chore: clean and speed up fhe training tests

src/concrete/ml/deployment/fhe_client_server.py

@@ -470,6 +470,6 @@ def deserialize_decrypt_dequantize(
 
         # In training mode, note that this step does not make much sense for now. Still, nothing
         # breaks since QuantizedModule don't do anything in post-processing
-        result = self.model.post_processing(*result)
+        result_post_processed = self.model.post_processing(*result)
 
-        return result
+        return result_post_processed

src/concrete/ml/quantization/quantized_module.py

@@ -886,8 +886,6 @@ def compile(
             global_p_error=global_p_error,
             verbose=verbose,
             single_precision=False,
-            fhe_simulation=False,
-            fhe_execution=True,
             compress_input_ciphertexts=enable_input_compression,
         )
 

src/concrete/ml/sklearn/base.py

@@ -579,8 +579,6 @@ def compile(
             global_p_error=global_p_error,
             verbose=verbose,
             single_precision=False,
-            fhe_simulation=False,
-            fhe_execution=True,
             compress_input_ciphertexts=enable_input_compression,
         )
 

src/concrete/ml/sklearn/linear_model.py

@@ -190,6 +190,7 @@ def __init__(
         self.learning_rate_value = 1.0
         self.batch_size = 8
         self.training_p_error = 0.01
+        self.training_fhe_configuration = None
 
         self.fit_encrypted = fit_encrypted
         self.parameters_range = parameters_range
@@ -344,10 +345,15 @@ def _get_training_quantized_module(
             fit_bias=self.fit_intercept,
         )
 
+        if self.training_fhe_configuration is None:
+            configuration = Configuration()
+        else:
+            configuration = self.training_fhe_configuration
+
         # Enable the underlying FHE circuit to be composed with itself
         # This feature is used in order to be able to iterate in the clear n times without having
         # to encrypt/decrypt the weight/bias values between each loop
-        configuration = Configuration(composable=True)
+        configuration.composable = True
 
         composition_mapping = {0: 2, 1: 3}
 

tests/deployment/test_client_server.py

@@ -63,11 +63,11 @@ def dev_send_clientspecs_and_modelspecs_to_client(self):
 @pytest.mark.parametrize("model_class, parameters", MODELS_AND_DATASETS)
 @pytest.mark.parametrize("n_bits", [2])
 def test_client_server_sklearn_inference(
-    default_configuration,
     model_class,
     parameters,
     n_bits,
     load_data,
+    default_configuration,
     check_is_good_execution_for_cml_vs_circuit,
     check_array_equal,
     check_float_array_equal,

tests/sklearn/test_fhe_training.py

@@ -312,6 +312,7 @@ def check_encrypted_fit(
     parameters_range,
     max_iter,
     fit_intercept,
+    configuration,
     check_accuracy=None,
     fhe=None,
     partial_fit=False,
@@ -356,6 +357,8 @@ def check_encrypted_fit(
     # We need to lower the p-error to make sure that the test passes
     model.training_p_error = 1e-15
 
+    model.training_fhe_configuration = configuration
+
     if partial_fit:
         # Check that we can swap between disable and simulation modes without any impact on the
         # final training performance
@@ -418,7 +421,13 @@ def check_encrypted_fit(
 @pytest.mark.parametrize("label_offset", [0, 1])
 @pytest.mark.parametrize("n_bits, max_iter, parameter_min_max", [pytest.param(7, 30, 1.0)])
 def test_encrypted_fit_coherence(
-    fit_intercept, label_offset, n_bits, max_iter, parameter_min_max, check_accuracy
+    fit_intercept,
+    label_offset,
+    n_bits,
+    max_iter,
+    parameter_min_max,
+    check_accuracy,
+    simulation_configuration,
 ):
     """Test that encrypted fitting works properly."""
 
@@ -439,6 +448,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             max_iter,
             fit_intercept,
+            simulation_configuration,
             check_accuracy=check_accuracy,
             fhe="disable",
         )
@@ -453,6 +463,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             max_iter,
             fit_intercept,
+            simulation_configuration,
             check_accuracy=check_accuracy,
             fhe="simulate",
         )
@@ -474,6 +485,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             max_iter,
             fit_intercept,
+            simulation_configuration,
             check_accuracy=check_accuracy,
             partial_fit=True,
         )
@@ -496,6 +508,7 @@ def test_encrypted_fit_coherence(
         parameters_range,
         max_iter,
         fit_intercept,
+        simulation_configuration,
         check_accuracy=check_accuracy,
         warm_fit=True,
         init_kwargs=warm_fit_init_kwargs,
@@ -519,6 +532,7 @@ def test_encrypted_fit_coherence(
         parameters_range,
         first_iterations,
         fit_intercept,
+        simulation_configuration,
         check_accuracy=check_accuracy,
         fhe="simulate",
     )
@@ -542,6 +556,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             last_iterations,
             fit_intercept,
+            simulation_configuration,
             check_accuracy=check_accuracy,
             fhe="simulate",
             random_number_generator=rng_coef_init,
@@ -569,6 +584,7 @@ def test_encrypted_fit_coherence(
         parameters_range,
         max_iter,
         fit_intercept,
+        simulation_configuration,
         check_accuracy=None,
         fhe="simulate",
         init_kwargs=early_break_kwargs,