_choices_feedback[-2][1]
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if highest_feedback / second_highest_feedback > self.get_confidence_threshold():
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return sorted_choices_feedback[-1][0]
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# We are not sure which choice is the best one
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return None
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class LearnedHeuristicDecision(LearnedHeuristic):
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def __init__(self) -> None:
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super().__init__()
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def get_choice(self, idx: int) -> Optional[str]:
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return None
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def get_decision(
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self, context: AHContext, choices: list[Choice]
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) -> Optional[Choice]:
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best_choices = self.get_best_choices(context)
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if not best_choices:
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return None
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(best_choice_proba, best_choice_idx) = best_choices[0]
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if best_choice_proba <= self.get_confidence_threshold():
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return None
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return self.get_choice(best_choice_idx)
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def get_decisions_ranked(self, context: AHContext) -> Optional[list[str]]:
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feedback_idx_list = self.get_best_choices(context)
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if feedback_idx_list is None:
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return None
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choices = [
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self.get_choice(feedback_idx[1]) for feedback_idx in feedback_idx_list
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]
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choices = [choice for choice in choices if choice is not None]
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return choices
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def get_best_choices(self, context: AHContext) -> Optional[list[tuple[float, int]]]:
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return []
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