default pattern search #1259
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default pattern search #1259
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Dec 14, 2025
| from ..runtime.kernel import BoundKernel | ||
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| class DefaultPatternSearch(PatternSearch): |
Contributor
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Suggested change
| class DefaultPatternSearch(PatternSearch): | |
| class QuickPatternSearch(PatternSearch): |
I worry people won't understand Default means the default config.
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| def _autotune(self) -> Config: | ||
| self.log( | ||
| f"Starting PatternSearch with initial_population={self.initial_population}, copies={self.copies}, max_generations={self.max_generations}" | ||
| ) | ||
| visited = set() | ||
| self.population = [] | ||
| for flat_config in [self.config_gen.default_flat()]: | ||
| member = self.make_unbenchmarked(flat_config) | ||
| if member.config not in visited: | ||
| visited.add(member.config) | ||
| self.population.append(member) | ||
| self.set_generation(0) | ||
| self.parallel_benchmark_population(self.population, desc="Initial population") | ||
| # again with higher accuracy | ||
| self.rebenchmark_population(self.population, desc="Verifying initial results") | ||
| self.population.sort(key=performance) | ||
| starting_points = [] | ||
| for member in self.population[: self.copies]: | ||
| if math.isfinite(member.perf): # filter failed compiles | ||
| starting_points.append(member) | ||
| self.log( | ||
| f"Initial random population of {len(self.population)}, {len(starting_points)} starting points:", | ||
| self.statistics, | ||
| ) | ||
| if not starting_points: | ||
| raise exc.NoConfigFound | ||
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| search_copies = [self._pattern_search_from(m, visited) for m in starting_points] | ||
| for generation in range(1, self.max_generations + 1): | ||
| prior_best = self.best | ||
| new_population = {id(prior_best): prior_best} | ||
| num_neighbors = 0 | ||
| num_active = 0 | ||
| for search_copy in search_copies: | ||
| added = next(search_copy, ()) | ||
| if added: | ||
| assert len(added) > 1 | ||
| num_active += 1 | ||
| num_neighbors += len(added) - 1 | ||
| for member in added: | ||
| new_population[id(member)] = member | ||
| if num_active == 0: | ||
| break | ||
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| # Log generation header before compiling/benchmarking | ||
| self.log( | ||
| f"Generation {generation} starting: {num_neighbors} neighbors, {num_active} active search path(s)" | ||
| ) | ||
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| self.population = [*new_population.values()] | ||
| # compile any unbenchmarked members in parallel | ||
| unbenchmarked = [m for m in self.population if len(m.perfs) == 0] | ||
| if unbenchmarked: | ||
| self.set_generation(generation) | ||
| self.parallel_benchmark_population( | ||
| unbenchmarked, desc=f"Generation {generation}:" | ||
| ) | ||
| # higher-accuracy rebenchmark | ||
| self.rebenchmark_population( | ||
| self.population, desc=f"Generation {generation}: verifying top configs" | ||
| ) | ||
| # Log final statistics for this generation | ||
| self.log(f"Generation {generation} complete:", self.statistics) | ||
| return self.best.config |
Contributor
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Can we refactor things so this shares more code with the base class?
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