fix: working tests

src/concrete/ml/onnx/ops_impl.py

@@ -16,7 +16,7 @@
 
 # pylint: disable=ungrouped-imports
 from concrete.ml.common import utils
-from concrete.ml.common.debugging import assert_true
+from concrete.ml.common.debugging import assert_false, assert_true
 from concrete.ml.onnx.onnx_impl_utils import (
     compute_onnx_pool_padding,
     numpy_onnx_pad,
@@ -1808,6 +1808,17 @@ def numpy_brevitas_quant(
     assert_true(signed in (1, 0), "Signed flag in Brevitas quantizer must be 0/1")
     assert_true(narrow in (1, 0), "Narrow range flag in Brevitas quantizer must be 0/1")
 
+    # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4544
+    # Remove this workaround when brevitas export is fixed
+    if signed == 0 and narrow == 1:
+        signed = 1
+        narrow = 0
+
+    assert_false(
+        signed == 0 and narrow == 1,
+        "Can not use narrow range for non-signed Brevitas quantizers",
+    )
+
     # Compute the re-scaled values
     y = x / scale
     y = y + zero_point

src/concrete/ml/quantization/quantized_ops.py

@@ -13,7 +13,7 @@
 from concrete.fhe import tag, univariate, zeros
 from typing_extensions import SupportsIndex
 
-from ..common.debugging import assert_true
+from ..common.debugging import assert_false, assert_true
 from ..onnx.onnx_impl_utils import (
     compute_onnx_pool_padding,
     numpy_onnx_pad,
@@ -1974,6 +1974,17 @@ def check_float(v, err_msg):
         self.is_signed = bool(attrs["signed"])
         self.is_narrow = bool(attrs["narrow"])
 
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4544
+        # Remove this workaround when brevitas export is fixed
+        if self.is_signed is False and self.is_narrow is False:
+            self.is_signed = True
+            self.is_narrow = False
+
+        assert_false(
+            not self.is_signed and self.is_narrow,
+            "Can not use narrow range for non-signed Brevitas quantizers",
+        )
+
         # To ensure de-quantization produces floats, the following parameters must be float.
         # This should be default export setting in Brevitas
         check_float(

src/concrete/ml/sklearn/base.py

@@ -1374,6 +1374,9 @@ def from_sklearn_model(
 
         # Get the onnx model, all operations needed to load it properly will be done on it.
         n_features = model.n_features_in_
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4545
+        # Execute with 2 example for efficiency in large data scenarios to prevent slowdown
+        # but also to work around the HB export issue.
         dummy_input = numpy.zeros((2, n_features))
         framework = "xgboost" if isinstance(sklearn_model, XGBModel) else "sklearn"
         onnx_model = get_onnx_model(

src/concrete/ml/sklearn/tree_to_numpy.py

@@ -370,7 +370,9 @@ def tree_to_numpy(
         f"framework={framework} is not supported. It must be either 'xgboost' or 'sklearn'",
     )
 
-    # Execute with 1 example for efficiency in large data scenarios to prevent slowdown
+    # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4545
+    # Execute with 2 example for efficiency in large data scenarios to prevent slowdown
+    # but also to work around the HB export issue
     onnx_model = get_onnx_model(model, x[:2] if x.shape[0] > 1 else x, framework)
 
     # Compute for tree-based models the LSB to remove in stage 1 and stage 2

src/concrete/ml/sklearn/xgb.py

@@ -217,6 +217,10 @@ def load_dict(cls, metadata: Dict):
         obj.onnx_model_ = metadata["onnx_model_"]
         obj.output_quantizers = metadata["output_quantizers"]
         obj._fhe_ensembling = metadata["_fhe_ensembling"]
+
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4545
+        # Execute with 2 example for efficiency in large data scenarios to prevent slowdown
+        # but also to work around the HB export issue.
         obj._tree_inference = tree_to_numpy(
             obj.sklearn_model,
             numpy.tile(numpy.zeros((len(obj.input_quantizers),))[None, ...], [2, 1]),
@@ -492,6 +496,10 @@ def load_dict(cls, metadata: Dict):
         obj.onnx_model_ = metadata["onnx_model_"]
         obj.output_quantizers = metadata["output_quantizers"]
         obj._fhe_ensembling = metadata["_fhe_ensembling"]
+
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4545
+        # Execute with 2 example for efficiency in large data scenarios to prevent slowdown
+        # but also to work around the HB export issue.
         obj._tree_inference = tree_to_numpy(
             obj.sklearn_model,
             numpy.tile(numpy.zeros((len(obj.input_quantizers),))[None, ...], [2, 1]),

tests/quantization/test_quantized_ops.py

@@ -1658,9 +1658,12 @@ def create_layer(is_signed, narrow):
         )
 
     if not is_signed and narrow:
-        with pytest.raises(AssertionError, match=r"Can not use narrow range.*"):
-            quant = create_layer(1 if is_signed else 0, 1 if narrow else 0)
-        return
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4544
+        # Reinstate warning check when brevitas export is fixed
+        pytest.skip("Skipping checking of invalid brevitas quant setting (signed=0,narrow=1)")
+    #        with pytest.raises(AssertionError, match=r"Can not use narrow range.*"):
+    #            quant = create_layer(1 if is_signed else 0, 1 if narrow else 0)
+    #        return
 
     quant = create_layer(1 if is_signed else 0, 1 if narrow else 0)