fix: skip _compute_latent_samples in PYAUTO_TEST_MODE

Short-circuit SearchUpdater._compute_latent_samples to return None when
autoconf.test_mode.skip_latents() is True. Test-mode runs already bypass
the sampler but the post-fit latent pass still draws 100 PDF samples and
dispatches analysis.compute_latent_samples per sample — under PyAutoGalaxy's
AnalysisDataset.LATENT_BATCH_MODE = "jit" override that is a Python loop
of 100 JIT dispatches per stage, ~750s per SLaM fit on CPU. End-to-end
SLaM smoke now completes in 29.6s instead of timing out at 5min.

PYAUTO_SKIP_LATENTS=1 also bypasses the path in real-mode fits where the
user explicitly wants to skip latent post-processing. Real-mode behaviour
with no env vars set is unchanged.

Requires rhayes777/PyAutoConf PR (skip_latents helper) merged first.

Fixes #1294.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Author: 2 people

autofit/non_linear/search/updater.py

@@ -8,6 +8,7 @@
 import psutil
 
 from autoconf import conf
+from autoconf.test_mode import skip_latents
 
 from autofit import exc
 from autofit.non_linear.paths.database import DatabasePaths
@@ -232,6 +233,8 @@ def _compute_latent_samples(
         during_analysis: bool,
     ) -> Optional[Samples]:
         """Compute and persist latent variable samples if configured."""
+        if skip_latents():
+            return None
         if not (
             (during_analysis and conf.instance["output"]["latent_during_fit"])
             or (not during_analysis and conf.instance["output"]["latent_after_fit"])

test_autofit/non_linear/search/test_updater.py

@@ -0,0 +1,82 @@
+import logging
+from unittest.mock import MagicMock
+
+from autoconf import conf
+
+from autofit.non_linear.search.updater import SearchUpdater
+
+
+def _make_updater() -> SearchUpdater:
+    return SearchUpdater(
+        paths=MagicMock(),
+        timer=MagicMock(),
+        search_logger=logging.getLogger("test_updater"),
+        plot_results_func=MagicMock(),
+        samples_from_func=MagicMock(),
+        disable_output=False,
+        iterations_per_full_update=1.0,
+    )
+
+
+def test__compute_latent_samples__skipped_in_test_mode(monkeypatch):
+    monkeypatch.setenv("PYAUTO_TEST_MODE", "1")
+    monkeypatch.delenv("PYAUTO_SKIP_LATENTS", raising=False)
+
+    analysis = MagicMock()
+    result = _make_updater()._compute_latent_samples(
+        samples=MagicMock(),
+        samples_save=MagicMock(),
+        analysis=analysis,
+        fitness=MagicMock(),
+        during_analysis=False,
+    )
+
+    assert result is None
+    analysis.compute_latent_samples.assert_not_called()
+
+
+def test__compute_latent_samples__skipped_by_explicit_env_var(monkeypatch):
+    monkeypatch.delenv("PYAUTO_TEST_MODE", raising=False)
+    monkeypatch.setenv("PYAUTO_SKIP_LATENTS", "1")
+
+    analysis = MagicMock()
+    result = _make_updater()._compute_latent_samples(
+        samples=MagicMock(),
+        samples_save=MagicMock(),
+        analysis=analysis,
+        fitness=MagicMock(),
+        during_analysis=False,
+    )
+
+    assert result is None
+    analysis.compute_latent_samples.assert_not_called()
+
+
+def test__compute_latent_samples__config_gate_still_works(monkeypatch):
+    """
+    With neither env var set, the existing ``latent_after_fit`` /
+    ``latent_during_fit`` config gate must still short-circuit
+    when both are disabled. Regression for the new skip_latents()
+    check not bypassing the original logic.
+    """
+    monkeypatch.delenv("PYAUTO_TEST_MODE", raising=False)
+    monkeypatch.delenv("PYAUTO_SKIP_LATENTS", raising=False)
+
+    original_after = conf.instance["output"]["latent_after_fit"]
+    original_during = conf.instance["output"]["latent_during_fit"]
+    conf.instance["output"]["latent_after_fit"] = False
+    conf.instance["output"]["latent_during_fit"] = False
+    try:
+        analysis = MagicMock()
+        result = _make_updater()._compute_latent_samples(
+            samples=MagicMock(),
+            samples_save=MagicMock(),
+            analysis=analysis,
+            fitness=MagicMock(),
+            during_analysis=False,
+        )
+        assert result is None
+        analysis.compute_latent_samples.assert_not_called()
+    finally:
+        conf.instance["output"]["latent_after_fit"] = original_after
+        conf.instance["output"]["latent_during_fit"] = original_during