Allow DASC quality gates above parity

modelopt/torch/sparsity/state_sparsity/config.py

@@ -142,9 +142,9 @@ def validate_wmax_candidates(cls, candidates: object) -> object:
     @field_validator("min_perplexity_retention")
     @classmethod
     def validate_perplexity_gate(cls, value: float) -> float:
-        """Require a finite retention gate in (0, 1]."""
-        if not math.isfinite(value) or not 0.0 < value <= 1.0:
-            raise ValueError("min_perplexity_retention must be finite and in (0, 1]")
+        """Require a finite positive retention gate."""
+        if not math.isfinite(value) or value <= 0.0:
+            raise ValueError("min_perplexity_retention must be finite and positive")
         return value
 
     @field_validator("min_top1_agreement")

tests/unit/torch/sparsity/state_sparsity/test_dasc.py

@@ -160,12 +160,20 @@ def test_config_fails_closed(override):
 
 
 def test_perplexity_retention_accepts_parity_improvements():
-    """Allow an observed DASC perplexity improvement instead of requiring clamping."""
-    measurement = mtss.DASCCalibrationMeasurement(**_candidate(7))
-    measurement.quality[0].perplexity_retention = 1.0004
+    """Allow improvement measurements and thresholds instead of requiring clamping."""
+    candidate = _candidate(7)
+    candidate["quality"][0]["perplexity_retention"] = 1.0004
+    measurement = mtss.DASCCalibrationMeasurement(**candidate)
 
     assert measurement.quality[0].perplexity_retention == 1.0004
 
+    model = mtss.calibrate(
+        TinyGatedDeltaNetForCausalLM(),
+        _config(wmax_candidates=[7], min_perplexity_retention=1.0002),
+        [candidate],
+    )
+    assert mtss.export_policy(model)["selected_wmax"] == 7
+
 
 def test_calibration_fails_closed_on_measurements_and_model_mismatch():
     """Reject incomplete evidence, failing gates, wrong geometry, and unsupported layers."""