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77 lines
2.4 KiB
77 lines
2.4 KiB
from math import log
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from random import random
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from typing import List, Optional, Tuple
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from gbso.program.test_case import Output, TestCase, eq_on_testcase
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from gbso.program.mutate import mutate_program
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from gbso.program.program import Program
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EPSILON = 0.00001
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DEFAULT_NUM_ITERS = 1_000_000
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DEFAULT_PROB_OPCODE = 0.25
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DEFAULT_PROB_OPERAND = 0.25
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DEFAULT_PROB_SWAP = 0.25
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DEFAULT_PROB_INSN = 0.25
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DEFAULT_PROB_INSN_UNUSED = 0.1
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def cost(orig_prgm, test_cases, outputs, prgm) -> Tuple[int, bool]:
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# Since each instruction executes in 4*k cycles (for some k), this can have
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# the undesirable effect of performance improvements being weighted much
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# higher than correctness. This hurts convergence pretty badly, so we scale
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# by 1/4 to compensate.
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perf = (prgm.perf() - orig_prgm.perf()) / 4.0
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eq = 0
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for test_case in test_cases:
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eq += eq_on_testcase(orig_prgm, prgm, test_case, outputs)
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return perf + eq, eq == 0
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def optimize(
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target_prgm: Program,
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max_size: int,
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test_cases: List[TestCase],
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outputs: List[Output],
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beta: int = 0.5, # How far away in cost you are allowed to search
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init_prgm: Optional[Program] = None,
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num_iters: int = DEFAULT_NUM_ITERS,
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prob_opcode: float = DEFAULT_PROB_OPCODE,
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prob_operand: float = DEFAULT_PROB_OPERAND,
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prob_swap: float = DEFAULT_PROB_SWAP,
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prob_insn: float = DEFAULT_PROB_INSN,
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prob_insn_unused: float = DEFAULT_PROB_INSN_UNUSED,
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) -> Program:
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padded_prgm = (init_prgm or target_prgm).pad(max_size)
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last_prgm = padded_prgm
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last_cost, _last_eq = cost(target_prgm, test_cases, outputs, last_prgm)
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best_prgm = target_prgm.pad(max_size)
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best_cost = 0
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num_candidates = 0
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for _ in range(num_iters):
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candidate_prgm = mutate_program(
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last_prgm, prob_opcode, prob_operand, prob_swap, prob_insn, prob_insn_unused
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)
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candidate_cost, candidate_eq = cost(
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target_prgm, test_cases, outputs, candidate_prgm
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)
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if candidate_cost < best_cost and candidate_eq:
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best_prgm = candidate_prgm
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best_cost = candidate_cost
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num_candidates += 1
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if candidate_cost < last_cost - log(random()) / beta:
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last_prgm = candidate_prgm
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last_cost = candidate_cost
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print(f"Optimization complete. Total candidates: {num_candidates}")
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return best_prgm
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