From f73f843473b430d051d3f7935a3bfb913664ad86 Mon Sep 17 00:00:00 2001
From: marksverdhei <marksverdhei@hotmail.com>
Date: Sun, 29 Mar 2026 02:21:20 +0000
Subject: [PATCH] test: add coverage for kl per_sample mode and
 disable_adapters

Tests per_sample=True tensor shape, zero values for identical distributions,
scalar/per-sample relationship, variable mask lengths, empty mask (clamp
safety), and temperature scaling. Tests disable_adapters for normal flow,
exception recovery, and yield value.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 tests/test_kl_extra.py | 95 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 tests/test_kl_extra.py

diff --git a/tests/test_kl_extra.py b/tests/test_kl_extra.py
new file mode 100644
index 0000000..306b1fb
--- /dev/null
+++ b/tests/test_kl_extra.py
@@ -0,0 +1,95 @@
+"""Additional tests for bakery.kl — per_sample mode and disable_adapters."""
+
+import torch
+from unittest.mock import MagicMock
+
+from bakery.kl import compute_kl_divergence, disable_adapters
+
+
+# ---------------------------------------------------------------------------
+# compute_kl_divergence — per_sample mode
+# ---------------------------------------------------------------------------
+
+class TestKLPerSample:
+    def test_per_sample_returns_batch_shaped_tensor(self):
+        batch = 3
+        logits = torch.randn(batch, 5, 100)
+        mask = torch.ones(batch, 5)
+        result = compute_kl_divergence(logits, logits, mask, per_sample=True)
+        assert result.shape == (batch,)
+
+    def test_per_sample_identical_distributions_near_zero(self):
+        logits = torch.randn(2, 4, 50)
+        mask = torch.ones(2, 4)
+        result = compute_kl_divergence(logits, logits, mask, per_sample=True)
+        assert (result < 1e-5).all()
+
+    def test_per_sample_vs_scalar_sum_relationship(self):
+        """Per-sample losses summed should be close to the scalar version
+        when all samples have the same mask length."""
+        teacher = torch.randn(2, 5, 50)
+        student = torch.randn(2, 5, 50)
+        mask = torch.ones(2, 5)
+        scalar = compute_kl_divergence(teacher, student, mask, per_sample=False)
+        per_sample = compute_kl_divergence(teacher, student, mask, per_sample=True)
+        # scalar = mean over all 10 tokens; per_sample mean = mean of 2 per-sample means
+        # They should match when both samples have equal token counts
+        assert torch.isclose(per_sample.mean(), scalar, atol=1e-5)
+
+    def test_per_sample_different_mask_lengths(self):
+        """Different token counts per sample should not produce equal per-sample losses."""
+        teacher = torch.randn(1, 6, 50)
+        student = torch.randn(1, 6, 50)
+        # mask A: all tokens; mask B: half tokens
+        mask_a = torch.ones(1, 6)
+        mask_b = torch.tensor([[1.0, 1.0, 1.0, 0.0, 0.0, 0.0]])
+        result_a = compute_kl_divergence(teacher, student, mask_a, per_sample=True)
+        result_b = compute_kl_divergence(teacher, student, mask_b, per_sample=True)
+        assert not torch.isclose(result_a[0], result_b[0])
+
+    def test_per_sample_empty_mask_single_sample(self):
+        """All-zero mask for a single sample should return 0 (safe clamp)."""
+        logits = torch.randn(1, 4, 50)
+        mask = torch.zeros(1, 4)
+        result = compute_kl_divergence(logits, logits, mask, per_sample=True)
+        assert result.shape == (1,)
+        assert result[0].item() == pytest.approx(0.0, abs=1e-5)
+
+    def test_per_sample_temperature_lowers_loss(self):
+        teacher = torch.randn(2, 5, 100)
+        student = torch.randn(2, 5, 100)
+        mask = torch.ones(2, 5)
+        loss_t1 = compute_kl_divergence(teacher, student, mask, temperature=1.0, per_sample=True)
+        loss_t10 = compute_kl_divergence(teacher, student, mask, temperature=10.0, per_sample=True)
+        assert (loss_t10 < loss_t1).all()
+
+
+# ---------------------------------------------------------------------------
+# disable_adapters context manager
+# ---------------------------------------------------------------------------
+
+class TestDisableAdapters:
+    def test_disables_then_re_enables(self):
+        model = MagicMock()
+        with disable_adapters(model):
+            model.disable_adapter_layers.assert_called_once()
+            model.enable_adapter_layers.assert_not_called()
+        model.enable_adapter_layers.assert_called_once()
+
+    def test_re_enables_on_exception(self):
+        model = MagicMock()
+        try:
+            with disable_adapters(model):
+                raise RuntimeError("training exploded")
+        except RuntimeError:
+            pass
+        model.disable_adapter_layers.assert_called_once()
+        model.enable_adapter_layers.assert_called_once()
+
+    def test_yields_nothing(self):
+        model = MagicMock()
+        with disable_adapters(model) as value:
+            assert value is None
+
+
+import pytest