chore: no save circuit with glwe backend

src/concrete/ml/torch/hybrid_model.py

@@ -267,6 +267,7 @@ def forward(self, x: torch.Tensor) -> Union[torch.Tensor, QuantTensor]:
 
         elif self.fhe_local_mode == HybridFHEMode.REMOTE:  # pragma:no cover
             # Remote call
+            # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4672
             assert self.executor is None, "Remote optimized linear layers are not yet implemented"
             y = self.remote_call(x)
 
@@ -410,8 +411,15 @@ def _replace_modules(self):
             # Optimal input dimension is 2048, below 512 the performance are too low.
             if is_pure_linear_layer:
                 module = self.private_modules[module_name]
-                input_dim = module.in_features if hasattr(module, "in_features") else 0
-                output_dim = module.out_features if hasattr(module, "out_features") else 0
+                # Use weight shape instead of in/out_features
+                if hasattr(module, "weight"):
+                    input_dim = module.weight.shape[
+                        1
+                    ]  # Input dimension is second dimension for Linear layers
+                    output_dim = module.weight.shape[0]  # Output dimension is first dimension
+                else:
+                    input_dim = output_dim = 0
+
                 is_pure_linear_layer = (
                     is_pure_linear_layer and input_dim >= 512 and output_dim >= 512
                 )
@@ -582,6 +590,7 @@ def compile_model(
                 # then simply quantize the model without compiling with
                 # Concrete Python.
                 if self._has_only_large_linear_layers and _HAS_GLWE_BACKEND:
+                    self.executor = GLWELinearLayerExecutor()
                     self.private_q_modules[name] = build_quantized_module(
                         self.private_modules[name],
                         calibration_data_tensor,
@@ -637,7 +646,15 @@ def save_and_clear_private_info(self, path: Path, via_mlir=True):
             path (Path): The directory where the model and the FHE circuit will be saved.
             via_mlir (bool): if fhe circuits should be serialized using via_mlir option
                 useful for cross-platform (compile on one architecture and run on another)
+
+        Raises:
+            NotImplementedError: GLWE backend deployment is not yet supported
         """
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4672
+        # GLWE backend deployment is not yet supported
+        if self.executor is not None:
+            raise NotImplementedError("GLWE backend deployment is not yet supported")
+
         path = Path(path)
         path.mkdir(parents=True, exist_ok=True)
 

tests/torch/test_hybrid_converter.py

@@ -73,6 +73,7 @@ def run_hybrid_llm_test(
 
         # Create a hybrid model
         hybrid_model = HybridFHEModel(model, module_names)
+        is_compiled = False
         try:
             hybrid_model.compile_model(
                 inputs,
@@ -81,6 +82,7 @@ def run_hybrid_llm_test(
                 rounding_threshold_bits=8,
                 configuration=configuration,
             )
+            is_compiled = True
         except RuntimeError as error:
             # When reshaping adds PBSs we sometimes encounter NoParametersFound
             # when compiling. In this case we skip the rest since we can't simulate
@@ -153,10 +155,20 @@ def run_hybrid_llm_test(
         # Get the temp directory path
 
         if not has_pbs and glwe_backend_installed:
-            # Deployment of GLWE backend hybrid models is not yet supported
-            with pytest.raises(AttributeError, match="The quantized module is not compiled.*"):
-                hybrid_model.save_and_clear_private_info(temp_dir_path)
 
+            if is_compiled:
+                # Deployment of GLWE backend hybrid models is not yet supported
+                with pytest.raises(
+                    NotImplementedError, match="GLWE backend deployment is not yet supported"
+                ):
+                    hybrid_model.save_and_clear_private_info(temp_dir_path)
+            else:
+                # Check that we get an error when trying to save a non-compiled model
+                with pytest.raises(
+                    AttributeError,
+                    match="The quantized module is not compiled. Please run compile*",
+                ):
+                    hybrid_model.save_and_clear_private_info(temp_dir_path)
         else:
             hybrid_model.save_and_clear_private_info(temp_dir_path)