test: add unit tests for skillopt.optimizer.scheduler
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黄云龙
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"""Tests for skillopt.optimizer.scheduler — edit-budget schedulers.
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ReflACT trainers use an edit-budget scheduler at each optimisation step to
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control how many skill edits are allowed (analogous to gradient clipping /
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learning-rate annealing in neural-network training).
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This module has zero LLM dependencies — all behaviour is deterministic pure
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math — making it an ideal target for precise unit tests.
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"""
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from __future__ import annotations
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import pytest
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from skillopt.optimizer.scheduler import (
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LRScheduler,
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ConstantScheduler,
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LinearScheduler,
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CosineScheduler,
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AutonomousScheduler,
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build_scheduler,
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)
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# ── ConstantScheduler ────────────────────────────────────────────────────────
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class TestConstantScheduler:
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"""ConstantScheduler — fixed edit budget regardless of step."""
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def test_always_returns_max_lr(self) -> None:
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s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
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for _ in range(10):
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assert s.step() == 8
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def test_step_advances_internal_counter(self) -> None:
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s = ConstantScheduler(max_lr=5, min_lr=1, total_steps=5)
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assert s._current_step == 0
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s.step()
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assert s._current_step == 1
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s.step()
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assert s._current_step == 2
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def test_get_lr_returns_max_for_arbitrary_step(self) -> None:
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s = ConstantScheduler(max_lr=12, min_lr=1, total_steps=100)
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assert s.get_lr(1) == 12
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assert s.get_lr(50) == 12
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assert s.get_lr(999) == 12
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def test_state_dict_and_load_state_dict_round_trip(self) -> None:
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s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
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for _ in range(3):
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s.step()
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assert s._current_step == 3
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state = s.state_dict()
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s2 = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
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s2.load_state_dict(state)
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assert s2._current_step == 3
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# Step after resume lands on the correct step
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assert s2.step() == 8
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assert s2._current_step == 4
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def test_load_state_dict_with_missing_key_defaults_to_zero(self) -> None:
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s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
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s.load_state_dict({})
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assert s._current_step == 0
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# ── LinearScheduler ──────────────────────────────────────────────────────────
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class TestLinearScheduler:
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"""LinearScheduler — linear decay from max_lr to min_lr."""
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def test_first_step_returns_max_lr(self) -> None:
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
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assert s.step() == 10
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def test_last_step_returns_min_lr(self) -> None:
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
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for _ in range(9):
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s.step()
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assert s.step() == 2
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def test_all_steps_return_integers(self) -> None:
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s = LinearScheduler(max_lr=8, min_lr=2, total_steps=6)
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for _ in range(6):
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lr = s.step()
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assert isinstance(lr, int)
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def test_total_steps_one_returns_max_lr(self) -> None:
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=1)
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assert s.step() == 10
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def test_total_steps_zero_returns_max_lr(self) -> None:
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"""Degenerate case: 0-step training still gets max_lr on the one call."""
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=0)
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assert s.step() == 10
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def test_monotonically_non_increasing(self) -> None:
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s = LinearScheduler(max_lr=20, min_lr=2, total_steps=100)
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prev: int = 999
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for _ in range(100):
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lr = s.step()
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assert lr <= prev
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prev = lr
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def test_never_below_min_lr(self) -> None:
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
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for _ in range(20): # overshoot
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assert s.step() >= 2
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def test_never_above_max_lr(self) -> None:
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
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for _ in range(20):
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assert s.step() <= 10
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def test_after_total_steps_stays_at_min_lr(self) -> None:
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s = LinearScheduler(max_lr=8, min_lr=2, total_steps=5)
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for _ in range(5):
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s.step()
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# Steps beyond total_steps should plateau at min_lr
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for _ in range(5):
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assert s.step() == 2
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def test_known_decay_sequence(self) -> None:
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"""Linear decay max_lr=10, min_lr=2, total_steps=4 (t=0.25,0.50,0.75,1.0).
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lr = 10 + (2-10)*t = 10 - 8t
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t=0.25: lr=8.0, t=0.50: lr=6.0, t=0.75: lr=4.0, t=1.0: lr=2.0
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"""
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s = LinearScheduler(max_lr=10, min_lr=2, total_steps=4)
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assert s.step() == 8
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assert s.step() == 6
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assert s.step() == 4
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assert s.step() == 2
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def test_step_state_dict_resume_consistent(self) -> None:
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"""After resume, the next step value is the same as without resume."""
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s1 = LinearScheduler(max_lr=10, min_lr=2, total_steps=5)
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for _ in range(3):
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s1.step()
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resumed_lr = s1.step() # step 4
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s2 = LinearScheduler(max_lr=10, min_lr=2, total_steps=5)
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s2.load_state_dict({"current_step": 3})
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assert s2.step() == resumed_lr
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def test_max_lr_equals_min_lr_yields_constant(self) -> None:
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s = LinearScheduler(max_lr=5, min_lr=5, total_steps=10)
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for _ in range(10):
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assert s.step() == 5
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# ── CosineScheduler ──────────────────────────────────────────────────────────
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class TestCosineScheduler:
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"""CosineScheduler — cosine annealing from max_lr to min_lr."""
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def test_first_step_returns_max_lr(self) -> None:
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s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
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assert s.step() == 10
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def test_last_step_returns_min_lr(self) -> None:
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s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
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for _ in range(9):
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s.step()
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assert s.step() == 2
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def test_all_steps_return_integers(self) -> None:
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s = CosineScheduler(max_lr=8, min_lr=2, total_steps=6)
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for _ in range(6):
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lr = s.step()
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assert isinstance(lr, int)
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def test_total_steps_one_returns_max_lr(self) -> None:
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s = CosineScheduler(max_lr=10, min_lr=2, total_steps=1)
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assert s.step() == 10
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def test_total_steps_zero_returns_max_lr(self) -> None:
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s = CosineScheduler(max_lr=10, min_lr=2, total_steps=0)
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assert s.step() == 10
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def test_monotonically_non_increasing(self) -> None:
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s = CosineScheduler(max_lr=20, min_lr=2, total_steps=100)
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prev: int = 999
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for _ in range(100):
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lr = s.step()
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assert lr <= prev
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prev = lr
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def test_never_below_min_lr(self) -> None:
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s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
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for _ in range(20):
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assert s.step() >= 2
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def test_never_above_max_lr(self) -> None:
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s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
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for _ in range(20):
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assert s.step() <= 10
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def test_after_total_steps_stays_at_min_lr(self) -> None:
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s = CosineScheduler(max_lr=8, min_lr=2, total_steps=5)
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for _ in range(5):
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s.step()
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for _ in range(5):
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assert s.step() == 2
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def test_midpoint_close_to_mean(self) -> None:
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"""At the half-way point, cosine gives (max+min)/2 exactly."""
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s = CosineScheduler(max_lr=20, min_lr=2, total_steps=100)
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for _ in range(49):
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s.step()
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mid = s.step() # step 50 / 100, t=0.5, cos(pi/2)=0
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# lr = min + 0.5*(max-min)*(1+0) = (max+min)/2 = 11
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assert mid == 11
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def test_step_state_dict_resume_consistent(self) -> None:
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s1 = CosineScheduler(max_lr=10, min_lr=2, total_steps=5)
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for _ in range(3):
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s1.step()
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resumed_lr = s1.step()
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s2 = CosineScheduler(max_lr=10, min_lr=2, total_steps=5)
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s2.load_state_dict({"current_step": 3})
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assert s2.step() == resumed_lr
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def test_max_lr_equals_min_lr_yields_constant(self) -> None:
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s = CosineScheduler(max_lr=5, min_lr=5, total_steps=10)
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for _ in range(10):
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assert s.step() == 5
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def test_early_steps_near_max(self) -> None:
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"""Cosine annealing stays flat near max_lr early on (cos(0)≈1)."""
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s = CosineScheduler(max_lr=100, min_lr=0, total_steps=100)
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# step 1: t=0.01, cos(0.01*pi)≈0.9995 → lr≈99.97 → 100
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assert s.step() == 100
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# step 2: t=0.02, cos≈0.9980 → lr≈99.9 → 100
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assert s.step() == 100
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def test_late_steps_near_min(self) -> None:
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"""Cosine annealing flattens near min_lr at the end (cos(pi)≈-1)."""
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s = CosineScheduler(max_lr=100, min_lr=0, total_steps=100)
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for _ in range(99):
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s.step()
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# step 100: t=1.0, cos(pi)=-1 → lr=0
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assert s.step() == 0
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# ── AutonomousScheduler ──────────────────────────────────────────────────────
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class TestAutonomousScheduler:
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"""AutonomousScheduler — no edit limit (model decides freely)."""
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def test_always_returns_no_limit(self) -> None:
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s = AutonomousScheduler(max_lr=8, min_lr=2, total_steps=10)
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for _ in range(20):
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assert s.step() == AutonomousScheduler.NO_LIMIT
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def test_step_advances_counter(self) -> None:
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s = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=5)
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assert s._current_step == 0
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s.step()
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assert s._current_step == 1
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def test_get_lr_returns_no_limit(self) -> None:
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s = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=10)
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assert s.get_lr(1) == AutonomousScheduler.NO_LIMIT
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assert s.get_lr(50) == AutonomousScheduler.NO_LIMIT
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def test_state_dict_round_trip(self) -> None:
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s = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=10)
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for _ in range(4):
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s.step()
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s2 = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=10)
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s2.load_state_dict(s.state_dict())
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assert s2._current_step == 4
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# ── build_scheduler factory ──────────────────────────────────────────────────
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class TestBuildScheduler:
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"""build_scheduler factory — creates the right scheduler from a mode name."""
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def test_constant(self) -> None:
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s = build_scheduler("constant", max_lr=8, min_lr=2, total_steps=10)
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assert isinstance(s, ConstantScheduler)
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assert s.max_lr == 8
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assert s.min_lr == 2
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assert s.total_steps == 10
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def test_linear(self) -> None:
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s = build_scheduler("linear", max_lr=12, min_lr=3, total_steps=20)
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assert isinstance(s, LinearScheduler)
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def test_cosine(self) -> None:
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s = build_scheduler("cosine", max_lr=16, min_lr=4, total_steps=30)
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assert isinstance(s, CosineScheduler)
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def test_autonomous(self) -> None:
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s = build_scheduler("autonomous", max_lr=8, min_lr=2, total_steps=10)
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assert isinstance(s, AutonomousScheduler)
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def test_unknown_mode_raises_value_error(self) -> None:
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with pytest.raises(ValueError, match="Unknown scheduler mode"):
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build_scheduler("exponential", max_lr=8, min_lr=2, total_steps=10)
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def test_default_mode_is_constant(self) -> None:
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s = build_scheduler(max_lr=8, min_lr=2, total_steps=10)
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assert isinstance(s, ConstantScheduler)
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# ── Abstract base class ──────────────────────────────────────────────────────
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class TestLRSchedulerBase:
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"""LRScheduler — abstract base: cannot be instantiated directly."""
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def test_cannot_instantiate_abstract(self) -> None:
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with pytest.raises(TypeError):
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LRScheduler(max_lr=8, min_lr=2, total_steps=10) # type: ignore[abstract]
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def test_concrete_subclass_instantiates_fine(self) -> None:
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"""ConstantScheduler (and others) work normally."""
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s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
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assert isinstance(s, LRScheduler)
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assert s.step() == 8
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