test: add unit tests for skillopt.optimizer.scheduler (edit-budget schedulers)

tests/test_scheduler.py

@@ -0,0 +1,329 @@
+"""Tests for skillopt.optimizer.scheduler — edit-budget schedulers.
+
+ReflACT trainers use an edit-budget scheduler at each optimisation step to
+control how many skill edits are allowed (analogous to gradient clipping /
+learning-rate annealing in neural-network training).
+
+This module has zero LLM dependencies — all behaviour is deterministic pure
+math — making it an ideal target for precise unit tests.
+"""
+from __future__ import annotations
+
+import pytest
+
+from skillopt.optimizer.scheduler import (
+    LRScheduler,
+    ConstantScheduler,
+    LinearScheduler,
+    CosineScheduler,
+    AutonomousScheduler,
+    build_scheduler,
+)
+
+
+# ── ConstantScheduler ────────────────────────────────────────────────────────
+
+
+class TestConstantScheduler:
+    """ConstantScheduler — fixed edit budget regardless of step."""
+
+    def test_always_returns_max_lr(self) -> None:
+        s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
+        for _ in range(10):
+            assert s.step() == 8
+
+    def test_step_advances_internal_counter(self) -> None:
+        s = ConstantScheduler(max_lr=5, min_lr=1, total_steps=5)
+        assert s._current_step == 0
+        s.step()
+        assert s._current_step == 1
+        s.step()
+        assert s._current_step == 2
+
+    def test_get_lr_returns_max_for_arbitrary_step(self) -> None:
+        s = ConstantScheduler(max_lr=12, min_lr=1, total_steps=100)
+        assert s.get_lr(1) == 12
+        assert s.get_lr(50) == 12
+        assert s.get_lr(999) == 12
+
+    def test_state_dict_and_load_state_dict_round_trip(self) -> None:
+        s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
+        for _ in range(3):
+            s.step()
+        assert s._current_step == 3
+
+        state = s.state_dict()
+        s2 = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
+        s2.load_state_dict(state)
+        assert s2._current_step == 3
+        # Step after resume lands on the correct step
+        assert s2.step() == 8
+        assert s2._current_step == 4
+
+    def test_load_state_dict_with_missing_key_defaults_to_zero(self) -> None:
+        s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
+        s.load_state_dict({})
+        assert s._current_step == 0
+
+
+# ── LinearScheduler ──────────────────────────────────────────────────────────
+
+
+class TestLinearScheduler:
+    """LinearScheduler — linear decay from max_lr to min_lr."""
+
+    def test_first_step_returns_max_lr(self) -> None:
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
+        assert s.step() == 10
+
+    def test_last_step_returns_min_lr(self) -> None:
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
+        for _ in range(9):
+            s.step()
+        assert s.step() == 2
+
+    def test_all_steps_return_integers(self) -> None:
+        s = LinearScheduler(max_lr=8, min_lr=2, total_steps=6)
+        for _ in range(6):
+            lr = s.step()
+            assert isinstance(lr, int)
+
+    def test_total_steps_one_returns_max_lr(self) -> None:
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=1)
+        assert s.step() == 10
+
+    def test_total_steps_zero_returns_max_lr(self) -> None:
+        """Degenerate case: 0-step training still gets max_lr on the one call."""
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=0)
+        assert s.step() == 10
+
+    def test_monotonically_non_increasing(self) -> None:
+        s = LinearScheduler(max_lr=20, min_lr=2, total_steps=100)
+        prev: int = 999
+        for _ in range(100):
+            lr = s.step()
+            assert lr <= prev
+            prev = lr
+
+    def test_never_below_min_lr(self) -> None:
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
+        for _ in range(20):  # overshoot
+            assert s.step() >= 2
+
+    def test_never_above_max_lr(self) -> None:
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=10)
+        for _ in range(20):
+            assert s.step() <= 10
+
+    def test_after_total_steps_stays_at_min_lr(self) -> None:
+        s = LinearScheduler(max_lr=8, min_lr=2, total_steps=5)
+        for _ in range(5):
+            s.step()
+        # Steps beyond total_steps should plateau at min_lr
+        for _ in range(5):
+            assert s.step() == 2
+
+    def test_known_decay_sequence(self) -> None:
+        """Linear decay max_lr=10, min_lr=2, total_steps=4 (t=0.25,0.50,0.75,1.0).
+           lr = 10 + (2-10)*t = 10 - 8t
+           t=0.25: lr=8.0, t=0.50: lr=6.0, t=0.75: lr=4.0, t=1.0: lr=2.0
+        """
+        s = LinearScheduler(max_lr=10, min_lr=2, total_steps=4)
+        assert s.step() == 8
+        assert s.step() == 6
+        assert s.step() == 4
+        assert s.step() == 2
+
+    def test_step_state_dict_resume_consistent(self) -> None:
+        """After resume, the next step value is the same as without resume."""
+        s1 = LinearScheduler(max_lr=10, min_lr=2, total_steps=5)
+        for _ in range(3):
+            s1.step()
+        resumed_lr = s1.step()  # step 4
+
+        s2 = LinearScheduler(max_lr=10, min_lr=2, total_steps=5)
+        s2.load_state_dict({"current_step": 3})
+        assert s2.step() == resumed_lr
+
+    def test_max_lr_equals_min_lr_yields_constant(self) -> None:
+        s = LinearScheduler(max_lr=5, min_lr=5, total_steps=10)
+        for _ in range(10):
+            assert s.step() == 5
+
+
+# ── CosineScheduler ──────────────────────────────────────────────────────────
+
+
+class TestCosineScheduler:
+    """CosineScheduler — cosine annealing from max_lr to min_lr."""
+
+    def test_first_step_returns_max_lr(self) -> None:
+        s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
+        assert s.step() == 10
+
+    def test_last_step_returns_min_lr(self) -> None:
+        s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
+        for _ in range(9):
+            s.step()
+        assert s.step() == 2
+
+    def test_all_steps_return_integers(self) -> None:
+        s = CosineScheduler(max_lr=8, min_lr=2, total_steps=6)
+        for _ in range(6):
+            lr = s.step()
+            assert isinstance(lr, int)
+
+    def test_total_steps_one_returns_max_lr(self) -> None:
+        s = CosineScheduler(max_lr=10, min_lr=2, total_steps=1)
+        assert s.step() == 10
+
+    def test_total_steps_zero_returns_max_lr(self) -> None:
+        s = CosineScheduler(max_lr=10, min_lr=2, total_steps=0)
+        assert s.step() == 10
+
+    def test_monotonically_non_increasing(self) -> None:
+        s = CosineScheduler(max_lr=20, min_lr=2, total_steps=100)
+        prev: int = 999
+        for _ in range(100):
+            lr = s.step()
+            assert lr <= prev
+            prev = lr
+
+    def test_never_below_min_lr(self) -> None:
+        s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
+        for _ in range(20):
+            assert s.step() >= 2
+
+    def test_never_above_max_lr(self) -> None:
+        s = CosineScheduler(max_lr=10, min_lr=2, total_steps=10)
+        for _ in range(20):
+            assert s.step() <= 10
+
+    def test_after_total_steps_stays_at_min_lr(self) -> None:
+        s = CosineScheduler(max_lr=8, min_lr=2, total_steps=5)
+        for _ in range(5):
+            s.step()
+        for _ in range(5):
+            assert s.step() == 2
+
+    def test_midpoint_close_to_mean(self) -> None:
+        """At the half-way point, cosine gives (max+min)/2 exactly."""
+        s = CosineScheduler(max_lr=20, min_lr=2, total_steps=100)
+        for _ in range(49):
+            s.step()
+        mid = s.step()  # step 50 / 100, t=0.5, cos(pi/2)=0
+        # lr = min + 0.5*(max-min)*(1+0) = (max+min)/2 = 11
+        assert mid == 11
+
+    def test_step_state_dict_resume_consistent(self) -> None:
+        s1 = CosineScheduler(max_lr=10, min_lr=2, total_steps=5)
+        for _ in range(3):
+            s1.step()
+        resumed_lr = s1.step()
+
+        s2 = CosineScheduler(max_lr=10, min_lr=2, total_steps=5)
+        s2.load_state_dict({"current_step": 3})
+        assert s2.step() == resumed_lr
+
+    def test_max_lr_equals_min_lr_yields_constant(self) -> None:
+        s = CosineScheduler(max_lr=5, min_lr=5, total_steps=10)
+        for _ in range(10):
+            assert s.step() == 5
+
+    def test_early_steps_near_max(self) -> None:
+        """Cosine annealing stays flat near max_lr early on (cos(0)≈1)."""
+        s = CosineScheduler(max_lr=100, min_lr=0, total_steps=100)
+        # step 1: t=0.01, cos(0.01*pi)≈0.9995 → lr≈99.97 → 100
+        assert s.step() == 100
+        # step 2: t=0.02, cos≈0.9980 → lr≈99.9 → 100
+        assert s.step() == 100
+
+    def test_late_steps_near_min(self) -> None:
+        """Cosine annealing flattens near min_lr at the end (cos(pi)≈-1)."""
+        s = CosineScheduler(max_lr=100, min_lr=0, total_steps=100)
+        for _ in range(99):
+            s.step()
+        # step 100: t=1.0, cos(pi)=-1 → lr=0
+        assert s.step() == 0
+
+
+# ── AutonomousScheduler ──────────────────────────────────────────────────────
+
+
+class TestAutonomousScheduler:
+    """AutonomousScheduler — no edit limit (model decides freely)."""
+
+    def test_always_returns_no_limit(self) -> None:
+        s = AutonomousScheduler(max_lr=8, min_lr=2, total_steps=10)
+        for _ in range(20):
+            assert s.step() == AutonomousScheduler.NO_LIMIT
+
+    def test_step_advances_counter(self) -> None:
+        s = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=5)
+        assert s._current_step == 0
+        s.step()
+        assert s._current_step == 1
+
+    def test_get_lr_returns_no_limit(self) -> None:
+        s = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=10)
+        assert s.get_lr(1) == AutonomousScheduler.NO_LIMIT
+        assert s.get_lr(50) == AutonomousScheduler.NO_LIMIT
+
+    def test_state_dict_round_trip(self) -> None:
+        s = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=10)
+        for _ in range(4):
+            s.step()
+        s2 = AutonomousScheduler(max_lr=5, min_lr=1, total_steps=10)
+        s2.load_state_dict(s.state_dict())
+        assert s2._current_step == 4
+
+
+# ── build_scheduler factory ──────────────────────────────────────────────────
+
+
+class TestBuildScheduler:
+    """build_scheduler factory — creates the right scheduler from a mode name."""
+
+    def test_constant(self) -> None:
+        s = build_scheduler("constant", max_lr=8, min_lr=2, total_steps=10)
+        assert isinstance(s, ConstantScheduler)
+        assert s.max_lr == 8
+        assert s.min_lr == 2
+        assert s.total_steps == 10
+
+    def test_linear(self) -> None:
+        s = build_scheduler("linear", max_lr=12, min_lr=3, total_steps=20)
+        assert isinstance(s, LinearScheduler)
+
+    def test_cosine(self) -> None:
+        s = build_scheduler("cosine", max_lr=16, min_lr=4, total_steps=30)
+        assert isinstance(s, CosineScheduler)
+
+    def test_autonomous(self) -> None:
+        s = build_scheduler("autonomous", max_lr=8, min_lr=2, total_steps=10)
+        assert isinstance(s, AutonomousScheduler)
+
+    def test_unknown_mode_raises_value_error(self) -> None:
+        with pytest.raises(ValueError, match="Unknown scheduler mode"):
+            build_scheduler("exponential", max_lr=8, min_lr=2, total_steps=10)
+
+    def test_default_mode_is_constant(self) -> None:
+        s = build_scheduler(max_lr=8, min_lr=2, total_steps=10)
+        assert isinstance(s, ConstantScheduler)
+
+
+# ── Abstract base class ──────────────────────────────────────────────────────
+
+
+class TestLRSchedulerBase:
+    """LRScheduler — abstract base: cannot be instantiated directly."""
+
+    def test_cannot_instantiate_abstract(self) -> None:
+        with pytest.raises(TypeError):
+            LRScheduler(max_lr=8, min_lr=2, total_steps=10)  # type: ignore[abstract]
+
+    def test_concrete_subclass_instantiates_fine(self) -> None:
+        """ConstantScheduler (and others) work normally."""
+        s = ConstantScheduler(max_lr=8, min_lr=2, total_steps=10)
+        assert isinstance(s, LRScheduler)
+        assert s.step() == 8